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Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this paper the author investigates how to utilize largescale human video to train dexterous robot manipulation skills to leverage the information from the internet videos the author proposes a handful of techniques to preprocess the video data to extract the action information then the network is trained on the extracted hand data and deployed to the real robot with some human demonstration collected by teleoperation for finetuning experiments show that the proposed pipeline can solve multiple manipulation tasks strength the direction explored in this paper is important utilizing the internet video data for robot learning is well motivated especially considering the similarity between human and multifinger hands this direction looks very promising the authors perform experiments with multiple realworld tasks with pick and place pushing and rotating objects weakness although the objective of this paper is very impressive the experiments can not support the introduction and there are multiple overclaims section 4 is titled videodex learning dexterity from youtube however i can not find any evidence that the author utilizes youtube data for learning dexterous manipulation as mentioned in the section on retargeting wrist pose orb slam and the cameras acceleration data are used to compute the camera pose trajectory this information is not readily available in the youtube data the experiments and methods are misaligned with this claim in the introduction line 42 the author mentioned that our key insight is to combine these visual and action priors from passive data with the physical constraints of how robots should move in the world however the method does not consider the surroundings of the human hand and the detection results itself is not accurate how to incorporate physical information into the training data missing literature discussion on previous learning from video works 1 dexmv imitation learning for dexterous manipulation from human videos 2021 this paper focuses also on how to learn dexterous manipulation from human videos the reviewer understands that this literature paper uses simulated tasks while the authors focus on the real robot settings but it seems that similar pipelines are also used in this paper estimating the human hand retargeting and learning from retargeted hand pose 2 the surprising effectiveness of representation learning for visual imitation 2021 this paper also focuses on how to leverage the video data for better learning it also uses a gopro camera to collect a video of each trajectory which is the same as the ego4d dataset used in this paper it shows that by learning from this video data the final manipulation performance can be improved a lot these literature works use very similar methods to achieve robot learning the novelty claims of this paper can also be found in this literature missing details for retargeting wrist pose the detection module frankmocap is a 2d hand detector it is not clear how the author can get 3d keypoints from the hand model in the camera frame also this section is important in the whole technical approach it is better to provide visualization of the final retargeted robot a hand wrist pose and robot arm should also be visualized in figure 3 if they are used in the training if the wrist pose and arm joint pose is not used how to pretrain the action prior missing details about transforms in the equation it is not clear why the author uses t and m to denote pose simultaneously what are the differences if m is also a se3 transformation how to compute the position part of the mworldc1 besides the reviewer can not find any information about how the trobotworld is determined heuristically in both the main paper and supplementary docsepthe authors demonstrate a system in which they combine a few different components to get interesting supervisedlearned open loop behavior of real robot hands doing several different tasks in particular the most notable part of the approach is using videos of human hands as an action prior which informs their supervised mapping strengths good core idea the overall idea of using action priors from human videos via hand tracking to make robots work better is a good idea there are a lot of closely related works but i think they are well referenced in this paper good execution on several key parts the execution details of handling moving cameras with camera pose tracking together with perframe hand tracking seems to be well done i also like just using r3m features out of the box this is smart and interesting to see external validation results of real robots with hands doing a variety of things weaknesses there are various unscientific elements of this paper in its current form while the work is interesting i cant recommend a strong accept for a paper in this form hopefully the list below will help the authors improve both this work and their future work if the authors can address all of the following weaknesses in their rebuttal which i think is all doable and within scope to do in a rebuttal id be happy to move from weak accept to strong accept 1 it seems like the authors are not very upfront about the fact that this method does not produce closed loop policies only on the last page or two is it mentioned that the whole method is open loop this is fine to study the task of i inputting an image of a scene and ii outputting an open loop trajectory but it of course is very limiting the tasks are carefully chosen such that they dont require any closed loop feedback this aspect of their approach is not what most researchers in the field would expect so a common experience of a researcher would be to look over the first handful of pages of this paper and only at the last page or so realize that this is an open loop method please just make this clear up front 2 several false statements in the introduction to build such robotic agents that can operate anywhere we need access to a lot of successful robot interaction data in many environments not necessarily true this is a reasonable hypothesis but one that isnt tested in this paper and it cant be stated as a fact however deploying inexperienced real world robots to collect experience must require constant supervision which is in feasible also not necessarily true but also a very reasonable hypothesis just need to say may require instead most of the inefficiency in robot learning is due to the exponentially large action space an opinion and cant be stated as fact 3 ndps can produce safe and smooth trajectories yes but this is a meaningless statement they can also produce trajectories that are completely unsafe there is nothing about ndpsdmps that provides safety other than a bit of smoothness that may arguably help but there is nothing that helps here with the presence of obstacles in the environment or humans etc this statement probably only serves to confusemislead inexperienced readers please removefix 4 the paper mentions a physical prior as a key component but this is just that it uses dynamic movement primitives it seems im not sure this is the best way to communicate this line 191 also says physicallyaware ndps they dont know anything about contact physics maybe just say second order system or dynamical system or something maybe physicallyinspired but not physicallyaware and whenever it says for example line 269 baselines without a physical prior it should just be instead clear that this just means they dont use dmps 5 line 213 is videodex able to perform general purpose manipulation since the method is open loop the answer is no thats fine and the results are still impressive but should be clarified this is not something that needs to be empirically evaluated its just a result of the formulation 6 its very confusing that citation 44 is used open loop this isnt an intention of the method also is the rnn version closed loop over time its not clear and if its not im not sure how the rnn would be any different if its not used sequentially over time 7 please state exactly how many demonstrations were used for the different experiments 8 in the conclusion this is because training rl in the real world is difficult due to hardware limitations yes but this isnt reason to make the used behavior cloning method open loop instead of closed loop minor dont worry about these too much but i mention these as opportunities to improve the paper further ego4d is not cited on page 2 mentioned but not cited hr is not defined in an equation also i would recommend not using two letters for a math symbol it looks like a matrix h multiplied by a matrix r why use orbslam3 rather than colmap for the poses already running colmap for the calibration docsepvideodex pretrains a policy network with videos with gyroscope and accelerometer data of humans performing a task then finetunes with demonstrating trajectories collected by teleoperating the robot in order to train with the human data they use the approach from 49 for mapping human pose to robot pose and use orbslam355 to account for the camera motion they feed the image data labeled with the outputted pose into a resnet1815 backbone initialized with r3ms6 features and use a neural dynamic policy ndp 13 network to generate actions the paper demonstrates that using human data allows improved performance on 67 tasks pros the paper presents a theoretically simple method of learning from videos of humans the method is demonstrated on 7 different tasks outperforming the baselines without human data on 6 of them cons the writing of the paper is somewhat scattered the analysis of why the proposed approach using ndp rather than a mlp works better with human data could be stronger the paper needs to be much clearer that it relies on gyroscope and accelerometer data from the human videos which is a barrier to truly using internetscale data ### Summary:

this paper studies how to learn dexterous manipulation from human videos in the initial review the reviewer appreciated the direction and realworld experiment but also raised concerns about the need of special sensor for tracking during rebuttal the authors effectively addressed this concern by providing additional experiment results and reviewers were satisfied with the response ac would like to recommend acceptance for this paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary of contributions this paper proposes a new framework to design new loss for gans the authors show that their framework is quite general and encompass a number of existing approaches eg the original gan formulation hinge loss etc they also propose a categorization in three different classes and derive new loss function they then compare experimentally the different existing loss and the new proposed loss that fall under their framework main comment the framework proposed in the paper is interesting since its quite general and the authors are able to derive a large number of existing as well as new loss from it however i think the framework has several limitations 1 the formulation is based on the likelihood ratio which is only defined if the support of g and f match this is known to not be the case in the context of gans 2 the benefit of the framework is not clear while it provides a way to derive new loss its not clear what are the advantages of the new loss theoretically the author argue that it is a hard question to answer and i agree the authors try to answer this question through experiments but i find the experiments not very convincing in particular the authors argue that subclass a objectives are more stable based on the celeba experiment however its not clear to me that the instability is due to a specific choice of objective function it might just be that the hyper parameters where slightly off for the other objectives i believe it would be interesting to understand better the results on celeba in particular maybe to show that some objectives are indeed more stable they can vary several hyperparameters and compare how often each objective is better than the other that would make the results and conclusion much more convincing minor comment the paper is overall clear but the clarity of some sections could be improved i think theorem 1 would be more clear if stated a bit differently simply saying that domegar maximize phidr psid and that r1 minimize phiomegarr psiomegar section 3 is a bit dense the subclasses also seem a bit arbitrary i believe section 5 could be improved by stating more clearly the different observations right now it looks more like a description of the figures than a clear statement of the question that the experiments try to answer and how they answer it docsepthis paper generalizes the minmax problem of gans to form a richer family of generative adversarial networks interestingly most of the wellknown variants of gans can be found in the spectrum of formulations covered by the family proposed in this work in terms of modeling it is evident that the family proposed in the paper is richer than that of fgan the family in this paper is shown to have a connection to wgan except that the lipschitz condition is omitted however under the light of existing works including fgan and other relevant works the obtained theoretical results are not surprising to me in addition apart from providing a richer family this work does not significantly influence the practical aspects of gans i have some following questions 1 if we solve the minmax problem in 2 subjected the fact that phi and psi satisfy eq 9 is it equivalent to minimizing any divergence between two distributions with pdfs f and g 2 dx is not a typical discriminator whose values between 01 providing the probability to distinguish true and fake data is not it d is more similar to a critique whose output values are realvalued is not itdocsepsummary in this paper the authors set out to find what scalar functions will make for a max part of the minmax gan objective they then find such a class of functions and show that only a ratio between two equal probabilities will be admitted as a solution pros the paper nicely introduces a different way of seeing gans not as a difference between the generated and real data but as a an integer of the ratio between generated and real distribution times the discriminator only if the ratio is 1 everywhere is the discriminator unable to maximize the max part of the gan objective further i liked the idea that the discriminator shouldnt just decide what class data belongs to but also estimate the probability ratio specifically in the formulation here the max part is maximized when dx omegarx so maximized iff omega1dx doesnt just classify but says the probability ratio between the two classes if this idea is expanded upon i think the authors could make a novel contribution cons unfortunately the authors have neglected to carefully explain how their contribution relates to previous work its telling that the paper cites only two papers from 2018 one from 2019 and none from 2020 all other citations are from previous years even though 20182020 has been a time of much gan research a key way in which the authors work hasnt been sufficiently compared to previous work is with their main claim we propose a simple methodology for constructing such minmax problems assuring at the same time consistency of the corresponding solution in liu they show a class of of functions where consistency is also guaranteed and the class shown by the authors here is a subset of the class in liu the details are at the bottom of my review further many of the techniques in this paper seem very similar to song where they also investigate the fgan divergence specifically the claims they make in theorem 1 seem very similar to prop 2 in song also the change of measure trick in the introduction can be found in song a detailed comparison of this work to that work would also be helpful since when reading this paper one simply doesnt know what is previous work which has already been done by others and what is the authors novel contribution once the authors address this and one is confident the contribution is indeed novel then the submission would be worth considering details of why this is a subset of whats already been shown in liu there they examine the difference between the target density d in this paper d is f but liu uses f for something else and the generated density g via supfinmathcal fmathbb exsim dysim gfxy so we find the function f in a class mathcal f which maximally separates the classes from d and g now this work proposes to do the same thing but with fxyphidx psidy where phiz intomega10z omega1tpt dt c1 and psizintomega10z pt dt c2 in liu they then split fxy up into two functions m and r such that fxymx y rxy where mxy has the form mxyvxvy this can be done in your case too resulting in here we drop the constants c1 and c2 for simplicity vx intomega10dx pt dt vy intomega10dy pt dt and rxy intomega10dx omega1t 1 ptdt since dx must be in mathcal jomega this integral has an infimum and theorem 4 from liu can be applied to achieve the same results as in this paper song song jiaming and stefano ermon bridging the gap between f gans and wasserstein gans arxiv preprint arxiv191009779 2019 liu liu shuang olivier bousquet and kamalika chaudhuri approximation and convergence properties of generative adversarial learning advances in neural information processing systems 2017 docsepoverall this paper provides impacts on understanding the core of generative models with adversarial optimization problems this paper shows the diverse possibilities of formulating the generative model optimization problems that the researchers can further investigate for better performances  also this paper shows that generative models with unexplored losses achieve the best results in various datasets which demonstrates the possibilities of future improvements of generative models overall this paper is valuable to the machine learning community especially for generative models and adversarial training the below are some concerns for this paper but those concerns are not bigger than the advantages of this paper 1 quantitative experiments although the authors provided two tables table 2 and 3 there were not much analyses about the results i understand that it is not an easy problem to understand when should we use which function however it would be great if the authors can discover some trends in the results to demonstrate which type of functions work well with which type of datasets i think it would be great to use some synthetic data with known characteristics of distributions as the target distribution to analyze for understanding this point 2 other types of dataset generative models are widely utilized in computer vision  however there are various other types of datasets that can get benefits of generative models such as tabular data and timeseries data it would be good if the authors can provide some simple experiments to demonstrate its generalizability 3 minor points it is not clear to transform between equation 3 and 4 i think this is a critical part in this paper thus it would be good to explain a little bit more for this part the authors explain the differences between fgan and this paper however it is not super clear to understand it would be good to clarify this point to highlight the novelty of this paper after reading other reviews are rebuttals after reading all the reviews from other reviewers and corresponding rebuttals i think this paper is a good paper and enough to be accepted in iclr 1 i think it has a clear difference from fgan it can provide a new loss function for the generative models which can further extend the success of generative models in the future 2 experiments are not super interesting but at least it has some intuitions corresponding to the authors claims 3 general theoretical results for the generative models such as when should we use which loss is a very difficult problem to solve maybe this paper can provide some intuitions for solving that large problem but it seems too much to ask this thing to the authors of this paper without that i think this paper is still worth to present to the iclr readers and participants therefore i am standing on my original score 7 ### Summary:

this paper proposed a new family of losses for gans and showed that this family is quite general and encompasses a number of existing losses as well as some new loss functions the paper compared experimentally the existing losses and the new proposed losses but the benefit of this family is not clear theoretically and this work did not also provide the very helpful insights for the practical application of gans

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper addresses the problem of moe routing under the cases of different network topologies by allocating another abstraction layer for the topology and designing an auxiliary objective to optimize experiments show very good improvement in terms of speed compared to strong baselines strength 1 the paper offers an important contribution to the ai community at the system level which is probably not difficult to approach for many people working in this field in fact in my humble opinion not so many ai people have the opportunity to access detailed hardware information as cloud users such as with azure or aws 2 the experiments show very good improvement over strong baselines system analysis is clearly presented weakness 1 the paper addresses the system level however since it claims a significant boost of speed without sacrificing the model accuracy it needs to show the accuracy eg at least the lmrelated one with nlprelated metrics 2 line 240 which claims without loss of generality is probably too strong my suggestion is if the solution is good with the current hardware settings the authors can run current codes for other many applications of which codes are available to further solidify their claims 3 likewise why not show the data dispatch distribution of other ranks but only rank 0 if space is limited appendix space is always there 4 in the era of gpus and large data the motivation is led by demonstrating only 128mb of data is probably inefficient probably at least some gbs or even stronger in a combination with different types of data would make a stronger motivation 5 no code is provided maybe not very relevant since the paper addresses the systemrelated level and thus is hard to judge those impacts docsepthe paper proposes a new algorithm to improve training efficiency of mixture of experts models in a distributed training setting by exploiting the network topology information to achieve this the authors propose a new auxiliary loss term incorporating communication bandwidth to encourage tokens to be routed to closer nodes rather than further nodes by applying this new algorithm authors claim that they could achiever faster throughput 101x 477x without losing accuracy on their several different clusters as a result they show a faster wallclock time convergence the communication overhead is one of the major issues for the moe model training and this paper proposes a new method to deal with this problem naturally given the increased usage of moe model technology this is a timely work having a soft guidance seems like a good idea not to hurt the original training dynamics while encouraging locality of token routing and as authors mentioned there have not been this kind of topology aware loss terms before as far as i know however there are a few missing details about model configurations and algorithms asked in the question section and the overall speed gain is minor this paper is focusing on the computation algorithm itself so it might not have direct societal impact docsepsparsely gated mixtureofexpert moe plays a vital role in largescale model training but suffers from both load imbalance and global communication in addition the existing even dispatch approach may cause network contention and worsen the previous challenges this work proposed a topologyaware largescale moe training method called tamoe that can adapt communication volume to fit the underlying network topology without interfering with the model convergence the key ideas are abstracting the dispatch problem as a communication cost optimization problem and then adding an auxiliary loss with patternrelated coefficients experiments show that tamoe provides up to 161x speedup and 477x speedup over deepspeedmoe and fastmoe without accuracy loss strengths this work tried to tackle a very significant and interesting challenge in moe system network topology may worsen the communication and load balance problems during the dispatch in moe the paper is well organized and easy to follow the proposed tamoe method is simple and effective extensive experiments show that tamoe is able to offer noticeable speedup over the stateoftheart under different hardware and model configurations weaknesses the experiments are mostly doen with gpt models it would be better to have models with different neural architectures in the evaluation benchmark it is unclear how tamoe works on other moe using models other than gpts the authors have adequately addressed the limitations and potential negative societal impact of their work ### Summary:

mixtureofexpert moe models have demonstrated a lot of success recently to further improve upon the existing literature this paper studies moe routing for different network topologies this is essentially to deal with the communication overhead of moe training the strategy is to add another layer on top for the topology along with a corresponding objective to optimize the authors also provide experiments demonstrating improved speed of convergence the reviewers were in general positive and liked the idea of the paper the reviewers did however raise issues about lack of clear demonstration that accuracy is not compromised lack of large data and a few other more technical concerns the reviewers concerns seem to be more or less addressed by the authors my overall assessment of the paper is positive i think the general premise of the paper is interesting and the paper has interesting ideas i do agree however that the experiments need to be more thorough i am recommending acceptance but request that the authors follow the reviewers comments to improve their experimental results

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper discusses applications of variants of rnns and gated cnn to acoustic modeling in embedded speech recognition systems and the main focus of the paper is computational memory efficiency when we deploy the system the paper well describes the problem of the current lstm especially focusing on the recurrent connection matrix operations which is a bottle neck in this scenario and introduces variants of rnns eg qrnn also these variants may not yield enough performance compared with lstm but 1d convolution andor deep structure helps to avoid the degradation one of the biggest issues of this paper is that they use ctc as an acoustic model while still many real speech recognition applications and major open source kaldi use hybrid hmmdnntdnn lstm cnn etc systems therefore the papers claim on ctc is not along with the current application trends it may be changed near future but still hybrid systems are dominant for example the wsj wer performance listed in table 3 is easily obtained by a simple feedforward dnn in the hybrid system the latest lattice free mmi with tdnn can achieve better performance 2x wer and this decoding is quite fast compared with lstm the authors should consider this current situation of stateoftheart speech recognition also the techniques described in the paper are all based on existing techniques and the paper lacks the technical novelty other comments in abstract and the first part of introduction as i mentioned above ctc based characterprediction modeling is not a major acoustic model the paper needs some discussions about tdnn which is a major acoustic modeling fast and accurate in kaldi p4 first line and  represents elementwise multiplication the elementwise multiplication operation was first appeared in eq 1 and it should be explained there section 32 i actually dont fully understand the claims of this experiment based on timit as it is phoneme recognition and not directly related to the real application which is the main target of this paper i think my suggestion is to place these timit based experiments as a preliminary experiment to investigate the variants of rnn or gated cnn before the wsj experiments i did not say that section 32 is useless this analysis is actually valuable and this suggested change about the position of this timit experiment can avoid some confusion of the main target of this paper docsepthis paper present a study on efficient acoustic modeling using neural networksbased model four approaches are presented and evaluated diag lstm qrnn gated convnet and adding a 1d convolution layer the evaluation is done on asr task using wsj and in phoneme classification task using the timit corpus the study show that the inference speed is improved with comparable of better performance than the standard lstm model the findings presented in this paper are interesting and quite useful when one wants to implement a lstmbased acoustic model on mobile devices the paper is well written and easy to ready the main issue of this paper is the lack of novelty the three evaluated approaches diag lstm qrnn and gated convnet are not novel the only novelty is the addition of a 1d convolution which is not enough for a conference like iclr minor comments on the experiments the network quantization approach has been shown to lead to efficient neural networks could the authors provide a comparison between their approach and the quantization approach on the timit experiment the authors could add a decoder and use the per metric instead of the frame accuracy so they could provide comparison with the literature wsj and timit are quite small corpora compared to the available corpora maybe the authors should consider using large corpora like librispeech it could be interesting to see the performance of the presented approaches overall this paper feels more like a technical report the findings could be useful but its novelty is too limited for iclr hence i argue for rejection and suggest that the authors consider submitting the paper to a speech conference like icasspdocsepthis paper investigates a number of techniques and neural network architectures for embedded acoustic modeling the goal is to reduce the memory access and make efficient computation in the meantime to sustain good asr performance overall the paper is well motivated and well written however i have following concerns 1 it is not clear from the paper whether both the training and inference are conducted on embedded devices or only the inference i assume it is the latter but cant find it explicitly mentioned in the paper 2 the exploration carried out in the paper is more on the system level and the novelty is not overwhelmingly significant 3 my major concern is that the reported wers on wsj and phoneme classification accuracy are quite off 2030 wers for wsj do not seem to be usable in real applications honestly i dont even think this performance is better than welltrained gmmhmm acoustic models using a viterbi decoder furthermore there is no clear winners across the investigated architectures in terms of performance one question is if one wants to deploy such an ondevice system which architecture shall be chosen 4 a more general comment on the work explored in the paper first of all the ondevice memory issue puts a heavy constraint on the capacity of acoustic models which will significantly hurt the modeling capability for the dnnbased acoustic models deep learning acoustic models can outperform gmmhmm because they can use large model capacity with very deep and complex architectures when a large amount of training data is available second for ctc when the training data is limited its performance is far worse than the hybrid dnnhmm model let alone a pure endtoend fashion without using external lm and dictionary if wfstbased decoders composition of wfsts of lm dictionary and deblankrepetition are used then the memory issue will surface again ### Summary:

in this work the authors conduct experiments using variants of rnns and gated cnns on a speech recognition task motivated by the goal of reducing the computational requirements when deploying these models on mobile devices while this is an important concern for practical deployment of asr systems the main concerns expressed by the reviewers is that the work lacks novelty further the authors choice to investigate ctc based systems which predict characters these models are not stateoftheart for asr and as such it is hard to judge the impact of this work on a stateoftheart embedded asr system finally it would be beneficial to replicate results on a much larger corpus such as librispeech or switchboard based on the unanimous decision from the reviewers the ac agrees that the work in the present form should be rejected

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors introduce the problem of telegraphic summarization given a sentence we want to reduce its size while retaining its meaning with no penalty for grammatical mistakes the main application presented by the author is that of summarizing fictional stories and plays the setting proposed by the author prescribes that the summarized sentence can be obtained by the input sentence by dropping some words so for example the simplest baseline for this problem would consist of simply dropping stop words the approach proposed is basically an autoencoder consisting of a 2step encoderdecoder network in the first step the sentence is encoded into a vector which is in turn decoded to a smooth indicator vector to mask words in the sentence in the second step the masked sentence is encoded into a vector which is in turn decoded into the output summarized sentence the optimization is a tradeoff between recoverability of the input sentence and norm of the indicator vector how many words are dropped in order for the network not to learn repetitive masking patterns eg drop first half of the sentence or drop every other word an additional loss is introduced that penalizes keeping easily inferable words or dropping hardtoinfer words concerns the problem doesnt seem to be wellmotivated also the length of the obtained summarized sentences is 70 that of the original sentences which makes the summaries seem not very useful the proposed complex architecture seems not to justify the goal especially considering that simply dropping stop words works already quite well in order for the presented architecture to beat the simple stopwords baseline an additional loss l4 linkage loss with retention weights which need to be tuned manually as hyperparameters is required theres not enough discussion about the related work by malireddy et al which is extremely similar to this paper a good part of that work overlaps with this paper comparison with literature about abstractive summarization is completely missing minor comments figure 1 indicator encoder should be indicator decoder are negations part of your stop words from your discussion you should make sure that not dont doesnt do not belong to your stop word set how did you optimize the hyperparameters r desired compression the regularization weights and the retention weights were pretrained word embeddings used as initialization whats the average compression of golden sentences docsepthe authors consider the problem of telegraphic sentence compression they train a system in an unsupervised fashion to predict which words can be dropped from a sentence without drastic loss of information to that end they propose a new autoencoding type architecture which uses the extracted words as latent code and most importantly a linkage loss which relates a words perplexity given the summary of its left context to its likelihood of being retained the model itself is sober and well motivated and the linkage loss is to the best of my knowledge original the authors show that their method outperforms some simple baselines in terms of rouge and compression on a small humanannotated test set the paper is generally well written although the initial presentation of the model could be made a little clearer it is not obvious from the text that the decoder takes the text as input figure 2 helps but comes a couple pages later however the authors fail to appropriately justify the choice of their hyperparameters eg the optimum value of r for our experiments was found to be 065 the best value of b was found to be 5 the weights 1 2 3 and 4 have been set to 3 2 50 and 3 respectively for our experiments how is best measured on the validation set which does not have gold references the choice of the specific sparsity constraint one could as well imagine using a simpe l1 regularization for the binarization loss and of chii why not simply use the likelihood could also be better motivated the model also relies on a handcrafted rules section 33 whose effect needs to be made more evident what weights are used in practice how were they chosen we observed that needs to be further developed the authors claim that the quantitative scores are not affected significantly but that is presumably only the rouge score what about annotators preferences most importantly however the task of telegraphic sentence compression whose usefulness is not a priori obvious is barely motivated the author refer to malireddy et al 2018 for a justification but it is important to note that the latter provides a telegraphic summary of a whole document with a compression factor of 037 the claim is that the concatenation of the telegraphic sentence compression can act as a summary of a whole document but given the fact that compression for individual sentences is closer to 069 this is yet to be demonstrated and even if that were true it is unclear whether the cognitive load of reading a sequence of telegraphic sentences would be that much lower than that of reading the original text this paper presents some interesting ideas and is well written but the content is not quite sufficient for publication in addition to the clarifications and justifications requested above the authors are encouraged to apply there methods to full lengths documents which would make for a more substantial contribution docsepthe paper explores unsupervised deep learning model for extractive telegraphic summaries which extracts text fragments eg fragments of a sentence as summaries the paper is in general well structured and is easy to follow however i think the submission does not have enough content to be accepted to the conference first in term of methodology as described in section 3 the paper has little novelty there has been intensive study using various deep learning models on summarization the models described in the paper contain little novelty compared with previous work using autoencoder and lstm for both extractive and abstractive summarization second the paper claims contributions on using deep learning models on telegraphic summarization but the advantage is not well demonstrated for example the advantage of the resulting summary is not compared with stateoftheart sentence compression models with intrinsic evaluation or probably better with extrinsic evaluation by the way it is interesting that the paper argues the advantage of using telegraphic summaries for fictional stories but actually gives an example which looks also very typical in news articles the earthquake tokyo 12 dead example third there has been much work on speech summarization that summarizes with the telegraphic style this is natural considering speech transcripts are often nongrammatical and telegraphic style summaries focusing on choosing informative fragments actually result in usable summaries the authors may consider discussing such work and compare the proposed methods to it ### Summary:

this paper presents methods for telegraphic summarization a task that generates extremely short summaries there are concerns about the utility of the task in general and also the novelty of the modeling framework there is overall consensus between reviewers regarding the papers assessment the feedback is lukewarm

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this work tackles the task of forecasting dynamics in different domains simultaneously using an encoder which is trained to determine the task the inferred latent vector is then used to adapt a forecasting network to the task at hand experiments on three datasets linked to fluid dynamics are then conducted to assess the proposed model pros this is an interesting problem which is quite timely given the development of the field of forecasting physical dynamics using neural networks the proposed solution seems sound and principled moreover it is well motivated and the writing was quite clear the different additions made to the forecaster network are also quite interesting i especially liked the adapad solution to deal with boundary conditions conducting an ablation study also considerably strengthens the paper cons all experiments are conducted on somewhat similar datasets which are based on fluid dynamics pdes it would be nice to see how the model deals with other families of dynamics especially given the fact that the contributions of this work seem geared towards practical considerations the setting of the experiments should be more precise and additional details should be given how are the different datasets constructed what supervision is there exactly regarding the different tasks how many domains are there in each dataset and what are the differences how is the balance between the different domains ect this is a good work on a timely subject the contribution is not groundbreaking but should be significant enough to warrant acceptance docsepthis paper addresses the problem of learning a deep learning model for dynamics forecasting which generalizes to changes in dynamics these changes can be induced by different parameters boundary conditions or external forces the proposed model takes a metalearning approach and proposes to partition data into different heterogeneous domains it consists of two components an encoder which infers timeinvariant features given observed domain data and a forecaster which predicts the dynamics given these features the paper evaluates the proposed approach on several datasets and provides some theoretical insights this paper addresses a new and interesting generalization problem for dynamics forecasting it proposes a model to address different changes in the dynamics evaluation is done on relevant datasets with several baselines and some ablation studies the applicability of the proposed approach is restricted to problems where relevant weak supervision from task parameters is available this seems like an important limitation in realworld applications how valid is this scenario the question of choosing relevant parameters for weak supervision is important for applying this model to other datasets yet the definition of these parameters is unclear how robust is the model when chosen parameters are not useful the performance of wrongenc table 2 tends to say that this model will then fail it is unclear why the model can adapt to changing boundary conditions with adapad as it generates them from features hatzc extracted from data inside the domain and weakly supervised by quantities unrelated to the boundary condition eg mean vorticity or season the theoretical analysis inspired by existing work in multitask learning domain adaptation has some limitations and does not add much value to the paper i have some concerns with the domain adaptation upperbound to the target error in theorem 34 and proposition 35 this upperbound is not minimized thus the target risk can be high ie the model is not guaranteed to adapt well moreover the validity of the theoretical analysis is unclear as several assumptions may not be verified eg bounded loss in theorem 31 proposition 33 lipschitz continuity in proposition 35 theorem 34 requires that the assumptions in theorem 2 in redko et al 2017 are verified yet these assumptions are not mentioned in the paper some ablation studies are missing 1 the contribution of each term in equation 2 and 2 the dimensionality of hatzc which is fixed arbitrarily other questions it would be good to better explain how the experiments include changing boundary conditions between domains the testing scenarios only mention different initial conditions or external forces why do the baselines resnetc and unetc not adapt well despite having access to relevant weak supervision p8 this is the same information used by the proposed model to adapt how redundant is the time invariance term 3rd term in equation 2 with the invariances enforced in the architecture of the encoder this paper tackles a new generalization problem for dynamics forecasting and proposes a model supported by experimental results however this model can only be applied to problems with relevant weak supervision which may not always be available in practise moreover the definition of relevant parameters is unclear and the robustness of the model to the choice of these parameters is not measured which may restrict its application to other datasets there are also unclarities on the ability of the model to adapt to changing boundary conditions with adapad some ablation studies are missing and i have concerns on the theoretical analysis which brings limited value to the paper for this reason i am giving this paper a weak reject postrebuttal comments i thank the authors for their response after studying it the theoretical results still have some major issues and feel disconnected from the model in particular key assumptions are not enforced in the model eg lipschitz continuity and the generalization error of the model in th33 is uncontrolled as the upperbound is not minimized by the model the wasserstein distance between domains is fixed and is high in all generality its use for the model is thus not very convincing on practical aspects the capability of handling boundary conditions should be better justified and evaluated for this reason i keep my score unchanged and recommend rejecting this paper docsepthe paper suggest a remediation for a common problem for dynamics forecasting which is the lack of generalization to other domainstasks the author suggest to tackle this with via a 2 component architecture one for learning the task and one for forecasting in empiricial experiments the authors show the practical feasibility of their approach as a caveat im not an expert in the area so my review remains on a superficial level consequently for which i apologize i overall liked the paper quite a bit the question discussed is relevant the empirical evaluation is very good the theoretical results seem as relevant as they would get and the related work discussed is crisply presented and relevant one question i would have is that results in table 1 are overwhelmingly good with only unetc coming close do we know for these tasks what the theoretical upper bound eg by the right pde system would be is it computationally even possible to compute this upper bound im wondering how much of a gap there still is too close in a similar vein what is the intuition behind dyad resnet being better than dyad unet mostly are there some complementary strengths between dyad and resnet that this combination can exploit better than dyad unet this is a good paper that id like to see accepted for its combination of theoretical results empirical results and methodological novelty docsepthis paper is interested in learning general forecasting models for physical dynamical processes the paper proposes a decomposition of such a model into an encoder that captures the innate properties of the system and a forecaster that autoregressively makes predictions conditioned on the encoded properties this is framed as a metalearning approach and is shown to substantially outperform singletask approaches and offtheshell metalearning approaches across multiple datasets the paper provides some theoretical analysis and qualitative analysis of what is learned overall the paper shows that learning shared models across domains is an important and fruitful way forward for modeling physical processes with machine learning strengths the problem statement is wellmotivated learning generalizable deep learning models across diverse settings is an important open problem experiments use interesting and realworld problems results are strong and appear reliable adapad is an interesting idea specialized to the case of physical complex systems since it is designed to address boundary condition issues visualizations show the model is behaving essentially as expected although there are many design choices that go in to the model each such design choice is wellmotivated aside from some aspects of the theory section the exposition is generally quite clear and wellorganized assumptions are made clear the fact that the encoder can be trained first and independently of the forecaster should be very useful for further rapid developments great to see ese metric used as a complement to raw error table in appendix showing alternatives to adain is very useful in increasing confidence in adain for this application weaknesses the biggest concern is the theory section the multitask learning and domain adaptation results are general results that are not adequately connected back to the specific model and problem the paper is considering yes it is widely accepted that multitask learning and domain adaptation can work well especially when tasks are related in some measurable way and it can be a useful exercise to restate existing theory in the language of your framework but what if any novel claims is the theory implying are there any predictions the theory makes about the particular approach which can be validated in experiments the theoretical bound on error that decomposes the error of the encoder and forecaster is similarly lacking in its interpretation yes it can be a useful exercise to show that the error can be decomposed along the lines of the model but does this bound somehow suggest that the decomposition results in lower error than a monolithic model or is it showing that you can work independently on improving either part of the model and improve the overall error where is there potential for practical value in this theorem for example one place there could be potential to validate the theory is to check in experiments that task pairs with lower wasserstein distance actually support better domain adaptation however in the introduction of the paper it acknowledges that even the slightest change in these features may lead to vastly different phenomena but doesnt that suggest that wasserstein distance may not be a useful metric here for measuring task similarity couldnt turbulence limit the usefulness of such a metric proposition 33 says the bound is strictly looser than the bound in theorem 31 for clarity it would be very helpful to combine the bounds into an inequality showing this strictlylooser property it is not immediately apparent from the statement of the theorems since the inequalities contain different terms as is the theory doesnt really hurt the paper but for the amount of space dedicated to it it doesnt add much the paper could be substantially improved by either 1 adding interpretationpredictionsvalidation of the theory that connect it back to the approach in the paper or 2 removing some of the less useful parts of the theory from the main paper to free up space for more of the interesting analysis of what the model actually learns also it is interesting but a bit counterintuitive that the theory section relies on results in multitask learning and domain adaptation instead of theoretical results from the metalearning literature as is since the paper relies on multitask learning so much it is missing references to related work in multitask learning ie related work outside of modeling physical dynamical systems similarly it would be helpful to mention why there are no comparisons to multitask learning or domain adaptation methods in the experiments why do they not apply here the three terms in the loss function of the encoder are wellmotivated but it is not clear how important each term is ablations on these terms would be very informative for the reader to understand whats generally required to train an encoder in section 5 it says varsepnet employs separation of variables through different loss terms what are these loss terms and how are they different from the ones in the paper in the ablations with no encoder how do adain and adapad work dont they require some z where does this come from if not from the encoder unet does seem it could be at a qualitative disadvantage compared to dyad in terms on number of parameters especially since unet c is one of the more competitive baselines it would be useful to see results for a larger unet c or at least some evidence that the unet is not underfitting the training data additional question of interest overall this is a very important a potentially deep line of research the most exciting promise of such work is the potential of revealing shared regularities across vastly disparate dynamic systems that is across complex physical processes and it seems the approach in the paper could be particularly wellsuited to such research for example the authors could train a single encoderforecaster model across all the datasets in the paper and analyze relationships in the learned encodings across datasets training models across highly diverse domains have been tried in multitask learning eg pretrained transformers as universal computation engines arxiv 2021 the traveling observer model iclr 2021 modular universal reparameterization neurips 2019 one model to learn them all arxiv 2017 is such a generalization part of the longer term vision for this line of work minor comments in section 24 some references would be useful in the sentence ending with the combined force equation there are several inconsistencies in the use of parentheses in citations throughout the paper correcting these would improve readability in last sentence of first paragraph of section 4 the word task could be changed to something like problem since task has another meaning in the paper should the 726 for unetc on ocean currents future be bolded in the last paragraph of section 51 we tried to vary we tried varying or we varied appendix a21 footnote for phiflow is on the wrong page appendix a21 the last paragraph seems like it should be the first paragraph of a22 in proof of proposition b5 there is an extra or missing set of norm bars in the first inequality overall this is very interesting and useful work the problem is wellmotivated and the approach and experiments are carefully designed and generally convincing if the concerns about the theory are addressed i would be happy to increase my score adding the additional info and experiments requested could increase it further and make this a particularly strong paper ### Summary:

the paper addresses the problem of domain generalization for learning spatiotemporal dynamics it proposes a solution where an encoder captures some characteristics of a given environment and a forecaster autoregressively predicts future dynamics conditioned on the characteristics learned by the encoder said otherwise the forecaster learns the general form of dynamics parameterized by an environment representation extracted by the encoder the conditioning is implemented via an adaptive instance normalization mechanism a form of padding is also introduced in order to take into account boundary conditions the two components encoder and forecaster are trained sequentially this approach is casted in a metalearning framework theoretical results inspired by multitask learning and domain adaptation are also demonstrated the model is evaluated and compared to different baselines on three problems and for two different settings varying initial conditions with a given dynamics and dynamics with varying parameters this is a borderline paper it targets a timely and important problem of domain generalization for dynamic environments the proposed solution is original and compares well experimentally to several baselines it allows for better generalization performance for the two test settings considered in the current version the paper however suffers from different weaknesses first there is the imprecision of the arguments and the description of the experiments some of the arguments and claims are vague and sometimes abusive not backed up by evidence for example a central claim is that the encoder learns time invariant quantities characterizing the environment when the learned representations indeed change with a time shift in the input for any environment the same goes for the argument developed for the padding construction it is claimed to model boundary conditions but this is not supported by any theoretical or empirical evidence as noted by the reviewers the theoretical analysis is disconnected from the algorithmic and experimental developments and does not bring much additional value to the paper what is more embarrassing is that some of the claims in this section are overstated and induce incorrect conclusions from theorem 31 and proposition 33 the authors suggest that multitask learning leads to better generalization than learning independently while this is not formally guaranteed by the results this is acknowledged by the authors in a later comment besides the conditions of validity are not discussed while they seem to only cover situations for which the train and the test distributions are the same the same holds for the second theoretical results theorem 34 it is claimed that this result supports the authors idea of training encoder and forecaster sequentially while it does not besides the bounds in this result cannot be controlled as noted by the reviewers and are not useful in practice overall the paper addresses an important topic and proposes new solutions the results are promising and it is indeed an interesting contribution however inaccuracies and incorrect or exaggerated claims make it difficult to accept the current version of the article the article would make a strong and innovative contribution if it were written as a purely experimental article with a detailed description of the experiments and comparisons

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper studies the mixture of experts moe architecture which has become popular in nlp recently as a way to increase the capacity of network without increasing depth the authors aim to develop a theoretical understanding of the moe modelconditional computation the authors begin with a formal model for conditionally activated sparse models which can capture common existing moe models the authors use lsh locally sensitive hashing for the gating in moe and use this to derive a few theoretical results regarding the ability to approximate real valued lipschitz functions in rd the authors perform some small scale experiments to back up and verify their theoretical findings post rebuttal thanks to the authors for running the experiments and for sharing the insights like i said earlier it is important to study the theoretical underlining of moes this paper starts with it although as a researchers actively working in moes i do not think that the paper exactly answers the key questions the results proven are expected and not surprising but on the other hand as pointed by the authors nontrivial to prove so i would say that it is a decent paper at the moment and would suggest the authors to keep going in this direction to develop a more thorough understanding so that they can uncover some more fundamental results strengths 1 relevant problem moes are becoming very popular in nlp thus it is important to study their underlying theoretical and working mechanisms the paper tackles this relevant problem 2 lsh locally sensitive hashing authors propose to use lsh for gating this can actually be quite promising in my opinion because it takes the local vicinity into consideration 3 well written it is a very well written paper and is easy to follow i really like the limitations section it is good to see for a change that there is somebody who knows and writes the limitations of their work weaknesses 1 weak experimental evaluation i think that lsh is infact good authors need to perform more experiments to show its effectiveness i am not suggesting to go to the huge model sizes but atleast medium scale models and datasets should be evaluated very well discussed docsepfrom my understanding the main contribution of the paper is as follows 1 the authors capture the sparsity structure of these popular transformers they model the transformers into dsm models 2 they show that the dsm model can represent the lsh model 3 they provide theory on the lsh model these theories can be used to interpret the success of switch and scaling transformers 4 motivated by the theory they proposed a new lshbased model and run toy experiments to show its efficacy see my comments below i have the following concerns 1 in the current manuscript the connection between contribution 12 34 is still a bit vague see my comments in the summary part when reading the current version it is easy to get confused about the main contribution of the paper is it explaining why general sparse model like scale transformer works well or is it designing new lhs methods to save inference costs for me the contribution of the explaining part outweighs the designing part this is because the author didnt provide any realdata experiments on lsh if no realdata experiment is provided lsh is a pure theoretical tool to prove the theory on dsm and the designing part is minor however the current script overemphasizes the designing part causing great confusion for me 2 the performance of dsm on cifar10 does not quite match the theory to support the theory i would suggest the authors run experiments on transformerbased nlp tasks instead of cv tasks on cifar 3 all the theories are built on the function lipschitz assumption it would be better if the authors verify the lipschitz condition is it a necessary condition or it is due to the limitation of the theory if it is the latter case what is the main technical challenge to relax this assumption 4 in line 255 why is random ddegree polynomial a lipschitz function docsepthis paper provides a theoretical treatment of modern sparsely activated networks with the datadependent sparse model dsm model the authors show that the dsm model can simulate modern sparsely activated networks and the locality sensitive hashing lsh model it is proven in the paper that the lsh model can be expressive as a dense network for approximating realvalued lipschitz functions while requiring much fewer flops furthermore experiments are conducted to validate the theoretical findings on lipschitz target functions as well as the cifar10 dataset strength 1 the paper is the first work to treat sparsely activated networks theoretically thus novel to me 2 the paper is wellorganized weaknesses 1 the theoretical analysis is based on the assumption of the llipschitz target function and i am not sure how significant the work is furthermore the neural network size used in the experiment is also very small 2 i am not very sure about the relation between the theoretical findings and experiments theorem 41 and theorem 43 conclude that lshbased sparsely activated networks can be expressive as their dense counterparts when their size and number of samples match as for size the lsh model is measured using hash table size and the dense model is measured using width as a result in experiments i am expecting to see the lsh model is as good as dense ones when buckets width of the dense network however in the figures the width of dense networks is compared to the number of activated units 3 for comparison of dsm and dense networks the authors mention that sparsity helps in both dsm and lsh models using the same number of activated units however it seems that the comparison may be unfair specifically with 64 activated units dsm chooses the best 64 units out of total of 1024 units while the dense one has only 64 units in total it seems unsurprising to me that dsm is better than its dense counterpart yes the authors have addressed the limitations and potential negative societal impact of the work docsepthis paper proposes the dsm model to sparsely approximate lipschitz functions the authors theoretically demonstrate their method in a wide range of scenarios from onelayer shallow neural networks to switch and scale transformers the original idea but i am not sure as i am not familiar with this domain of interpreting dsm as knn is very interesting however the experiment setting is a bit weak and seems to be finished in a rush i have increased the rating from 4 to 5 after rebuttal clarity strengths this paper offers a detailed introduction to the lsm model and other background knowledge weaknesses if the authors can further unify the usage of notations the overall readability will be better for example the authors use s for the sparsity parameter but in sec 30 it switches to k and later k is used as the intrinsic dimension of input distributions the usage of notation ax is also a bit confusing i also recommend the authors add some figures to illustrate their idea for example the euclidean lsm and sec 30 can be well explained by figures originality i am not familiar with this domain so i may not be able to judge this point but still i find the argument in sec 30 interesting it points out a potential direction in that we may interpret neural networks as knn operators the current content can be enhanced in some directions the authors may try to remove the constraints of unit b rows typical network blocks like cnns attention networks and residual connections do not have such unit structures also extending it to deep neural networks will be more attractive quality strengths the theory analysis is careful and in depth but some settings and assumptions need either explanation to justify the necessity or adjustment to cater the practice demands weaknesses weird experiment setting 1 now that the main point is the efficiency of the proposed method why not report inference time flops is a good metric but not enough 2 needs more detailed ablation study specifically detailed study on how sparsity parameter s influence the model accuracy approximation mse and inference time 3 now that the paper put much attention on discussing input distributions the authors should also use input distributions on low dimensional manifold in rn currently it is unclear how the input is sampled 4 from numerical perspectives polynomial may not be good choices as they tend to be extreamly ill conditioned when degree is high the authors may consider bspline or bzier curve for some realistic industrial scenarios also random neural networks even shallow ones may be good candidates theoretical settings need clarify and adjustments 1 it is a bit weird to assume input distributions be uniform can it be replaced as absultely continuous with respect to the uniform distribution lebesgue measure 2 now that the proof is based on euclidean lsh it should be clearly stated in the theorems significance the theory results is good but needs stronger empirical evidence to support it 1 i strongly encourage the authors to add figures to illustrate their concepts and ideas 2 experiments on sota neural networks will be much appreciated 3 needs a more detailed ablation study to justify the theory results ### Summary:

the paper provides a theoretical analysis of sparsely activated neural networks they introduce lsh local sensitive hashing as a new routing function for theoretical analysis and proved a few results on representation power and inference time one reviewer pointed out that the theoretical results are expected and do not provide much interesting insight which i agree with nevertheless this is one of the early papers that study sparsely activated networks and may serve as a starting point i recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper proposes a new approach to inject knowledge into pretrained language representation models plms instead of tuning the original plm parameters the paper plugs in new adapters for knowledge injection to avoid catastrophic forgetting pros injecting knowledge into plms is an advanced topic the authors focus on the catastrophic forgetting problem during knowledge injection evaluation is solid the authors evaluate their model on three downstream tasks and show that the adapters improve the performance the paper is well written and can be easily understood cons the approach is simple but achieves good performance over a variety of tasks i appreciate that the authors conduct the knowledge probing experiment but its p1 is quite low and worse than bert some more explanations are expected docsepsummary the paper proposes a novel approach of incorporating different types of world knowledge sources contained in texts such as facts or linguistic syntax to do this they introduce additional transformers layers between the layers of a pretrained language model such as roberta and term this model as kadapters where the k stands for k different streams of knowledge pros incorporating different sources of information into a pretrained model such as roberta is an interesting idea the proposed approach is simple and interesting and it scales to many different types of information as the different adapters can be trained in parallel with the weights of the pretrained lm being fixed performance gains on different classification tasks such as entity tying questionanswering and relation classification highlights the utility of the approach cons sec 1 introduction in the introduction there are multiple mentions of the phrase rich knowledge but it is unclear what do the authors mean by that in the context of pretrained language models some recent works such as httpsarxivorgabs200208910 httpsarxivorgabs190901066 suggest that pretrained language models do indeed contain a lot of world factual knowledge hence the statement in the paper that pertained lms lack world knowledge is contradicting these works there is also a frequent mention of catastrophic forgetting of knowledge during the finetuning step i tend to disagree that this is necessarily bad for a pretrained model because it has been shown that finetuning pretrained lms perform well in opendomain question answering where some degree of world knowledge is needed furthermore producing entangled representations may not necessarily be a negative thing if multitask learning approaches are able to show an increase in performance due to knowledge injection in table 1 dependency parser doesnt really fall under the same class of knowledge sources such as wordnet or wikidata a dependency parser may be able to provide some sort of syntactic structure of the underlying text moreover such syntactic information is not always generalizable to different domains and thus has the limitation of not being accurate enough the introduction section is not wellmotivated and does not present convincing arguments as to why external knowledge infusion is really required in some tasks it just states that knowledge infusion using kadapter model outperforms roberta models in different tasks in section 31 not enough space has been allocated to explain the adapter model in detail if the authors had used mathematical notation or equations for explanation then it would have been much more clear in section 4 it is mentioned that they select three downstream tasks for evaluating their models however the paper doesnt provide justifications as to why these tasks were selected how can these tasks highlight the importance of kadapter model etc in the results table 2 and table 4 as the performance improvements are somewhat marginal it is important to know if these improvements are statistically significant or not the paper doesnt report if the results are from single run or the mean of multiple runs i have concerns about data leakage during the pretraining step as the factual adapter makes use of supervised relation classification dataset trex i feel that there might be some overlap between entity typing and relation classification datasets used for evaluating the performance of the model the authors should present an analysis as to what degree of overlap if any is present during the pretraining and evaluation tasks the paper lacks a detailed analysis section that could explain as to which test examples are getting correctly classified when using kadapter model in tasks like relation classification entity typing compared to other baseline approaches such as roberta roberta multitask currently the paper pays just too much emphasis on raw numbers or performance improvements in various tasks the results of the probing experiments suggests that bertlarge model vastly outperforms kadapter model on googlere and trex datasets probing datasets this raises an important question over the validity of the results in different downstream tasks for a fair comparison with baselines the authors should compare the performance of the kadapter model with bertlarge multitask across different tasks in almost all of the experiments the authors use roberta as the underlying pretrained language model for demonstrating generalization to different pretrained lms the paper should also evaluate when kadapter model is trained when bertlarge or t5large are used as underlying models in place of roberta grammar errors page 1 3rd line from bottom remains retains section 33 information that concerned information that is concerned section 34 father index is commonly referred to as head index of a worddocsep summary this submission proposes a general method kadapter for injecting knowledge either factual or linguistic into pretrained language models the key architectural property of the approach is that kadapters are isolated from one another allowing the use of multiple adapters without interference these kadapter modules take hidden layer inputs from the main pretrained model eg bert and are pretrained on their knowledge outputs before a finetuning phase where they feed into a joint downstream taskspecific model along with the pretrained model outputs strong and weak points the set of baselines seem strong and the experimental results consistently show that using either factual or linguistic knowledge kadapters improves while using both yields the best results the lama probing experiment is a nice sanity or validation test that the knowledge injection is achieving the desired effect being able to hardcode knowledge into the model in this way could be useful in a variety of applications it is overselling it a bit to say the model captures richer commonsense knowledge however the basic architectural idea is wellmotivated and simple in a good way the supplemental materials mostly provide additional reproducibility details on architectures hardware used learning rates etc recommendation accept or reject with one or two key reasons for this choice i recommend to accept the proposed approach yields strong quantitative performance against solid and relevant baselines and the lama experiments give some support to the hypothesis that it is doing so by capturing knowledge as intended the general design pattern could spur further innovations in modular network designs or knowledge capture strategies as well questions to clarify additional evidence required bertmk inegrates fact triples from the knowledge graph how i can follow the cite but this sentence provides little information inject different types of knowledge independently is it correct to say then that by design there can be no beneficial interactions or synergies among different types of knowledge alternatively in the finetuning phase could different adapters interact or affect each other via the downstream coupling in the taskspecific layers is this observed in practice how should the reader think about the relative magnitude of the presented improvements at one point i see kadapter fl makes significant improvement of but i believe significance is only meant coloquially here section 31 how was this structure chosen what was the motivation or intuition here what limits if any do you foresee with the use of separate parallel knowledge modules like this could we use 10 100 1000 kadapters additional feedback to improve it would be helpful to cite ling and weld 2012 or similar for the definition of loose micromacro f1 or briefly explain it inline in the evaluation setup likewise for the catastrophic forgetting phenomenon affecting other knowledge injection attempts is there some previous work explicitly demonstrating this problem when using multiple knowledge sources if not it would have been interesting to have an experiment of this sort in this work docsep reasons for score the authors propose a plugin based adapter approach to allow for task specific parameter settings without updating the original pretrained model which prevents the potential for catastrophic forgetting while also removing the need for separate models for separate tasks the work seems to build off houlsby 19 as briefly cited but its in plugin nature seems easier to adopt for multiple tasks there is however not direct comparison with it or cooper et al 19 httpsarxivorgpdf190202671pdf which makes it difficult to assess the way in which the adaptors were pretrained was a little unclear to me the experiments are extensive and well done pros 1 the number of experiments run 3 tasks on 6 datasets total are extensive and shows the kadaptor approach can benefit from the factual adaptor in particular in giving better performance over roberta with or without multitask learning 2 the proposed adaptor seems concise and easily expanded to incorporate other knowledge sources though there are few details which could help clarify things see 2 in next section 3 the probing task using lama to show how much factual knowledge has been memorized by the k adaptor roberta facadapter was well done and its discussion was very interesting cons 1 the proposed adapter solution is somewhat similar in nature to that of houlsby 19 and to a lesser extent cooper 19 httpsarxivorgpdf190202671pdf and it feels like an omission to not discuss houlsby 19 and make experimental comparisons against it discussing proscons more thoroughly especially since in the extensive experiments done in this work it is shown the linguistic adapter usually only adds a tenth of a percentage point when using roberta with a single factual adapter in this single adapter case then its not immediately evident how these models would differ and what the advantage is both houlsby and cooper are evaluated on the glue benchmark and provide code 2 i was a little confused as to how the adapters were specifically pretrained and it might be a question of figure 1b but also sections 33 and 34 could have been expanded to clarify it a bit it is my understanding that when pretraining the facadapter on the relation classification task for instance in section 33 for a given example in trexrc two entities and context are passed into roberta whose weights remain fixed while those of the kia units of the facadapter are updated and the final hidden representations of roberta and the facadapter are concatenated to form an input representation of the entities given their context and this used for the actual task is my understanding correct if so im confused as to how the subsequent pooling and concatenation actions are done clarifying this process for 33 and 34 would be beneficial for clarity purposes and its not discussed in the supplemental materials either 3 your roberta large baseline already beats most of what you are comparing against which is fine as your adapters give gains again particularly the facadapter but it also would have been interesting to see what sort of gains would have been achieved using a different less powerful model as the base roberta small or just plain bert and additionally some sort of ablation testing or explanation on the choices made for the adapter networks themselves ie n2 transformers etc hidden layer size etc though its possible this could be left for future work for clarity in figure 2 where you show n x transformer layer and n2 im assuming the first transformer layer feeds directly into the second transformer layer which then feeds into the up projection layer correct if so it might be better just to show two transformer layers like that instead and additionally naming the projection layers up and down projection layer respectively questions during rebuttal period please address and clarify the cons above small typos in abstract we propose kadapter which remains the original parameters remains keeps or retains in introduction they fail to continual learning fail at continual learning it remains the original representation remains leaves pg2 while remaining the original parameters of roberta frozen remaining keeping section 3 it remains the original representation remains keeps 31 different from houlsby et al 2019 add adapter layers in contrast to houlsby et al 2019 who add adapter layers 33 all relations having lees than lees less ### Summary:

the paper augments pretrained language models by introducing adapter where each adapter is another language model pretrained for a specific knowledge source eg wikidata and an objective eg relation classification the representation from each adapter is concatenated to the representation from the generic lm specifically they introduce two adaptors factual mostly derived from wikipedia and linguistic from dependency parser and the experiment shows modest improvements over various benchmarks this is a borderline paper as both methods and experiments are reasonable yet not very novel or strong the clarity of the paper can be improved as pointed by r1 and r4 without any mathematical notations model details are to be interpolated from figures the novelty is limited and experimental rigor can be improved ie for many settings gains are fairly small and no variance reported

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper presents a formal analysis for the impact of graph reordering ie ordering the inmemory storage sequence of graph node embeddings on the cache efficiency of near neighbour searches using near neighbour graphs the connection of the graph ordering ie memory layout of the graph nodes and the cache complexity is formulated based on which the cache complexity of the gorder wei et al 2016 is analysed and two other graph ordering corder and porder are proposed experimental results on large real datasets confirm the effectiveness of the analysis and the importance of graph reordering for near neighbour search efficiency strengths 1 the paper has presented a solid analysis for the impact of graph reordering ie ordering the inmemory storage sequence of graph node embeddings on the cache efficiency of near neighbour searches using near neighbour graphs 2 experimental results on large real datasets are presented with an interesting discussion on the results 3 source code of the paper is available weaknesses 1 the proposed method corder is ineffective as discussed in the experimental results perhaps this method can be dropped to make room for adding more details on the experimental settings to the main content of the paper 2 the other proposed method porder is only somewhat better than the existing method gorder table 5 3 a lot of contents have been included as supplementary material which makes the paper somewhat difficult to follow typo degreebased groupingfaldu et al 2019 studies show that studies show that the paper included an interesting discussion on the results and limitations docsepthe paper studies practical performance of different methods for rearranging the node layout in the memory for graphbased approximate nearest neighbor search algorithms hnsw specifically it also proposes a simple modification of the existing methods based on query profiling the paper claims to have up to 3050 improvement in query latency on 100m datasets and is accompanied by code though not clear if it is going to be released strengths simple change that is very likely to be agnostic to the type of the graph algorithm used sizable gains from the algorithm on large datasets sourcecode hopefully will be released with the publication not clear from the text long discussions of the results weaknesses the paper does not have a clear description of the methods no pseudocode or even text description of stepbystep actions i guess the readers are suggested to follow the cited papers but imo there should be at least a sketch of the best solution the source of 1000 queries for porder not discussed in the paper were they used from the train set or the test set there is no implementation of porder in the code which is confusing the code also has bugs eg nonexistent openmp flag it does not compile without fixing the dependencies could had been fixed if you provide dockerfile and there are errors in its description judging the code the construction is done in a single thread if index construction time provided in the paper is for this regime which is not clear from the paper but seems to be the case it should be redone in the multithreaded regime i do not think there is any negative societal impact docsepthis paper proposes to use graph reordering to improve the cache locality of graphbased nearest neighbor search algorithms an analysis is conducted to show why graph reordering works and the experiments show that graph reordering significantly improves performance strength 1 graphbased nearest neighbor search algorithms are very popular and graph reordering improves the performance of graphbased nearest neighbor search algorithms 2 although the conditions are restricted the analysis explains why graph reordering improves performance 3 the profilingbased ordering scheme makes sense weakness 1 the authors should dig deeper to show why recording improves performance currently the explanations are vague eg due to software prefetcher and auxiliary functions the authors may want to make them more specific by explaining how software prefetcher works or conduct some experiments to show where the reduction comes from 2 i believe that graph reorder works for graphbased algorithms in general but it helps to show by experiments that graph reorder improves the performance for algorithms other than hnsw eg nsg or ngt 3 the legends in the figures are too small to read yes ### Summary:

this paper studies how to order inmemory sequences for graph embedding there was a positive consensus that the studied problem is interesting and results are sufficiently discussed there were some concerns on missing results which were addressed during rebuttals

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper introduces the pacbayes information bottleneck pib starting from the generalization bound eq 4 which shows that the generalization gap is upper bounded by a function of iws the authors proposes pib which has an additional regularization term of beta iws since the computation of iws is intractable the authors then make several assumptions to simplify its computation arriving at an estimate of iws by tildeiws eq 15 and use sgld to compute it in practice experiments show that 1 there is a twophase transition in sgd training as indicated by tildeiws 2 tildeiws seems to correlate with the generalization gap under different variations of experiment hyperparameters number of hidden layers noise ratio and number of random label data 3 it improves performance compared to l2 and dropout regularization strength this paper addresses an interesting and important problem the proposed pib is novel the experiments show that the proposed tildeiws correlates with the generalization gap and helps improving the performance weakness in order to make the computation of iws tractable the authors make several important assumptions it would strengthen the paper a lot if the paper discuss and perform experiments to show if the assumptions are valid in the experiments the authors run furthermore in section 55 can the authors show the generalization gap together with the train and test acc with different regularization ideally we should see that with pib as the objective the generalization gap is much smaller than the other methods with this we can then be confident that the improvement is due to reduced generalization gap instead of better training in summary this paper is novel but the experiment should be strengthened as detailed in the main review docsepthis paper proposes a new version of the information bottleneck objective for training neural networks this is in part motivated by previouslyderived pacbayes bounds on the generalization error that are proportional to the square root of the mutual information of the weights and the training dataset iws thus this new information bottleneck objective attempts to minimize both the empirical risk and this mutual informationthe paper derives a computationallytractable algorithm for estimating iws then this algorithm is used to show that this quantity is inversely correlated with generalization loss on a variety of neural network architectures strengths the paper proposes an exciting general principle of deep learning as far as i know the contributions here are novel and will be of high interest to the community the authors build on previous work by showing how their ib objective addresses the shortcomings of previous work in this area it is very well written this is a highlytechnical paper and the details are presented in a careful and thoughtful way the experiments are well done and the results support the conclusions specifically this objective is motivated by a pacbound the tightness of which is not clear and various approximations are used to estimate iws the accuracy of these are not immediately clear the experiments address these issues by showing that the motivation and the approximations are reasonable weaknesses the limitations of this method are not discussed clearly for example the paper provides an algorithm for sampling from the weight posterior pws but how does this compare computationally to standard training of a neural network or a estimating the posterior in a bayesian neural network there are some minor grammatical and spelling typos throughout eg infection point an excellent paper with exciting ideas clear presentation and technical depth docsepthe authors propose new interpretation of the information bottleneck ib dubbed pacbayes information bottleneck pib where the ib is defined wrt the mutual information between feature representations t and either inputs x or targets y pib is defined wrt the empirical risk over the dataset s xi yii1n and the mutual information between model parameters and s imathbfw s or information stored in weights the authors show that pib is a bound on generalization error the authors derive a tractable estimator for imathbfw s the authors present an approximate inference method for pmathbfw mid s that utilizes the proposed pib the authors show that pib reflects the hypothesized twophase fitting and compression modes of neural networks across different activation functions network depth and network width they show that imathbfw s yields a good estimator of the generalization error that is robust to label noise they show that their inference method improves generalization across several benchmark datasets strengths to my knowledge this paper has significant technical and empirical novelty the authors do a good job of summarizing previous work and differentiating their contributions this is not my area of expertise but the derivation of pib the estimator for imathbfw s the proposed optimal posterior all look novel and correct the experiments are well done thorough and support the main claims of the paper weaknesses the main weaknesses of this paper are language and clarity i would recommend a thorough grammarly or perhaps external advice the graphs should report means and standard error intervals over multiple random seeds some specifics some technical terms are used before definition eg phase transition in abstract in the abstract ib cannot explain maybe ib theory can as you use in the intro iiw and imathbfw s are redundant would recommend just using imathbfw s third mutual information becomes trivial in deterministic cases please elaborate cite 2 we derive a solution to the intractable can something intractable have a solution maybe approximation is better optimal posterior of pib does pib have a posterior or is the posterior over the weights figure 2 iiw only shows compression phase can the loss also be included in these plots to my knowledge this paper demonstrates significant technical and empirical novelty i believe the main weaknesses can be addressed prior to publication therefore i recommend acceptance however i am not an expert on this topic so my confidence is only a 2 docsepthe authors propose a formulation of the information bottleneck problem replacing the mutual information between input x and latent representation z via the mutual information between the sample s and the weights w obtained from the sample they derive closedform solutions for this mutual information in the gaussian setting and propose an sgld scheme to optimize the objective using this objective and optimization algorithm the authors investigate several interesting scenarios including different activation functions and noisy labels the paper is generally well written and treats an interesting and timely topic the idea to limit the information about the sample that is contained in the weights is not new the authors cite several works that bound the generalization error via this information but this is the first time that i have seen a corresponding cost function implemented in practice there are however a few issues that are not perfectly clear to me the authors cite the literature stating that the generalization gap is limited by isw if the loss is sigmasubgaussian does this hold for the negative loglikelihood in 6 also in 6 is s a random variable or not 4 requires that isw is computed as an expectation over ps while the loglikelihood in 6 is an expectation over pws ie not over ps but over a concrete s how can this be understood connected to this is it safe to call the resulting cost function an information bottleneck cost function i assume that this is better called an iiwregularization rather than an ib cost the ib cost is a very specific formulation that combines a mutual information cost with a mutual information utility whereas here we have a general cost with an additional mutual information cost as regularization term the authors correctly claim that ixt becomes trivial if the network is deterministic more precisely this mutual information becomes infinite in many of these cases see learning representations for neural networkbased classification using the information bottleneck principle by amjad and geiger i believe that this result carries over to isw being infinite for deterministic learning algorithms this may not hold for all learning algorithms but certainly for some my own gut feeling suggests that isw is infinite for sgd with finitely many epochs eg by the fact that there are only combinatorially many options to shuffle the batches but that it is finite for sgld where noise is added to the weights it is therefore not clear to me in which settings the analysis in section 3 is a valid approximation in other words in which settings is the assumption that pws is gaussian valid does it only hold for sgld connected to the point above in which cases is the assumption that pw is gaussian a valid approximation can this gaussian assumption about pw be used to bound isw from above eg for a gaussian learning algorithm can it be shown that the term isw is maximized if w becomes gaussian as well this would be at least intuitive from a channel coding perspective where a gaussian channel input is known to maximize the mutual information through a gaussian channel and which is then known to produce a gaussian channel output in algorithms 1 pls compare line 9 with your equation 15 in 15 you sum over squared inner products in line 9 and 11 you square over the resulting sum of inner products is this difference intended and if so how can it be explained also do we have t0 ge t1 in algorithm 1 in fig 1 why is the mutual information iws evaluated for different layers what is the exact meaning of splitting the iiw between layers in terms of the generalization bound i was assuming that the generalization bounds all consider the entire set of weights and that the proposed pib should do so as well also in fig 1 the discussion of the inflection point is not fully clear in section 51 it is claimed that the variance of the information explodes can this be made more precise eg by writing down the mathematical symbol for this variance furthermore this is not shown in the figures if i remember correctly in all figures why is the mutual information isw so small these numbers do not seem right i would assume that it is necessary to learn more than 102 bitsnats to successfully solve a classification problem in other words while the general trend of iiw seems to be correct i am not convinced of the correctness of the absolute numbers can you provide some intuition about these small numbers is this connected with the proportionality symbol in 14 but going from 8 to 9 it seems to be that additive constants are dropped not multiplicative constants for the sake of clarity i would prefer that footnote 3 is in the main text also in some instances the notation and terminology is not clear eg is s sampled iid in 4 why is the oracle prior called an oracle how exactly is the bootstrapping resampling weight zetak defined why is the temperature beta called the annealing temperature just before 18 at the end of section 52 you write that the l2norm keeps increasing the norm of what a very interesting paper dealing with an interesting and timely topic unfortunately the paper is not perfectly clear throughout all sections ### Summary:

this paper revisits the information bottleneck principle but in terms of the compression inherent in the weights of a neural network rather than the representation this gives the resulting ib principle a pacbayes flavor the key contribution is a generalization bound based on optimizing the objective dictated by this principle which is then tractably approximated and experimentally verified reviews raise concerns about assumptions made to achieve the tractable version and a public discussion debates whether this is truly a pacbayes bound the authors address these adequately another concern is whether improvements in experiments can be ascribed to the new objective authors add new experiments in support of this additional concerns about the clarity of certain aspects of the paper were or were promised to be addressed by the authors overall the perspective of this paper its technical contributions and experimental evaluations appear to be worthwhile to share with the community as they advance the applicability of the information bottleneck principle

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: i liked this paper quite a lot although this paper does not belong to my area of expertise i was able to understand the paper clearly because of its lucid exposition experimentally the authors show a novel gnn design with an attention module that has comparable performance to the mlp and outperforms other gnn designs i believe that this will be a valuable contribution to many practical problems unfortunately this work does not have any theoretical results and evaluating the experimental results is outside my range of expertise therefore i would like to defer this paper to my fellow reviewersdocsepmain idea in this paper the authors study the problem of gcn for disassortative graphs the authors proposed the gnan method to allow attention on distant nodes indeed of limiting to local neighbors the authors generalized the idea of graph wavelet with mlp to generate the attention score and utilized it to generate multiple attention heads the authors carried out experiments on several realworld networks 4 assortative and 3 disassortative with comparison to several stateofart gcn methods strength the authors study a very interesting problem of gcngraph embedding or disassortative graphs the proposed method is well motivated with solid theoretical motivation from graph wavelets the proposed model is very intuitive generalization of graph wavelet methods the empirical evaluation is very thorough on seven networks with comparison to about 10 baselines of different kinds weakness though the authors mentioned the use of sparsification of attention for speedup however it mentioned that t is set to zero it is interesting to see how scalable the proposed method is as it needs to have global attention to possibly all nodes an empirical comparison of running time would be very helpful the authors only carry out experiments on three disassortative which are all very small it would be interesting to see more experiments on disassortative graphs alternatively it would be interesting to have an experiment on synthetic graphs where the beta can be controlled and varied smoothly to see how it affects the performance of different algorithms the authors picked only node classification of evaluation tasks it is interesting to see how the disassortative could impact other tasks like graph reconstruction and link prediction docsepthis work propose a new gnn architecture to help gnn break its limitation on only working over homophilic networks the technical is to use introduce graph global attention i think the paper is written okay the motivation is clear the solution is reasonable however i have following criticisms 1 this work has limited novelty observing that gcn cannot work well over heterophilic networks is not a new idea and observation using attention to capture the features from faraway nodes is natural but not novel i do not think that it is reasonable to argue against other works eg 1 that adopts the above idea by saying they are not expressive enough expressiveness sometimes may lead to model overfitting actually chevnet 2 can also capture faraways nodes and be expressive enough why does it not work well i guess that it is due to some overfitting issue moreover if i understand it correctly the limited difference between this work and 3 is most likely the global attention which has very limited contribution 2 although the work claims everywhere to tend to decrease the complexity when computing the global attention one still needs to do computation for every pair of nodes which is of course not scalable for even mediumsized graphs 3 the heterophilic networks used for evaluation are very small with only several hundred nodes why not try larger ones say actor cham in 4 i guess the computational issue comes from the global attention 1 nonlocal graph neural networks 2 convolutional neural networks on graphs with fast localized spectral filtering 3 graph wavelet neural network 4 geomgcn geometric graph convolutional networks postdiscussion update i would like to thank the authors for preparing the rebuttal and attending our discussion however i still think the complexity is a concern of this work i do not think that eq 3 can be implemented within the complexity that the authors claimed moreover if the authors use another way to compute the attention scores that way should be very clearly stated instead of written in a different form given the high complexity i cannot clearly see the advantage of this work in comparison to 1 as the nonlocal attention has been proposed in 1 already 1 nonlocal graph neural networks ### Summary:

this paper proposes a gnn that uses global attention based on graph wavelet transform for more flexible and datadependent gnn feature aggregation without the assumption of local homophily three reviewers gave conflicting opinions on this paper the reviewer claiming rejection questioned the novelty of the paper and the complexity of the global attention mentioned in the paper even through the authors responses and subsequent private discussions concerns about complexity and novelty were not completely resolved considering the authors claim that the core contribution of this paper is to design fully learnable spectral filters without compromising computational efficiency it is necessary to consider why it is meaningful to perform global attention based on graph wavelet transform in the first place in terms of complexity although the wavelet coefficient can be efficiently calculated using the chebyshev polynomials mentioned by the authors in the attention sparsification part n log n is required for each node in sorting resulting in complexity of n2 or more there may still be an advantage of complexity over using global attention in a messagepassing architecture but it will be necessary to clarify and verify that given that the proposed method uses an approximation that limits global attention within k hops also this paper modifies the graph wavelet transform in graph theory which requires a deeper discussion for example as the authors mentioned the original wavelet coefficient psiuv can be interpreted as the amount of energy that node v has received from node u in its local neighborhood the psiuv defined by the learnable filter as shown in equation 3 has a different meaning from the original wavelet coefficient there is insufficient insight as to whether it is justifiable to use this value as an attention coefficient overall the paper proposes potentially interesting ideas but it seems to require further development for publication

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper proposes a novel framework for semisupervised learning that solves two issues of previous methods 1 overreliance on labeled data and 2 error accumulation it shows that jointly solving the main task together with another task that discriminates whether the data label is real or not leads to better performance strengths the proposed framework seems to be novel it works well in experiments on a wide range of tasks classification label propagation and data imputation it seems to be potentially beneficial for many domains since it does not have domainrestrictions while many previous ssl methods rely on certain image domain techniques such as consistency regularization and data augmentation weaknesses since the proposed method is only compared with the original pseudolabel method comparing with other extensions of pseudolabelling methods that are mentioned in section 5 will make the contributions more clear in addition to the papers mentioned in section 5 there are a few papers that try to address the error accumulation in semisupervised learning methods that is observed in pseudolabelling for example in defense of pseudolabeling an uncertaintyaware pseudolabel selection framework for semisupervised learning from iclr 2021 and repetitive reprediction deep decipher for semisupervised learning from aaai2020 questions i am not sure if i understood the experiments correctly as the missing rate goes higher do we have more unlabeled samples as explained in the last paragraph of page 6 or do we have more noisylabelled samples as explained in 1st paragraph of section 41 can we show the 3rd task data imputation in figures 2 to 4 one of the benefits of the method seems to be that it can be incorporated into a wide range of ssl algorithms i think the paper demonstrated that it can be used to enhance pseudolabelling method but what kind of other ssl algorithms can scl incorporate minor questions and comments ssl is a very hot topic and there has recently been many advances since the experiments do not compare with many of the recent works it would be better to emphasize why they were not compared for example section 1 has a discussion on how recent ssl methods utilize consistency regularization which relies on heavy data augmentation techniques that is only available in certain domains what kind of value for parameter alpha is used in the image classification for the other two tasks i think the appendix explains that alpha is 1 if we are given a labeled dataset l and unlabeled dataset u it seems we can automatically construct vector m which is explained in end of page 2 if this is correct then why do we need m as an input in algorithm 1 in page 6 what is p introduced in the beginning of section 22 it seems like it is a set from the p in p notation but since it compares with m in the loss function it also looks like a vector typo perforamnce in page 6 should mi mj in the beginning of page 3 be mi mj is y a label space y in y or is it the full set of labels in the training dataset y yl cup yu ideally it would be better to perform several trials and report meanstandard error in table 1 after rebuttal thank you for answering my questions the additional experiments are helpful to have a better understanding about the proposed method it looks like the advatangeous points of the proposed method is now about the low computational costs according to the new experiments including ups rather than better performance although this still may be beneficial for the research community it seems to be slightly less significant and also may affect the storyline i would like to also recommend to put the new experiments with ups in the main paper instead of the appendix the proposed method seems to have some nice benefits but i feel there are a few weaknesses that should be addressed i also have a few questions and it would be helpful if the authors can take a look at the previous section main review docsepthe paper introduces selfinterested coalitional learning scl which is a novel approach to semisupervised learning scl combines the traditional selftraining approach to semisupervised learning with an auxiliary task that infers label observability the empirical results show that in a variety of scenarios scl outperforms both selftraining and the original model this is an interesting paper on a topic with important practical applications semisupervised learning the contribution appears to be original and it is likely to influence future work in the field the authors are explicitly calling out and addressing the two main weaknesses of traditional selflearning approaches error accumulation and overreliance on the labeled data the paper would greatly benefit from an additional section that would provide an intuitive illustrative example of how and why the proposed approach outperforms selftraining ideally it should compare and contrast the convergence of 1 self training 2 the auxiliary task and 3 scl the paper would also benefit by tightening the narrative around the alpha parameter which in the main paper is only discussed in the theoretical framework appendix a provides no value of alpha for the first dataset and it proposed without any justification a value of 1 for the other two domains appendix b is extremely brief and not very helpful the authors make no recommendation on how to tune alpha and the argument that even the worst alpha in the 01 09 range is better than the original model is fairly weak given the wide variations of the accuracy due to changes the value of alpha other comments for table 1 please add three more rows 0 90 and 99 the former is critical to understanding the upperbound performance while the later two will bring scl into a more realistic semisupervised regime where unlabeled data is one or two orders of magnitude more abundant than the labeled data please add to figure 6 the horizontal lines with the accuracy of the original model for each of the three missing rates it is still unclear why did you choose to use only 10of the data for image classification page 6 is scalability to large datasets a concern please spellcheck the paper eg perforamnce on page 4 page 2 please replace more sufficient page 3 jointly solving above two tasks jointly solving the above two tasks page 3 there are some other works embody there are some other works that embody page 4 are impacted the influence are impacted by the influence page 7 please replace well learn overall this paper uses a novel idea to improve the state of the art for semisupervised training docsepthis paper proposes a new semisupervised learning method motivated by the error accumulation problem of typical selftraining paradigms the authors propose to explicitly model the confidence of pseudo labels as an auxiliary task they come up with a selfinterested coalitional learning scl strategy to solve both tasks jointly under the new framework the main task is transformed into a costsensitive learning problem experiments demonstrate that pseudo labels are substantially more accurate with the new method and better performance of the main tasks at different label missing rates pros overall the paper is wellstructured and easy to follow the new method achieves its original goals and improves ssl effectiveness by jointly solving the main and the auxiliary tasks the authors introduce a new scl strategy to solve the problems which can be applied to a broader class of learning problems cons lack of experiments the proposed method is only compared with the selflearning method with the same base learner while this demonstrates how the model is improved with scl it is also necessary to compare with stateofart ssl methods its also valuable to include the supervised method with fullylabeled dataset as a reference in all experiments for data imputation a more common case is that missing state is correlated with inputoutput instead of simply random missing it also checks the method robustness against labeledunlabeled distribution shift compared with original selflearning method the new method has an extra discriminator model which are based on the same base learners as for the main tasks its meaningful and more fair to compare with supervised models of higher capacity the paper doesnt cover how scl can work together with consistency regularization which is commonly used together with selflearning besides i have a few questions although table 1 doesnt have a row for missing rate 0 full dataset it seems scl methods have better accuracy than model trained with full dataset for the first two tasks is this because the scl has double model capacity due to the extra discriminator why is the test accuracy of pseudolabels 100 for scl method in figure 4 are they calculated differently this is an interesting paper from technique perspective but it definitely needs more empirical studies to demonstrate practical value docsepthis paper proposes a new semisupervised learning framework by introducing an auxiliary task that distinguishes whether the pseudolabels are truly labeled or not then this information is used to add a reweighting loss to the main objective experiments on several simple benchmark datasets show that the proposed method outperforms some naive baselines the idea of introducing the auxiliary task that discriminates whether an instance is labeled is quite interesting in effect such a strategy is first introduced in active learning 1 in the vaal method 1 a similar discriminator is introduced to identify where an example is labeled or not which is then used to indicate the uncertainty of an example for active selection therefore the proposed method has a close connection to a recent work in ssl 2 that also employs the uncertainty measure to select highquality pseudolabels i have the following concerns 1 the derivation in section 32 is confusing for example in eq 3 the second equality is incorrect and the term fracdddx should be added also it would be better to change the notation of d discriminator to another one since derivative dx also uses the notation d besides i actually did not understand why mathcallb depends on f since f and mathcallb are from two different branches without sharing network blocks figure 1 2 the experimental section is not convincing and this is my main concern the datasets and the baselines are too simple stateoftheart ssl methods should be employed to support the claims in particular the uncertaintybased ssl method 2 should be compared as i have discussed above the proposed method can implicitly be equal to existing techniques in ssl is the proposed method complementary to existing methods or it is contradictory to some techniques these questions require an indepth empirical analysis overall this work is below the bar of an iclr paper regarding its poor experiments 1 sinha s ebrahimi s darrell t variational adversarial active learningcproceedings of the ieeecvf international conference on computer vision 2019 59725981 2 rizve m n duarte k rawat y s et al in defense of pseudolabeling an uncertaintyaware pseudolabel selection framework for semisupervised learningj arxiv preprint arxiv210106329 2021 interesting idea poor experiments and confusing derivation ### Summary:

this paper proposes a new method for the important problem of semisupervised learning this method relies on an auxiliary task label observability prediction to weight the examples according to the confidence in their pseudolabels so as to avoid the propagation of errors encountered in selftraining limited experiments show that the proposed method can compete with other methods in terms of performance or training time on the positive side all evaluators agree on the potential value of the proposed approach which is generic in nature on the negative side the experimental evaluation although strengthened during the discussion is not yet strong enough to have really convinced of the real merits of the method in particular comparisons with the state of the art still need to be improved in addition the paper would benefit from some rewriting in particular of the mathematics eg the d notation for task b should be avoided as suggested by one reviewer there is a misplaced partial derivative in equation 6 the authors could also simplify their derivation by using the envelope theorem i therefore recommend rejection with an encouragement to strengthen the experimental part and to improve the derivation of the proposed method

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the main goal of this paper is to introduce a simple methodology for optimizing transformer based models for efficiency and effectiveness the paper introduces two main ideas 1a topdown strategy for pruning components of a transformer model given a specific focus say speed the strategy is to consider pruning large coarsegrained components first followed by smaller finergrained components the pruning decision is made based on a significance analysis a component is considered significant for pruning if it from the model does not result in a substantial increase in the models loss as decided by a pruning threshold 2 pruning and approximating techniques for different components for example feedforward networks are pruned by removing weights in groups determined via a hyperparameter for approximating selfattention a signmatching technique for deciding which top k keys to use for computing query x key dot products the main strengths of this work are as follows 1 the techniques do not require training networks from scratch and can be applied directly during finetuning 2 the techniques are simple and should apply widely to most transformerbased models 3 the empirical results support the claim that the technique can yield significant speedup and memoryreductions while maintaining accuracy and even provide improvements in accuracy if that is the pruning goal they show that technique is orthogonal to other models explicitly designed for speed and memory footprint q8bert distillbert and can provide further improvements in both efficiency and effectiveness 4 this is a practical and useful approach that should be widely applicable along with many useful insights about optimizing transformerbased systems i appreciate that the experimental results are reported with averages across multiple runs i dont see any major weaknesses in the paper here are some areas that can be improved 1 the description of the pruning strategies was hard to follow and needed to be tightened up possibly adding equations and some pseudocode to the description should help 2 i am curious to know what components get pruned cross the different models that were optimized i wonder if there are systematic differences between original and distilled models and between autoregressive gpt and autoencoding style models 3 also some level of ablation analysis on the strategies used will be helpful for example if the elements were not ordered based on the granularity would the results be any different since this is an iterative strategy the order should play an important role in selection and utility of the subsequent pruning steps same goes for the set of pruning strategies a related question would be what gives the biggest gains 4 what is the impact on the finetuning time the baseline only requires one finetuning pass does this method require multiple finetuning passes or can the loss thresholds be computed on a smaller subset of the target data this may be a good future work to look into for tasks where the training data is relatively large where one cannot afford to exhaustively search through all the pruning strategies docsepafter reading the rebuttal some of my concerns are addressed by the additional experiments but i also agree with other reviewers that the result is not very surprising as r4 mentioned the proposed method depends on the a specific downstream task where the small general bert can be further pruned for a fair comparison to previous work baselines that are applied to a specific finetuning task need to be compared this paper presents a new framework for creating small finetuned pretrained language models the framework has 3 components 1 a set of transformer components to be pruned 2 a significant analysis for identifying unimportant elements 3 a set of techniques to prune or approximate the transformer element pros 1 the framework is very adaptive by considering different basic elements of the transformer 2 the framework is very efficient by removing large components eg layers attention blocks ffd layers at first and small components eg weight group later 3 the framework gathers multiple different pruningapproximation techniques and tries to explore the limit of pruning pretrained models which is appreciated consquestions 1 is the loss used in significant analysis computed using the development set if the validation loss is used the experiment results in table 1 are not reliable 2 there are many bert pruning papers providing comparison to these papers is very important to evaluate the proposed method can the model prune more weight at the same performance level or can the model perform better at the same pruning ratio 3 it is also helpful to present how much computing resource is needed to prune the network eg how many prunefinetune cycles are needed 4 lack of results of pruning bertbase on glue which is a very standard and common setting 5 in figure 3 why q8bert speed focus is even largerslower than q8bert with the same speed q8bert speed focus is significantly worse than q8bert minor page 5 less the minimum loss seen less than the minimum lossdocsepthis paper presents a method for improving a fineturned transformer in terms of a specific metric such as size speed or accuracy the candidates of removed elements are considered hierarchically with some heuristics and are evaluated in terms of training and validation loss to determine whether they should actually be removed from the model the authors apply their method to several stateoftheart transformer models and show that they can produce fast and compact models without losing much accuracy although the individual techniques employed to realize the whole pruning process are not particularly novel the paper presents a wellthoughtout approach to combine those and reports very promising experimental results i think this is a nice contribution to the community given that the computation cost is increasingly important in dealing with bertlike models it seems to me that the authors used transformers whose weights are shared between different layers like universal transformers or albert maybe i missed something but i think the authors should clarify if this is really the case in the manuscript the entire process of pruning is a bit vague and hard to replicate would it be possible to describe the whole process in pseudo code is algorithm 1 the whole process i think the authors should also describe the computational cost or maybe wallclock time required to perform the proposed pruning processes it seems to me that the search space is rather large and requires a considerable amount of computation p5 we prune the element only if the trainingvalidation loss i think you should be more specific here how did you actually use both the training and validation loss why do you need to look at the training loss when you are interested in the generalization error p5 weight groups of wn why is this wn i thought this should be w minor comments p5 less the less than the p6 doesnt does not p6 attention attention p7 second order secondorder docsepthanks to the authors for the detailed feedback i still have concerns about the clarity of the presentation and some contributions of the papers are not strong enough so ill keep my score summary this paper presents a framework to systematically perform pruning and layer approximation the framework includes a queue of potential elements for compression at each time step the framework will evaluate the head element of the queue try to prune the whole element or perform approximation quantizing pruning attention heads and approximating with signmatching attention and keep the transformation only if the loss in performance is acceptable the paper performs experiments with various models on glue and shows speedups or compressions compared to the original model reasons for score the techniques used in the paper are not novel and the choices on how to apply multiple compression techniques need more justification the experiment results are okay but not surprising the presentation of the paper needs to be polished see below for more details pros 1 i like the insight that approximate finetune approximate cycles doesnt work for finetuning 2 i like the insights used to determine which elements to be examined first start from the larger blocks and later layers i hope this point can be emphasized more and compared with more bruteforce and lessefficient algorithms for example for each round one can choose a layer that causes the least loss of performance to prune you can compare your greedy algorithm with this algorithm to show the gain of using a less efficient algorithm is not significant 3 the signmatching attention proposed in the paper is new i would like to see more emphasis and ablation studies on the effectiveness of this special module cons 1 it is wellknown that compressing the model is easier during the finetuning phase 1 2 i dont think this should be a contribution to emphasize for the paper 2 the whole compression framework has a single global error bound combining this with the greedy layerbylayer approach taken by the framework will the following case be possible a layer that is early in the queue causes a huge drop of accuracy and thus makes all the future layers impossible to remove because the global error bound has been reached a better way is to only remove the layer with the lowest loss reduction it will be better to justify this point with an ablation study or at least show the final pruned model doesnt have this issue in the paper 3 at the end of page 5 when optimizing for speed however removing weight groups with low significance from arbitrary locations does not help since it introduces unstructured sparsity in the weight matrix that can be difficult to exploit to achieve speedups its true that if you remove random entries in a matrix will not help for the actual speedups but you can remove an arbitrary set of rows of the matrix and then restructure the weight matrix ie concatenate all the remaining rows to form a new matrix to make it efficient for modern parallel hardwares 4 i dont really understand the point of using accuracy as the final goal if the framework is for compression the goal should be about speedup or size if accuracy really matters it should be enforced as the threshold instead of as the final goal also i dont see the difference in the framework between using speedup or size as the goal since all the thresholds are defined by loss 5 the results in the paper are okay but compared to previous works in computer vision 3 it seems that the model size can be further compressed 6 there are multiple places where the presentation can be improved a its more clear to use a pseudocode instead of a diagram in figure 2 b it should be more clear to present table 1 as multiple tables c its better to put the results comparing with previous works in a table in the middle of page 8 minor comments on page 5 3rd paragraph from the bottom less the minimum loss less than minimum loss references 1 jiao xiaoqi et al tinybert distilling bert for natural language understanding arxiv preprint arxiv190910351 2019 2 shen sheng et al qbert hessian based ultra low precision quantization of bert aaai 2020 3 han song huizi mao and william j dally deep compression compressing deep neural networks with pruning trained quantization and huffman coding arxiv preprint arxiv151000149 2015 ### Summary:

this paper introduces a set of techniques that can be used to obtain smaller models on downstream tasks when finetuning large pretrained models such as bert some reviewers have noted the limited technical novelty of the paper which can be seen more as a combination of existing methods this should not be a reason for rejection alone but unfortunately the results in the experimental section are also a bit weak eg see 14 there are not very insightful analysis and it is hard to compare to existing work for these reasons i believe that the paper should be rejected 1 dynabert dynamic bert with adaptive width and depth 2 training with quantization noise for extreme model compression 3 mobilebert a compact taskagnostic bert for resourcelimited devices 4 squeezebert what can computer vision teach nlp about efficient neural networks

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper is a natural extension of 1 which shows the importance of spectral normalization to encourage diversity of the discriminator weights in a gan a simple and effective parametrization of the weights similar to svd is used w usvt is used along with an orthonormal penalty on u and v and spectral penalty to control the decay of the spectrum unlike other parametrizations of orthogonal matrices which are exact but computationally expensive the proposed one tends to be very accurate in practice and much faster a generalization bound is provided that shows the benefit of controlling the spectral norm experimental results show that the method is accurate in constraining the orthonormality of u and v and in controlling the spectrum the experiments also show a marginal improvement of the proposed method over sngan 1 however the following it is unclear why one would want to control the whole spectrum when theorem 2 only involves the spectral norm in 1 it is argued that this encourages diversity in the weights which seems intuitive however it seems enough to use spectral normalization to achieve such purpose empirically according to that same paper it would be perhaps good to have an example where sn fails to control the spectrum in a way that significantly impacts the performance of the algorithm while the proposed method doesnt overall the paper is clearly written and the proposed algorithm effectively controls the spectrum as shown experimentally however given that the idea is rather simple it is important to show its significance with examples that clearly emphasize the importance of controlling the whole spectrum versus the spectral norm only revision figure 1 is convincing and hints to why sngan acheives slow decay while in principle it only tries to control the spectral norm i think this paper is a good contribution as it provides a simple and efficient algorithm to precisely control the spectrum moreover a recent work 2 theorem 1 provides theoretical evidence for the importance of controling the whole spectrum which makes this contribution even more relevant 1 t miyato t kataoka m koyama and y yoshida spectral normalization for generative adversarial networks feb 2018 2 m arbel d j sutherland m bin kowski and a gretton on gradient regularizers for mmd gans nips 2018 docsepthe paper builds on the experimental observations made in miyato et al 2018 in which the authors highlight the utility of spectral normalization of weight matrices in the discriminator of a gan to improve the stability of the training process the paper proposes to reparameterize the weight matrices by something that looks like the singular value decomposition ie w u e vt four different techniques to control the spectrum of w by imposing various constraints on e have been discussed for maintaining the orthonormality of u and v penalties are added to the cost function the paper also derives a bound on the generalization error and experimentally shows the desirable slow decay of singular values in weight matrices of the discriminator other experiments which compare the proposed approach with the sngan have also been given 1the paper puts a lot of stress on the stability of the training process in the beginning but clear experiments supporting their claim related to improved stability are lacking 2it would be helpful for the readers if more clarity is added to the paper with respect to the desirability of slow decay of singular values and spectral normalization 3the point regarding convolutional layers should be part of the main paper docsepthis paper proposes to parameterize the weight matrices of neural nets using the svd with approximate orthogonality enforced on the singular vectors using orthogonal regularization as opposed to eg the cayley transform or optimizing on the stiefel manifold allowing for direct efficient control over the spectra the method is applied to gan discriminators to stabilize training as a natural extension of spectral normalization this method incurs a slight memory and compute cost and achieves a minor performance improvement over spectral normalization on two benchmark image generation tasks im a bit back and forth on this paper on the one hand i think the ideas this paper proposes are very interesting and could provide a strong basis off which future work can be builtthe extension of spectral normalization to further study and manipulation of the spectra is natural and very promising however the results obtained are not particularly strong and as they stand do not in my opinion justify the increased compute and memory cost of the proposed methods the papers presentation also wavers between being strong there were some sections i read and immediately understood and impenetrable there were other sections which i had to read 510 times just to try and grip what was going on ultimately my vote is for acceptance i think that we should not throw out a work with interesting and potentially useful ideas just because it does not set a new sota especially when the current trend with gans seems to suggest that top performance comes at a compute cost that all but a few groups do not have access to with another editing pass to improve language and presentation this would be a strong relevant paper worthy of the attention of the iclr community my notes the key idea of parameterizing matrices as the svd by construction but using a regularizer to properly constrain u and v instead of the expensive cayley transform or trying to pin the matrices to the stifel manifold is very intriguing and i think there is a lot of potential here this paper suffers from a high degree of mathiness substituting dense notation in places where verbal explanation would be more appropriate there are several spots where explaining the intuition behind a given idea particularly when proposing the various spectrum regularizers would be far more effective than the huge amount of notation in the authors defense the notation is generally used as effectively as it could be my issue is that it often is just insufficient and communication would be better served with more illustrative figures andor language i found the way the paper references figure 1 confusing the decays are substantially different for each layerare these all supposed to be examples of slow decay layer 6 appears to have 90 of its singular values below 05 while layer 0 has more than 50 if this is slow decay what does an undesirable fast decay look like isnt the fast decay as shown in figure 2 almost exactly what we see for layer 6 in figure 1 what is the significance of the sharp drop that occurs after some set number of singular values the figure itself is easy to understand but the way the authors repeatedly refer to it as an example of smooth singular decays is confusing what is doptimal design this is not something commonly known in the ml literature the authors should explain what exactly that doptimal regularizer does and elucidate its backward dynamics in an appendix if space does not permit it in the main body does it encourage all singular values to have similar values does it push them all towards 1 i found the brief explanation encourages a slow singular value decay to be too briefconsider adding a plot of the doptimal spectrum to figure 1 so that the reader can easily see how it would compare to the observed spectra ideally the authors would show an example of the target spectra for each of the proposed regularizers in figure 1 this might also help elucidate what the authors consider a desirable singular value decay and mollify some of the issues i take with the way the paper references figure 1 the explanation of the divergence regularizer is similarly confusing and suffers from mathiness a fact which i believe is further exacerbated by its somewhat odd motivation why if the end result is a reference curve toward which the spectra will be regularized do the authors propose 1 a random variable which is a transformation of a gaussian 2 to take the pdf of that random variable 3 discretize the pdf 4 take the kl between a uniform discrete distribution and the discretized pmf and 5 ignore the normalization term if the authors were actually working with random variables and proposing a divergence this might make sense but the items under consideration are singular values which are nonstochastic parameters of a model so treating them this way seems very odd based on figure 2 it looks like the resulting reference curves are fine but the explanation of how to arrive there is quite convolutedi would honestly have been more satisfied if the authors had simply designed a function a polynomial logarithmic function perhaps with a hyperparameter or two to control the curvature our experimental results show that both combinations achieve an impressive results on cifar10 and stl10 datasets please do not use subjective adjectives like impressive a 65 improvement is okay but not very impressive and when you use subjective language you run the risk of readers and reviewers subjectively disagreeing with you as is the case with this reviewer please also fix the typo in this sentence it should at least be achieve impressive results or achieve an impressive improvement on section 3 what is generalization supposed to mean in this context its unclear to me why this is at all relevantis this supposed to be indicating the bounds for which the discriminator will correctly distinguish real vs generated images or is there some other definition of generalization which is relevant does it actually matter for what we care about training implicit generative models what exactly is the use of this generalization bound what does it tell us what are the actual situations in which it holds is it possible that it will ever be relevant to training gans or to developing new methods for training gans experiments i appreciate that results are taken over 10 different random seeds if the choice of gamma is unimportant then why is it different for one experiment i found footnote 4 confusing and contradictory for figure 3 i do not think that the margin is significantit constitutes a relative 65 improvement which i do not believe really justifies the increased complexity and compute cost of the method i appreciate table 1 and figure 4 for elucidating a how orthogonal the u and v matrices end up and b the observed decay of the spectra appendix please change table 7 to be more readable with captions underneath each figure rather than listed at the top and forcing readers to count the rows and match them to the caption what is the difference between sngan and spectral norm in this table or is that a typo and it should be spectralconstraint i would like to see a discussion of table 7 interpretation of why the spectra look that way and why they evolve that way over training for each regularizer minor typos and grammatical mistakes throughout as per the cifar10100 website httpswwwcstorontoedukrizcifarhtml the torralba citation is not the proper one for the cifar datasets despite several recent papers which have used it intro last paragraph generation bound should be generalization bound page 4 paragraph 2 last sentence problem is misspelled ### Summary:

all the reviewers agree that the paper has an interesting idea on regularizing the spectral norm of the weight matrices in gans and a generalization bound has been shown the empirical result shows that indeed regularization improves the performance of the gans based on these the ac suggested acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper is about a method for synthesizing binaural audio from a mono recording of a single speakers speech first i think the title is too general the paper does not attempt to convert all possible sounds but it tries to convert a single speakers monaural speech signal to binaural audio where the speaker is moving i think this inherent assumption is important since the method will probably not work for multiple overlapping audio sources i suggest changing the title to neural synthesis of binaural speech of a single moving speaker the first part of the network neural time warping is an interesting component that is capable of adjusting the delays conditioned on the location and orientations of the source and microphone such that a location dependent binaural audio is formed by estimating timevarying delays of the original mono recording separately for two channels it is believable that such a module would be helpful for a single moving speaker however such a model would not help or work when there are more than two active audio sources a separation module would be required for that scenario neural time warping is an autoregressive model which can work online the second stage convolutional network which uses conditioned hyper convolutions is also an interesting architecture that takes the warped signals and applies timeconvolutions with kernels obtained from the conditioning input which has the timevarying locations and orientations of the source and the microphone the section about the loss function is also interesting in that the time domain l2 loss is shown to not work well for accurate phase estimation so the authors propose to add a separate phase loss term to compensate for that i think it would be better if figure 2 is replaced with a plot of epsilonyhat versus amplitude error divided by yhat in a and versus the phase error in b it could be clearer than the current 2d figure which is hard to interpret the use of sine activation is not well justified sine activation is useful in the first layer of a signal representation network which is different from a signal prediction network i do not see how and why that could be helpful here in terms of comparisons 25d method uses visual information as conditional information to generate complex masks to produce binaural audio in this paper visual information is replaced with the spatial conditioning information it would help to get more information about the window size and hop size used in 25d since they may be an important factor that relates to the amount of delays they can introduce for wavenet comparison it was not clear how the wavenet model was trained to generate binaural data did it use any conditioning information if so how was it applied in an autoregressive way with randomized sampling the wavenet audio example sounded noisy which is not typical of wavenet generated audio it looks like the dsp method can utilize a listener specific hrtf which may be difficult to incorporate for the proposed neural model is it an important issue how does the model generalize to unseen speakers and rooms the training and testing strategy uses the same room and the same speakers would we have any problem when the monaural audio is recorded in some other room with some other speaker in figure 8 maybe it is ok not to draw the original binaural signal for every method in general i liked the neural warping and conditional convolutional components which are interesting and i liked the analysis about the loss function the approach is an interesting way to obtain binaural version of a monaural mono speaker recording in a room the dataset produced for the paper would also be useful for research update after revision the revision improved the paper thanks for taking care of my comments justification of sine activations generalization to unseen speakers experiment are nice additions the new title is a bit better and i think it may be ok since the goal is to perform a moving source simulation for single speech sources multiple speech sources can be simulated separately and added together as mentioned the authors may consider possibly a better name neural binaural synthesis from mono speech which emphasizes that the synthesized target is binaural speech from a single speech recording just a few more points 1 i think it is essential in wavenet to apply the model in an autoregressive fashion over samples just using the network architecture and the loss function from wavenet is not equivalent to using a wavenet model since an essential part of the model is the autoregressive sampling which makes sure the samples are dependent and coherent without autoregressive sampling the resulting sound is poor as observed by the authors so i suggest to emphasize that autoregressive sampling is not performed in the paper to avoid misleading the readers 2 more explanation of 25d is appropriate one wonders if using a larger stft window size would improve its resultsdocsepstrengths 1 the paper is well written it includes clear math notations and figures readers can easily follow the thought process of the authors for example figure 2 shows the relation of l2 loss and phase loss with respect to target energy indicating the importance of penalizing phase loss in the end to end system the same observation is supported by figure 3 2 strong results the proposed end2end model significantly outperforms previous sota in terms of objective measures and subject tests the video demo is very convincing the model improved spatialization and sound quality 3 high novelty this paper proposes to impose monotonicity and causality to the learned warping function which incorporates the physics of sound propagation i am excited to another example of applying domain knowledge to an endtoend model the model includes two novel components the neural warp network compensates the errors from geometry warp the temporal convolution works as a post processing module to account for reverberation and other effects ablation study shows both components are critical to be improved 1 the caption for figure 4a seems to be incomplete 2 it would be good to include a table to compare the proposed model with baselines in terms of model size and inference speeddocsepthis paper presents a neural networkbased model to generate binaural audio given a singlechannel audio and positions of sourcelistener their angles the authors developed a dataset of binaural audio which will be released on acceptance technical details and model architecture are available in the body of the paper whereas additional details such as baseline dspbased approach proof and dataset are available in the appendix the model was evaluated using the dataset developed in this work a demo video demonstrating the capability of the model is also provided as a supplementary material there are a few parts need to be addressed 1 it is unclear why dtwbased warping is required iirc the warpfield here can represent not only a shift but also other monotonic causal such as repeating if there is only delay between left and right just having a shift is enough isnt it it is great if the authors can explain the motivation to use warpfield more clearly 2 the use of hyperconvolution is an interesting idea the equation 5 uses conditional temporal convolution however audio generative models such as wavenet uses a different architecture gated convolution the gating mechanism can give additional nonlinearity and so im wondering if you can evaluate the performance of hyperconvolution against gated convolution 3 too large confidence intervals in table 4 although there were many evaluations the confidence intervals were pretty large and there were overlaps among them eg small overlap between dsp and ours in cleanliness large overlaps between spatialization and realism between dsp and ours with this result it is difficult to claim that there was a significant improvement over the baseline system please check your results and design the experiment more carefully to figure out whether there is any significant difference between them conducting sidebyside comparision is one possiblity comments this paper claims that it works in real time but no information about speed such as realtime factor hardware specification are provided sampling rate information is not explicitly provided in the experiment section 06 mos difference is large not a bit modern wavenet models often use mixtureoflogistics refer to parallel wavenet paper for details as output rather than mulaw to achieve better quality ### Summary:

interesting method for binaural synthesis from moving monoaudio nice insight into why l2 isnt the best loss for binaural reconstructions interesting architectural choice with nice results nicely motivated and clearly presented idea especially after addressing the reviewers comments i agree with the idea of a title change while i think its implied that the source is probably single source making it explicit would make it clearer for those not working in a closely related topic hence neural synthesis of binaural speech from mono audio as suggested in the review process sounds quite reasonable

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary the paper considers the adversarial attacks via a surrogate model constructed using data from a different domain the authors propose a defense from such attacks by a special kind of adversarial training inspired by the idea of domain adaptation the idea can be useful but raises a lot of questions especially when looking at the evaluation of the proposed approach reasons for score i vote for a reject as some findings are intriguing while the experimental results are questionable the first major concern is why do authors consider nlp models and attacks in the paper it is much easier to work with image datasets and if the general idea is new i suggest to start from this point to verify that the considered domain adaption works well in this scenario also the proposed attack is not new it is just a surrogate model attack but using a surrogate model training on the data from a different domain as the authors suggest due to the unavailability of the initial domain data also for this new attack the authors dont compare a surrogate model attack trained using the same domain data which would be interesting to compare the authors use only one dataset which is a bit strange for modern papers for this dataset they dont provide a full study limiting the scope of experiments to particular pairs of sourcetarget domains from the paper it is not clear how widely applicable are obtained results the comparison is not full there are a lot of options to be tuned for alternative approaches like adversarial training or other defenses the hyperparameter selection for them has a crucial effect on their success so we cant say that the proposed approach works better than others major concerns only one dataset considered i think that the inclusion of additional datasets at least three would improve the paper and make the conclusion by the authors more solid usage of surrogate models trained on other dataset is not new for general adversarial attacks 1 mentioned in the paper 2 and for adversarial attacks in nlp 3 lstm is not the state of the art model for the processing of nlp data 42 what attack do you use not explicitly specified so the results cant be verified by replication of the described experiments table 2 will benefit from adding afterattack accuracy for the original domain if it is similar to the presented accuracies then why bother with a new method table 3 comparison is not fair as we have no details about training for each approach eg we dont know how many additional examples we add during adversarial training also note that the stateoftheart for adversarial training is different from described in the paper see 4 5 table 4 afterdefense accuracy for what model is presented because it should be different for lstmgrucnn attack model tables 234 i suggest to keep the list of pairs target domain substitute domain similar for all tables to be sure that the presented examples are not cherrypicked also please consider running your approach on all pairs target domain substitute domain and aggregating all these results domain adaptation models from my experience are not easy to train it is interesting to access the quality of the models for different runs of learn2weight is it stable etc 1 nicolas papernot patrick mcdaniel and ian goodfellow transferability in machine learning from phenomena to blackbox attacks using adversarial samples arxiv preprint arxiv160507277 2016a 2 cheng s dong y pang t su h zhu j 2019 improving blackbox adversarial attacks with a transferbased prior in advances in neural information processing systems pp 1093410944 3 fursov i zaytsev a kluchnikov n kravchenko a burnaev e 2020 differentiable language model adversarial attacks on categorical sequence classifiers arxiv preprint arxiv200611078 4 shafahi a najibi m ghiasi m a xu z dickerson j studer c goldstein t 2019 adversarial training for free in advances in neural information processing systems pp 33583369 5 aleksander madry aleksandar makelov ludwig schmidt dimitris tsipras and adrian vladu towards deep learning models resistant to adversarial attacks iclr 2017 proposed minor improvements table 1 demonstrates one example that breaks the semantics of the attacked sentence can you provide good examples of why your approach work definition 1 is not a definition is x one instance or many instances in this definition also not specified that x and x should be similar equation 1 why you avoid standard number of equations beginequation labeleqsampleequation sample text endequationdocsepsummary this paper is about generating adversarial examples for some target model and protecting from such attacks authors consider a setting when an adversary has access to some similar to target domain data and can use this data to generate a surrogate model using this surrogate model an adversary can generate adversarial examples that apparently also fool the target model then authors also propose a defense mechanism from this type of attack learn2weight this is a learnt network that for a given example returns perturbation of weights to the target model which will be applied to the target before inference this model is trained by a defender on synthetic domains generated as perturbations to the target data overall this type of an attack is interesting the paper is well organized and written and easy to follow enough background is given for a reader to follow without the need to research around or going to appendix well done on clarity i do have a problem understanding how effective this attack is compared to other blackbox attacks and how the proposed defense compares to standard domain generalization methods like learning domain invariant features 1 one concern i have is about practicality an availability of such similar domains for testing authors used amazon multidomain sentitment classification where domains are easily available but how would you attack a pretrained imagenet for example what domains are similar and further more how much data for these similar domains you need to have to train a good enough surrogate model also you dont really have a way to calculate that your data is close to the actual target data 2 definition 2 fa wt fsa requires an access to your model f so i would not call this type of attack without access to the target model 3 how does this attack compares to any other black box attack that uses target model it really should be in table 2 if other attacks are able to make target model performance worse than this type of attack it is of less value to defend from a weaker attack 4 algo 3 what are the adversarial perturbations you are talking about  5 i am not sure algorithm 2 is the best way of doing it why not to try any of domain generalization techniques eg train on all domains with an adversarial head tries to distinguish between domains or mmd or whatever may be this way you wont need learn2weight model at all since you are already learning domain invariant features only minor table 2 what are u boldening i would expect the bolden result to be per source model book and the worse performance you get so dvd attack gives the lowest after attack accuracy you are boldening baby which is the weakest domain on which your attack mode is trained for an attack  algo 2 compute weights of f trained on tywtwt just assign 0s docsepin this paper the authors propose a learn2weight framework to defend against similardomain adversarial attacks experimental studies on amazon dataset are done to verify the proposed learn2 weight the paper is not easy to follow the presentation and organization should be further improved here are the detailed comments 1 adversarial attacks are widely used in various application domains eg computer vision ref1 and reinforcement learning ref2 it is necessary to discuss with these related works and highlight the difference and importance of adversarial attack methods on nlp tasks ref1 adversarial examples that fool both computer vision and timelimited humans ref2 minimalistic attacks how little it takes to fool deep reinforcement learning policies 2 the authors highlight domain adaptation theory several times please give a clear description on what it is 3 where is table 1 used in the main content 4 regarding definition 2 the following two points are unclear 1 is fsa the true label of a based on the figure 1 a only correctly classified source samples are used while the definition does not show this 2 why fawt fsa f is the target classifier are you generating the domaininvariant samples 5 the rationale of similar domain adversarial attack is confused it is more reasonable to use source data to help generate target adversarial samples x which confuse the classifier to deviate the label fx neq fx where x is the original target sample however the paper generates source adversarial samples which naturally may confuse the target classifier due to the domain divergence it is unclear why and how these source adversarial samples can contribute to the robustness of the target classifier 6 regarding the accuracy drops in table 2 it is highly possible caused by the data shift between different domains how to differentiate the importance of the data shift and adversarial in the accuracy drops 7 the technical part is not easy to follow the sections 51 to 53 are not linked well it is necessary to give more contents on the motivation and flow of these algorithms instead of just putting them in algorithm charts 8 why target data is used in algorithm 2 and also transfer loss optimization in the introduction target domain information is assumed to be unavailable moreover algorithm 2 is to reduce the domain divergence if i understand correctly i am quite curious how the proposed method differentiates from other transfer learning methods update thanks for the authors response after reading the response and the other reviewers comments i think the paper needs to be further improved and thus i will keep my score ### Summary:

the submission considers a new attack model for adversarial perturbation in a framework where the attacker has neither access to the trained model nor the data used for training the model the submission suggests adomain adaptation inspired attack learn a different model on a similar domain and generate the adversarial perturbations using that model the authors then also develop a defense for this type of attack and provide some empirical evaluations of the resulting losses on a few nlp benchmark datasets the paper refers to the literature on domain adaptation theory to motivate their suggested defense but this analysis remains on an intuitive rather than a formally rigorous level furthermore the empirical evaluation does not compare to a variety of attacks and the defense is only evaluated with respected to the selfsuggested attack this is a very minimal bar for a defense to meet the reviewers have criticized the submission for the rather minimal extend of empirical evaluation given that the submission also doesnt provide a sound theoretical analysis for the proposed attack and defense i agree with the reviewers that the submission does not provide sufficient novel insight for publication at iclr in contrast to some of the reviewers i do find it legitimate and maybe recommendable even to focus on one chosen application area such as nlp i dont see a requirement to also present experiments on image data or reinforcement learning applciations however i would recommend that the authors highlight more explicitly what general lessons a reader would learn from their study this could be done through a more extensive and systematic set of experiments or a through analysis in a well defined theoretical framework

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a detailed analysis on pruning heuristics and its applications to early pruning it thoroughly analyzed magnitudebased pruning losspreservation based pruning and gradientnorm based pruning the paper demonstrated the results on cifar10 and cifar100 datasets its very timely research to guide the audience which heuristic is better my major concern is the novelty over existing pruning heuristics since the techniques have all been proposed before the other concern is the evaluation and the scale of the dataset given the results in table 2 different by less than a percent and cifar training is very noisy its hard to tell the difference just like the lottery ticket hypothesis works on cifar but does not work on imagenet different pruning heuristics needs to be verified on the large scale imagenet dataset in order to be convincing docsep summary this paper studies different families of pruning criteria and their impact on training dynamics especially early training stemming from the observations authors provide improvements to the 1st and 2nd order saliency methods pros authors provide simple and useful explanations to various pruning criteria that are based on the taylor approximation of the loss functions even the authors dont mention this in the contributions they propose some improved versions of existing criteria for example the updated taylor score with theta2gtheta or absolute valued grasp this is great and it might worth focusing on these criteria further providing further evidence on their usefulness currently they seem a bit arbitrary for example why not third power theta3gtheta or additive biasing of magnitude gthetactheta i recommend authors to run their versions in unstructured setting too cons authors choose to focus on structured pruning since resulting networks are dense and acceleration is straightforward however they miss an important work on structured pruning 1 this relatively wellknown work shows that pruned structured networks can be trained to full accuracy from scratch in other words their value lies on doing some kind of an architecture search over layer widths the motivation of the work needs to be revisited in the light of these results since we can retrain pruned networks from scratch it probably doesnt matter which neuron we choose and therefore which criteria is better unstructured pruning doesnt have this training from scratch issue and i recommend authors to at least include and maybe shift the focus to unstructured pruning but requires specially designed hardware han et al 2016a or software elsen et al 2020 while results in this paper are applicable in both settings our experimental evaluation focuses on structured pruning due to its higher relevance to practitioners all neural networks require special hardware if you want to accelerate them i think a better motivation here is to point out to the difficulties at accelerating sparse operations and limited availabilitysupport for such operations in existing frameworks and i am not sure how useful structured pruning algorithms are given the results of 1 the larger the magnitude of parameters at a particular instant the smaller the model loss at that instant will be this is likely to be true in simple settings however it is not a sufficient condition especially for networks with batch norm you can arbitrarily scale neurons if there is a batchnorm and you can comeup with arbitrary ordering if needed i recommend rephrasing this observation andor stating the assumptions better i dont remember seeing any assumption on the network itself how the regularization or gradient noise will effect this statement thus the parameter with the most negative value for tgt is likely to also have a large negative value for thtgt this is not clear to me assume 1d case where t 1 gt2 ht1 tgt2 thtgt2 i can see the correlation in the figure but it doesnt seem like an obvious thing maybe because hessian dont have many negative eigenvalues rating i found the results and analysis interesting however motivation needs to be updated the work would also benefit from including unstructured pruning experiments minor points recent works focus on pruning models at initialization frankle carbin 2019 lottery ticket paper prunes after training and show existence of some initializations that achieve good performance equations 67 fracdldt gtheta2 assuming gradient descent shouldnt be a learning rate than magnitudeagnostic techniques which methods are these as far as i see all methods use magnitude information in their formulas directly or indirectly in table1 i recommend authors to bold both scores if they lie within the std of each other so that we can identify which improvements are significant it would be nice to show how the temperature parameter is used for grasp 1 httpsarxivorgabs181005270 docsepsummary the authors study proposed importance metrics for pruning neuronschannels in deep neural networks and analyze what properties of parameters are favored by each approach by studying the relationship between model parameters gradients 2nd order derivatives and loss through this analysis they develop a rich understanding of the consequences of different pruning criteria and use their understanding to propose modifications to existing techniques that produce higher quality models across different settings pros the framework used by the authors is clear and easy to understand but also very general the authors mix of empirical results and theoretical analysis makes a convincing case for the accuracy of their observations the authors go beyond observation and analysis and use their insights to design new approaches to pruning that outperform existing techniques the paper is well written and well organized cons this paper has few limitations the main limitation is that all experiments were conducted on relatively small datasets cifar given that is has been shown that some techniques in model compression produce stateoftheart results on small tasks but fail on larger models and datasets 1 2 id encourage their authors to further validate their insights on a larger dataset ie imagenet comments i found that the authors waited a long time to explain the term gradient flow which was important in sections 13 but not fully detailed until the start of section 4 on page 1 the authors say in parenthesis that gradient flow is gradient descent with infinitesimal learning rate but i found this explanation was not clear the second sentence of section 4 the evolution over time of model parameters gradient and loss was much more clear id encourage the authors to potentially work some of these details earlier into the text references 1 httpsarxivorgabs190209574 2 httpsarxivorgabs200303033 docsepthe paper contributes to explaining why saliency measures used for pruning trained models may or may not also be effective for pruning untrained or minimally trained models by developing the relationship between those saliency measures and different forms of the norm of model parameters based on the evolution of model parameters via gradient flow basically derivatives wrt time this result leads to several interesting interpretations that could shed some light on ongoing efforts to understand recent methods of pruning earlyon eg pruning at initialization or after minimal training and potential extensions to existing saliency measures the idea of employing gradient flow is novel for its purpose and seems to be accurately executed the main concern is that there is a gap between the flow model and the actual optimization method used in this work sgd with momentum or more generally standard optimization methods for deep learning in this regard the claim of evolution dynamics seems a bit exaggerated and remains as theoretical experiments are strictly speaking not entirely valid to support it either minor related work is written as if pruning is only done via saliencybased methods eg pruning frameworks generally define importance measures without taking into account various others such as optimization based methods employing sparsity inducing penalty terms on a different but related note the writing becomes a bit loose when it comes to referencing existing methods it is worth correcting them and clarifying the scopefocus of this work further questions why do you study structured pruning only the provided reasons unstructured pruning requires specially designed hardwares or softwares or higher relevance to practitioners dont seem valid enough if the purpose really lies in analyzing can you provide any results for unstructured pruning can you provide evidence to support the claim grasp without large temperature chooses to prune earlier layers aggressively besides raghu et al 2017 based on tables 1 and 2 the proposed extension to losspreservation method works the best while the differences across different methods seem a bit marginal is my understanding correct ### Summary:

this paper proposes a broad framework for unifying various pruning approaches and performs detailed analyses to make recommendations about the settings in which various approaches may be most useful reviewers were generally excited by the framework and analyses but had some concerns regarding scale and the papers focus on structured pruning the authors included new experiments however which mostly addressed reviewer concerns overall i think is a strong paper which will likely be provide needed grounding for pruning frameworks and recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary this paper aims to explain dropout from the lens of game theoretic interactions let x denote the input of a deep neural net dnn intuitively the interaction between two variables xi and xj quantifies how much the presenceabsence of the jth variable affects the contribution of the ith variable to the output of the dnn with the above definition in place the authors show theoretically and empirically that dropout reduces the interactions between input variables of dnns as this type of interactions turn out to be strongly correlated with overfitting the authors suggest that dropout alleviates overfitting by reducing interactions between input variables or activation units of dnns based on this understanding of dropout an alternative regularization technique is proposed which explicitly penalizes pairwise interactions between variables strengths 1 the paper is well written and clearly presented 2 although it is already well known or at least widely accepted in the community that dropout reduces dependencies among activation units in dnns the explanation of dropout from the perspective of game theoretic interactions is interesting and it is supported both theoretically as well as empirically weakness 1 hyperparameter settings eg optimizationrelated ones to reproduce the results are not provided it is not clear what dropout rate was used in the experiments is it 05 in all cases different dropout rates should be investigated before claiming the superiority of the proposed interaction loss regularization over dropout 2 experiments are curried out on only one task classification one type of data images and one family of dnns convolutional neural nets however the paper draws quite general conclusions regarding the understanding of dropout from the perspective of game theoretic interactions therefore considering at least one more task involving a different type of data and another family of dnns would reinforce the findings of this paper 3 computational time analysis of the proposed interaction loss and training time comparisons with dropout are lacking additional comments 1 dropout is used both at convolutional and at fully connected layers however one can argue that applying dropout to convolutional layers does not make sense owing to the sparsity of connections in this type of layers 2 i would recommend revising the title of the paper what is proposed is more of an alternative regularization form to dropout than an improvement for the latter docseppaper summary the authors provide a novel interpretation of dropout regularization using banzhaf interactions a tool from game theory pros the authors are able to mathematically prove that dropout is capable of suppressing neural coadaptations the latter being one of the reasons for overfitting visualizations are also provided in this respect on a dataset for face analysis through their mathematical analysis authors are able to improve upon the classical dropout training by making it more compatible with batch normalization so that these two classical regularization strategies show a better complementarity cons some of the results does not read well like table 3 or figure 4 but this is really minor and fixable preliminary evaluation i believe that the overall analysis provided by authors is complete and interesting so i am akin to call for a full acceptance of the paper which i deem suitable for such a venue like iclr in order to improve their paper i would encourage authors to better investigate over the following aspect since many times authors established a principled connections between dropout and neural activations it would be very interesting to discuss the relationship with the present work and another paper gomez et al targeted dropout neurips workshops 2018 in which a computational variant of dropout is proposed such that the dropout rate depends upon neural activations postrebuttal evaluation i have carefully read the response provided by authors and checked the revised manuscript i confirm my preliminary acceptance ratedocsepsummary this paper analyzes the effect of dropout on interaction between units in a neural network the strength of the interaction is measured using a metric that is used in game theory to quantify interaction between players in a cooperative game the paper shows that dropout reduces highorder interaction as measured by this metric and that reduction in interaction is correlated with better generalization the paper introduces a new regularizer that explicitly minimizes the metric and claims that using this regularizer instead of dropout has some advantages pros the idea that dropout reduces overfitting by breaking up complex coadaptations and regularizing interactions is widely believed to be true however this paper tries to explicitly quantify the amount of interaction and presents theoretical and experimental evidence that interaction reduces as a result of having dropout cons the proposed metric is hard to compute exactly since it requires summing over exponentially many terms each term requiring a forward prop through the network the assumptions made in computing this metric approximately seem unclear to me appendix h i could not understand what probability distributions are being expressed and why in particular how is the term in eq 38 approximated by the one in the first line of eq 41 the paragraph after eq 40 was also unclear it is not discussed how this metric for evaluating interaction strength compares to something conceptually simpler like the hessian nabla2ij l which directly measures the dependence of the networks loss on pairs of input variables and its magnitude is proportional to the interaction strength the paper mentions that an advantage of the proposed loss is that the weight lambda applied to the interaction loss can be explicit controlled whereas the strength of dropout cannot be controlled section 4 advantages unlike the interaction loss people cannot explicitly control the strength of dropout this does not seem correct the dropout probability provides such as control mechanism for dropout for the experimental results in table 3 it is not mentioned what value of the dropout probability was used whether this value was tuned for each architecture and which network layers was dropout applied in these factors can have a significant impact on overall performance on the other hand the lambda parameter for the proposed interaction loss is tuned so the resulting comparison is not fair it is not clear what additional insight this metric provides about dropout beyond confirming what is intuitively apparent that having randomly dropped neurons will make it harder for the network to learn highorder interactions other comments and suggestions the introduction includes a discussion around banzhaf value without describing what it means the concept of banzhaf value might be new to many readers in the ml community i would suggest including a short explanation to give some intuition about what it means before discussing it in more detail the output of the dnn corresponds to the score f would it make sense to say that negative loss corresponds to the score f rather than the output of the network award reward or utility im not familiar with game theory literature so im not sure if award is a commonly used term there the title of the paper is a bit misleading as it seems to suggest that the paper is about using dropout in game theory ie solving problems in game theory using dropout post rebuttal the authors addressed the concerns around the clarity of the paper and added useful additional experiments i will increase my score to 7docsepsummary the paper proves that dropout can suppress the strength of interactions between input variables from the perspective of game theory it further improves the utility of dropout by introducing an explicit interaction loss experimental results verify the theoretic proof and the effectiveness of the proposed loss strengths 1 the paper introduces a new perspective of game theory to understand dropout 2 experiments are conducted on various datasets to support the theoretic proof and the proposed interaction loss concerns 1 although i have no background in game theory i try my best to understand the terminology and the analysis however i do not have the ability to verify the correctness of its proof thus i cannot evaluate the main contribution of this paper for experimental results the conclusion that dropout suppressing the input interactions is not a new story 2 it would be more interesting if the author can further explain the disharmony between dropout and bn from the perspective of game theory ### Summary:

the paper introduces a gametheoretic framework to improve our understanding of dropout all reviewers appreciated the contribution of the paper while they had a number of questionssuggestions almost all of them were adequately addressed three reviewers are satisfied and recommend acceptance while a lone reviewer is on the fence heshe admits heshe is less knowledgeable about game theory overall i think this paper makes a solid contribution to iclr

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper first thoroughly analyzes the difference in distributions of weights and activations in addernet and then proposes a new quantization algorithm by redistributing the weights and the activations strengths 1 this paper conducts a thorough study of the dilemma in addernet quantization and proposes an effective method to solve this problem 2 the paper is clearly presented 3 i am glad to see that the accuracy drop is within 2 for imagenet even at 4 bits weaknesses suggestions 1 the accuracy and energy comparisons with quantized cnn seem to be not very adequate better to compare the accuracy drops in quantized cnns as well as the currently presented accuracy drop in addernet so the reader can get the full information whether addernet is more advanced as compared to cnns in terms of quantization 2 addernet is a specific neural network it is not clear whether the proposed methods can be generalized to other neural networks will similar distribution properties 3 only classification results are shown how about other downstrem tasks eg detection segemetation or other network architectures eg vits with adder kernels 4 does the addernet compatiable with ssl pretraining eg mae pretraining and how the quantization scheme different for pretraining stage and finetuning or normal training stages 5 how is the latency or throughput in real devices i am curious of this since there is no available efficient cuda implementation of addernet opensourced as of now also quantization is also not efficient for general cpugpu devices have you deployed the trained models to real devices yet i am open to further boost the score or champion this paper if the rebuttal is also sound and can somewhat solve my quesionsconcerns the comparison with cnn quantization method seems to be not very adequate docsepthe paper proposes a new quantization scheme for addernets specifically the authors propose to cluster model weights in the channel dimension where each cluster is assigned its own scale value for quantization this ensures that the scales can better represent the range of values which may be different for different weight channels the authors further propose to absorb the error caused by clamping the weights inside the layer bias which helps restore accuracy finally the proposed method removes outliers when quantizing the activations therefore tailoring the scale to better represent the valid range of data strengths the paper is wellwritten and the ideas are clearly explained the proposed method significantly improves the accuracy after quantization compared to prior methods weaknesses some of the claims are not backed up by the method specifically the authors mention that a shortcoming of prior work is using the same scale for weights and activations which is decided based on either of the two and therefore may not best fit the other the proposed method also adopts the same scheme where the scales are still determined by either the weights or the activations with the only difference being the increased granularity of the scale choices due to the channel clustering please find more details on this in the next section some questions remain regarding applying the method to new models eg how to determine the number of clusters for new benchmarks the authors have not discussed the limitations or potential negative social impact of their work docsepthis manuscript focuses on the problem of the quantization of addernet the author has investigated the difference between addernet and traditional networks based on the differences the dedicated quantization is achieved by redistributing the weights and activation of addernet in the quantization method three techniques are proposed to overcome the bit waste and over clamp problems including clusteringbased grouping quantization range clamp of weights and outlier clamp of activations experimental results show the effectiveness of the proposed method for quantizing addernet with different bit widths pros the manuscript is easy to follow the analysis of the difference between conventional quantization methods for cnn and that for addernet is interesting the statistics of the activations and weights of a pretrained addernet are good as a new kind of efficient neural network addernet how to effectively quantize it is a challenging problem quantization of such nn would put forward a faster and more energyefficient model the proposed method includes clusteringbased grouping quantization range clamp of weights and outlier clamp of activations is promising for addressing the bit waste or overclamp issues within addernet which is also verified by the extension experiments cons besides the flops and energy it would be great to report the inference time of the proposed method it is highly recommended to add a more detailed recap about addernet which will make the whole manuscript smoother especially for those who do not familiar with it in fig 3 how is the 1262 calculated there is no detailed explanation about it in line 45 l1norm quantization is unclear does it mean an l1normbased quantization method or quantization for l1norm operation minor issues there is a strange rec symbol in line 193 there are several minor grammar issues for example in line 21 wellverified should be well verified yes docsepquantization is an effective method to further reduce the energy consumption of addernets however previous addernets quantization methods cannot properly handle the challenge of large differences in weights and activations and often lead to a large accuracy degradation especially in the case of lowbit 4bit this paper first reveals the key reasons for the poor accuracy of previous addernets quantization methods namely over clamp and bits waste then a novel quantization method for addernets is proposed experiments on several datasets and models demonstrate the effectiveness of the proposed method strengths 1 the paper is extremely well structured and easy to follow with motivation wellexplained 2 to my knowledge this paper is by far the most comprehensive and systematic study of the quantization of addernets through thorough analysis this paper concludes two main reasons for the poor accuracy of previous addernets quantization methods namely over clamp and bits waste which are insightful the proposed scheme of the clusteringbased weights grouping and the lossless range clamp for weights are interesting and novel 3 extensive experiments on different models and datasets superior performance compared to other addernets quantization methods the thorough ablation studies verifies the effectiveness of each components the distributions of weights and activations fig1 in appendix demonstrate that the proposed method can effectively solve the problem of over clamp and bits waste leading to a higher quantized performance weaknesses 1 the values in fig 4 are too small to read the authors are required to refine them 2 the histogram for int4 weights adjacent to over clamp is significantly higher fig1 in appendix however this phenomenon is not expressed in the top of fig1 c the authors are advised to revise this detail for better presentation the authors have discussed the limitations and potential negative societal impact of their work in appendix ### Summary:

the reviewers were mostly positive about this paper 8664 while the negative reviewer did not update the review or respond after the authors response i do not see any major issues remaining the suggested method seems interesting novel and achieves good empirical results

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper introduces a new model architecture using lstm for image classification by adapting 2dimensional lstm bilstm for vertical and horizontal directions into the transformerlike architecture the model outperforms vitbased and sota cnnbased architectures with less number of parameters strengths 1 the paper is clearly written 2 the paper proposes a simple yet effective framework using lstm the model outperforms transformer and cnnbased models for image classification this work provides a great alternative to transformer and cnns for image classification 3 the proposed model is especially efficient for higher resolutions weaknesses 1 lack of related work there are a number of studies using multidirectional lstmrnn for vision tasks that are very relevant to this work eg 14 the authors should cite and discuss the similarities and differences renet 69 is very relevant to this work the authors pointed out that the major difference is to use a transformerlike block structure however the benefit of this structure and what it provides to the model compared to renet or other related works 14 are missing 2 due to lstms sequence nature lstmbased models are not easily parallelizable especially compared to transformer and cnnbased models i see that throughput is much worse than other models i assume training time would be especially slow it is unclear to me how throughput improves with higher resolutions 1 multidimensional recurrent neural networks icann 2007 2 pixel recurrent neural networks icml 2016 3 scene labeling with lstm recurrent neural networks cvpr 2015 4 semantic object parsing with localglobal long shortterm memory cvpr 2016 the authors explained the limitations and potential negative societal impact of their work in the paper docsepthis paper proposes an architecture for image classification named sequencer which utilizes the bilstm module to replace the selfattention module in the vision transformer model the bilstm module is further improved by processing the vertical and horizontal axes in parallel from topbotton and leftright directions experiments on image classification tasks demonstrate that the proposed method can acheive similar performance with existing classification models with similar number of paramters strengths this paper is wellwritten the idea is easy to understand the proposed method is the first work to empirically show the effectiveness of lstm modules in large scale image classification tasks which would have a board impact in investigating the potential of lstmlike architectures in the computer vision field ablations and visualization results are rich which present the validity of the proposed method in terms of the importance of each component weaknesses the novelty of the proposed method is limited the proposed sequencer replaces the selfattention module in vit with the existing bilstm module besides r1 shows that the selfattention module in vit can be replaced with a simple spatial pooling operator which suggests that such replacement is incremental although the proposed model can achieve similar performance with existing sota architecures it requires much higher flops and throughput as shown in table 1 evaluation is only conducted on image classification it would be better to evaluate the proposed architecture on more vision tasks such as detection and segmetation to show its generalization ability r1 metaformer is actually what you need for vision cvpr 2022 the limitations are mainly about the limited novelty of the proposed method and the poor experimental results much higher flops lack of experiments on other vision tasks docsepthis paper proposes a new sequencer architecture that replaces selfattention in vit with bilstm2d for the image classification task on imagenet1k dataset sequencer achieves better performance than current other similar scale models the authors also show sequencer is more robust to resolution variation and suffers from less severe accuracy degradation when the input resolution is increased pros 1 this paper makes an attempt to use lstm an unexplored inductive bias to replace selfattention in vit for image classification and shows its effectiveness this line of research helps the community understand what is indeed essential for vision tasks 2 strong results and extensive experiments it compares with a series of related works based on various inductive biases and shows that it has superior performance and transferability under a similar scale of parameters besides ablation studies are conducted cons 1 the computational cost is too high as shown in table 1 under a similar scale of model parameters sequencer usually needs 2x flops and is 2x10x lower throughput compared with other methods although this is not surprising due to the recursion in lstm i am still concerned about the practicality of this model with such a high computational cost 2 lack of reasoning on how using lstm captures the spatial information and why it is so effective in bilstm2d it uses lstm to capture dependencies from horizontal and vertical patches respectively from my point of view this design should not be as effective as global dependencies in selfattention since you may need to involve patches that are not necessarily in the same horizontal and vertical line to understand the objects in the images besides i am also curious about what role the memory in lstm plays in processing spatial information the above analysis is critical for readers to understand the model but is missing in the paper the authors have discussed the limitations in the conclusion actually it would be better if the authors can test the models effectiveness on tasks that require sequence modeling such as video action recognition in the main paper docsepthis paper proposed sequencer by using deep lstms instead of selfattention for image classification and many related works were compared in experiments to validate the performance of sequencer strength this paper proposed sequencer which uses lstm instead of the selfattention for sequence modeling this paper also proposed a twodimensional version of sequencer module where an lstm is decomposed into vertical and horizontal lstms to enhance performance experiments showed the advantages of sequencers compared to the selfattention mechanism in transferability and resolution adaptability the work is clearly stated and the manuscript is well written weakness some experimental results are not clearly explained some experimental results are not clearly explained ### Summary:

four reviewers provided detailed feedback on this paper the authors responded to the reviews and i appreciate the authors comments and clarifications specifically that each questioncomment is addressed in detail the authors also uploaded a revised version of the paper after the two discussion periods all four reviewers suggest to accept the paper although the scores do not exceed a weak accept after considering the reviewers and authors comments i believe that the paper should be accepted to neurips weaknesses include some concerns about experimental results eg highlighting accuracy vs number of parameters but not also highlighting limitations when looking throughput comparing only parameters or flops can sometimes be misleading see also the efficiency misnomer iclr22httpsarxivorgabs211012894 but its good that throughput numbers are presented in the paper and the paper acknowledges this limitation related concerns about computational cost some concerns regarding relevant related literature addressed in comments and revision and novelty of the approach limitation to image classification only in the experiments partially addressed in comments and revision more interpretation of the effect of using lstms could be helpful to the reader partially addressed in comments strengths include interesting conceptually simple approach that revisits lstms for images which could be specifically useful for high resolution images reviewers agree that the paper is wellwritten experimental results and ablations are strong with respect to the claims made minor points not affecting this decision but potentially useful to authors when preparing the final revision mlpbased methods cannot cope with flexible input sizes during inference i think this is only partially true even the original mlpmixer paper shows how this can be solved eg in finetuning by modifying the shape of mixers tokenmixing mlp blocks minor typo i randomly encountered table 3 row 3 column flowers 895 985 it is demonstrated that modeling longrange dependencies by selfattention is not necessarily essential in computer vision to some degree similar demonstrations are visible in cnns and mlpmixers so this claim seems a bit strong maybe

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: nocturne introduces a 2d simulation environment to support rlil approaches to multiagent planning in the context of autonomous driving nocturne seeks to improve on other work in two key ways first nocturne is able to generate 2d views of the world that are visible to any actor second nocturne is fast allowing for 2000 stepssecond speed is absolutely essential due to the sample inefficiency of rl algorithms nocture achieves these fast visibility queries by leveraging techniques from the computer graphics community while nocturne can be applied to multiple datasets the authors introduce a benchmark based on scenarios from the waymo open motion dataset the nocturne dataset includes all scenarios which do not interact with a traffic light 134k487k scenarios the authors then train rl and il agents on the benchmark investigating the performance of the agents as a function of the number of training scenarios nocturne is highly performant 2000 stepssecond makes this a feasible environment for rl learning nocturne supports both vectorized and rasterized representations this allows for a greater diversity of approaches to be developed the nocture benchmark is built upon a wellrespected and utilized selfdriving dataset the visibility map seems pessimistic it precludes the ability of a driver to see over the hood of a neighboring car for example this is not discussed and constitutes a real weakness of the fast method introduced here in comparison with those generated from imagery or lidar given the source of the data there are regions of space that are visible to actor a but were occluded from the waymo vehicle that collected the data there does not appear to be any way to represent potentially missing data within nocturne i would like to see a more compelling argument for the value added by a dynamic visibility query it seems a reasonable alternative would be to precompute a visibility map as a function of time given the expert trajectory clearly these could diverge but do they are the differences between a dynamic vs a static visibility map significant in this context at this point bc seems to be quite a weak baseline there are a plethora of goalconditioned forecasting methods which would be more appropriate here intersections as a proxy for interactions seem to be a weak proxy vehicles that merge into the same lane 8s apart will be considered interacting while two vehicles performing complementary left turns will not the zero shot learning section seems like it should be supported by a simple statistical test ie there is no significant difference between selfplay and crossplay i would really like to see some discussion or perhaps ablation experiments wrt to the conditioning information provided ie what happens to performance if we do not provide the final velocity docsepthe authors introduce a new 2d driving simulator with a focus on multiagent coordination under partial observability hence the name nocturne crucially unlike previously published driving simulators nocturne makes use of statebased partial observability computed through efficient intersection methods disposing of the need of rendering camera images to acquire the set of visible objects in a given time thus the simulator is able to run at over 2000 stepspersecond using realworld data the simulator is run on realworld data at a highfrequency rate providing an accurate account of driving situations it combines coordination and cooperation where the agents parameters are tuned to match experts capabilities the work probes further into the ability of baseline rl multiagents to handle complex scenes and provides an overview of their limitations mainly during cooperation tasks given the simulator builds a 2d birdseye view of the scene it is inherently limited when datasets built from cameras of driving cars such as waymo are employed as for instance pedestrians had to be excluded even though they represent one of the main challenges to tackle in autonomous driving docsepnocturne is a 2ddriving simulator constructed on realworld data and designed for partially observed marl research it first reconstructs maps and replay objects trajectories contained in realworld datasets such as the waymo motion dataset after that traffic vehicles are turned into controllable agents with partial observability and actuated to arrive goal region according to a learnable policy the policy will control vehicles with discrete actions and follow the dynamics of the bicycle model due to the efficient ray casting implemented by cbackend nocturne is efficient and can run up to 2000 fps with rasterized image observation or vectorized observation in the experiment the authors conduct generalization experiments on the scenarios imported from the waymo motion dataset with rl and il methods the experiment shows that increasing the number of scenarios contained in training set will improve the test performance on the unseen holdout set also the failure mode analysis and zsc test indicate that improvement on current algorithms is required to solve the partiallyobserved coordination problem 1 designing marl methods under partiallyobserved realworld scenarios can be challenging and valuable nocturne provides a good starting point and provides adequate benchmark results 2 according to the benchmark result of smarts increasing the number of agents will degrade the simulation efficiency also generalization experiments usually take billions of steps for each training dataset therefore the simulator can run up to 2000 fps which is important in marl generalization experiments 3 the experiment results are sound and insightful 1 the reward function encourages the agent to follow the expert trajectories which are produced with the observation of all traffic participants therefore i still suggest including pedestrians and cyclists in scenarios in the next version 2 waymo motion dataset has a 20s version where 20s trajectories are further divided into 9s fragments for motion prediction see httpswaymocomopendatamotionoverview consider using the 20s version to tame the short trajectory problem 3 i am not sure whether the maps in waymo data contain overpass bridges or not if so please filter these maps since nocturne is a 2d simulator 4 experiments changing the ratio of controllable vehicles in the scene could be conducted in the future ie 50 replay from data 50 marl agents it would be interesting to discuss the similarity and differences between agents trained in heterogeneous and homogeneous populations 5 for picking up trajectory intersection i suggest using time to collision ttc which additionally considers the temporal intersection typo in table 1 the reference of vista simulator is incorrect docsepthis paper introduces a driving benchmark relative to previous work the authors claim nocturne is the only available simulator that can compute an agents visible objects and step the agents dynamics at above 2000 stepspersecond the paper compares the performance of expert playback appo and bc in different driving scenarios paper makes a good case for the importance of studying the driving benchmark compared to previous works nocturne has more efficient environment interaction efficiency the paper describes the task state action space design and algorithm details it is convenient for readers to reproduce the result and adopt this benchmark the paper lacks the necessary algorithm comparison the author claims it is a multiagent benchmark but the paper only includes ppo and bc the paper mentions that the purpose of this benchmark is to study the multiagent learning process in the real world but does not clearly point out what advantages this paper has over previous simulators for this purpose ### Summary:

overall this paper provides a great starting point for future benchmarking experiments the reviewers engaged in a lively discussion with the authors and provided valuable suggestions for future improvements which the authors have integrated in their submission

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper leverages clip for zeroshot segmentation which is a very hot topic currently the authors proposed a clipretrievalbased way to build gallery candidates for the semantics segmentation class and then use denseclip to generate reference image embedding then they proposed several way to boost the performances including languageguided cosegment and context elimination to remove the bias of background experiments show the proposed method achieves stateoftheart performance strength outperforming stateoftheart performance weaknesses 1 the paper is not well written the authors make it hard to understand even for some very clear concepts 2 novelty is somehow limited technical contribution is not enough it is just like to find a prototype for a class and then use it for normal clip inference although there are some modifications such as context elimination however these are more like tricks which does not have technical depth 3 why the used numbers in table 2 are different for denseclip 92 table 1 and table 3 in original denseclip 92 yes docsepthis paper proposes a retrieve and cosegment approach that leverages a pretrained imagetext model eg clip for unsupervised semantic segmentation the results on existing benchmarks are good compared to other unsupervised segmentation approaches strength retrieve and cosegment is an intuitive and reasonable approach for unsupervised segmentation using imagetext models eg clip for retrieval makes sense and is effective adaptation to target distribution by training on pseudo labels is reasonable and effective performance of reco seems better than existing unsupervised segmentation approaches at system level weakness compared with the unsupervised segmentation approaches i think reco has a clear advantage by using clip which makes the approaches not directly comparable clip has seen lots of imagetext pairs and acquired reasonable pixel localization ability while existing approaches such as picie have no access to this kind of knowledge in addition if i understand correctly reco has access to the category names of the target dataset while existing approaches do not reco uses vitl14 for retrieval which is larger and stronger than the models used by existing works eg resnet18 of picie how does the performance of reco compare to existing unsupervised segmentation methods if we use smaller clip models eg r50 or vitb32 for retrieval and inference how does the performance change if you pick more than one seed pixel per image the steps to identify seed pixels l158168 seem highly heuristicsbased alternatively would clustering approaches work there yes docsepthis paper addresses the task of zeroshot segmentation in images by leveraging powerful largescale pretrained visionandlanguage models such as clip interestingly the proposed approach does not require costly and timeconsuming pixelwise annotations for training instead it uses clip to select groups of relevant images that correspond to the natural language queries based on nearest neighbors next it uses pretrained visual encoders to identify seed pixels in the images that have strong support across the entire group of relevant images these seed pixels are used to compute a reference feature for each language query to produce a segmentation attention map for each new query image which is further refined by another segmentation mask that is computed by clip strengths 1 the paper is largely wellwritten and easy to follow in particular the mathematical definitions that are provided are very helpful for understanding the proposed approach 2 the proposed approach is theoretically sound and intuitive while it is not entirely original due to the existence of approaches including denseclip the idea of discovering common spatial regions that occur in images containing the same concept is very interesting more importantly it leverages the largescale pretrained clip model to retrieve related images for a language query this allows the proposed approach to be trained on any unlabeled image sets 3 the task of image segmentation often requires finegrained pixelwise annotations which is an especially costly process being able to leverage powerful and largescale pretrained models to circumvent this process is especially significant coupled with the empirical evidence that it outperforms stateoftheart approaches this can be an important area of research given the availability of increasingly larger multimodal datasets such as laion5b weaknesses it would be helpful to see some qualitative visualizations of cosegmentation with seed pixels given that these seed pixels are used to compute a reference embedding for new query images and concepts during inference time it seems to be a very important component of the proposed approach it may help a reader to determine if the regions selected by the seed pixels are consistent across most images that contain a concept yes the authors have addressed the limitations docsepthis paper proposed a method for zeroshot transfer in semantic segmentation to solve this problem it first performs a imagetext retrieval by clip to get image archive then it use a pretrained encoder to perform cosegmentation during inference it combines the results from reference image embedding and dense clip to get the final segmentation results strengths the proposed pipeline which combines retrieval and cosegmentation is novel it outperforms the compared methods significantly weakness the method is complicated and requires two encoders during inference which slow down the speed i want to know the comparisons in fps when compared to other methods missing important citations there are some concurrent works for open vocabulary semantic segmentation1 2 which are not cited it would be better if related discussions are included 1 a simple baseline for zeroshot semantic segmentation with pretrained visionlanguage model arxiv 2 decoupling zeroshot semantic segmentation cvpr 2022 yes docsepthis paper utilizes the clip model for zeroshot transfer at first they leverage the clip to dynamically curate training sets from unlabelled images for arbitrary collections of concept names and leverage the robust correspondences offered by modern image representations to cosegment entities among the resulting collections the synthetic segment collections are then employed to construct a segmentation model whose knowledge of concepts is inherited from the scalable pretraining process of clip in this way the proposed method could perform unsupervised segmentation approaches while inheriting the convenience of nameable predictions and zeroshot transfer strengths 1 the paper is well written and easy to understand 2 leveraging the clip for unsupervised segmentation is interesting 3 the proposed training pipeline is reasonable 3 the experiments are sufficient to show the effectiveness of the proposed method weaknesses 1 the whole training pipeline seems a little complex for example the proposed method should utilize clip to filter some candidate images from numerous unlabeled data and the identification of seed pixels includes four steps 2 also the adjacency matrix a is the computation cost and is sensitive to k in the first step of the identification of seed pixels i am very curious about the potential of employing the proposed method in instance segmentation and the whole training pipeline is a little complicated ### Summary:

after author response and the discussion the paper received 1x borderline reject 1x borderline accept 3x weak accept note that one reviewer mentioned the score increase only in the discussion the main strength are overall novel framework for zeroshot segmentation strong performance the authors revised the paper and addressed manymost of the reviewers concernssuggestions in the author response i recommend acceptance with the expectation the authors provide the additional revisions as promised if possible address the comment of reviewer 1qtt what if remove eq 3 it seems pcnew is already good enough from figure 2

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: problem the paper addresses the problem of discovering and segmenting multiple foreground objects in videos without using supervision solution the proposed solution involves training on synthetic data and only leveraging optical flow not rgb to facilitate easier sim2real generalization the paper introduces a novel model architecture which tasks as input a sequence of optical flow frames and produces k amodal segmentation masks each associated with an estimated depth ordering these amodal segmentation maps can be combined using the ordering information to form the final estimated multiobject segmentation map the architecture involves a transformer based model inspired from detr which uses k learned queries to produce the k unique outputs strengths writing the writing of the paper is very clear and easy to follow the paper is organized very well to facilitate easier understanding of the details of the model and proposed data generation pipeline the details of the proposed model is presented in a concise manner covering most necessary aspects the paper includes detailed discussions of the pros and cons of most design choices these discussions make it much easier to follow and verify the claims proposed model the paper proposes a novel architecture that is interesting and might be applicable to a wider range of domains the model makes several interesting design choices the choice of leveraging optical flow only from synthetic data is not novel and has been used in other domains however the application to the problem of multiobject segmentation is unique the model goes beyond segmenting objects in individual frames and produces amodal segmentation maps for each frame this is an interesting design choice that seems to be very effective see more about this in weakness below the core of the model is heavily inspired from detr but the ideas of using this architecture to estimate layer depth and amodal masks is still novel and interesting in addition to modifying and adopting the objective of detr for estimating the masks the paper introduces a layer ordering loss to accurately estimate the depth order of each object synthetic data generation the data generation pipeline proposed in this work is novel the utility of this data beyond the task of multiobject segmentation is unclear however i believe this pipeline could be adopted by other researchers in this domain evaluation the experimental evaluation in this work exhaustively covers standard benchmarks and the necessary ablative studies to verify the claims across two tasks single object and multiobject segmentation the proposed model outperforms existing unsupervised learning methods the ablative studies show the benefits of using amodal segmentation maps as the intermediate output which is a key design choice of the proposed model weakness supervision and comparisons the paper claims to be an unsupervised method for video object segmentation im not sure if this is true based on the conventional usage of the term unsupervised the proposed method uses synthetic supervision this can obviously be corrected in the text however the bigger issue is the comparisons to existing work if this work were to be categorized as a supervised learning method the single object results are only as good as other supervised learning methods and in the multiobject case there is no comparison to supervised learning methods since the proposed method uses synthetic supervision it is also somewhat unfair to compare to a model trained with realworld human supervision but i think it is at least important to demonstrate the benefit of synthetic data ie scalability since generating synthetic data is not expensive if the proposed model can scale in performance with volume of synthetic data and outperform supervised methods that use limited supervision that would be a compelling result for adopting the proposed model amodal evaluation one of the interesting aspects of the proposed model is the choice of producing amodal maps as the intermediate step however the evaluation of this output is limited to the ablation of comparing to a model using modal maps it would have been interesting to see how well this model performs on the amodal segmentation task see the following for evaluation protocol zhan xiaohang et al selfsupervised scene deocclusion proceedings of the ieeecvf conference on computer vision and pattern recognition 2020 xiao yuting et al amodal segmentation based on visible region segmentation and shape prior proceedings of the aaai conference on artificial intelligence vol 35 no 4 2021 yes the limitations have been discussion fairly well there is no discussion on the societal impact docsepthe authors propose a new model for amodal segmentation of videos given optical flow a transformerbased neural network predicts an amodal ie unoccluded segmentation mask and depth for each object in the input frame the model is trained using ground truth supervision on a synthetic dataset created by the authors when evaluated on common video instance segmentation benchmarks the model shows promising results without requiring further training the proposed model clearly outperforms previous unsupervised segmentation methods and even outperform some supervised methods trained on the respective benchmarks video object segmentation is an active area of research predicting layered amodal segmentation masks that can be combined into a modal segmentation is an uncommon but very natural approach with this approach the model is tasked to not only learn about visible object parts but complete objects occlusions arise naturally when masks are combined compared to the more common approach of only reasoning about visible object fragments this may result in a more useful object representation the main weakness of the paper in my view is the comparison with other models the proposed model is trained supervisedly on synthetic data and then transferred without supervision none of the other models is trained in this way but either completely unsupervised or trained supervisedly on the respective benchmark the performance improvements on the benchmark are therefore not necessarily due to the proposed model architecture but might as well arise due to the different data seen during training a fair comparison would be to also train the other methods on the synthetic dataset and evaluate on the benchmarks the key question then would be whether the transfer performance of the proposed model is better than that of previous models which would be especially interesting for other models using optical flow one limitation of the model arises due to using optical flow as primary input which makes it impossible to segment nonmoving objects this limitation is discussed openly by the authors and subjected to future work what i believe is justified docsepthis paper considers the problem of segmeting multiple moving objects in a scene by taking optical flow as inputs the solution is composed of two key ideas first the inputs are optical flow so they can employ simulated videos for training and the learned model is able to generalize to real videos second a depthoredered layered representation is used to handle mutual occlusion experimental results show improvements upon methods with nonlayered representation on singlemultiple object segmentation datasets originality good the key innovations are three fold 1 objectcentric representation 2 layered representation 3 amodel representation although all three techniques have already been explored in previous literature there is no such an attempt before to combine them for video object segmentation furthermore most relevant works experiment on simulated datasets while in this work all experiments are conducted on real world videos which is much more difficult and more convincing quality good the proposed method is easy to understand and shows good performance on all dataset considered surpassing the most relevant method motion grouping by a significant margin clarity good but can be improved in general the paper reads smoothly and is easy to follow but perhaps due to limited space some important details are defered to the supplementary material which from my point of view would be better to be placed in the main text for example testtime adaptation seems bring large improvements and is important to the final performance it would be clearer to put relevant details in the main text significance the sutdied topic is of great importance objectcentric layered repersentation shows promise to become the next generation of vision paradigm the authors have discussed the limitations of their work docsepthis paper proposes a unet for generating a motion segmentation of a given motion field in particular the method assumes t snapshots of the field and generates t amodal segmentation masks the novelty of the paper lies in combining the unet with a transformer ie the transformer receives the latent embeddings of the unet encoder and generates output embeddings from which the segmentations masks and an ordering of these masks is inferred the authors follow the idea of layered motion and assume that each motion segment has unique depth by that the ordering is interpreted as a depth ordering the model is limited to a fixed size of layers queries in this paper three layers the method is then trained on new synthetic flow data without any manual labelling and applied to a variety of existing datasets and also to new data the paper is very well written very well structured the methodological approach sounds appropriate and the presentation of the results is well done i am not working on this specific problem but i believe the authors that the proposed neural network model is novel and not published before it shows how this generative problem can be elegantly solved by combining a unet for the task of segmentation together with a transformer for the task of inferring spatiotemporal correlations to guide the segmentation the evaluation seems properly done with partially very good results although some statements go to far eg in line 227 where it is argued large margin although improvements are not that large egfor fbms59 i believe that it has been shown that motion segmentation is basically learnable from synthetic data only if so one of the coreviewers more expert than i will definitely refer to this what i miss in the paper is a clear distinction between motion segmentation and occlusion reasoning 21d sketch as the model can only infer motion boundaries it might miss some of the occlusion boundaries especially for surfaces with interior occlusion boundaries it is also not clear to the reader how the model behaves for corrupted flow inputs or for more than three motions eg also for a moving camera finally the reader might also be interested to understand why longer sequences give better results or why object boundaries can help improving the layer ordering line 223 monet is also a new method to infer occlusion regions and motion boundaries h kim et al bmvc21 i suggest to clarify this in the paper i also suggest to add x zhan et als cvpr20 paper to the related work that shows a nice way to compute amodal maps yes ### Summary:

this paper uses synthetic data to train a cnn transformer architecture for amodal object segmentation from optical flow input the model architecture can be viewed as an adaptation of detr 12 to a different task reviewer ratings lean positive although there are concerns about experimental validation as the combination of training regime using synthetic data and input modality optical flow does not match that of other methods tested on the same datasets the proposed oclr system outperforms selfsupervised methods but falls behind the stateoftheart systems trained on real data while using different training resources than either class the author response partially alleviates this ambiguity with an additional ablation study comparing to an optical flow based mask rcnn model trained on synthetic data

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper studies differential privacy for pretrained certifiers that offer certified robustness through input perturbation the key insight is to analyze differential privacy afforded by the input perturbation by noise transformation computing corresponding gradient noise and augmenting with gradient perturbation when needed for differential privacy guarantees this improves upon past work by reducing the differential privacy budget needed by showing that some differential privacy is obtained from the input perturbation in addition to new analysis techniques for providing the privacy bounds experiments show gains over prior methods eg better differential privacy under adversarial attack strengths it may be desirable to defend the same classifier against adversarial input perturbations and privacy attacks so approaches which simultaneously guarantee certified robustness and differential privacy are interesting and useful prior work has noted that certified robustness techniques provide differential privacy current work provides a quantification of this claim experiments show gain over prior methods weaknesses the proposed perturbation mechanism is a simple combination of input and gradient noise the transformation process is only for analyzing the differential privacy guarantee and involves a simple taylor estimate and weakly uses the properties of the loss function the multivariate gaussian mechanism is a simple generalization of known mechanism in prior work questions it is not clear what is the proportion of negative and nonnegative examples or what effect this proportion has on differential privacy how are the perturbation scale thresholds xilow and xiup set in the experiments can gradient perturbation be useful for certified robustness experiments compare with other approaches that simultaneously try certified robustness and dp but what is the gap from approaches that optimize for exactly one or the other other suggestionscomments formal statements are sometimes unclear or without sufficient detail eg what is the domain of input xi the readability of the paper would benefit from a brief consolidated notationterminology section eg defining epsilondeltadp theorem 1 simultaneously defines multivariate gaussian mechanism and proves a property for it would be clearer to separate out a complete formal definition in equation 2 is o the littleoh notation it appears to not be the case in algorithm 1 a clarification would be useful if there is abuse of notation or if the littleoh choice is just coincidental in abstract and elsewhere the dp bounds with moments accountant are said to be tight but perhaps better to just say relatively tighter the paper considers an interesting and relevant question but the technical novelty and significance of the provided results does not seem to be sufficient algorithmic additions over prior work include using a combination of input and gradient noise and adding general multivariate gaussian noise the key analytic insight is to use taylor approximation to estimate dp afforded by input noise it is not clear how to set the hyperparameters of the proposed algorithm also formal presentation is somewhat lacking even though the approach beats some prior works empirically i believe the work is marginally below acceptance threshold due to above issues docsepthis paper focuses on providing both differential privacy and certified adversarial robustness to machine learning models the authors propose an algorithm called transdenoiser to achieve such a goal transdenoiser consists a denoiser through both input and gradient perturbation for achieving dp and certified robustness and following by a pretrained classifier for classification the privacy guarantee is carefully analyzed extensive experiments demonstrate the effectiveness of proposed method from model utility and adversarial robustness strengths 1 this paper considers transforming input perturbation into gradient perturbation then the noise introduced by random smoothing can be quantified with the explicit gradient perturbation for the privacy guarantee 2 to analyze the privacy guarantee multivariate gaussian mechanism is proposed by considering multivariate gaussian perturbation 3 the proposed method are conducted on various datasets and adversial attacks to show the effectiveness weaknesses 1 multivariate gaussian mechanism is not new and many previous works are also investigated multivariate gaussian differential privacy to achieve dp for example chanyaswad et al in 1 proposed a mvg mechanism which adds a matrixvalued noise drawn from a matrixvariate gaussian distribution and also introduce the directional noise in mvg that can further imporve the utility further yang et al in 2 proposed a matrix gaussian mechanisms for matrix value with better utility 1 chanyaswad thee alex dytso h vincent poor and prateek mittal mvg mechanism differential privacy under matrixvalued query in proceedings of the 2018 acm sigsac conference on computer and communications security pp 230246 2018 2 yang jungang liyao xiang jiahao yu xinbing wang bin guo zhetao li and baochun li matrix gaussian mechanisms for differentiallyprivate learning ieee transactions on mobile computing 2021 this paper investigates how the random smoothing noise can be transformed into gradient perturbation and then carefully compute the privacy loss which seems an interesting method docsepin this paper authors studied the problem of achieving both the overall differential privacy and certified robustness simultaneously for pretrained models they proposed a framework called transdenoiser based on an existing framework salman et al 2020 1 by adding additional and transformed gradient perturbations for the overall dp authors analyzed dp guarantee provided by these perturbations and empirically evaluate their methods on mnist and cifar10 and shown that transdenoiser is effective against fgsm and pgd attacks with guaranteed dp strengths the idea is well described the authors provided detailed analysis both theoretically and experimentally weaknesses discussion questions the proposed transdenoiser is lack of novelty this paper mainly builds off the work by salman et al 2020 1 which proposed to train a denoiser on input perturbations and leveraged randomized smoothing to achieve certified robustness the main difference is that in transdenoiser additional gradient perturbations are generated however the comparison with 1 is missing the lack of comparison with the most relevant baseline reduces the confidence throughout the paper it is not made clear why achieving dp is important and what is the difference between partial and overall dp i think it would be good to include some brief background on dp in the related work section instead of in the appendix a good chunk of your introduction could be moved to the related work section as well in terms of clarity the overall writing could be greatly improved there are several typos confusing sentences and symbol choices i listed several in minor issues specifically its hard to follow your transdenoiser training algorithm does the proposed method work for linfinity norm as well both theoretically and experimentally the experimental results are not entirely convincing to me for a thorough evaluation it would be better to report robust accuracy against pgd using madryetal is very confucing cw and auto attack my specific concerns are the following all the methods should be given with a better name current versions like xxxsct xxxprt xxxsepdp are not easy to follow the captions of figure 2 are mixed together and confusing im not sure whats going on in figure 3 there is no figure reference in main text i just suppose the corresponding explanations are under empirical defense please correct me if i am wrong only clean example for transdenoiser is provided what about other methods besides the better robustness may be caused by the tradeoff between natural accuracy and robustness with lack of the clean example given by other baselines to me it is not convincing to directly draw the conclusion that the certified accuracy on clean examples provides a good estimation for the empirical robustness of the model minor i found authors make their statements misleading for example in page 2 the paper says compared with 1 transdenoiser can without retraining the pretrained models however 1 fixed the pretrained models instead of retraining in page 3 the paper says different from 1 the objective function we use to optimize the denoiser contains the standard reconstruction mse however 1 also used mse under empirical defense the authors are mainly describe figures in the appendix the proposed methods should be evaluated on larger datasets eg cifar100 and more popular models eg resnetxx wrnxx to demonstrate its effectiveness thoroughly this paper mainly builds off the work by salman et al 2020 1 although dp analysis and tighter bound on dp guarantee are of some significance the authors are suggested to 1 compare their proposed method with 1 2 improve overall writing clarity and 3 significant improvements over experiment settings docsepthis paper studies the problem of integrating differential privacy and robustness to adversarial examples for pretrained machine learning models specifically this work aims at designing methods that guarantee both privacy and robustness without having to retrain the model at hand to achieve this goal the authors build upon an existing technique in the adversarial example literature that involves placing a denoising autoencoder in front of a pretrained model before applying a noise injection scheme known as randomized smoothing 1 while this technique is known to provide stateoftheart certified accuracy against adversarial examples its privacy guarantees remained to be studied this work proposes to do just that by adapting the algorithm to guarantee differential privacy for the dataset used to train the autoencoder the authors claim three main contributions 1 exploiting the intrinsic traintime input perturbation that existed in the previous implementation of the algorithm and composing it with an explicit gradient perturbation to satisfy differential privacy the authors claim that their treatment of this input perturbation allows a finer analysis of the algorithms privacy which ultimately leads to better accuracy for the same privacy guarantees 2 introducing two new analytical tools namely mgm and mmga for analyzing the privacy guarantees of multivariate gaussian noise injection 3 conducting extensive experiments on several benchmark datasets to demonstrate that their algorithm called  transdenoiser  provides better privacy guarantees and achieves similar level of certified robustness compared to previous works 1 provably robust deep learning via adversarially trained smoothed classifiers hadi salman greg yang jerry li pengchuan zhang huan zhang ilya razenshteyn sebastien bubeck strengths privacy and robustness to adversarial examples are two hot topics within the ml community especially when considering large models for image or speech recognition therefore i believe that the main focus of this paper ie the integration of these two notions for pretrained classifiers is very relevant to the iclr community also the main point of the article is quite simple and easy to understand from a highlevel perspective finally the idea of trying to translate the input noise injection into gradient perturbation to simplify the privacy analysis seems interesting weaknesses my main concern is with the technical quality of the article in fact i am not sure that the claims of the paper are technically correct especially with respect to lemmas 1 and 2 although from a general perspective the concepts of input noise and gradient perturbation seem related in my opinion neither lemma 1 nor lemma 2 demonstrate a clear connection i provide some details below 1 lemma 1 states that for a certain type of perturbed examples zinon defined in section 22 the gradient computed at zinon can be lower bounded by the gradient computed at the initial point xinon plus some noise that depends on the jacobian matrix of the loss function at xinon first the statement itself appears to be very confusing to me because the authors compare two random vectors with infinite support without explaining the meaning of the term geq second the analysis that the authors provide by stating that according to lemma 1 the dp guarantee provided by the perturbation of the transformed gradient is the lower bound of the one provided by the perturbation of the input lacks justification especially since the lemma only holds for a specific family of perturbed inputs finally looking at the proof i have some additional concerns among which a the reason for the jump from 9 to 10 is not clear to me and b the transition from 10 to the equality between the gradient of zi and the perturbed gradient of xi is also not clear to me 2 lemma 2 provides a similar statement with an equality but was not provided with a formal proof instead the authors present another lemma in the appendices lemma 3 also without a proof which is very similar to lemma 1 and claim that lemma 2 can be derived from lemma 3 this claim does not seem sufficiently conclusive to me finally i have the impression that the paper claims too much its technical contribution on the analysis of multivariate gaussian noise injection in fact as i understand it this work can be considered as a special case of previous work studying matrixvalued gaussian mechanisms 2 i think the authors should compare with this previous work additional comments and questions in experiments i am not sure that the comparison of transdenoiser with previous methods is fair in terms of privacy preservation as i understand it in 3 and 4 the model is directly learned with differential privacy thus protecting the dataset used to learn the model however in this paper the authors only claim to preserve privacy on the finetuning dataset thus leaving the dataset used in the pretrained model unprotected i have two concerns with this a the authors are comparing methods that do not preserve privacy on the same dataset which makes the comparison unfair compared to previous methods and b i checked the pretrained classifiers and it appears that they use the same datasets as the authors trained autoencoder since these models are not trained with privacy i think this represents a clear privacy breach in thereom 3 the authors present a result on privacy preservation for algorithm 1 based on a previous result in 5 this result is only valid if we consider algorithms that use poisson sampling to select the minibatch at each round however algorithm 1 does not seem to be using poisson sampling since the size of the minibatch is constant and equal to b see line 25 of the algorithm i think the presentation of the technical contribution could be improved the appendix presents several statements redemonstrating existing results in the literature on privacy and adversarial robustness theorem 5 6 and appendix b from my point of view these do not help the overall understanding of the contributions of the paper i suggest presenting only the proofs of the original contributions in the appendix and simply citing the existing papers for the earlier work when needed 2 mvg mechanism differential privacy under matrixvalued query thee chanyaswad alex dytso h vincent poor prateek mittal 3 heterogeneous gaussian mechanism preserving differential privacy in deep learning with provable robustness nhathai phan minh vu yang liu ruoming jin dejing dou xintao wu my t thai 4 scalable differential privacy with certified robustness in adversarial learning nhathai phan my t thai han hu ruoming jin tong sun dejing dou 5 deep learning with differential privacy martn abadi andy chu ian goodfellow h brendan mcmahan ilya mironov kunal talwar li zhang while i think this article studies an interesting problem i do not think it presents its contributions convincingly enough in particular i have concerns about the technical quality and the novelty of the article that lead me to recommend its rejection ### Summary:

this paper develops a technique to provide both privacy and robustness at the same time using differential privacy unfortunately the paper in its current form does not have meaningfully interpretable security or privacy claims the reviewers point at a number of these flaws that the authors do not address to the satisfaction of the reviewers but there are a few others as well what is actually private at the end of this whole procedure if the actual pretrained classifier is not made private then whats the purpose of the entire privacy setup in this paper why does the denoiser need to be private if the classifier isnt the proof of lemma 1 appears incorrect the proof in appendix e says that equation 10 is true but this sweeps all of the remaining taylor series terms under the rug and doesnt deal with them how are they handled in figure 4a what does it even mean to have a fgsm privacy budget epsilon or a mim privacy budget epsilon a privacy budget is almost always something defined with respect to the training data privacy how does this relate to the attack in this paper how does this paper compare prior canonical defenses both on the robustness and privacy side in particular comparisons to adversarial training on the robustness side and some recent dpsgd result on the privacy side

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: 1 important step in understanding the inductive bias of modern neural networks 2 convincing theoretical and empirical analysis 3 excellent presentation 1 only one hidden layer is considered but this is sufficient to represent readonce dnf 2 only uniform distributions 3 no sample complexity results i really enjoyed reading your paper and as a nonexpert got a lot out of it it is very wellpresented and i have only a few minor comments i dont think the abbreviation kkt is explained anywhere in the paper at the bottom right of p3 the citation amos et al 2017 should be amos et al 2017 the paper makes quite specific contributions and i think the title doesnt adequately reflect this docsepthe authors contribution rests both on experimental evaluation and on new theoretical results experimentally the authors show that the studied networks converge to solutions which generalize well in particular better than twolayer mlp and another method for learning dnfs moreover the trained networks have neurons aligned with terms of the dnfs theoretically the authors show under certain assumptions that i gradient flow does not learn solutions that memorize the data and ii that the learned network learns to reconstruct the dnf interesting question the paper studies an interesting question which is the inductive bias for learning readonce dnfs i really appreciate not only the results but also the problem and its specification nice mix of theory and experiments the theory and experiments nicely complement each other it is nice that the authors also study settings that go beyond their theoretical analysis dnfs that violate the readonce property especially for the second result however there are stronger assumptions used first the sample is assumed to be the whole set x ie all possible instances and ii it is assumed that the learned solution will be a minimumnorm solution the authors give compelling evidence from the literature but a proof does not existit may likely be very difficult so this is not a criticism the assumption on the population setting is quite strong however i think that such assumptions might be necessary for a work like this so i do not consider these to be big weaknesses overall the paper is well written i was just wondering what happens if the dnf contains just one term that contains all propositional variables will the solution not be memorizing if not then why not docsep there is a good theoretical analysis of the behavior of neural networks on this type of problem and experiments that align with this theory there are also experiments that show that the readonce restriction is needed which means that the results are tight and it gives an interesting example of an unlearnable function it would be helpful to define dnf i think the authors are talking about boolean functions in disjunctive normal form but they never explicitly say the problem of learning readonce dnfs seems to be mostly theoretical the experiments are limited to a a few fixed dnfs the experiments are not described in enough detail what learning method is used what parameters what are the lines in figure 2a notation is not introduced properly some of the related work seems only distantly related the theorems are obviousnot very surprising theorem 31 looks like a variant of the universal approximation theorem theorem 51 is an obvious consequence from gradient flow being a norm minimizing solution as mentioned in section 3 notation is not introduced properly d which seems to mean the set 12d x prod bernoulli05 but x has entries in 11 bernoulli usually implies 01 figure 1 how were these networks constructed there are no weights for second network layer in 1 is that too limiting figure 3 what is small gaussian initialization definition 62 and all other neurons are zero what does that mean theorem 61 is readonce an implicit assumption here it should be explicit in the statement of the theorem the experiment in figure 4a is the most interesting part of the paper to me showing a big contrast between the ability of a neural network to learn general dnfs vs readonce dnfs docsep the work seems thorough it answers several questions and also empirically covers cases where the initial assumptions do not hold novel paper is well structured reproducibility provided code and mentioned experimental parameters in the appendix the problem formulation is not clearly written cf detailed comments while the work appears thorough details of proofs and experiments are often referred to the appendix which makes the paper a lot less selfcontained and harder to readverify the theoretical contributions are mostly limited to readonce dnfs which appears very constraining for applications comments the problem formulation is not clear start with formally defining readonce dnf in logical terminology literals term disjunction of terms before describing the more numeric encoding of it the current problem formulation is quite hard to read also write at least once in full all used abbreviations disjunctive normal form dnf does not occur in the text but it really should is n in x sim prodi1n bernoulli05 supposed to be d mention d number of variables the task is to learn fx does that mean that the output must be a boolean function in readonce dnf format or does it mean that it must be a boolean function in any representational form that can be represented as a readonce dnf so the learned function fx is not necessarily in a dnf format while the overall work appears thorough very often the main text refers to the appendix for detailsproofs the main text on its own becomes a lot less selfcontained in case the population size is too large to check the actual accuracy it would be insightful to also report on top of the sample based test accuracy the model count of the actual dnf f the learned dnf f and f vee f i did not verify the proofs in the appendix and found the proofs within the paper not always easy to follow in particular for the proof of theorem 61 i would have liked to see more details within the main paper itself questions q1 figure 1s axes are not labelled what are they i assumed xaxis is the hidden units and yaxis is all potential inputs but there should be 29 of those instead of 600 so im confused q2 nonlinear readonce dnf was never defined what is a nonlinear readonce dnf in logical terminology literals terms q3 sect 7 the fact that sgd recovers simple boolean formulas is very attractive in the context of interpretability but the previous paragraph empirically showed that when learning from data sourced from a dnf with overlapping terms the solution is not a dnf recovery solution those would not be easily interpretable q4 sect 7 non readonce dnfs are the literals still all positive or both negatives and positives q5 to show each property we assume by contradiction that it holds and construct a perfect solution with lower norm this leads to a contradiction since the solution has minimal norm to show that each property holds the proof is not very clear to me how does it rely on the properties q6 the focus is on learning a readonce dnf from training data problem formulation if the learned function did not have to be a readonce dnf then the problem reads like a binary classification problem for which methods exist eg a learned decision tree can easily be turned in a dnf what makes the readonce dnf constraint specifically interesting are those commona good approximation in practice i understand that from a theoretical perspective it could be interesting to initially restrict the input to readonce dnfs to make the analysis easier but why constrain the output to a readonce dnf too dowe see comment later on problem formulation q7 which proofstheoretical statements rely on mathcald being uniform q8 which proofstheoretical statements rely on the readonce restriction q9 in figure 2a only with training set size 2000 does the sq algorithm seem to more consistently achieve 100 accuracy however i had the impression that if sq was given the entire population 29 samples here it would be 100 accurate is this not true or did the training set just not happen to contain the entire population q10 in figure 2a did each run only differ in initialization or also a different training and test set did the 3 approaches learn from the same training set and evaluated on the same test set textual remarks fig 3b and similar are missing axis labels learning dnfs is hard pitt and valiant 1998 learning readonce dnfs is hard to be more specific the a set the set we perform experiments on dnfs of higher dimension higher dimension is not clear does this mean more terms or more variables or should this be readonce dnf fig 4 caption the training size was 8 500 for all dnfs is this 8500 without space their results suggest that the inductive bias of gf is to kkt points or global solutions of minimum norm problems and later in our theoretical analysis we apply the results of lyu and li 2020 ji and telgarsky 2020 which show that gf is biased towards kkt points of minnorm problems did they proof show this or suggest this in assumption 51 what is n ### Summary:

meta review most reviewers appreciated the insights provided on learning neural networks some reviewers also had some concerns about readability hopefully they provided enough feedback to improve the paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper considers the problem that personalization methods in federated learning may cause the personalized models to overfit on spurious features thereby increasing the accuracy disparity compared to the global model to mitigate this accuracy disparity the paper investigates adversarial transferability which is shown to correlate with disparity thus the paper proposes a federated personalization approach based on adversarial transferability and catastrophic forgetting that reduces accuracy disparity to the level of the global model while maintaining the higher accuracy of prior personalization methods the paper evaluates the approach on three realworld datasets strengths the paper identifies a relevant problem the paper empirically shows that prior work on personalized federated learning induces high accuracy disparity which is a relevant problem to the fairness community the method is somewhat novel and technically sound the paper demonstrates a link between adversarial transferability and accuracy disparity of personalized models thus the paper employs adversarial transferability and weight regularization which has already been proposed by prior work as the paper acknowledges to learn personalized federated models that achieve high accuracy and lower accuracy disparity than prior work weaknesses certain presentational aspects could be improved for example biasconflicting examples are mentioned in the introduction but only defined in section 4 leaving the reader to guess what these biasconflicting examples are furthermore the experimental setup for figures 4 and 5 is not provided eg which personalization method was used moreover in section 61 two sentences are repeated there are 650 blond and there are 650 blond finally there are a few typos section 41 models on biased and biasconflicting section 61 throughout our experiments section 61 residual the method lacks key motivation adversarial transferability is introduced to reduce the accuracy disparity of the personalized models by forcing them to be vulnerable to the same adversarial examples as the global model however the adversarial transferability between the global and local models is just a proxy for the similarity of the two models similar to eg kl divergence baselines are misrepresented in figure 3 the centralized model is trained on a dataset where the spurious correlations are fixed whereas the federated model is trained on multiple client datasets each with different spurious correlations accordingly it is unsurprising that the centralized model has a significantly lower accuracy on the biasconflicting dataset as it was not exposed to the same data distribution shifts during training as the federated model i would expect the centralized model to perform on par with the federated model when trained on the same data invalidating the papers claim results are misrepresented in section 5 the paper states that low adversarial transferability indicates high accuracy disparity for the personalized models however the paper merely shows a correlation between the two metrics as they both estimate the similarity between the global and personalized models i would be very surprised if one could not train global and personalized models that achieve high transferability and high disparity or low transferability but high disparity as i believe that transferability and accuracy disparity are only weakly correlated unfortunately the paper does not provide empirical evidence to substantiate these claims in fact it provides evidence for the opposite as accuracy disparity still increases even if the adversarial transferability remains high the paper makes unsubstantiated claims in section 5 the paper claims that both methods are relatively lightweight from a computational perspective but does not provide any further evidence for or analysis of this statement results are unclear figure 7 compares the losses on the biased and biasconflicting datasets however the loss is only an approximation of the accuracy which is the quantity that we are ultimately interested in therefore it would be more meaningful to compare the accuracies on the different datasets results are insignificant comparing the results for the global model and the proposed method in table 1 the method achieves roughly the same accuracy on the biasconflicting dataset and only a minor increase in accuracy on the biased dataset except for the mnist dataset moreover the prior personalization approaches achieve significantly higher accuracies on the biased datasets thus the method just provides a little less personalization yielding personalized models that are more similar to the global model with corresponding performance the paper identifies an interesting shortcoming of prior personalization approaches however given the various weaknesses outlined above eg insignificant results misrepresentation of results and the limited novelty i do not believe that the paper meets the bar for publication in its current form docsepthis work explores the possibility of personalisation methods entangling spurious features that can undermine their generalization in case of federated learning it proposes to use a combination of a consistency term for adversarial transferability and an l2 regularisation term to help reduce this disparity the approach is evaluated on artificial settings with spurious features strength the work exposes a possible generalisation issues in personalised federated learning and proposes a novel approach to tackle it the idea is well motivated paper is generally well written and experiments are provided to substantiate the claims weakness use of some nonstandard hyperparams like 0031 eps budget for mnist and the batches of 96 40 30 similarly the 5 epochs of local training seem larger than the conventional 1 or 2 could the authors provide an explanation doesnt include exploration for other modalities like text or large scale setups like femnist few open questions any thoughts on how the method could compare to say the adversarial training objective in combination with personalisation i think this work tackles an interesting hypothesis that can limit generalization in case of personalised fl the proposed solution is principled and well motivated with appropriate ablation study the only drawback would be lack of experimentation on large scale problems which would certainly make this a valuable piece of work docsepthe authors have proposed a new fl training strategy to reduce the performance discrepancy between the central model and the client models the pgd generated adversarial examples are fed into both central model and client models and their outputs are used to minimize the entropy loss the computation is further simplified using taylor expansion strength the idea of using adversarial examples and minimizing entropy loss of global and local models outputs is normal the intuitions are straightforward to understand authors have spent a decent amount of effort explaining the relationship between the spurious features and adversarial transferability it is helpful for audiences the results do show certain improvements over the other baselines weakness the presentation has space for improvement please explicitly explain all the critical terms accuracy disparity biasconflicting etc at the first occurrence in this paper many subjective descriptions exist in the paper eg if you claim the distribution shift of spurious features is a major effect of accuracy disparity it would be really important that you give theoretical proof andor empirical support to verify your claim it seems like the spurious features are handcrafted and we dont have a clear solution of how to automatically choose the spurious features in real applications the adversarial examples are generated using a global model however the way of generating adversarial examples in fl worth a lot of analysis and description of the details there exist some papers that discuss the best way of improving adversarial robustness with adversarial training here the same strategy should be applied to compare the experiments are a bit disappointing without a comprehensive comparison a replication of the results is almost impossible so many factors in fl can dramatically change the results the authors didnt provide a fair and reproducible setting for the results the experiment is incomplete and the results are not convincing the idea is good and novel however the presentation is disappointing and the experiments are weak i recommend rejection ### Summary:

the paper talks about a novel setting in federated learning and argues that personalization methods may cause the personalized models to overfit on spurious features thereby increasing the accuracy disparity compared to the global model to this end the authors propose a debiasing strategy using a global model and adversarial tranferability there were some positive opinion about the problem being interesting however reviewers had several concerns about the validity of assumption and hand wavy arguments used in the solutions for existence adversarial tranferability overall the settings and the need for removing personalization bias needs to be validated more convincingly and rigorously with concrete real scenarios and experiments

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper presents a hypergrid transformer approach to finetuning where one takes a pretrained transformer model and then modifies it by introducing hypernetworks that modify the 2nd ffn in each transformer block by generating additional weights conditioned on input these hypernetworks are trained on all tasks in gluesuperglue datasets simultaneously and are task aware through prefixing of a task specific token to input this allows one to finetune only a small number of parameters and end up with a model that performs quite well on all tasks at the same time not much worse than finetuning the entire transformer model on all of these tasks this is an interesting paper in the area of hypernetworks with results suggesting potential for impact where one can achieve good accuracies on tasks without having to fully finetune gigantic pretrained models i do have some questions for the authors though local vs global global seems to be just like learning another weight matrix not conditioned on anything from the name one would expect local to be conditioned on some specific parts of the input while global is conditioned on entire input what is the intuition on why they help whats the intuition between the differences of performance of the various setups lg gl l2 etc figure 9 doesnt exist if coarser variants are better what happens when the entire weight matrix is treated as 1 block so you just learn a scalar weight what about learning a single scalar for each weight in the ffn ie block size is 1 have you tried adding dynamic weights to projections in the multi head self attention modules eg to projections for qkv table 1 why are qnli results so much worse for hgtlg than all other results but seems to be doing better on most other tasks how stable are all of your results ie what is the variance across seeds parameter counts are confusing how does one compute that a multiple networks needs 32b params but a single network needs 02b params are these the trainable weights for each setup so you count 16 tasks x 02b weights maybe it is better to report total num of params trained params or else somehow make it more clear what the number of parameters means have you tried finetuning the hypernetworks on individual tasks update thanks for the update i guess the intuition is driven mostly by empirical results which i suppose is ok but may be worth digging into a bit more i have updated my rating docsepthis manuscript presents a hypergrid transformer which is engaged in learning a single model to account for multitasks in nlp the core idea of hypergrid transformer is to learn taskconditional dynamic weights in a gridwise manner in the feedforward layers where the weights are factorized in local and global components this idea is simple materializing the goal of reducing the parameter cost for the used multitask network however the conducted experiments look nice showing promising performance on gluesuperglue therefore from my point of view this work is worthy of a publication at iclr docsepthe authors propose hypergrid transformers with a decomposable hypernetwork that learns gridwise projections to specialize regions in weight matrices for different tasks usually people would use different models to solve different tasks respectively in this paper the authors focus on using a single model to solve all tasks and it will save a lot of model parameters for natural language understanding and the authors have done comprehensive experiments on glue and superglue and prove that the proposed single model can achieve much better performance than baseline and competitive performance with multiple taskspecific models pros 1 the idea to make use of decomposable grid wise projection is interesting this is to somehow add regularization to the weights 2 the proposed method has been widely evaluated on gluesuperglue tasks and achieve good performance cons 1 the baseline details are not clear when using a single model as baseline how many layers are shared across tasks whats the sample strategy for different tasks is it possible to train a single model on multitasks for some steps then fix most layers and only finetune some task specific layers i feel the baseline is a bit weak although i cannot come up with a stronger one that can be easily adapted to the pertained model 2 it seems task conditioning is a very important trick the authors should have some ablation study on it or maybe add it to the baseline overall its great to see people working a single model to solve all tasks and i would be happy to increase my score if the authors could convince me regarding the baseline which is quite tricky update the experiment results are not surprised but strong enough still no very strong baseline provided in this submission but it might be good to set up a benchmark in this direction however t5 model needs more computational resource and the experiment results are hard to replicate overall i would like to keep rating ### Summary:

the paper proposes hypergrid transformers a modified transformer architecture for learning a single model for multiple tasks in nlp the proposed method was evaluated on popular gluesuperglue tasks and reported competitive results with the baselines the improvements are somewhat marginal the paper contains some interesting idea of using a decomposable hypernetwork to learn gridwise projections for different tasks which may not be particularly novel in machine learning context but seems new for multitask nlp reviewers generally agree the paper is above acceptance bar however some concerns were raised about clarity of baselines and fairness of experimental comparison as well as stronger baselines authors improved some of them in the rebuttal but there is still some room to further improve the quality of presentation and writing in the final version

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this work provides lower bounds in the noise free setting for learning two hidden layer networks in the gaussian space basically the whole concept is to embed hard problems over the uniform in hypercube to the gaussian space this is not something new this has been done before in 1 for proving lower bound in the agnostic learning halfspaces over the gaussian distribution in contrast this work provides noisefree lower bounds they provide a superpolynomial sq lower bound a cryptographic lower bound under the lwr assumption moreover they also provide lower bounds for the query model which is more powerful than the pac learning model the whole concept is the following to embed hard problems from the hypercube to the gaussian space we can use a similar idea like in 1 ie using the sign function the dv lift basically does that by adding 2 more hidden layers with relu components so a hard problem with llayers can provide lower bounds for l2 layers in gaussian space the authors decrease the number of layers from 2 layers to 1 to do that first they show a way to do it using a very large network basically they start from an exponential construction eq11 and then they decrease it to dm by make the network more sparse using the distributional properties of the gaussian they introduce some error in the construction but they show that this is indeed very small after that the hardness proofs follow from a reduction 1 adam klivans and pravesh kothari embedding hard learning problems into gaussian space pros 1 this is good result the authors provide lower bounds under several assumptionsmodels 2 this work is very wellwritten checked almost all the proofs and the claims are sound cons not really a con just a comment the lower bounds are for 2hidden layer networks where there are results for 1hidden layer networks for the csq model ggj20dkkz20 basically the tradeoff is stronger model and 2hidden layer instead of 1 in general i would expect stronger lower bounds for 2hidden layer network overall i recommend for acceptance everything is good docsepthe authors prove statistical query lower bounds for learning polynomialsized neural networks with two hidden layers the bound is superpolynomial in the input dimension d or the query tolerance is negligible in d no cryptographic assumptions are needed for these bounds to hold the authors also show that under the learning with rounding with polynomial modulus cryptographic assumption no polynomialtime algorithm can learn neural networks with two hidden layers from gaussian examples the result is extended to neural networks with one hidden layer over the uniform distribution on the boolean hypercube the paper is wellwritten and the review of the related work is thorough a great deal of effort has been put into making sure that the paper is clear and accessible for a wide audience particularly in the technical overview this paper is a bit outside my expertise but the results and techniques used are interesting and could be of independent interest i believe this paper is relevant to the learning theory community limitations yes impact na docsepthe paper establishes hardness of learning neural networks with gaussian inputs under various assumptions statistical query learning two hidden layers cryptographic hardness of learning with rounding two hidden layers label query learning existence of a family of pseudorandom functions any fixed number of hidden layers the main tool for obtaining these results is a modified danielivardy transform 2021 that maps a boolean example x y to a gaussian example z y while remaining in the realizability setup strengths a solid theoretical work that establishes new hardness results for the now ubiquitous neural networks weaknesses the paper does not explicitly indicate the limitations of the results for example lines 6768 say that theorem 11 rules out almost all known approaches for provably learning neural networks but the most wellknown approach to learn neural networkssgdis not ruled out by thm 11 see weaknesses above ### Summary:

this work provides lower bounds in the noise free setting for learning two hidden layer networks in the gaussian space overall it is a fundamental result well within the scope of neurips continuing a solid line of work and i cannot see any reason for rejection the authors have engaged with the reviewers and have committed to make minor revisions and clarifications which i am sure they will do

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary this paper provided the definition of the temporallycontingent planning tcp problem together with some concrete planning domains modeled in terms of this framework on top of that the authors pointed out several directions toward how to provide explanations about some important questions that a user may raise in the context of tcp i found this paper is easy to read and follow in general however there are some concepts that are not clearly specified which may result in some reader being confused apart from this the remaining part is sound thus from my perspective i think this paper can be accepted detailed comments 1 in the definition of the temporallycontingent planning problem the authors said that the problem ptc is defined as pt cup pc but the formal definitions of pt and pc are not given 2 in definition 2 the set of physical actions consists of instantaneous actions and duration actions which both are the tuple apre aeff adur based upon this definition it seems that there is no difference between an instantaneous action and a duration action moreover the authors said that adur is a set of duration constraints but they did not clarify what are duration constraints i guess adur is a set of variables which must hold when the action is executing since those definitions play an important role in this paper i think it would be better if the authors can clarify these conceptsdocsep summary this paper analyses the explainability of temporal and contingent planning problems for settings with noisy sensing and incomplete knowledge the work defines what a temporallycontingent planning problem is and what its solution can look like it then defines the notion of explainable planning for such problems and discusses possible questions and answers for these settings feedback the paper is wellwritten easy to read and relevant to the xaip community the family of xaiptc problems extends the current notions of explainable planning to domains with numeric temporal and contingent features the authors comprehensively discuss the type of questions and answers that might be needed to facilitate the explainable planning for such domains in my opinion the discussion of complexity in section 5 can be extended as the current discussion seems informal more importantly what will be the complexity of reasoning generated by the ai planner also planning with k propositions seems computationally hard the work can include a formal discussion of this minor edits 1 at many places i think citet would be a better choice to use instead of cite eg section 1 para3 line1 section 1 para4 line1 etc 2 def 1 last sentence it is not clear at this point what is p in til it becomes clear later but this def is incomplete without it 3 def 1 does not talk about mathcalpc or mathcalpt as mentioned in the paragraph just before def 1 4 use empheff in math mode instead of eff eg def 2 5 ai solver is used on page 3 domain 2s description directly with no reference to it before this point 6 what is deltad in def 4 seems to be a typo 7 the paper consistently used the phrase tcp problem i think it should either be tc problem or just tcp 8 text in fig 1 is difficult to read 9 page 4 left side last para the second example is is a fixed set and the second questions in each branch these sentences seem to be incompleteincorrect ### Summary:

we thank the authors for their contribution itll be a great addition to the workshop program please refer to the feedback provided by the reviewers when creating your cameraready version particularly to improve the clarity of the paper consider formally defining important termsnotions used throughout the paper such as the problem ptc which is the main consideration in this paper it will also be interesting to consider expanding the discussion in section 5 as suggested by reviewer 2 to include some analysis on the practical aspect of this work ie what is the overall complexity involved in finding solutions for some of the queries considered in this work we are looking forward to an interesting and fruitful discussion at the workshop

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper studies the use of random weights together with learnable masks the learnable masks are learned with straight through operator the authors argue that such training approach for neural network would reduce the model storage requirements and has applications to network compression the model is validated on cifar 10 and cifar 100 datasets showing that the proposed layers underperforms approximately between 1 and 10 accuracy points wrt dense layers depending on the model architecture and number of parameters post rebuttal based on authors responses i updated the overall score from 3 to 6 and increased soundness and presentation both by 1 overall the discussed ideas are interesting using a random layer with learnable masks to achieve competitive performance however the validation and the presentation of the paper require improvements for details see comments below title the title might be a bit to generic not very informative and a bit misleading for more complex datasets one would probably need more layer architectures how about the following title on learning masking operators for network random weights abstract to strengthen the abstract please add quantification of improvements in terms of space complexity also based on the experimental section the improvements come at the expense of model accuracy however this tradeoff is not captured in the abstract introduction introduction section is in general well written and easy to follow however it could benefit from some rewriting shortening and refocusing the introduction does not discuss the obtained results making it hard to assess the significance of the proposed approach it is also unclear what the ml community gains with the results of this study please add such discussion to the introduction section methodology this section would benefit the most from rewriting and restructuring in the current form it is difficult to follow and do not allow to fully appreciate the presented ideas figure 2 should be better discussed and better formatted please extend the caption to clarify the figure in general all figures in the paper should be selfexplanatory making it possible to understand the figures just by reading the captions in its current form it is impossible to understand the drawings based on the description the process of updating the prototype weights into target weights is unclear could the authors clarify how different networks are updated eq 4 and eq 7 differ only in the dimensionality of w why changing the dimensionality leads to new paradigm of random weights padding moreover please boldface vectors in eq 7 to differentiate them further from scalars in eq 4 the section lacks motivations behind different choices results the introduced layer is not compared to previously published models would it be possible to compare the model to supermaks and popup adding comparisons to previous art would make the validation stronger cifar datasets are small scale would the observations generalize to larger scale datasets adding another dataset would make the observations more compelling the results are missing stds making it hard to assess the significance of the results in general the proposed layers are underperforming wrt dense layers that would be expected however the results section lacks discussion and positioning of the reported results eg why the reported results are interesting what do we learn as a community from the results what is the impact of the reported results adding more indepth discussion would make the paper stronger the paper does not discuss the limitations of the discussed ideas the paper does not mention societal impacts docsepthis paper aims to handle the difficulty of restoringtransmitting models caused by the increasing model size for recent largescale neural networks inspired by recent works eg lth popup on random networks the paper starts by answering a scientific question what is the potential of a random network specifically the authors propose a series of strategies to study the random network with different masks to map different features through the exploration for the answer a new model compression paradigm is proposed by only restoring onelayer random weights and a bunch of masks to represent a model experiments were conducted based on using different cnntransformer architectures extensive results validate the rationality of the motivation and show the feasibility of the new compression paradigm strengths 1 this work tries to reduce the model storage size which is a clear and practical motivation compared with typical model size compression methods that remove partial parameters it is a novel way to represent a model by using different masks on fixed random weights 2 this work is driven by studying the random weight capacity which is an interesting yet underexplored studying point it is novel to use onelayer weights with different masks to learn a model 3 experiment is extensive using different model architectures firstly it answers the question about random weight potential using a series of proposed strategies to construct a network using random weights secondly it shows the feasibility of a new compression paradigm compared with the typical model compression method weakness 1 it is encouraged to revise the draft title to a more appropriate one after reading the draft i think the current title doesnt convey the key factor of this paper iteratively selecting different masks on a set of fixed random weights for different feature mappings should be the main point therefore the usage of onelayer in the title is inaccurate on the other side all you need is a too vague description it needs to be concretized to eliminate confusion 2 some related works are supplemented in the appendix i suggest moving them into the main draft and providing necessary discussions about them the discussion should include the difference between the submitted work with these existing works since they look highly related to this work even if they are in a different setting 3 technically the proposed random vector padding rp repeats the given set of random weights in the same order if randomly shuffling the random set and then doing the padding to construct the model can it improve the capacity 4 minors 1 in alg3 it seems the output is written in the wrong way which should be the output of mp strategy in alg2 but not consistent with rp strategy in alg3 2 around eq 5 and eq 6 the explanation of tp is missing it should be further clarified and consistent with alg1 3 in eq9 the dl should be the dimension of wl instead of the number of vector vpro please make it clear to eliminate confusion the authors have addressed the limitations and potential negative societal impact docsepthis paper proposed a new paradigm for neural network compression the authors randomly initialize a set of weights the actual parameters of each layer are represented as the initialized weights with binary masks the weights are shared by multiple layers while the masks are different for each layer the weights are fixed while the masks are learnable in this way the total bytes are significantly reduced experiments show that the proposed method achieves better compression than baselines strengths 1 this paper is well organized and the core method is clearly represented 2 this paper represents each layer as shared weights with different masks the idea for model compression is interesting and novel 3 experiments show that the proposed method achieves good compression for image classification models weaknesses 1 the title of this paper is unsuitable and the authors should change it first people will not associate the title with model compression second the word one layer in this paper is misleading although some parts are shared cross all layers there are differences between layers thus we cant say them one layer in my opinion masks are also parameters of the model 2 it is better to compare the compression performance with stronger baselines or bigger datasets such as imagenet 3 in general the proposed method achieves compression by sharing some parts of parameters while adjusting the others several previous works have explored this direction such as 1 and 2 the authors should discuss them in related works 1 residual connections encourage iterative inference international conference on learning representations 2017 2 recurrent convolutions a model compression point of view nips workshops compact deep neural network representation with industrial applications 2018 authors discussed the limitations and potential negative societal impact of their work in supplementary material docsepthis paper proposes a new way of representing a neural network in a compressed way coined one layer is all you need the idea is to keep a single fixed and randomly initialized weight vector as prototype for each layer of the network whereas each layer is saved as a learned mask determining which weights of the prototype are used since saving bit masks is more memory efficient than floating point the network can be efficiently stored experiments with resnet32 resnet56 convmixer and vit on cifar10 and cifar100 show that this method achieves improved results in terms of accuracy compared to sparse network training baselines while maintaining larger compression ratios strengths s1 the problem of storing neural networks in an efficient manner is significant and the proposed idea improves in this direction s2 the tradeoff between network compression and accuracy is improved in comparison to sparse network training baselines s3 the writing is wellstructured and easy to follow weaknesses w1 experiments only performed on lowresolution datasets cifar10 cifar100 tinyimagenet w2 it is not clear if experimental settings are repeated with different seeds the checklist refers to the supplementary material but i cannot find any results for multiple seeds there either i believe all experiments should be conducted for multiple seeds w3 no code is included for reproducibility w4 the writing should be improved in terms of typos and grammar see below for some instances typos the sentence in lines 2021 seems incomplete lines 126150 rewrited rewritten line 259 compreesion compression line 263 the sentence is confusing because you train two networks with different strategies and not one network with both line 307 foundamental fundamental w2 is addressed by the authors during the discussion furthermore they ensured they will resolve w3 and w4 i updated my score respectively the authors discussed limitations in the supplementary material the fact that this method cannot be used to compress already pretrained models but requires training from scratch is an important limitation and should be mentioned in the main paper ### Summary:

the paper studies the use of random weights together with learnable masks authors demonstrate that such training approach for neural network can reduce the model storage requirements and has applications to network compression reviewer appreciated the novelty of the idea and the extensive experiments on various architectures adding experiments that would go beyond smallscale datasets would further strengthen the quality of the paper and its potential impact

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper introduces an extendable framework to train different vaebased generative models a usecase is presented benchmarking 19 different autoencoders on 5 different tasks image reconstruction image generation classification clustering interpolation s1 the framework enables researches to train custom vaebased models in few lines of code and thus reduces friction for further research in this area s2 the framework seems to be welldocumented and is readily available s3 the experimental section in the paper is interesting especially the part about varying sizes of latent spaces however see w1 w1 my main concern is that the experimental section is evaluated with isfid that use inception as feature extractor several works address problems with these metrics eg see 15 if time permits i would suggest to run more metrics eg 4 or 5 and additionally report those in the paper otherwise at least mentioning these concerns suggesting additional alternatives and stating that benchmarking on isfid alone is insufficient should be mandatory w2 the linked framework does not seem to contain the code of the experimental section in the paper ie benchmarking different vaes and implementations of metrics like fidis please clarify if this is the case or if i was not able to find it w3 in relation to w2 the submission is neither a dataset nor a benchmarking tool but a framework to train custom vaebased generative models in few lines of code im unsure whether this fits the scope of the dataset and benchmark track 1 a note on the inception score httpsarxivorgabs180101973 2 effectively unbiased fid and inception score and where to find them httpsarxivorgabs191107023 3 internalized biases in frchet inception distance httpsopenreviewnetforumidmlg96upmbyz 4 on selfsupervised image representations for gan evaluation httpsopenreviewnetforumidnerdbetionn 5 the role of imagenet classes in frchet inception distance httpsarxivorgabs220306026 docsepthis paper provides a python toolbox called pythae to train and evaluate various ae models under a unified framework this library has aroused vast interest among related users in particular there have already been over 800 stars in the opensource repository this work also extensively compare various ae models from various aspects and provide insightful dicussions 1 this paper is overall wellwritten and the documentation in the opensource library is very clear 2 besides a clear introduction to the code structure and project the authors also extensively compare various ae variants from different aspects including image reconstruction and generation latent vector classification and clustering and image interpolation 3 experiment results also provide insight into how different components used in different ae models affect the five aspects studied 1 experiments are performed on relatively smallscale datasets like mnist and cifar experimenting on largerscale datasets like imagenet would better illustrate the efficacy of the proposed toolbox 2 many recent unimodalmultimodal pretrained models eg beit dallefor textconditioned imagevideo generation are also based on vqvae it would be interesting to also include some results on vae used in these pretrained models 1 beit bert pretraining of image transformers iclr 2022 2 dalle creating images from text 2021 docsepthe paper proposes pythae a python library that provides implementations for various types of popular autoencoder ae architectures and modeling choices beside an introductory description of the library and a summary of each individually considered ae also in supplementary pdf 19 different aes are benchmarked on various tasks ranging form measuring reconstruction losses to proxies to assess generation quality post discussion update i believe many of my concerns have been addressed improvements have been made and more outlined for a camera ready version as detailed in the response below i encourage the authors to continue tuning the presentation and am raising my rating to recommend acceptance of the paper previously 5 to now 7 there are various strengths to the proposes pythae environment primarily perhaps that it comes with a promise of providing a framework to easily experiment and compare various autoencoder based approaches having the pythae tool holds a promise towards more reproducible research although simultaneously see first weakness below although the considered approaches are all autoencoders the breath of considered methods is impressive there are of course a couple of examples that may be still added but as the authors say the library should be subject to continuous development and as such will likely only grow to be more exhaustive over time the supplementary material overall is very helpful similarly the usage examples and readme instructions in the provided github repo are pretty comprehensive and are likely to facilitate adoption appendix section d4 is much appreciated in particular the effort to provide a short summary of the main mathematical advance in each respectively model as mentioned in the first point on strengths the work holds a promise towards reproducing existing works at the same time when looking at the provided experimentation in the paper some of the choices are a bit puzzling in this regard for instance there does not seem to be any mention or measuring of eg kullbackleibler kl divergences in any of the variational approaches similarly reconstruction loss seems to be measured in a mean squared error rather than what is typically reported in almost any generative modeling paper its not clear to me why the choice has been made here to deviate from the by now fairly standardized convention to stick to reporting of negative log likelihoods naturally this does not hinder the relative comparison between methods but it does make it harder to assess the correctness with respect to the original papers and hinders their direct comparisonreproduction a bit this points feels particularly important as the scale of values mse lying around 001 and log likelihoods typically being multiple orders of magnitude larger can significantly affect choice of hyperparameters etc the discussion of enhancing the model of section 22 is rather shallow i understand the space constraint here but it feels like crucial and heavily investigated arguments are missing for example any mention with respect to desiredundesired priorposterior mismatches or any discussion on lossy compression is omitted here in favor of a too simplified narrative this is a bit problematic because the disentanglement picture is rather naive and it is unclear whether it is an agreed upon perspective there have been several well cited published papers to name a few disentangling disentanglement in variational autoencoders resampled priors for variational autoencoders rethinking lossy compression the ratedistortionperception tradeoff that challenge the narrative that autoencoders and variational versions of it are all about finding a better form for the posterior or that a weighting of the kl term betavae somehow induces disentanglement in whichever way disentanglement is actually defined following up on the above two arguments i am a bit worried that the experimental evaluation may be somewhat misleading to the reader in particular the fact that the tasks are separated in the way they are does not seem very intuitive to me for instance what is the purpose of running an experiment measuring mse reconstruction i na comparison between an ae a vae and a betavae and then concluding that the betavae is a better model if one simply turns down the divergence term similarly task 3 does seem to be somewhat oddly formulated and catered heavily towards the ae again if we are interested in classification why first do unsupervised pretraining and then train a linear classifier on top naturally the space of autoencoders is much less constrained than that of any vae but what we would actually be interested in seems to be learning the joint pxy rather than freezing the architecture like in the semisupervised variants of kingma et al as a third point there should be a discussion around fid and its brittleness with respect to how useful it is as a measure see eg paper pros and cons of gan evaluation measures as one example why would we be interested in measuring an inception distance which is imagenet based if we train a model on grayscale or binarized handwritten digits finally perhaps the individual investigations are not troublesome in the sense that they were conducted but the way that they are described in a seemingly constant attempt to select a clear winner per category is rather misleading to the reader evaluation of generative models has been an ongoing debate for several years now and it is clear that is complex and challenging it would be great to have some of this flavor depicted in the experimentation and discussion rather than going for an overly simplified takeaway see related work section below on the library not being the first of its kind and lacking a comparison docsepauthors propose pythae an opensource python library that implements 21 stateoftheart generative autoencoders gaes in addition authors perform benchmarking of 19 gaes on 5 downstream tasks using mnist cifar10 and celeba datasets throughout these tasks authors deduce several conclusions on the benefits of different gaes for different tasks over each other the main strength of this work comes from the proposed python library that should allow for a unified hub to compare different gaes and find the best suited one for ones downstream task authors tackle a crucial topic and compare performance of many gaes under same settings this is often neglected and hence it is not trivial to compare empirical performance of different methodologies in the literature through comparative experiments authors make valuable observations for the different tasks especially for the image generation task in short there are two major weaknesses i it is not clarified whether the author implementations can replicate the performance of the original works and ii benchmarks are conducted on simple low resolution datasets rendering conclusions of the manuscript unconvincing it has been seen in the computer vision community that findings in simple image domains content and resolution often do not translate well to more complex vision domains with higher resolution data authors conduct more than half of the experiments exclusively on mnist and cifar10 datasets and derive conclusions based on their observations accordingly these findings can be misleading and outright false for more complex datasets in sec 421 task 2 image generation authors themselves also emphasize that using more advanced density estimation models as opposed to gmm was perhaps not useful because of the simplicity of the dataset they experimented with it would be vital to see if same results can be concluded after experimenting on imagenet or other higher resolution natural datasets in sec 421 it is not clear how the latent dimensions are set for each dataset for example especially for cifar10 latent dimension of 256 for input images of 32x32 size seem unnecessarily high in sec 422 authors find out that often 16 or 512 latent dimensions were optimal depending on the task andor gaes unfortunately these are boundary values within the search space the authors have used therefore it is not right to conclude that 16 or 512 is optimal unless they observe the findings do not change after trying 16 and 512 dimensions ### Summary:

acceptposter the reviews recognized the importance of contributions by the paper the rebuttal by authors addressed a number of concerns it would be helpful if the authors can address the pending concerns in the camera ready version

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary this paper addresses the problem of vertex classification using a new graph convolutional neural network nn architecture the linear operator within each of the layers of the gnnn is formed by a polynomial graph filter ie a matrix polynomial of either the adjacency or the laplacian novelty rather than working on the frequency domain the paper focuses on learning the polynomial coefficients of the filter on the vertex domain the key novelty is the consideration of a stack architecture for which the polynomial filter is formed by the successive application ie matrix multiplication of filters of order one numerical experiments with real datasets showcase the merits including superior classification performance of the proposed architecture strong points the paper is timely and fits nicely the scope of the conference the numerical experiments are convincing offering insights and demonstrating some of the advantages of the proposed architecture the writing is clear making the paper easy to follow weak points except for the numerical experiments i find that the contribution is quite limited the postulation of gcnn architectures based on polynomial graph filters where the focus is on learning the polynomial coefficients has been studied thoroughly in the literature in general the paper does a good job listing relevant works in that area although some are missing eg gama ribeiro some of the existing works look at arma structures and recursive orderone filter implementations i acknowledge that the architecture considered in those papers may not be exactly the same as the one proposed by the authors in this paper i also appreciate that the application at hand vertex classification was not the goal of many of those papers however i still feel that the contribution falls short especially for a top conference such as iclr in any case i am open to change my mind if the authors are able to strengthen their theoretical claims or address my concerns in their rebuttal i believe that the title should be changed gcnn are not mentioned the current title places the focus on stacked graph filters my first concern is that within the linear paradigm ie as polynomials of the adjacencylaplacian matrix this type of architectures have already been investigated more importantly the paper focuses on nn architectures so i think it is reasonable to have that on the title overall recommendation marginal reject the paper is topical timely and nicely written it addresses a problem of interest and does so with contemporary machine learning tools the results in realworld datasets are convincing however the contribution and novelty are limited falling short of the average contribution at iclr additional recommendations being able to obtain additional theoretical results would make the contribution more solid further elaborating on the robustness of the architecture it is another change that would strengthen the manuscript docsepadaptive stacked graph filter the paper proposes a relatively simple formulation for a graph convolutional filter that has the advantage of providing useful insights on the characteristic of the considered datasets many points of the paper are however not convincing in the present form mainly regarding the novelty of the proposed formulation the paper proposes a graph convolution operator that is inspired by the wellknown approximation of a graph filter using polynomials of the graph laplacian pros the paper proposes a simple filter formulation that allows to study the dependency on the neighborhood radius on different datasets the visualisation of the filters is interesting the reported experimental results are positive even though in many cases the improvement does not seem significant cons the proposed model is very similar to gcnii graph convolution by kipf and welling with a single scalar parameters instead of a parameter matrix skip connections the main difference with gcnii is the lack of the identity mapping in fact eq of hl in page 4 is very similar to eq 5 in httpsarxivorgpdf200702133pdf authors should deeply discuss the differences between their proposal and other works in literature clarifying their novel contribution comments about specific sections follow experimental section in page 6 authors state that they fix the theta hyperparameter of gcnii to 05 even though the recommended values are around 15 can you justify this choice also since you run the experiments on gcnii it would be interesting to see its performance on the bipartite dataset with theta 15 in table 3 the results from literature do not report the variance in general it seems like the results of the proposed method and baselines are pretty close and in many cases inside the variance range appendix a the horizontal stacking variant is not explained in detail from the figure it looks like several stacked layers with an aggregation that sums the weighted representation computed at each layer i dont see why this should be horizontal probably writing down the equations of this model would help b2 while authors state that for each dataset and for each run they select the hyperparameters using the validation set later in the same section they state that the results in the main paper are referred to the hyperparameters in bold i dont understand how the hyperparameter selection procedure is adopted minor table 3 chamaleon dataset missing bold on sgc texas mlp is in bold while it shouldnt be page 6 note that we also the extact we use the rebuttal i acknowledge having checked authors rebuttal and the revised version of the manuscriptdocsepthis paper proposes to stack the graph filters with learnable polynomial parameters to construct the new graph neural network model generally this paper is well organized and easy to read here are my concerns 1essentially this paper argues that the approximation of chebyshev polynomials in gcn can only capture the lowfrequency features in the spectral domain and proposes a more general approximation scheme by stacking the graph filter in the spatial domain however the lowfrequency property of gcn is highly related to the localized firstorder approximation of graph convolutions without this firstorder approximation gcn model can capture the highfrequency information in graphs eg chebynet 2 with large enough order k its better to add more discussionscomparisons with this kind of gcns moreover my core concern is the superiority of why the proposed polynomial approximation in equation 7 is better than the previous chebyshev approximation from both theoretical and practical justifications in graph signal processing using a polynomial series to approximate the graph filter has been well studied in the literature as pointed out by 1 chebyshev polynomial is a good approximator to approximate graph filters it is better to add more justifications eg numerical analysis about the proposed approximation scheme 2another concern is the experiment dataset splitting it seems like that this paper adopts the new splitting plan stratified 060202 splits for all datasets meanwhile the paper also reports the best results reported in the literature however i think its improper to put them in the same table since we cant make a fair comparison under different data splitting moreover i would like to see the results of sgf on the public splitting of these datasets hyperprameters in appendix b4 the authors claim that they follow the hyperparameter recommendation in the original paper of baselines however it seems that some of the given hyperparameters are not the best hyperparameters for example for cora alpha of gcnii is set to 02 while in appendix b4 alpha05 which inconsistent with the original paper 3 on the other hand in appendix b2 the authors adopt the random strategy to search the hyperparameters of sgf since the authors rerun all the experiments of baselines in the new splits its better to conduct the same hyperparameter search process for each baseline to ensure a fair comparison the filter parameters visualization from the model construction perspective since the only difference between sgf and gcniiappnp is the trainable filter parameters therefore im curious about the value of alpha and beta after the training could you visualize the value of two parameters in each layer from sgf overall i think this paper is marginally below the acceptance threshold 1 david k hammond pierre vandergheynst and re mi gribonval wavelets on graphs via spectral graph theory applied and computational harmonic analysis 302129150 2011 2 defferrard michal xavier bresson and pierre vandergheynst convolutional neural networks on graphs with fast localized spectral filtering advances in neural information processing systems 2016 3 chen m wei z huang z ding b li y 2020 simple and deep graph convolutional networks arxiv preprint arxiv200702133 docsepsummary the authors proposed to learn the polynomial graph filter in their model it can be viewed as adaptively learning the propagation part of appnp and follows by a linear transformation in features they show the proposed model can perform well on both homophilic and heterophilic graphs pros 1 the idea of adaptively learn the polynomial filter seems correct and reasonable 2 results on filter visualization and structural noise are interesting cons 1 the proposed methodology is not novel a very similar idea has been proposed previously see detail comments 2 problems of oversmoothing 3 results on experiment section table 2 and 3 are questionable detail comments while the proposed idea of adaptively learning the polynomial graph filter is interesting it has been proposed previously in not only the gnn literature 2 but also pagerank based methods 3 both of them proposed the idea of adaptively learn the polynomial graph filter or equivalently the generalized pagerank weights hence i do not think the current paper is completely novel nevertheless the proposed methodology seems to be the correct answer for gnn to adapt to both homophilic and heterophilic graphs one problem of the current proposed method is that why it can avoid oversmoothing when stacking many layers the authors use a fixed initialization alpha 05 which is the same as appnp so at least at the very beginning it wont suffer from oversmoothing however it is unclear how will the coefficients behave during and after training also it is not clear how to initialize beta in the model furthermore if the proposed model can indeed adaptively learn the good polynomial graph filter why doesnt the random initialization work does that mean the implicit bias of the specific initialization proposed in the paper is necessary if that is the case then i do not see why the claim of adaptive learning is correct since it is actually sensitive to the initialization beside the methodology and novelty i also find the experiment section questionable firstly since the main theme of the paper is learning the polynomial filter the authors should at least compare their method with chebnet gcncheby5 which also use polynomial filter note that in both 4 and 2 they all show that chebnet can better adapt to heterophilic graphs compare to gcn and gat on the other hand according to appendix b4 the authors use k2 propagation step for appnp this is not the suggested hyperparameter reported in 1 k10 note that the authors of 1 even show that if we choose a larger kgeq 10 the performance can be slightly improved on cora citeseer and pubmed in contrast sgf use k16 which is not a fair comparison to appnp there should be a experiment that compares appnp with sgf under the same k finally the authors claim the performance of most baseline methods are found in the literature however this is also problematic to me note that in the original gcn and gat paper the date split is much sparse then the 060202 split proposed by the authors also in the geomgcn paper they do test their model on chameleon in the split 060202 why is it stated as not available even if we assume all the problem above can be well explained the improvement of the proposed model seems not statistically significant for example on wisconsin cornell and texas although sgf has the highest accuracy in average the standard deviation is very large mlp is within 1 standard deviation please report the confidence interval to show that the gain of sgf is indeed statistically significant on the other hand sgf is worse than not only sgc but also gcnii by a large margin on chameleon if sgf can indeed learn the nearoptimal polynomial filter then why this is the case at last in the original geomgcn paper they also have the actor dataset i think it would be great if the authors can put this result at least in the appendix besides these weaknesses i still find the paper well written also the experiment on filter visualization and structural noise are quite interesting i believe the paper can be greatly improved if all the concerns above can be addressed minor comments in page 2 the authors state that the normalized adjacency matrix with added selfloops is tildea id12a d12 c where c is some diagonal matrix this is incorrect note that when we add selfloops the degree matrix d has to changed accordingly please see the correct expression in 1 for example in page 2 the rayleigh quotient rmathcallx is defined with two input arguments but later the authors ignore mathcall while it is clear from the context the notation is not rigorous in page 1 introduction section the authors mention that the model does not need to tune the hyperparameters however in the same page contribution section the authors mention that they use one hyperparameter setting according to their experiment section i think what they mean is the previous it would be great to clarify the ambiguity here reference 1 predict then propagate graph neural networks meet personalized pagerank klicpera et al iclr 2018 2 adaptive universal generalized pagerank graph neural network chien et al arxiv200607988 3 adaptive diffusions for scalable learning over graphs berberidis et al in mining and learning with graphs workshop acm kdd 2018 pp 1 8 2018 4 generalizing graph neural networks beyond homophily zhu et al neurips 2020 arxiv200611468 5 convolutional neural networkson graphs with fast localized spectral filtering defferrard et al neurips 2016 ### Summary:

the topic covered by the paper is timely and the way the authors have addressed the problem seems correct the provided empirical evidence seems to be sufficient to support the main claim of the paper presentation is well structured and clear notwithstanding the above merits the proposed approach seems to confirm other similar proposals presented in the literature so the contribution of the paper seems to be limited although presentation is good it is not highlighting enough the differences wrt those proposals and the basic approximation result given by chebyshev polynomials especially a better theoretical characterisation wrt to approximation capabilities by chebyshev polynomials with no truncation would have helped to better gain understanding of the merits of the proposed approach finally some of the experimental results do not seem to have a significant statistical difference wrt to the baselines so it would have helped to have the result of a statistical test

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors propose a method that allows training of uv methods without sharing any user exemplar or class embeddings with the server or other uses models are trained using gradient averaging on the server so any leakage through that is not addressed in this work the paper shows experimental results on speaker identification face and handwriting verification tasks the authors argue that this is the first work that considers secure training in a federated setup with neither raw inputs nor exemplar or class embeddings being shared with the server or other users pros the paper is clearly written and the derivations are sound for the most part see questions below the idea appears to be novel and a significant delta compared to the sota in terms of security and the novelty of a secure embedding learning protocol in the federated setup were only one positive classes are available for training the experimental results are promising albeit cant compete with existing less secure methods cons clarity of experiments especially for the face verification task the code length seems to play a major role any discussion giving an understanding of this would be appreciated specifically how and why does dmin affect the accuracy bottom of page 5 mentions that increasing the codewords and presumably dmin increases the performance but no reasoning is provided additional insights of how the baselines softmax fedaws were trained and what the emedding sizes are would be helpful is the embedding size 64 in all cases questions comments the assumption of z sqrtc should be put into context what are the practical applications for this assumption is it merely there for the math to work out the theorems show that lneg is redundant for when lpos0 however it is not clear to me that minimizing lpos also corresponds to minimizing lneg in practice lpos will likely never reach 0 and a negative loss term could have a significant contribution to the loss surface page 6 mentions that increasing lr reduces the minimum distance of the code for a given code length why is this the case is it because ru is sampled by the clients and no guarantees can be made a more detailed discussion would be helpful this work proposes a new idea that allows training embeddings for verification with only positive classes in a federated setting while ensuring security some areas could be clarified in the paper especially why it is sufficient to proof the redundancy of the negative loss term only for the global minimum of when lpos0 assuming the authors can provide a satisfying explanation i recommend accepting this workdocsepthe paper leverages federated learning to train user verification models the authors claim that their new federated learning addresses the security and privacy issues of previous methods in particular for privacy the users do not need to send their class embedding vectors to server nor other users for security the paper claims that the proposed method is secure against poisoning attacks and evasion attacks strengths i think the major strength of the paper is to design a loss function and a way of modeling embedding vectors for users such that the embedding models can be learnt without sharing the embedded vectors to the server nor other users weaknesses the paper is weak on its security and privacy claims 1 for privacy can you quantify the privacy leakage of sharing embedded vectors with the server without a formal quantification it is hard to claim your method is more private 2 poisoning attack i dont think the paper addresses the poisoning attacks the paper considers that the server may poison the learnt model however in the proposed method the server can still poison the model in particular the server can send arbitrary new model to each user in general it is hard to defend against malicious server who performs poison attacks also malicious users can poison the model training which are more realistic poisoning attacks but such poisoning attacks are not considered i dont see how the proposed method can address these poisoning attacks some references on poisoning attacks httpsarxivorgabs180700459 httpsarxivorgabs191111815 httpsopenreviewnetforumidrkgys0vfvr 3 evasion attack the proposed cannot address evasion attack at all 4 experimetal details can you add more details on experimental details eg learning rate how is experiment on softmax loss function implemented 5 can you also report auc to compare different methods since you already show the true positive rate vs false positive rate curvesdocsepsummary federated learning takes advantage of the fact that private user data does not need to be transferred and shared across devices or servers this makes fl particularly attractive for the user verification scenario where privacysensitive biometric data are used to train verification models one crucial hurdle in this scenario is that per device only positive data are present potentially turning the devicewise training objective illposed all embedding are likely to collapse to a single point as a way to introduce negative examples fedaws has been developed and presented at icml 2020 this paper recognizes a crucial security risk in the fedaws system that embeddings of user data are transferred to the server and proposes a more secure training methodology feduv that involves the errorcorrecting codes feduv enjoys stronger security guarantees while showing comparable roc curves as fedaws at nearly identical computational costs though not entirely sure about the computational cost bit pros the motivation is spot on having to see any form of negative samples is the itchy point of the flbased user verification system feduv magically solves this issue by predefining a unique prototype vector for each user which are not shared across users and are by design far apart from each other this is the crucial trick by employing a technique in errorcorrecting codes ecc as a result each users endeavour to get closer to the own prototype vector ensures the maximisation of distance from the others prototype vectors three experiments that are quite close to realworld scenarios speaker face and handwritingbased verification show that the performance of feduv is comparable to fedaws the state of the art framework from icml 2020 with weaker security guarantees writing is nearly flawless highly enjoyable paper cons no major cons perhaps explain in a bit more depth on the bch code to illustrate at least a highlevel handwavy description how it assigns the codes in a distancemaximizing manner section 23 only explains the desiderata for bch rather than how bch achieves it please also confirm that feduv spends nearly identical computational cost as fedaws somehow i got this from the paper but have not found a solid reference that confirms this if not please explain too nits please add grid lines and row titles training set test set with known users test set with unknown users in figure 2 plots baslines baselines flatten the last part of section 1 as paragraphs rather than itemize yu et al 2020 fedaws is an icml paper not arxiv please fix the reference key reasons for the rating i dont find any major rationale to reject this paper however its novelty is also eclipsed by the yu et al 2020 fedaws paper though i really like this paper i believe the best scores should be reserved for more innovative papers after rebuttal discussion i still tend to think that the papers scope can be adjusted relatively easily it is not too difficult to insert more disclaimers and change the title and we can force apply the adjustment by conferring a conditional acceptance but im sold on the point that there is a lack of argumentation on whether undisclosing the userspecific embedding will improve the privacy guarantee i had taken this argument as granted but this is indeed not so obvious given that there exist many attacks that are applicable in this kind of scenario as r4 has argued it would be great if the authors could quantify the improved privacy guarantee im okay with rejecting the paper then i still like the paper quite a lot but rejecting it will also give the authors a good chance to assimilate more points of views in the paperdocsepin this paper the authors focus on designing a federated user verification solution specifically the authors address two fundamental challenges associated with user verification ie oneclass data positive data only and privacy protection ie the raw data and the embeddings of the users and class technically the authors extend a very recent work called fedaws by yu et al 2020 and introduce a userspecific codewords which not only protect users privacy ie not sharing the embedding with other users or the server but also do not need the negative samples ie the two loss functions in eq5 reduces to one due to equivalence shown in theorem 1 we can see that the main idea of rewriting the eq2 into two loss functions in eq4 and eq5 and introducing codewords are novel and effective which also address the two challenges well empirical studies on three user verification cases show the effectiveness of the proposed solution feduv overall the technique is novel and i like this idea and the paper is well presented i recommend acceptance ### Summary:

in this paper the authors propose to adapt the recent paper by yu et al icml 2020 namely fedaws in that paper the authors solved a potential failure mode in federated learning when all the users only have access to one class in their devices in this paper the authors extend fedaws to a setting in which federated learning is used for user verification uv namely feduv the authors argue that the previous paper could not be the solution to learning uv because fedaws share the embedding vectors with the server the authors then show a procedure in which they can learn a classifier in which the embedding vectors are not needed to be shared with the classifier they use errorcorrecting codes to make the mapping sufficiently different and that allows the training to succeed without sharing the embedding the proposed change is only marginally worse than fedaws and centralized learning this is the part of the paper that has attracted positive comments and is praised by all the reviewers the authors take as given that by not sharing the embedding vectors and by using randomly generated errorcorrecting codes the whole procedure is privacypreserving and secure the 4th reviewer indicates that these guarantees need to be proven and points out several references that hint toward flaws in the argument by the authors reviewer 4th does say that not sharing the embeddings might not be enough but that selfevident arguments are not enough this paper provides a significant improvement for a federated machine learning algorithm that deserves publication but the rationale of the paper is flawed from a privacy and security viewpoint i think if the paper is published as is especially with the proposed title it will create a negative reaction by the security and privacy community for not adhering to their standards we cannot lower those standards i suggest to the authors that they can follow two potential paths for publishing this work 1 change the scope of their algorithm for example i can imagine that by not sharing the embedding the communication load with the server might be significantly reduced or that adding new users with new classes can be easier 2 follow the recommendation from reviewer 4 and show that the proposed method is robust against the different attacks minor comments for a paper that is trying to solve the au problem i would expect a discussion about why learning is better than a private algorithm in a way learning is sharing and that increases the risk of mischief by malicious users the discussion about errorcorrecting codes and the minimum distance is quite old fashion in high dimensions the minimum distance is not the whole story ldpc codes make sense when we stop focusing on minimum distance codes and minimum distance decoding i would recommend having a look at the berlekamps bat discussion in david mackays book chapter 13

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes to use hypernetwork to personalize the federated model by encoding new user data and using this embedding as a parametrization argument the results demonstrate significant average improvement over the new clients furthermore the authors evaluate the possible use of dp to encode the user embedding vectors strength the proposed idea contributes to the hypernetwork research and ourperforms current stateoftheart results weaknesses its clear how the proposed paper is different from 1 but i would dedicate more space for the comparison specifically section 53 of 1 suggests using the nearest client as you describe in table 1 its clear that cifar100 or inaturalist have more diverse data where your method excels but i wonder if in real scenario with millions of users participating eg language model for redditstackoverflow dataset the same conclusion would persist it would be useful to see the distribution and not the average accuracy values for new user performance it might be that the proposed method benefits or harms the users with diverse data which is an essential question for fl fairness it leads to the following point evaluating the method on the text data is important for fl as language modeling is one of the few industrydeployed use cases and widely used in fl literature and also the personalization of lm models is an important problem for dp please clarify what d in d is is it a single input of the user lastly other personalization works that use local adaptation 2 such as finetuning global model on the local data should be considered as baselines and i wonder what is their performance wrt the proposed method 1 shamsian a navon a fetaya e chechik g personalized federated learning using hypernetworks in icml21 2 li tian shengyuan hu ahmad beirami and virginia smith ditto fair and robust federated learning through personalization in icml21 the paper is wellwritten and presents a new algorithm that uses encoder to adapt for new clients the paper lacks few baselines and explanations but can be beneficial for fl community docsepthis paper studies the problem of in personalized federated learning the current paradigm does not allow for new clients to join during the inference time federated learning does not generalize well to new data distribution that is very different from training personalized fl are not designed to apply to a client that is not being trained this paper defines a new task inferencetime personalized federated learning itpfl to close the gap the proposed approach itpflhn first trains a client encoder then maps a full client dataset to descriptor a hyper network maps the descriptors on to a space during inference time the client description can be computed locally and request the personalized model ondemand the paper demonstrate the effectiveness of the proposed approach on both cifar and realworld datasets pros 1 the paper overall in clearly written and easy to follow the overall flow of the paper and descriptions of the proposed approach is clear 2 the problem being study is wellmotivated and can be useful 3 it also provide differential privacy analysis to provide insights into the privacy protection perspective of the proposed approach cons 1 some of the experiment results are not explained for example in table one the cifar100 pathological split the proposed approach is not outperforming the baseline approaches however it lacks any explanation or analysis about why it is the case 2 question in the realworld dataset for the models both target model client encoder and hyper network these are all simple fully connected learns with global operations are these models realistic for realworld usecases if these base model change will the framework performance differ it would be good to demonstrate the effectiveness of the proposed approach with different types of neural networks 3 in the experiment section 64 it methods the pfedhnensemble suffers from large communication and computation cost however there are not numbers supporting the claim it would be good to provide numbers to see if these are realconcerns 4 i was wondering how much difference it will make in comparison with the new client participate in training it would be good to add experiments to compare with that minors 1 in section 4 meta mechanism the quote is not right same for many other quotes overall the problem being studied in this paper is interesting and wellmotivated the paper is clearly written the novelty perspective of this paper can be highlighted better it seems like a lot of the aspect like hyper network are proposed by other approaches some other major concerns are in terms of the experiments there are results are not explained or claims without number supported please see main review for details 
 docsepthe paper proposes a new task named as inferencetime personalized federated learning itpfl specifically given a new client with unlabeled data joining in the federated learning system after the training process has finished itpfl aims to deploy personalized fl model to it the strategy is based on recent works on hypernetwork the training strategy is in an endtoend framework while the authors claimed that itpfl is a novel problem the solution that the authors gave is similar to 1 which utilizes hypernetwork in personalized federated learning task at least to me the difference between 1 and this paper is marginal which makes me question the novelty of the paper i think the authors need to make clear what is the novel contribution given the existence of 1 this paper does give some results on generalization bound and differential privacy but i believe they are just results of simple application of existing theorems the theoretical contributions from these two sections are not strong enough or at least the authors did not make clear what are theoretical challenges besides the novelty concern i have two additional suggestions first i think it will be interesting to compare hypernetworks and metalearning these two approaches are solving essentially the same problem in the end and they are both applied to personalized federated learning a deep understanding of the connections and differences between the two approaches will be beneficial to the community second i think in related work the authors should summarize the literature about hypernetworks hns and especially its recent application in personalized federated learning 1 because there is obviously an intimate connection between that line of works and this paper 1 aviv shamsian aviv navon ethan fetaya and gal chechik personalized federated learning using hypernetworks arxiv preprint arxiv210304628 2021 given the major novelty concern i do not think the paper is appropriate for acceptance in its current shape the authors should try to make clear what is the novel part of the paper how challenging it is to get those results and add more literature review in related work docsepthe authors propose a new personalized federated learning paradigm composed of a hypernetwork module and an encoder module in server and an extra novel client with unlabeled data the encoder module is enhanced thanks to the unlabeled data preliminary experiments demonstrate the efficacy of the proposed algorithm this paper is clearly written the authors adopt an additional novel client to enhance the accuracy of personalized fl however the reviewer still has several concerns 1 the proposed generalization bound is established for the novel client how does this generalizationbound reflect the efficacy of the proposed personalized federated learning framework 2 the compared baselines are not sufficient to demonstrate the efficacy of the proposed algorithm in fact there exist several pfl algorithms such as ditto subfedavg etc 3 the authors should do an additional ablation study to evaluate the influence of the size of the unlabeled dataset it seems that is the novel client merely has limited data the proposed algorithm in this work reduces a smile variant of pfedhn see the comment above ### Summary:

this paper proposes a personalized federated learning method using a hypernetwork to encode unlabeled data from new clients at inference time new clients can use unlabeled data as input to this hypernetwork in order to obtain a personalized version of the model the key strength of the paper is that the idea is interesting and timely personalization has been studied for clients that participate from the beginning of training but personalization of models for new clients that join later on has not been considered in most previous works the experimental results also show a reasonable improvement over the baselines however the following concerns remain 1 novelty in comparison with reference 1 please add a detailed comparison when you revise the paper 2 explanation of the experimental results and comparison with baselines was deemed insufficient by some of the reviewers 3 the generalization bound and the dp results seem standard extensions of existing works and do not add much novelty to the paper there wasnt much postrebuttal discussion and the reviewers decided to stick to their original scores therefore i recommend rejection of the paper i hope that the authors will take the reviewers constructive comments into account when revising the paper for a future resubmission

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this work introduces a new perceptual super resolution method for 3d brain image segmentation the method uses a carefully designed upsampling and a novel loss function to obtain the desired performance and this method is evaluated based on a clinical relevant metric due to the lack of high resolution ground truth data the authors find that the proposed method consistently improves the ability to detect regional atrophy both longitudinally and crosssectionally in five relevant diseases strength this work addresses an important problem in the field of medical imaging and the authors have demonstrated deep understanding and expertise in this field the method is discussed in a thorough manner and important decisions are well justified truly quality work rigorous experiments to evaluate the proposed methods performance its great to see ci evaluations weakness this work would be of a stronger stance if its originality is more welljustified 1 the paper writes that three perceptual loss functions for psr two of which are new while the neural network and the loss functions used reconstruction error tv perceptual loss dice are not unheard in the field of medical imaging the authors might elaborate which parts of the loss functions are new 2 while the proposed evaluation paradigm is in a sense new i would like to see a deeper discussion of why this is new eg is this a novel way of seeing the problem of lacking sr ground truth label the contribution bullet states that demonstration of the impact of loss choice on performance differences in improving detection power in population studies of neurodegenerative disease the presented results havent directly shown the power of the proposed method on a populationscale the authors might elaborate more about the stated populationlevel effect of the proposed method yes docsepthis paper outlines a new method for enhancing 3d neuroimaging resolution and improving image segmentation using perception super resolution in a technique labelled concurrent super resolution and segmentation csrs the authors evaluate the effectiveness of the technique on publicly available clinical datasets from the human connectome project the parkinsons progression markers initiative and the frontotemporal lobar degeneration neuroimaging initiative nifd 4repeat tauopathy neuroimaging initiative 4rtni they test this method in quantifying changes across crosssectional and longitudinal mri datasets for different neurodegenerative diseases the results however only report comparisons of the new technique to the originalresolution model using bootstrapped ttests which are not corrected for multiple testing the implications are very briefly discussed i do not comment on paper originality as it is a flawed measure of paper quality the overall quality of the paper is moderate while the technique is interesting there is insufficient detail about preprocessing of the mri data to replicate the results and the methods are not communicated very clearly in addition more of a clinical andor neuroscience perspective in the experimental design would have been useful the discussion is overly brief and lacking in depth or detail the supplementary materials are quite poorly organized and do not meet neurips communication standards however the technique itself does seem very promising and with clearer communication this could be an interesting and important development the authors do not address the limitations or potential negative social impact of their work docsepthe paper presents a new methodology for concurrent super resolution and segmentation csrs on 3d volumetric mri data to consistently upsample both an image intensity channel and associated segmentation labels to this end a primary contribution is in adapting perceptual super resolution frameworks designed for 2d data to 3d data within the 2d deep back projection dbpn network since the ground truth high resolution images may not be present for a given dataset the authors propose an indirect evaluation via the quantification of crosssectional and longitudinal change in diseased cohorts specifically they choose a set of phenotypically heterogeneous but related disorders that are associated with known patterns of brain atrophy their experiments examine the effect of various choices in loss function in terms of identification of neurodegenerative diseases within the human connectome project hcp data parkinsons progression markers initiative ppmi frontotemporal lobar degeneration neuroimaging initiative nifd andrepeat 123 tauopathy neuroimaging initiative 4rtni datasets strengths the clinical problem that the paper examines concurrent super resolution and segmentation is interesting as is the usecase of tracking brain atrophy within neurodegenetative diseases the proposed approach to extending existing perceptual super resolution techniques is interesting though a bit incremental on the technical front weaknesses 1 the evaluation section is rather poorly explained and hard to follow several points are unclear and the main arguments are not very convincing in light of the quantitative results a some key details such as dataset splits for training testing validation are not clearly mentioned b it is unclear which datasetdisorder all datasets the results in table 1 and 2 and figures correspond to c the differences across different loss functions and configurations in table 1 are rather minor third decimal place in terms of effect sizes dice psnr it is unclear whether these improvements are consistent across dataset splits d table 2 is really hard to parse lacking in description and in general confusing as per my understanding csrsrtvvgg vs the proposed framework should be compared across the same anatomical class if so the differences in effect sizes appear very minor third decimal place for several comparisons 2 if my understanding is correct the baseline comparisons in the paper correspond to evaluation against ablated versions of the framework various loss functions evaluation in the original resolution and linear interpolation given that the authors listed a couple of recent approaches proposed in literature in section 1 it is not obvious why they did not include these as baseline comparisons the main limitations discussed in section 6 including computation and quality of annotations given that the framework is so computationally expensive that 1 epoch takes 12 hours seems to greatly limit the practical utility docsepthis work developed a super resolution method for 3d neuroimaging and evaluate its performance in detecting brain changes due to neurodegenerative disease it trained on 3d brain data to upsample both the raw intensity image and associated segmentation labels the method was mainly based on the 2d deep back projection network dbpn 5 which was extended to three dimension and multiple output the method was evaluated in downstream clinically relevant signal detection problem quantifying crosssectional and longitudinal change across a set of phenotypically heterogeneous but related disorders that exhibit known and differentiable patterns of brain atrophy strength this work conducted the evaluation of super resolution in the downstream clinically relevant tasks eg quantifying crosssectional and longitudinal change across a set of phenotypically heterogeneous but related disorders weakness this work overall has limited methodology contribution the organization of this work is not clear and hard to follow for some sections this work fits more to a dedicated medical imaging conference or journal no docsepthe authors present an extension of a 2d perceptual superresolution architecture extended to 3d within neuroimaging the authors additionally extend the network to simultaneously produce upsampled segmentation maps the upsampled segmentation maps are evaluated against the ground truth as well as against detection power across a variety of frontotemporal disorders the effect of different loss functions used in training are also evaluated against prediction power whilst the superresolution techniques improved standard evaluation metrics psnr ssim when compared against the low resolution data these metrics did not significantly improve when compared against models trained with the different loss functions used prediction power for frontotemporal disorders was improved in many regions when using the superresolution models however some regions suffered in prediction power when superresolved overall this paper is clear and concise and forms a well constructed manuscript the background information and methods are extensive and are presented in logical clearly defined sections the overall study is fairly in depth for the given research question and presents a nice analysis of the downstream effect of segmentation superresolution within a disease model the authors include the relevant previous work done in this area and appropriately outline their contribution to this field the biggest limitation of this work i feel is the limitation of impact in some of novel contributions outlined specifically extending a model from 2d to 3d whilst relevant within neuroimaging data is not particularly impactful additionally adding segmentation maps as an additional output channel does improve user convenience and likely a computational efficiency gain but is ultimately not overly impactful as the segmentation maps could be recalculated with the inferred highresolution dataset overall however it is my opinion that the strengths do clearly outweigh the weaknesses in this work the authors have addressed the limitations of their work in the conclusions section docsepthis paper proposed a 2x from 2mm to 1mm superresolution method for 3d medical image volumes and the corresponding segmentation mask for better brain diagnosis the proposed method is a natural extension of the 2d superresolution model called deep backprojection network dbpn compared with linear upsampling and nearestneighbor upsampling the proposed method achieved a better dice score as well as a better visual quality strength this paper provides good implementationtechnical details to illustrate the problem the method and the experiments it also provides a good visualbased comparison to interpret the methods performance it is interesting to consider upsampling both the image along with the segmentation mask weakness the method proposed in the paper doesnt seem to be novel given the fact that it is a natural extension from 2d to 3d based on the deep backprojection network dbpn though the paper proposed a few loss functions to adjust the models objective the performance does not show a significant difference between different combinations of loss functions moreover the paper doesnt provide a good description of the related work and the main comparison is done between the variants of the proposed method and the trivial methods linearnearestneighbor upsampling as stated in the weakness the method is not novel to me and the experimental comparison is not satisfactory for a neuroips paper the paper lacks a thorough description of related work and a better comparison with other existing methods other than linearnearestneighbor upsampling ### Summary:

this paper has mixed evaluations with two reviewers recommending accept and three recommending reject after carefully reading the paper and the discussion i agree with reviewers hygi ho1b akcw uzm9 in their main criticisms the paper still requires major revisions before it can be accepted including but not limited to an improvement in the clarity of the presentation and more experimental comparisons against other perhaps even simpler approaches

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper tackles the issue of segmentation uncertainty using stateoftheart ssns authors view these as factor models they derive flow probabilities on these factors to visualize and quantify uncertainty associated with them while looking for a minimal rotation of factors through orthogonal rotations they show that this technique is suited to derive finegrained maps for assessing uncertainty in segmentation results as well as to tweak computed segmentations which could prove useful for experts using such tools i find this piece of work sound and interesting flow probabilities fp after factors have been rotated in a meaningful way make for a nice object to intuitively visualize uncertainty and authors show a convincing piece of code allowing users to update segmentations thanks to these fp significance the described method could prove very useful if it can indeed help domain experts perform fast segmentation tasks while providing them with intuitive uncertainty measures however it is yet unclear to me wether it can help discard entire segmented zones when the number of classes is higher than 2 or 3 see question 1 originality this paper could be considered mildly original as it mostly combines existing results from ssns factor analysis and flow probabilities however i think the idea to view lowrank models underlying ssns as factor models sheds a nice perspective on these models and that this aspect is more important than pure originality quality i find this piece of work to be well written and illustrated i appreciate that authors bundled a repo that i could use out of the box i tested the notebook and read a great deal of the code as is the codebase is hard to grasp and use though and i think it would greatly benefit from being documented more extensively docstrings for all methods would be nice and tested tests make for a nice way to understand the overall structure of the codebase and usage of each method please cite used packages in the main document numpy scipy sklearn torch einops to name a few clarity although this work calls for very visual and easily understood experiments i found the article a bit difficult to dive in i think it would benefit from having a figure describing the overall procedure training steps and connection between concepts loadings factors latent variables fps rotations and rotation criteria it could be included in the supp mat i think intuitions leading to proposition 1 would also benefit from being illustrated in a figure authors mention computation time of flowprobability based rotation but i think question 1 could be an important limitation of this work docsepthe manuscripts reinterprets stochastic segmentation networks ssn 2020 as a factor model and thus adds latent factors governing the noise components within the single covariance of ssns additionally rotation of the factors with imposed sparsity leads to a parsimonious and supposedly more interpretable representation of the factors the manuscript provides derivations of the reasoning behind the proposed representation and performs a rigorous empirical comparison of already available rotation approaches the results in the main manuscript and the supplement including the video demonstrate that the approach works in providing uncertainty factors that could be individually manipulated strengths a simple and pragmatic approach to extending ssn with an ability to control uncertainty components independently a needed take on sparsity and uncertainty to address interpretability of uncertainty representation weaknesses the major weakness in my view is that besides meeting the goals of factorization and sparsification of uncertainty the manuscript is not convincing in that the created tool is interpretable and thus useful the value of the method for interpretation of the model is not coming through in any of the examples of the manuscript supplement and the video the most intuitively interpretable examples from the camvid dataset does not add any information and looks like uncertainty of all classes but possibly the cars is simply mixed around all the objects other demonstrations are also not helpful and it is unclear how would a user of the model benefit from the new approach in either of the remaining examples i do not look at the satellite images every day and that may be the reason the flagship example in the main manuscript and the supplied video does not convey much information since the segmentation seems to be very poor the dice score would be really low the value of the proposed approach is not clear from the paper this limits further impact of the paper since practitioners wont be able to appreciate the need for this method minor computational complexity as noted by the authors docsepthe paper proposes a novel method for structuring uncertainty in the context of stochastic segmentation networksssns they use lowrank multivariate gaussian distribution to solve ssns model uncertainty they also develop a tool for the analysis of factor models in ssns and apply rotation criteria to provide simple and wellseparated control variables in the experimental part the proposed method has been outperformed the current state of the art on several datasets the idea of this proposed method appears to be a novel combination of stochastic segmentation networks the authors try to solve the segmentation uncertainty by using smaller latent factor variables based on the recent work called ssns the model proposed by the paper is clearly explained and it includes sufficient and detailed experiments in the supplement one possible weakness maybe somewhat missing the explanations of the so called significant effect on output segmentations another weakness maybe more detailed description about the rotation criteria it will be great if the authors could explain more about the interface of finegrained sampling in the supplement the proposed methods seems to have heavy computation cost i expect them to better address this issue if possible one limitation of this work is the heavy computation of the flowprobability rotation ### Summary:

the manuscript interprets stochastic segmentation networks ssn 2020 as a factor model and thus adds latent factors governing the noise components within the single covariance of ssns additionally wellchosen rotations of the factors with imposed sparsity lead to a parsimonious and supposedly more interpretable representation of the factors the manuscript provides derivations of the reasoning behind the proposed representation and performs a rigorous empirical comparison of already available rotation approaches the results in the main manuscript and the supplement including the video demonstrate that the approach works in providing uncertainty factors that could be individually manipulated according to all reviewers the paper is well written and the technical work looks solid the proposed solution is pragamatic experiments are sufficient the main issue of the paper is that the value of the method for interpretation of the model does not come through the examples of the manuscript supplement and the video it is unclear how would a user of the model benefit from the new approach in either of the examples the issue is that according to the authors the new feature for finegrained sampling and fineadjustments of segmentations should be integrated alongside with classic manual techniques for editing segmentations in a full user application however the present work only focused on the new technique and not on building a full user application given this there remains a doubt wehther the proposed technique is actually useful or not other issues related to presentation clarity and quality of the visual material have been well addressed the consensus is that the paper should be accepted at neurips 2022

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper provides a theoretical study of the popular selfsupervised learning ssl techniques vicreg simclr and barlowtwins the main results are closedform optimal representations for each method as a function of the training data and the samplerelation matrix which indicates which samples are from the same class eg this could be constructed as the matrix that indicates which samples are augmentations of the same image the paper further provides simplified versions of these expressions in linear settings and uses them to show equivalence between the ssl methods and various spectral methods and provides a study on downstream task performances strengths 1 the paper studies the important problem of theoretically understanding increasingly popular ssl methods 2 to my knowledge no other work has derived such expressions concerning ssl optimal solutions or made the connections between ssl and spectral embedding methods 3 the scope of the paper is comprehensive all of the most popular ssl techniques are analyzed clearly a lot of work went into writing this paper weaknesses 1 it is difficult for me to understand the insights of this work because the writing is so dense often expressions are introduced without sufficient background and conclusions drawn without sufficient explanation andor motivation eg mathcallvar and mathcallcov in theorem 2 and equations 13 and 14 intuitively how do these equations arise and why are they useful the figures are especially informationoverloaded and difficult to parse the claims relating to downstream task performance are not clear overall it should be abundantly clear what the key messages are from this paper but they are lost in the density of the work 2 there is insufficient discussion of other theoretical studies of ssl only a brief mention in the introduction 3 some of the statements and proofs are not rigorous as it is not formally defined what it means for one method to recover another the proof of theorem 3 requires an assumption that is not made in the theorem statement other proofs can use more formality only english text is given and the reader is left to fill in the details and the proof of lemma 1 is missing the limitations of the theory are not discussed and an assumption is missing see above there are no potential negative societal impacts docsepthis paper introduces a general framework that leverages spectral methods to unify the representative selfsupervised learning ssl methods such as vicreg simclr barlowtwins the authors provide a detailed theoretical analysis of different ssl frameworks and demonstrate the properties of their learned representations strength unifying existing selfsupervised learning methods with spectral methods looks interesting it also provides new insights into the community to help understand how ssl works both the motivation and the technical details in the experiment designs seem sound the authors have cited most of the relevant papers and well summarized previous works weakness although the paper provides an insightful view of understanding the mechanism of ssl most of the analysis is only valid in the preassumptions some of which might have a gap in practical usage for example the properties of vicreg are based on the linear network assumption while in practice the encoder backbones are often nonlinear as the authors claim this paper aims to provide some guidelines to practitioners these gaps could limit their insights to them the example in eq 4 and 5 is a little bit hard to follow it is not obvious see the logical relation between these two formulations the presentation quality is fairly good but needs further polishing it feels like the paper is finished in a rush and some paragraphs are not clear to the readers some abbreviations are not explained with the full name or corresponding citation when first appearing in the text eg da dn which might confuse the readers the experimentsimulation settings are not specified in the paper although most validations are conducted on toy examples for example what datasets are used for these proof of concepts most of the discussions are based on the assumption that the downstream task is classification while in practice ssl methods are widely used in various settings including detection segmentation generative modeling etc the insight to practitioners might be limited in the scope of the classification minors the notation h in eq 3 is not used simclr simclr line 91 loose lose line 98 variosu various the authors have discussed the limitation of the paper docsepthis paper explores the links between selfsupervised losses metric learning and spectral embedding methods it specifically investigates which representations are learnt when the embedding is chosen to be linear and carefully investigates how the choice of loss affects their rank strengths though many connections between metric learning ssl and spectral embedding methods have been previously explored 1 2 3 this paper is a welcome addition explicitly writing out the optimal representations in the linear regime is useful explicitly showing how the choice of ssl loss impacts the rank of the learnt representation in comparison of the rank of the downstream information encoded in g is a welcome contribution to recent investigations on the dimensional collapse of ssl representations weaknesses better referencing known terminology and problems given the authors are connecting ssl to known spectral embedding methods it would add significant clarity to draw on known terminology as well for example vicreg is composed of three terms that are eponymously described as varianceinvariancecovariance with a rather new vocabulary of dimensional collapse and semantic similarity on a simpler level the vicreg loss is simply robust nonlinear pca where terms 1 and 2 whitens the signal and term 3 encourages robustness to perturbations eg jitter or rotation encoded in g specifically term 1 removes the scaling ambiguity of pca by setting the variances to 1 term 2 encourages orthogonal representations term 3 encourages robustness to userspecified transforms eg rotation relating this to known terminology pca is useful because it explains the observations made by the authors when they say the optimum is not unique or there exist many local minima this is due to wellknown indeterminacies for pca eg permutation offset and is not a new problem fig 2 gamma 0 vs gamma 01 is essentially pca vs robust pca expectedly pca has known indeterminacies panel 3 and robust pca blurs the landscape panel 3 and smoothens the latent space panel 2 as it desensitizes the loss to local perturbations it seems like these observations are framed as a new problem of vicreg when they in fact inherit from pca abbreviation ssl selfsupervised learning 1 agrawal ali boyd 2021 minimum distortion embedding 2 lee lei saunshi zhuo 2021 predicting what you already know helps provable selfsupervised learning 3 pfau petersen agarwal barrett stachenfeld 2019 spectral inference networks unifying deep and spectral learning i have no potential negative societal impact to raise docsepthis paper proposed a unfied framework for selfsupervised learning with the help of spectral manifold learning the authors show that vicreg simclr barlowtwins are specical cases of their proposed framwork they points out the prerequisite about the production of optimal selfsupervised representations for downstream tasks strengths 1 this paper is well written and easy to follow 2 the idea of specral manifold learning for unifying simclrnnclr barlowtwins and vicreg seems to interesting for me 3 this paper is technically sound weakness 1 i think the authors overclaim their contributions of this paper from my view they have only consider vicreg barlowtwins and simclr in this paper although they have mentioned dino in this paper they do not reveal the connection between dino and others no ### Summary:

this paper focuses on providing some theoretical intuitionunderstandings of popular selfsupervised learning ssl methods the authors develop closedform optimal representations for various method as a function of the training data and the samplerelation matrix the authors also provide further intuition by developing simplified versions of these expressions in linear settings which they use to show an equivalence of sorts between ssl and various spectral methods and how it affects downstream tasks overall the reviewers were positive and thought the paper had nice insights they did raise some concerns about the quality of exposition and various detailed technical issues most of the technical issues seems to have been addressed by the authors in their response i concur with the reviewers the paper has nice insights and therefore i recommend acceptance i do however recommend that the authors further polish the paper for the camera ready version by addressing the issues raised by the reviewers especially about the exposition

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this paper the authors propose an extension to the non autoregressive translation model by gu et al to improve the accuracy of non autoregressive models as compared to the autoregressive translation models the authors propose using hints which can occur as 1 hidden output matching by incurring a penalty if the cosine distance between the representation differ according to a threshold the authors state that this reduces same output word repetition which is common for nart models 2 reducing the kl divergence between the attention distribution of the teacher and the student model in the encoderdecoder attention part of the model we see experimental evidence from 3 tasks showing the effectiveness of this technique the strengths of this paper are the speedup improvements of using these techniques on the student model while also improving bleu scores the paper is easy to read and the visualisations are useful the main issue with this paper is the delta contribution as compared to the nart model is gu et al the 2 techniques although simple dont make up for technical novelty it would also be good to see more analysis on how much the word repetition reduces using these techniques quantitatively and performance especially on longer length sequences another issue is the comparison of latency measurements for decoding the authors state that the hardware and the setting under which the latency measurements are done might be different as compared to previous numbers though still impressive speedup improvements it somehow becomes fuzzy to understand the actual gainsdocsepthis paper proposes to distill knowledge from intermediary hidden states and attention weights to improve nonautoregressive neural machine translation strengths results are sufficiently strong inference is much faster than for autoregressive models while bleu scores are reasonably close the approach is simple only necessitating two auxiliary loss functions during training and rescoring for inference weaknesses the discussion of related work is deficient learning from hints is a variant of knowledge distillation kd another form of kd using the autoregressive model output instead of the reference was shown to be useful for nonautoregressive neural machine translation gu et al 2017 already cited the authors mention using that technique in section 41 but dont discuss how it relates to their work 1 should also probably be cited hu et al 2 apply a slightly different form of attention weight distillation however the preprint of that paper was available just over one month before the iclr submission deadline questions and other remarks do the baselines use greedy or beam search why batch size 1 for decoding with larger batch sizes the speedup may be limited by how many candidates fit in memory for rescoring please fix are not commonly appeared on page 4 section 31 1 kim yoon and alexander m rush sequencelevel knowledge distillation emnlp 2016 2 hu minghao et al attentionguided answer distillation for machine reading comprehension emnlp 2018docsepthis work proposes a nonautoregressive neural machine translation model which the authors call nart as opposed to an autoregressive model which is referred to as an art model the main idea behind this work is to leverage a well trained art model to inform the hidden states and the word alignment of nart models the joint distribution of the targets y given the inputs x is factorized into two components as in previous works on nonautoregressive mt an intermediate z which is first predicted from x which captures the autoregressive part while the prediction of y given z is nonautoregressive this is the approach taken eg in gu et al kaiser et al roy et al and this also seems to be the approach of this work the authors argue that improving the expressiveness of z as was done in kaiser et al roy et al is expensive and so the authors propose a simple formulation for z in particular z is a sequence of the same length as the targets where the jth entry zj is a weighted sum of the embedding of the inputs x the weights depend in a deterministic fashion on j given this z the model predicts the targets completely nonautoregressively however this by itself is not entirely sufficient and so the authors also utilize hints 1 if the pairwise cosine similarity between two successive hidden states in the student nart model is above a certain threshold while the similarity is lower than another threshold in the art model then the nart model incurs a cost proportional to this similarity 2 a kl term is used to encourage the distribution of attention weights of the student art model to match that of the teacher nart model these two loss terms are used in different proportions using additional hyperparameters together with maximizing the likelihood term quality the paper is not very well written and is often hard to follow in parts here are some examples of the writing that feel awkward consequently people start to develop nonautoregressive neural machine translation nart models to speed up the inference process gu et al 2017 kaiser et al 2018 lee et al 2018 in order to speed up to the inference process a line of works begin to develop nonautoregressive translation models originality the idea of using an autoregressive teacher model to improve a nonautoregressive translation model has been used in gu et al roy et al where knowledge distillation is used so knowledge distillation paper from hinton et al should be cited moreover the authors have missed comparing their work to that of roy et al httpsarxivorgabs180511063 which greatly improves on the work of kaiser et al and almost closes the gap between a nonautoregressive model and an autoregressive model 267 bleu vs 27 bleu on ende while being orders of magnitude faster so it is not true that while the nart models achieve significant speedup during inference gu et al 2017 their accuracy is considerably lower than their art counterpart nonautoregressive translation nart models have suffered from lowquality translation results significance the work introduces the idea of using hints for nonautoregressive machine translation however i have a technical concern it seems that the authors complain that previous works like kaiser et al roy et al use sophisticated submodules to help the expressiveness of z and this was the cause for slowness however the way the authors define z seems to have some problems zj does not depend on z1 zj1 so where is the autoregressive dependencies being captured z1 z2 zty depends only on the length of y and does not depend on y in any other way given x predicting z is trivial and i dont see why that should help the model fy z x help at all given such a trivial z one can just assume that your model is completely factorial ie pyx prodi pyix since the intermediate z has no information on the ys except its length this is quite suspicious to me and it seems that if this works then a completely factorial model should work as well if we only use the hints from the art teacher model this is a red flag to me and i am finding this hard to believe ### Summary:

sufficiently strong results a fast parallelizable model novelty with respect to previous work is not as great see anonreviewer1 and anonreviewer2s comments the same reviewers raised concerns about the discussion of related work eg positioning with respect to work on knowledge distillation i agree that the very related work of roy et al should be mentioned even though it has not been published it has been on arxiv since may ablation studies are only on smaller iwslt datasets confirming that the hints from an autoregressive model are beneficial whereas the main results are on wmt i agree with r1 that the important modeling details eg describing how the latent structure is generated should not be described only in the appendix esp given nonstandard modeling choices r1 is concerned that a model which does not have any autoregressive components ie not even for the latent state may have trouble representing multiple modes i do find it surprising that the model with nonautoregressive latent state works well however i do not find this a sufficient ground for rejection on its own however emphasizing this point and discussing the implication in the paper makes a lot of sense and should have been done as of now it is downplayed r1 is concerned that such model may be gaming bleu as bleu is less sensitive to longdistance dependencies they may get damaged for the model which does not have any autoregressive components again given the standards in the field i do not think it is fair to require human evaluation but i agree that including it would strengthen the paper and the arguments overall i do believe that the paper is sufficiently interesting and should get published but i also believe that it needs further revisions further experiments

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes an activation quantization method acsgd for the finetuning step on language models with slow network setup acsgd compresses the change of the activations instead of activation values directly this paper shows this compression method can converge well and it can lead to throughput improvement under slow network systems strengths this paper doesnt aim at compressing activations directly instead by compressing the changes of the activation the proposed algorithm can achieve efficient convergence with a slow network system ie the portion of communication overheads is quite big during endtoend distributed finetuning process this algorithm can achieve higher throughput with smaller communication overhead weaknesses as weaknesses of this paper i have two concerns as below im very confused about what slow networks are it seems to be hard for me to imagine which scenario this paper assumes v100 multigpus with 100mbps network the only network i can imagine is that single gpus are distributed and interconnected with slow ethernet systems it is so weird i dont understand why we should assume extremely slow interconnection between highend gpus for finetuning large plms for the gpt results in figure 4 i think showing training loss is not sufficient to prove that the compression method works well for generative language models especially for finetuning steps since the pretrained model consists of an enormous number of parameters and is trained with a large dataset it is hard to trust the train loss and validation loss for finetuning dataset it tends to be easily overfitted and there is rare correlation between loss results and measured scores since there is a limitation on scale of bertlike models this paper should show the inference results on generation tasks and large generation models as i mentioned this paper should describe why they assume this slow network system in detail as far as i know it seems to be not common the experimental results are not sufficient to show that this method works well showing profiling results of the finetuning process with this method and various speeds of the network would be better for understanding docsepthe authors proposed a differential activation quantization scheme with proven convergence for distributed modelparallel training clear presentation in general realworld demonstration limitation of practical usefulness from storage requirement lack of stability study see above docsepthis work examines the feasibility to compress the activations for models trained with pipeline parallelism to this end the scheme acsgd is proposed which aims to make pipelineparallel training more communicationefficient over slot networks different from previous efforts in activation compression instead of compressing activation values directly acsgd compresses the changes of the activations the most novel insight is that one can still achieve o1pt convergence rate for nonconvex objectives under activation compression without making assumptions on gradient unbiasedness that do not hold for deep learning models with nonlinear activation functions the this work shows that acsgd can be optimized and implemented efficiently without additional endtoend runtime overhead the evaluations on acsgd are shown on finetuned language models with up to 15 billion parameters compressing activations to 24 bits acsgd provides up to 43 endtoend speedup in slower networks without sacrificing model quality strengths an interesting insight on pipelineparallel training the solution is effective and the optimization space is covered extensive evaluation results source codes available weaknesses the work may need more rationales upfront to motivate the problems ie slow networks more breakdown analysis to showcase the effectiveness of the approach i think the paper has good potentials and it can be strengthened by addressing the two points raised in the weakness part i assume the geodistributed network is a more realistic scenario for this proposed approach hence i would suggest the authors to get more literature on this part and back up both their motivation and simulation setup better both qualitatively and quantitatively also i believe breaking down the optimizations in acsgd can add more values in this work so that the readers can better understand how essential your optimizations are docseplarge language models are trained or finetuned using a combination of pipeline and data parallelism methods pipeline parallelism leads to activation and activation gradient being communicated between devices while data parallelism leads to weight gradients being communicated between devices a lot of work has focussed on the later but the former has received less focus compressing activations is non trivial as compressing it in a stochastic unbiased way will lead to biases in the gradient and breaks the unbiasedness assumption made by most gradient compression results previous work either failed to provide theoretical guarantees this paper proposes acsgd an activation compression algorithm for communication efficient pipeline parallelism this method helps accelerate pipeline parallelism over slow networks they provide theoretical guarantees to prove convergence and show that the method can be implemented with minimum runtime overhead albeit at a huge memory cost they compare their method with recent work and show better accuracy at lower precision depending on the communication bw used as baseline they can achieve 43x end to end speedup strengths 1 the claims in the paper are evaluated against strong baselines 2 the method comes with a theoretical guarantee of convergence weakness 1 the idea relies on data being repeated over multiple epochs this can limit the applicability of this techniques to more important workloads like pretraining and limit to the idea to only finetuning of llm this reduces the potential impact of the work 2 the requirement to store activations for new samples in the finetuning datasets makes it harder to scale this technique with larger finetuning datasets have the authors adequately addressed the limitations and potential negative societal impact of their work yes ### Summary:

in this paper authors propose to speed up the fine tuning of large models over slow networks by compressing deltas of activations vs activations themselves so as to reduce the computation cost original reviews were mixed but at the end of the discussion period all reviewers are leaning towards acceptance the main issues that were raised are the motivation for training very large models over slow networks the limited amount of metrics to validate the quality and robustness of the optimization process concerns about the scalability of the method storage requirements and its applicability to the online setting i consider that these concerns have been mostly addressed during the discussion period by the authors who also remained honest about some of the limitations of their method in my opinion the pros of this work a practically useful idea outweigh the cons it may only be useful in somewhat niche settings and i thus recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this work the authors propose the warm method to help conduct iterative and interactive weaklysupervised learning active learning is used here to refine the labeling functions by focusing on data points that are once labeled the authors further incorporated gradient propagation to alternatively update the lf parameters and the dp model experimental results show that the warm method can improve the quality of training data strength the proposed method is clearly described and the writing is easy to follow weakness 1 the theoretical analysis is insufficient and i recommend the authors provide more analyses on their main contributions 2 the general idea of active learning with weak supervision is not novel which can be seen in 1 the methods proposed in this work are combinations of existing ones and the authors also fail to contribute novel theoretical results 3 the layout should be improved i recommend the authors separate the key notations eg labeling functions from the contexts for better presentation there are also some typos eg dependant should be dependent in the last paragraph of page 2 1 chicheng zhang and kamalika chaudhuri active learning from weak and strong labelers nips 2015 703711 the authors should compare with more related works and provide some theoretical analyses to make this work more convincing the ideas are totally heuristic and the experimental results are also not satisfying docsepthis paper proposes warm an active learning approach to weaklyprogrammatically supervised learning in the warm approach which bases off of the data programmingsnorkel paradigm for weak supervision users write labeling functions lfs to programmatically label training data these labeling functions are then modeled by the snorkel framework for weak supervision and used to train downstream models in the warm setup these lfs are assumed to be or cast as differentiable the paper then proposes an active learning approach to sampling labeled data points to tune the parameters of these lfs and validates this approach on several medical datasets strengths s1 this paper tackles an important problem with an intuitive approach of complementing recent programmaticweak supervision approaches with expert feedback via an active learningstyle approach s2 this paper introduces a clean formulation of argument for lfs being cast as differentiable functions whereas to date most lfs have been nondifferentiable s3 the paper shows some strong results relative to recent approaches s4 the paper includes a range of datasets from synthetic data lfs to semisynthetic real data synthetically generated lfs to a real eeg taskdataset lfs developed in conjunction with medical smes an impressive and real worldrelevant contribution weaknesses w1 simple method while not a major drawback in isolation it is worth nothing that the proposed approach is fairly simple and standard from a methodologicalalgorithmic standpoint from an active learning perspective the query function is just based on model uncertainty which is the most basic type of active learning query function the only tweak being that it is the label model ie model over lfs but this does not change anything from an algorithm perspective then these data points are used to tune the parameters of the lfs in a manner that is also straightforward the only tweak here being that the approach alternates between two formulations of the label model objective which is not necessarily needed see w1a w1a the authors state that the data programming label model is not differentiable with respect to the tunable lf parameters introduced in warm which is not true w2 lack of exploration of effect of differentiable lfs given the above lack of methodological novelty this reviewer at least saw one of the main points of novelty and overall contribution of the paper being around the differentiable lfs themselves and the overall setup here however unfortunately this contribution was not explored in any depth eg what are the tradeoffs of softening lfs to make them differentiable how should we think about this more broadly beyond the medical settings treated etc which would have been interesting and significantly strengthened this aspect of the overall contribution w3 lack of relevant ablations in general there were a range of ablations of the overall approach i would have thought natural for example what is the impact of an active learning setup vs just using some randomly sampled labeled data to tune the lfs internal parameters could these internal parameters also be learned without labeled data following the basic snorkel modeling approach and how would that do how would the approach do without tuning the internal lf params etc w4 weakimproper comparisons since two of the other approaches compared to have no access to tune the internal params of the lfs and warm does this seems like a somewhat handicapped comparison overall the proposed approach introduces some interesting and practical ideas with some exciting experimental applications however does not sufficiently explore the most novel elements of the contribution ablate them sufficiently or compare them to prior methods appropriately docsepthis paper proposes a new method for data programming ie using weak supervision to generate probabilistic training labels for unlabelled points using heuristics devised by domain experts in particular the authors propose warm a framework for iteratively improving these weakly supervised models by modifying the parameters of labeling functions and directing users to a subset of data points that when labelled would most improve the model pros 1 the proposed method would be of great use in many realworld scenarios where labelled data is scarce eg medicine 2 to my knowledge the authors proposed method ie actively refining the voting weights of each labeling function and the parameters of the labeling function is novel 3 generally i found the writing in the manuscript to be of high quality as someone who is not an expert in data programming i found the manuscript easy to follow with some minor exceptions see point 3 in cons 4 the authors experiment with their method on a variety of datasets including one for which they use human domain experts to craft labeling functions i greatly appreciate the application to a realworld scenario cons listed in order of importance to my score 1 i found it difficult to assess the significance of the knowledge shift experiment results presented in figure 2 and figure 3 due to a lack of any results from baseline models as such i would appreciate it if the authors could add results from their baseline models to these figures to 1 illustrate the severity of the knowledge shift problem and 2 potentially better illustrate the advantages of warm over previous work 2 as mentioned by the authors in section 5 the active learning baseline outperforms warm on half of the tested datasets the authors say that this is due to some datasets being simpler than others though i was not clear as to what simpler meant here i would be willing to raise my score if the authors could provide experimental results that clearly illustrate the reasons behind the poor performance of warm on these simpler datasets for example if simpler means having fewer data points an experiment that assesses warms performance on simulated data with varying dataset size would be helpful to better understand when warm may outperform existing methods vs when it may underperform them 3 some of the terminology relating to the problem formulationlabeling functions is not precisely defined within the manuscript and can be confusing for readers not already familiar with data programming eg i had to look outside the manuscript for a definition of the polarity of a labeling function 4 the literature review in section 2 is very dataprogramming specific and does not discuss other recent approaches to active learning eg 123 expanding the related works section would help put warm in a broader context and assist the reviewer in assessing the significance of warm 1 learning active learning from data neurips 2017 2 learning algorithms for active learning icml 2017 3 active learning with partial feedback iclr 2019 overall i enjoyed reading this paper the authors method appears methodologically sound and it seems to provide considerable benefits when applied to complex datasets however there are some weaknesses in the manuscript that somewhat undermine the papers story for now i am recommending a weak accept and i am willing to revise my score upwards if the authors address my concerns docsepthis paper proposes an algorithm for choosing a small set of labels to improve labeling function model performance both directly and for downstream tasks additionally the authors provide a general method to convert standard labeling functions to soft labeling functions which are differentiable with respect to some parameters eg a threshold if the labeling functions are differentiable this paper provides a method to update the labeling function parameters finally experimental results show that the method introduced outperforms other active labeling approaches for weak supervision in equation 1 the propto seems like the wrong relation since the left hand side is actually the softmax of the right hand side why are the labels not used in estimating the labeling function accuracies in figure 3i why is the performance not monotonic in particular the curve without noise peaks with 20 labels and then starts to deteriorate its interesting to note that warms downstream performance never improves over the accuracy of the label model it seems that a piece of the weak supervision pipeline is broken here perhaps using a more expressive model random forests would be more appropriate for the downstream model im surprised that the active learning baseline generally outperformed the weak supervision methods even though the weak supervision methods have access to extra information the labeling functions trained on the whole dataset minor things i think the arguments of ptheta are swapped throughout the paper look at eq 2 eq 3 and line 4 of the algorithm the method proposed in this paper does not contain any particularly novel ideas and seems to be based on heuristics maybe the proposed quantities could be derived from more general principles additionally it appears that weak supervision is not appropriate for the papers empirical settings as seen by the lack of improvement from the downstream model and the stronger performance of nonweaklysupervised methods active learning docsepthe paper gives a method to iteratively and interactively improve the label model in weak supervision the approach consists of two steps first is standard weak supervision way of weighted combination of labelling functions to generate labels novelty and improvement mainly comes from the second step where true label for most uncertain data point is queried using which the parameters of the labelling functions are improved which in turn lead to a more accurate weak supervision model a key requirement and assumption in this papers setup is that labelling functions are given by some learnable parameters ie they can be differentiated wrt their parameters which allows parameters updates using the true labels acquired empirical results on various real world datasets in medical domain show that in some cases this approach can yield a more accurate model in comparison to pure active learning approach and some recent baselines which combine weak supervision with active learning these results also show that the papers approach can get accuracy comparable to fully supervised model as well the main strengths of the paper lies in effectively combining active learning and weak supervision to generate high quality labels the proposed approach incrementally improves the labeling functions by using the true labels obtained in each round to achieve this they need lfs to be differentiable which can be a drawback in some cases except these few changes it is still using most of the existing weaksupervision machinery ie label model accuracy estimation and weighted combination of labeling functions to generate final labels etc so while the novelty might seem limited i think it is useful in the sense that it may require minimal changes in existing weaksupervision setups to enhance it using active learning i dont see major problems with this work except a few firstly interpreting the accuracy results in table 2 is slightly difficult since class distributions in the datasets are not provided ie are the datasets imbalanced from the results it looks like the cases where one can obtain good labeling functions from experts then this approach works better than active learning otherwise not there might be cases where obtaining expert labeling functions is more costly than obtaining labels which is probably easier than writing lfs it might be worthwhile to discuss these limitations in the paper overall i think its a nice paper with a sound and simple approach to improve weak supervisions label quality using active learning the contributions are novel and useful in practice i am inclined towards accepting this paper i need some clarifications in the experiments section to be more confident in this assessment ### Summary:

the authors propose warm a novel method that actively queries a small set of true labels to improve the label function in weak supervision in particular the authors propose a methodology that converts the label function to soft versions that are differentiable which are in term learnable with true labels using proper updates of parameters empirical results on several realworld data sets demonstrate that the method yields a pretty strong performance the reviewers generally agree that the idea of making the labeling functions differentiable is conceptually interesting they are also positive about the simplicity and the promising performance they share joint concerns on whether the idea has been sufficiently studied in terms of the design choices and completeness of the experiments for instance the authors can conduct deeper exploration of the tradeoff for differentiable lfs they can also study active learning strategies that are beyond basic uncertainty sampling while the authors have provided more studies about those exploration and ablation studies during the rebuttal generally the results are not sufficient to convince most of the reviewers in future revisions the authors are encouraged to clarify its position with respect to existing works that combine active learning and weaklysupervised learning the authors position the paper as more empirical than theoretical so the suggestion from some reviewers about more theoretical study is viewed as nicetohave but not a must

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper introduces an environment called honor of kings arena which is derived from the popular mobile game honor of kings the environment can be used for evaluating the generalization ability of agents in competitive games the authors publish the environment engine and provide easytouse interfaces along with detailed specifications by comparing the performance of two different rlbased methods ie ppo and dqn and one rulebased method ie bt the results show that the environment is efficient for training learning algorithms finally the paper demonstrates that the environment imposes generalization challenges across opponents and targets on the agents the honor of kings arena environment focuses on generalization ability evaluation in competitive games which is a research hotspot in the reinforcement learning domain the authors conducted several experiments to show that the environment is of high performance and is feasible for comparing the generalization ability between different agents the engine is accessible and the apis are welldesigned rl research related to generalization is expected to benefit from the environment the generalization challenges of honor of kings arena come from different opponents and different targets actually more challenges can be manufactured in this environment simply by introducing various goals or small tasks such as killing the opponent hero as fast as possible gaining as much money as possible or cooperating with other heroes on some small tasks different goals can make the agents more generalizable and small tasks can lower the bar for training and learning docsepthis paper presents a benchmark based on the game honor of kings where a player can take on one of many different roles and compete against a similarly wide selection of opponents the motivation is that generalization across these two axes targets and opponents is equivalent to generalization across several game dynamics including action control the key contributions are an optimized and simplified 1v1 honor of kings game engine and interface for rl research a series of baselines and experiments speed and accessibility of implementation existing interest in this game environment from the community comparison of direct transfer learning multitask and distillation setups was interesting the generalization framing is not very well supported motivated or contextualized with regards to prior works its not really explained or justified why the type of variation captured by this benchmark aka certain gameplay elements via the choice of target and opponent is significant or interesting compared to existing benchmarks it is unclear if this is a challenging enough benchmark to drive forward research progress given the performance of the baselines docsep this paper introduces a new rl simulation environment called honor of kings arena for training agents to play 1 vs 1 honor of kings a popular multiplayer game worldwide honor of kings arena has an efficient game engine and combined with the authors training pipeline researchers can train a viable agent in just 7 hours with 128 cpu cores and presumably a gpu further the authors suggest honor of kings arena can be an excellent testbed for measuring generalization because the agent needs to learn to 1 control different heroes with different skills and 2 play against different heroes they have conducted a series of experiments showing that the agents could overfit and lose to the heroes they were not trained against to mitigate this issue the authors have experimented with more diverse training heroes and model distillation that showed promising results the authors clearly put tremendous effort into opensourcing this testbed and this work has great potential however i cant give out a high rating given the accessibility and documentation issues described below the paper presents the first opensource rl interface to a commercially successful moba game the rl interface is welldesigned and easy to follow the authors show detailed experiments on the effects of computational resources the authors have conducted preliminary experiments examining the rl agents generalizability and explored ways to improve generalization accessibility is a big issue while honor of kings arena the rl interface is licensed under apache 20 running it requires downloading the hok gamecore which requires users to fill out a questionnaire to request access and sign a noncommercial agreement this practice may violate the key criteria listed in the dataset and benchmark track submission instructions datasets should be available and accessible ie the data can be found and obtained without a personal request to the pi and any required code should be open source while i understand the hok gamecore can be difficult to open source i think the authors should consider dropping the access request and the noncommercial agreement such as the case for pysc2 starcraft ii learning environment httpsgithubcomdeepmindpysc2httpsgithubcomdeepmindpysc2 if eventually an access request and signing a noncommercial agreement are required this requirement should be clearly communicated in the paper which is not done in section 3 the documentation and setup seem poorly organized see comments below docsepthe paper presents a benchmark for a competitive 1v1 digital game honor of kings oriented toward testing rl algorithm generalization when facing different opponents and using different characters with differing action spaces the benchmark provides access to run a performant game simulator and a gymlike reinforcement learning environment api to the simulator included are baseline algorithms including human authored rulebased behavior and baseline rl algorithm implementations stemming from prior research on the game the paper contributes a scalable game simulator that can stress rl agent generalization across action spaces and different opponent types initial examples demonstrate the failures of baseline rl techniques dqn ppo to generalize in either sense on this benchmark showing it is an open problem real world game simulator the game provided is a popular and highly competitive game prior work has not provided access to this level of game complexity instead focusing on more toy tasks contributing access to this type of game can spur further research in rl in general for these types of environments including the generalization topic the paper emphasizes while practitioners have worked on these domains in partnership with companies openai with valve for dota2 deepmind with blizzard for starcraft ii there has not been access for other researchers performant and scalable benchmark a single research machine can run experiments in a reasonable amount of time 1 day with a cluster the benchmark can scale to much larger training rl research is typically limited by one of these problems being too slow for a single researcher to experiment on a local machine or being too limited in infrastructure to leverage more resources when needed this makes the benchmark valuable to a wide range of research applications and practitioners important open problem despite successes in rl for targeted problems in games generalizing well across opponents in the multiagent setting remains an open challenge to date no publicly available systems have exposed this problem limiting research on the topic the topic is important in games in particular and rl in general unclear relevance for action space generalization the paper does not articulate why using different action spaces would be relevant outside the games context why is it helpful to have robotic agents that can control different arms the example given in the paper of the two types of generalization exposed this form seems more niche to the games rl community currently requires windows for better or worse most research in ml favors using linux the requirement for the simulator to use windows will be a minor impediment to adoption the forthcoming linux release mentioned will alleviate this but it is not currently available and has no timeline for release stated docsepreasonably useful dataset for rl unfortunately i am not an expert in this field but the author seems to provide suitable tools to efficiently explore the action space benchmarks demonstrate that basic hardware is capable of utilizing this data generator and training a rl model generalization across heroes can be of interest for transfer learning dataset appears to be useful for future research isnt exactly innovative other sources for rl data generation are available generalization across heroes is not particularly innovative as starcraft 2 provides generalization across races missed potential for multi agent team based rl ### Summary:

this paper introduces a novel rl benchmark based on the popular computer game honor of kings arena there are two modes of evaluation one is a single agent task of beating the computer ai and the second one is a competitive setting two player zerosum the game offers interesting challenges from a generalisation and transfer point of view and will make a valuable contribution to the research landscape the authors have included basis baselines in the evaluation one suggestion i have is to implement the training on the gpu to reduce computational overhead for scientists

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: while there is an extensive literature on both multiagent reinforcement learning marl and offline rl separately this work explores a setting that combines the two in the marl setting the size of the joint action space grows exponentially with the number of agents which is a key challenge in this subarea this paper investigates how to tackle this problem and whether we can find a nash equilibrium strategy in the offline marl setting given unilateral coverage specifically to construct a confidence interval for each stateaction pair in the offline marl setting the authors proposes to estimate each strategy separately to circumvent the dependence of the joint action space which grows exponentially with the number of agents besides based on this the authors also develop two different algorithm frameworks under the tabular case for both the offline twoplayer zerosum markov games as well as the offline multiplayer generalsum markov games separately this paper helps to improve our current understanding of algorithms for the offline marl under the tabular setting the major contribution is the proposed strategywise bonus which firstly estimates the state value functions for a strategy and then add the bonus on them however if the strategies are complicated and therefore the class space not well structured the calculation for state value functions for the strategy could be challenging given this for the twoplayer zerosum markov games the proposed framework utilizes a strategywise bonus as well as maximinoptimizationtype algorithm for the multiplayer generalsum markov game as there is no saddle point structure this paper proposes a surrogate function to minimize the performance gap which however requires to enumerate the strategy class in the worst case and thus not computationally efficient another concern regarding the current version is that the data assumption adopted in this work assumption 1 assumes that the all the tuples in the logged dataset are independent while this is not true in most realworld scenarios as in many realworld applications this assumption is very restrictive on the dataset and therefore usually does not hold to derive practical algorithms based on the theoretical analysis and test the efficacy of the proposed methods in practice the work should consider the impact of the violation of this assumption on the algorithm performance the authors has addressed some potential limitations docsepin this paper the authors study the offline multiagent rl they propose the strategywise concentration principle in contrast to the pointwise concentration principle in existing works by the new concentration principle they propose novel algorithms with strategywise bonuses for both zerosum and generalsum games which achieve better sample complexities than the existing works the sample complexity of the new method does not scale with the size of the joint action space moreover the proposed algorithms can take a prespecified strategy class as input and output a strategy that is close to the best strategy in such a class this paper proposes novel algorithms with the strategywise bonus which leads to better sample complexities than the existing works i think it is an important contribution to the theoretical analysis of the offline markov games as the new sample complexity result does not scale with the size of the joint action space in addition the writing of this work is good in general and this paper is well structured however i was not able to find the discussion of the limitation in the paper although the author answered yes in the checklist it would be much better if the authors can explicitly summarize the limitation of their work in the conclusion section or in the checklist besides is it possible to apply such a strategywise bonus to the online markov game setting i was not able to find the discussion of the limitation it would be much better if the authors can explicitly summarize the limitation of their work in the conclusion section or in the checklist docsepthis paper considers offline multiagent reinforcement learning the sample complexity for multiagent general sum mdp is at least n prodi1m ai where ais denote the effective action space size for each agent ie the number of actions obeying piia 0 specifically to ensure hatc infty one needs at least n samples such that hatdhs a 0 when dhpis a 0 in addition the assumption c infty implies there are at least n actions satisfying dhs a 0 which leads to 1pmin ge n hence all results for the multiagent case in section 32 exhibit an exponential dependence on the number of agents except the case that almost all agents will choose only one action on the other hand it is well known to find the ne for multiagent case is computationally hard then is it possible to compute cce effectively for offline multiagent rl the sample complexity for multiagent general sum mdp is exponential ### Summary:

reviewers appreciate the papers contribution to a novel intersection of fields offline and multiagent rl while feasibility of the results is limited to cases where prior knowledge allows strategywise decomposition it is nonetheless an interesting step in this field reviewers are concerned that the above substantial limitation of the work has not been sufficiently discussed in the paper and the authors are asked to clarify this aspect in a subsequent revision

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors introduce a physical systems evaluation dataset framework that focuses on evaluating machine learning algorithms for simulation problems they provide four systems of increasing difficulty to evaluate baseline methods spring wave naivestrokes and spring mesh explore the tradeoffs between derivativebased prediction and step prediction and advocate the usage of knearestneighbors to better understand the complexity of different simulation tasks the paper is very concise easytofollow and wellillustrated the authors do a great job motivating the four representative benchmark physical systems and provide a comprehensive array of baseline datadriven methods while neither of these are exhaustive the flexibility of their framework allows for the integration of other learning tasks or machine learning methods all in all their contribution promisingly lays the groundwork for future research in the field of scientific computing while the graphs are typically easytofollow i was slightly confused by repeated colors in figures 3 and 4 my understanding is that samecolor marks are different architectures of the baseline methods however different architectures could have significantly different strengths and weaknesses for instance shallow mlps are typically more robust to noisy datasets than deep mlps do you believe that readers would benefit from having more finegrained labels for the methods eg shallow vs deep mlpcnn in figures 3 and 4 further due to the nondeterministic nature of some nnbased approaches it would make sense to average results over multiple runs in the main paper there doesnt seem to be any indication of this were the results presented as averages over multiple runs docsepthe present work is motivated by the need for a thorough evaluation of datadriven approaches in scientific computing pipelines and b the lack of standardized benchmarks in the literature the authors focus on physical simulation benchmarks that a map a highdimensional state space into another highdimensional space as in temporal integration schemes mapping the state of the system at one time step to the next or b from a highdimensional input space to a lowerdimensional output as in surrogate models mapping the initial conditions to a functional of the solution in addition the paper addresses the narrow data regime where initial conditions are sampled from a lowdimensional manifold even within a highdimensional state space and the wide regime where initial conditions span a truly highdimensional space the main contribution of the paper is the presentation of a suite of simple representative physics problems along with reference numerical solutions for traditional time integration schemes to benchmark datadriven methods mlps cnns kernel machines nearest neighbors the key conclusion of the paper is that even in the simplest physical models current datadriven pipelines while providing qualitatively acceptable solutions are quantitatively far from directly numerically integrating physical models and this performance gap appears unfeasible to close by merely scaling up the models andor the dataset size another finding is that a simple l2based nearest neighbor regressor outperforms most deep learning models in the narrow regime for complex systems such as incompressible navierstokes systems provides a standard benchmark for contrasting traditional solutions for numerical computation with physics based models against datadriven methods i like the progression of complexity of the benchmarks and the exploration of narrow and wide data regimes such a standard benchmark allows for systematic evaluation and tradeoff analysis the experimental results provide sufficient insights the code and data is made accessible and is extensible no ethicalsocial implications exist apart from highlighting the deviations in results between data driven and traditional methods it is not absolutely clear how the benchmark results points to ml algorithm improvements the authors state this as a point of strength of the paper but it is not obvious what they imply by this while i like the organization of the benchmark in the paper i would have assumed that a more simpler differential system with well known closed form solution for the result may be an interesting option as a baseline the paper will be strengthened significantly if other aspects not covered by the authors described in the limitation sections are included including the missing timing analysis docsepthis paper introduced a benchmark for the methods of datadrivenbased physical system simulation they provided a dataset of four physical systems spring wave spring mesh navierstokes including data data generation methods codes and documents then they conducted a variety of experiments using knn nn kernel mlps and cnns this work is interesting and important for learning physical systems the descriptions are also detailed however the value of this benchmark needs to be more explained please see weaknesses this is hard work 850h on gpu 2180h on cpu it is really difficult to process such a large amount of data and keep it unified and correct the data source codes and documents are all well organized the generation methods documents and experiments are described in detail for a paper of dataset and benchmark the contributions should be noveloriginality data comparison and analysis of stateoftheart methods and the impact on the future of related fields i hope the authors could give more explanation about these points the paper claimed that the proposed benchmark takes a step towards unified evaluation protocols and metrics but it is unclear data the complex system is more important for simulation problems the value of simple system data needs to be explained the data of four systems are also generated in the existing paper such as spring in 7 navierstokes in 48 what is the difference between the provided data and the previous data benchmarks only simple baseline models are tested except for unet on naiverstokes and the paper claim that these models could not solve the problem well however advanced models have been proposed and compared with the baseline models in the existing papers sota models should be tested and analyzed ### Summary:

this paper introduces a physical evaluation dataset framework for scientific computing pipelines that map one highdim state space into another high or lowdim one providing a suite of simple representative physics problems reviewers appreciated for motivation clarity comprehensiveness and overall contribution to the space

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a deep state space model for videos the dynamics are defined by linear hamiltonian dynamics and the motion matrix is further assumed to be block diagonal in order to separate different categories of actions like previous works a latent variable z is introduced for explaining content and kept fixed for all frames experiments are carried out on sprites and mug to demonstrate the efficacy strengths the paper is well written and easy to follow the idea of introducing hamiltonian dynamics as an inductive bias for explaining repetitive or cycled motions in videos are reasonable and natural the theoretical derivation is technically sound weaknesses my main concern is that the proposed method is not well supported by the experimental results i dont understand how ssim and psnr can be used for evaluating generation quality as generated samples are supposed to be different from the training datasets i can only imagine that the numbers in table 1 are reported for reconstruction in which case it is not for generation quality as described by the paper also no baseline methods are compared in terms of reconstruction theres no commonly used metrics reported that are designed for really evaluating sample quality such as fvd or inception scores for disentanglement evaluation it is not fair to use the conditional halo model to compared with the baselines which are trained unconditionally the only fair way is the compare unconditional halo models with the baselines where the performance of the proposed model does not stand out in the ablation study section it makes me confused that the paper mentioned rnn or linear dynamic model cannot make image move but it also showed the results of swapping motions of the two baselines where the video sequences are changing over time also it sounds wired to me that the linearrnn dynamics cannot do imagetoseq as those have been applied by many classical state space models in experiments two operators h and skewh are compared but theres no official definition of skewh in the previous sections the model assumes that the action space can be divided into subspaces where each subspace represents a unique action this representation can be highly ineffective if the number of actions goes huge yes docsepthe paper proposes halo a novel type of variational autoencoder with structured latent space and demonstrates its applications to different types of controlled video generation tasks the main contribution is a principled decomposition of the latent space into a content space and a motion space where the motion space is modeled using hamiltonian dynamics the structural constraints eg symmetries imposed by these dynamics induce desirable properties like reversibility and volumepreservation enabling the conservation of learned quantities strengths video generation is an important and challenging task with a rich history the proposed approach takes a fresh perspective on this topic and explores the benefits of inductive bias based on principles rooted in the physics community sections 13 introduction related work method are wellorganized and easy to follow the main contributions are clearly formulated the figures are helpful in understanding architectural details and the mathematical notation is mostly consistent the related work section is commendable and provides a comprehensive overview of the field the paper does have a fairly strong physics flavour and i would recommend to provide stronger guidance for an audience which may not be familiar with topics that are not part of core ml such as group action phase space conservation law and symplectic geometry the hamiltonian design of the latent space is interesting and novel and the advantages of reversible and symplectic latent dynamics make intuitive sense i also appreciate the principled derivation of the dynamical model f from a constantenergy perspective l206l214 the variational inference section is less clear and i would encourage the authors to move at least some intuition about the elbo from the appendix into the main paper weaknesses the weakest part of the paper are its experiments both in terms of their presentation and design presentation the structure of the experiments is confusing throughout section 4 for example the description of the sprites and mug datasets starts in the middle of the rotating balls paragraph l262 likewise the description of the baseline comparison starts in the middle of the quantitative evaluation section l302 grammar and text flow in the experiment section also feel unpolished finally none of the figures in this section have proper axeslabels and the reader needs to count rows and infer the content from the caption or even the main text l348350 for figure 5 left design since table 1 does not include a comparison to other baselines it is not possible to assess whether the presented ssimpsnrmse scores are competitive or not why not use the same metrics as in table 2 table 3 is not mentioned in the text and seems to be based on the single example of figure 5 right which is not enough to make any general statements the positional encoding mentioned in this table is not explained and not supported by any qualitative evidence in figure 4 left the reconstructed and generated sequences look fairly similar which can be an indication of low diversity it is unclear how the sequences of the rotating balls dataset were generated as the mentioned constraint does not specify any temporal pattern what is the dynamic model used here the sequences are very short 816 frames and small 64 x 64 what is the main bottleneck that prevents application to highfidelity image sequences minor comments typos figure 1 alongwith l224 long term term l234 6 l258 asblue l283 evaluationwe the appendix provides valuable information about the elbo objective terminology and network structures but the main paper does not refer to it often enough eg contentpositionmomentum network the paper follows a topdown approach first introducing highlevel structures and then filling in the details while that is a reasonable approach it does mean that readers will have to read the paper twice or go back to previous paragraphs because the motivation for some design choices remains initially unclear one example is the structure of the phase space summary i appreciate the technical formulation of this paper but am on the fence due to the weak and unconvincing experiments i encourage the authors to address the concerns above as well as the questions below the paper flags potential misuse in the area of fake video data generation the paper does not contain a limitations section docsepthis paper deals with the task of generating image sequences specifically the authors propose a method called halo that allows to disentangle the content from the motion in image sequences in the vae framework they do so by separating the latent space in two spaces 1 the content space a global content vector that summarizes the image sequence 2 the motion space a sequence of timedependent vectors that capture the dynamics of the sequence the main contribution of the authors is to model the motion space with hamiltonian dynamics the authors claim that hamiltonian dynamics have good inductive biases for sequence generation such as reversibility of the motion experiments on simple image sequences are performed to prove the quality of their model strengths 1 the latent hamiltonian operator is quite generic it could be extended and used with other families of deep generative models for sequences and thus be of great interest for practitioners 2 halo achieves sota scores on motioncontent disentanglement metrics ablations with similar architecture and other sequence models are convincing especially table 1 of supplementary material weaknesses 1 stochasticity is only allowed by the gaussian sampling but there is no stochasticity in the hamiltonian operator thus halo can only generate one motion vector given input frames however trajectory prediction is a highly stochastic process this could be a limitation that makes scaling to more complex environments difficult 2 how does the method guarantee that no motion information leak in the global content vector since the encoder lstm that generate the global vector see the whole input sequence it could also capture some information about the motion 3 limited evaluation the model is tested on three simple datasets could be interesting to see how it performs on more complex datasets with differentmoving backgrounds it also misses comparisons with recent works eg a franceschi et al stochastic latent residual video prediction icml 2020 b wang et al g3an disentangling appearance and motion for video generation cvpr 2020 4 clarity while the paper is well written there lacks some implementation details on the main component of the paper the hamiltonian operator see questions this affects the understandability the authors addressed the limitations ### Summary:

this paper proposes a novel type of variational auto encoder referred to as helo the latent space is decomposed into a content space and a motion space and the main contribution is the proposal to model the motion space using hamiltonian dynamics all reviewers agree that the idea of using hamiltonian dynamics is interesting and novel one main critique that the authors agreed on was that the operator does not contain any stochasticity and that this might be a limitation when applying the idea to model more complex data another remark was that the experiments are limited and experiments on less constraint data are missing a quick look at the baseline methods revealed that they also use the same kind of data sets to evaluate their methods so this latter concern might be of minor importance all in all the potential positive outcomes of this paper outweight its current limitations so we recommend acceptance at this point while urging authors to address the remaining concerns in the final version

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this paper the authors propose an enhanced gat model named supergat by adding link prediction as an auxiliary task when conducting node classification and compare different methods of attention forms the authors have also conducted evaluation based on both synthetic datasets and realworld datasets to analyze how different attention methods perform on various data and task types in general the paper is written well and easy to follow however there are still several issues the authors need to address first the authors need to better justify the novelty of the proposed method the claimed selfsupervision task can be considered as general link prediction task where the attention weights is used as features though they authors propose two new types of attention forms ie 1 scaled dotproduct and 2 mixed go and dp they are normalized and combined version of existing attention mechanisms i suggest the authors to better justify the novelty of the proposed technique and how they differ from existing work another major concern is the experiment settings it is good to see that the authors raise several research questions to guide experiment design however some assumptions in these research questions are questionable for example in rq1 the authors claim that ideal node representation can be generated by aggregating only neighbors with the same label as the neighborhood information of a node can be also informative when predicting node labels in certain cases a good node representation does not necessarily need to only aggregates neighbors of the same labels which makes the proposed method that uses kl divergence to compare labelagreement and graph attention questionable i suggest the authors to provide more justification on this assumption rq2s primary goal is to understand how different graph attention methods perform for the link prediction task it would be better if the authors can justify why they didnt conduct experiments where only link prediction selfsupervised loss is used and discard the node classification task in rq3 the authors hypothesize that different graph attention will have different abilities to model graphs under various homophily and average degree this is probably true however given the so many graph properties eg degree distribution graph diameter and average clustering coefficient and model configuration eg of layers and task type it is unclear why the authors choose these two controlled variables and why they believe they are the most important ones i suggest the authors provide more rationale on how they choose the controlled variables and how other factors may impact model performance another minor question is that why do the authors add an activation function for eij dp in eqn 4 given that eij dp is already a dot product that indicates the weight of a link it would be better if the authors can elaborate more on the design rationale in summary i think the authors focuses on an interesting problem but need to further address the issues listed above docsepsummary the paper provides an interesting direction for improving the graph attention networks more specifically the authors propose a selfsupervised graph attention network supergat designed for noisy graphs they encode positive and negative edges so that supergat learns more expressive attention they focus on two characteristics that influence the effectiveness of attention and selfsupervision homophily and average degree they show the superiority of their method 4 variations by comparing it with many stateoftheart methods in 144 synthetic datasets with varying homophily and average node degree and 17 realworld datasets again with various reasons for score overall i vote for accepting i find very interesting the idea of using selfsupervision to improve graph attention networks and the experiments are nicely done and convincing the authors do an impressive work to include as much information and results as they can in the given space strengths the paper is about an interesting problem in the iclr community graph attention networks have gained a lot of attention in the recent years from researchers in the field of graph and node representations with applications in node classification and link prediction the idea of adding the advancements in recent direction of selfsupervised learning to improve the learnt representations seems very promising the authors have done a great job in the structure and the presentation of the paper the paper is wellwritten and especially the sections experiments and results are wellstructured and contain a lot of packed details in the design and the outcome of the experiments more specifically figure 4 stands out as in a very limited space contains the information for the best performed model in all 14417 graphs the contributions of this work include the proposed method gans with selfsupervision but also an analysis for the selection of the best model depending on two important features of the graph homophily and average degree the authors compare their method with all stateoftheart methods and also four variations of their own model and exhaust their evaluation by testing the performance in 144 synthetic graphs and 17 realworld datasets including the benchmark datasets that are usually being used in this domain weaknesses the proposed method uses two known graph attention mechanism as building blocks they use negative sampling and they add crossentropy loss for all node labels and selfsupervised graph attention losses for all layers these building blocks and mechanisms are known in the literature and as a result the proposed method adds incremental novelty compared to the related works in appendix a3 the description and discussion for tsne plots is limited or absent it would be better to add more details to it as for example why this is a good representation and how the representations improve or not based on the hyperparameters also how the results in the subfigures differ in terms of representations it is difficult to get any insights from these plots questions during rebuttal overall my recommendations for more analysis and insights of the results are all responded from the appendices i would like a comment and clarification from the authors regarding appendix a3 figure 5 tsne plots even though it is not in the main paper submission my understanding is that the authors are going to release the code upon acceptance is this correct in the repository that the code will be released it will be useful to also add links to all 17 public datasets to ease research in the field docsepthis paper proposes a new attention mechanism supergat with various flavours for graph neural networks that is selfsupervised they exploit the presenceabsence of an edge between a pair of nodes to guide the attention the authors then make the observation that the homophily and average degree of a graph influence the design of the attention mechanism extensive experiments are shown where the various versions of supergat are tested on 17 realworld dataset and many synthetic ones and these results are compared against other stateoftheart models including the original gat work the paper is wellwritten and it is clear that the authors have done extensive experimentation to test their hypothesis however the paper has some weaknesses that i try to summarize below to obtain supergat the authors have made some tweaks made to the original gat formulation these tweaks are minor and are not surprising or inspired from a deepnovel insight the choice of studying two graph properties homophily and average degree seem arbitrary what is the reasoning behind these properties were there other properties eg diameter degree sequence etc that the authors have studied that did not yield good results this is particularly of interest due to the fact that experiments do not entirely supportexplain the importance of these two properties such as in the case of flickr and crocodile datasets the above reasons would still not be a major disadvantage had the experimental results shown strong superiority of supergat over previous models in all the experiments that the authors perform supergats performance is only slightly better than older models eg in table 2 difference between an accuracy of 725 vs 726 can hardly be considered superior and even then supergat does not have the best performance across all datasets proposition 1 and its proof are interesting contributions of this work but they may not be enough perhaps instead of concentrating on performance superiority the authors can look at the explainability aspect of their proposed architecture and look deeper into other graph properties that may guide the designuse of selfsupervision another minor comment on page 3 the authors say if the number of nodes is large it is not efficient to use all possible negative cases the number of negative cases is a function of the number of edges and not the number of nodes if a graph contains all possible edges then the number of all possible negative cases is zero no matter how many nodes there are docsepsummary in this paper they introduced selfsupervised graph attention network supergat which is claimed to perform well in noisy graphs they used information in the edges as an indicator of importance of relations in the graph then they learn the relational importance using selfsupervised attention after learning the attentions values using their selfsupervised method they can predict the likelihood of an edge between nodes they worked on two popular attention mechanisms go and dp and showed in their experiments dp has better performance than go for link prediction task and go has better performance in labelagreements between nodes the other question they answered in their experiments was what the best attention model is to choose they introduce a recipe based on two graph characteristics homophily and average degree positives one thing i liked about this paper was thorough and neat experiments i enjoyed the way they designed their experiments by mentioning several important questions followed by their answered backed up with their experiments they used two attention mechanism as the base then applied their method on these two methods for link prediction and labelagreements tasks and compare their results i also liked that they examined their recommendation for the choice of attention model on real world datasets and their answer for realword data was almost similar to synthetics data the paper was wellorganized and wellwritten they clearly explained their method notes i would recommend adding a picture showing their architecture and compare it with other two attention models i sortof understand the reading as to why go learns labelagreement better than dp based on the argument on page 6 a strong argument would be helpful to explain why dp predicts edge presence better than go minor noteno need the parenthesis in this sentence line 8 interestingly for datasets cs physics coraml and flickr in which reason to accept i am in general positive about this paper the innovation is not significant however their experiments were interesting and they prove how well their method works well empirically i think this research will be useful for people in this area after rebuttal i have read the authors response ### Summary:

two reviewers are very positive about this paper and recommend acceptance one indicates rejection and one is on the fence although all referees appreciate the extensive experiments and analysis presented in the paper their main concerns are related to the limited superiority of the method wrt state of the art r1 seemingly arbitrary choices and questionable assumptions r4 the rebuttal adequately addresses r1s concerns by highlighting statistical significance of the results and partially covers r4s concerns although the proposed approach may be perceived as incremental r1 r2 r3 r4 the authors argue that introducing selfsupervision to graph attention is not trivial and emphasize their findings on howwhen this is beneficial moreover r2 and r3 acknowledge that the contribution of the paper holds promise is worth exploring and may be useful to the research community most reviewers are satisfied with the answers in the rebuttal after discussion three referees lean towards acceptance and the fourth reviewer does not oppose the decision i agree with their assessment and therefore recommend acceptance please do include your comments regarding the choice of average degree and homophily in the final version of paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this paper the authors propose a metalearning method for fewshot learning the propose approach mlti creates new artificial tasks by interpolating two existing tasks form the training set during metatraining the new tasks are generated by interpolating featureslabels of two sampled tasks from the training set the authors show that the proposed approach achieve good results in multiple datasets both in regression and classification multiple settings label sharing and nonlabel sharing and different algorithms maml and protonets pros the paper is well written and easy to follow the idea of interpolating tasks in a metalearning setting is novel intuitive and simple although previous work exists that augment the number of tasks this is the first approach that augments across tasks rather than within task the authors shows good result on different datasets settings and backbones cons i feel like results in more traditional and larger fsl datasets are missing for example it would be nice to see results in tieredimagernet or metadataset i also feel that the authors introduce the method as being a general metalearning approach but only show results on image classificationregression it would be nice to see results in other domains such as rlnlpetc tasks i find the theoretical analysis difficult to follow and potentially not very informative to the rest of the paper that been said i am not an expert on generalization theoryrademacher complexity and cannot properly validate it i recommend this paper for acceptance the proposed idea is simple and novel the paper is well written and the empirical evaluation is well executed postrebuttal update i thank the reviewers for the rebuttal and i keep my rating of 8 congratulations for the nice work docsepsummary this work tackles a scenario where there may not be a large number of training tasks available which increases the susceptibility of metalearning algorithms to metalevel overfittingmemorization problem in particular to cope with the scarcity of tasks the paper proposes to augment the given task set through interpolation of tasks the paper reports better performance than other methods on benchmarks that have fewer training tasks strengths the work provides extensive theoretical analysis to provide theoretical guarantees as to how the proposed mlti task interpolation method achieves better generalization in contrast previous methods that have employed common augmentation methods eg label noise cutmix mixup without theoretical guarantees the work introduces scenarios that are more challenging than standard benchmarks by limiting the number of metatraining tasks the work provides extensive experiments across various datasets under such challenging scenarios and demonstrates better performance than previous methods providing empirical support for the effectiveness of the proposed task interpolation method weaknesses i believe the work has minor technical novelty compared to the related work by ni et al 1 in particular ni et al 1 performs several augmentations for metalearning one of which is mixup for tasks using mixup between any given pair of classes ni et al 1 also creates new tasks comments in related work section the work states that compared to work by ni et al 1 the proposed method directly desnifies the task distribution but doesnt 1 effectively densify the task distribution where a new task can consist of new classes that are constructed by using mixup on pairs of classes as such i believe more discussions on this issue should help better differentiate the work from the related work why does the proposed method randomly sample a location where features are to be interpolated is there an ablation study on the sampled location i wonder if this technique is what makes the proposed method perform better than other works im curious as to whether the proposed method without this technique still performs better than other works the ablation study on this would be helpful for better understanding of differences from other works also how does it compare with related works on standard benchmarks such as miniimagenet i think that the proposed method should still work with a larger number of tasks and believe that these experimental comparisons can strengthen the contributions of the proposed method 1 ni et al data augmentation for metalearning recommendation despite strong experimental results and analysis at this point i believe the technical novelties are not significantly different from the work by ni et al thus i believe the work is marginally below the acceptance threshold if the above comments are addressed im willing to increase the score postrebuttal i thank the authors for the response along with clarifications and updates in the manuscript as the authors have addressed most of my concerns im happy to increase the score accordingly docsepthe paper proposes an interpolation strategy for metalearning to improve the learned models generalizability the interpolation strategy is quite simple interpolate between a pair of tasks in contrast to existing methods such as adding label noise or data augmentation on each task individually the authors show that the resulting gradient or metricbased metalearning framework mlti induces a datadependent regularizer that controls the rademacher complexity leading to better generalization mlti is tested on specially curated datasets derived from standard benchmarking datasets furthermore mlti is also compared against existing data augmentation and interpolation strategies for metalearning to illustrate its effectiveness strengths although nifty the idea of pairwise task interpolation is an incremental change over the existing data augmentation approaches the theoretical results highlighting the relationship between task interpolation and the rademacher complexity are nontrivial extensions of the zhang et al iclr 2021 and yao et al icml2021 to account for pairwise task interpolation i view this as the primary contribution of the paper the comparison against existing dataaugmentation baselines for both metric and gradientbased metalearning approaches is quite exhaustive furthermore mlti is tested on a wide variety of datasets while the improvement on each dataset is only marginal the consistent improvement in all datasets and across all approaches strengthens the papers contribution the paper is wellwritten and easy to follow concerns current approaches in metalearning rely on heavier backbones such as resnet as the goal of all the metalearning methods is to improve the models generalizability i think it is fair to evaluate the effectiveness of mlti with heavier feature extraction backbones such a comparison is relevant as the proposed task interpolation is conducted on the features extracted from some intermediate layer of the network overall the paper proposes a simple extension to standard datatask augmentation methods for metalearning but justifies it with rigorous theory the theoretical results are nontrivial extensionscombinations of existing work the effectiveness of the approach is evident from the extensive empirical evaluation the contributions are strong albeit limited to the metalearning research community docsepthis paper proposes a task augmentation method via taskinterpolation for data efficient metalearning while the traditional metalearning methods highly rely on a large amount of data to retain diverse training tasks the proposed method mlti generates tasks by interpolating the tasks which are obtained from training data the experimental results on variety of fewshot learning dataset show that mlti is effective when the metatraining data for constructing training tasks is not enough for both gradientbased and metricbased fewshot learner strength 1 this paper proposes a novel taskaugmentation method which is affected by manifold mixup which can be applied to many existing fewshot learning tasks 2 the theoretical analysis shows that the proposed mlti augmentation has a regularization effect and leads the metalearner to have a better generalization capability 3 extensive simulation results on variety of fewshot learning datasets and two representative fewshot learning methods show that the proposed mlti is highly effective for metalearning with fewer data weakness 1 comparison with the prior methods in large dataset is missing for example in table 3 the comparison results are provided only for small datasets or reduced version of large datasets however the proposed method is not restricted to small dataset the ablation experimental result in figure 2 shows that proposed mlti is still effective when the full miniimagenetdermnet dataset is used although the performance gain becomes small when the full dataset is used i suggest the authors to include the comparison of mlti and prior methods with full size of miniimagenet and dermnet question 1 in section 3 the authors mentioned that it is intractable to calculate prototypes with mixed labels however in prior work on semisupervised fewshot learning 1 the prototypes are computed using softlabels what happens if we compute prototypes using softlabels as done in 1 2 some additional studies on the interpolation layer would be helpful for understanding the proposed method in algorithm 1 and 3 the interpolation layer l is randomly chosen in step 7 what happens if we fix l instead of randomly sampling l for every iteration in that case how are interpolating at lower layer and interpolating higher layer different typo in last line of page 4 there is a typo regularizaiton regularization 1 ren mengye et al metalearning for semisupervised fewshot classification international conference on learning representations 2018 this paper proposes a novel taskaugmentation method of mlti and shows the effectiveness of proposed mlti through the extensive simulation results and theoretical analysis the proposed mlti can be applied to both optimizationbased and metricbased fewshot learning methods adding some experimental results would make the readers to better understand the proposed method however i believe the idea of this paper is valuable for fewshot learning field and i recommend to accept this paper docsepthis paper describes a method for augmenting task selection in metalearning by interpolating support and query sets between two random tasks from the base dataset this is examined in two scenarios labelshared ls and nonlabelshared nls differing in whether the label space is the same between tasks eg pose estimation or different classification to different discrete class sets in the former label targets are interpolated as well as support set inputs while in the latter new classes are constructed by random crosstask pairings comparisons are made to other interpolation augmentation approaches including metamix which interpolates in query set but not the support set the approach results in significant performance gains on multiple benchmarks in both settings i found the approach to be simple and relatively well explained including ablations studies on largepoint questions i had while reading including its behavior and effectiveness for different sizes and number of classes in the original base dataset as well as effects of inter and intra task interpolations the key difference between this work and metamix yao et al 2020 is incremental but important metamix will run the inner loop on the unmodified support set only and use a mix of supportquery in outer loop comparison optimization whereas this work interpolates support set in inner loop as well this difference enables betweentask interpolation which adds additional augmentation particularly in settings where few tasks can be drawn from the base data i didnt follow much of the theoretical sections in detail and had to look at the appendix proofs to even understand some of the notation in the main text in my somewhat limited understanding they seem reasonable these claim to show a theoretical generalization improvement in simplified settings binary classification of single layer model linear protonet feautres additional questions nls in addition to a single set of correspondence pairs the input examples for each class can be mixed with allpairwisecombinations how many combinations are used that is for two sets of k examples xsi and xqj ij in 1k one can form k2 interpolated examples a xsi 1a xqj using each ij combination are all of these combinations formed or just a single set of k pairings if using more than k pairings this would change the task from kshot to k2shot but the llayer features for each of the k2 combinations could be computed and then up to k2 choose k tasks could be selected from these and used in the upper layer loss comparisons for k5 that would increase interpolated pairs from 5 to 25 but potentially get up to 53130 upper layer loss comparisons from each task pair sample would this get even more benefit from this task augmentation technique eq 5 what does the name of the subscript cr mean does it stand for something it could be useful to have a more explicit explanation of differences with metamix metamix will run the inner loop on the unmodified support set only and use a mix of supportquery in outer loop comparison optimization whereas this work interpolates support set in inner loop as well this is already mentioned at a high level fig 1 caption and sec 5 last paragraph but i think it could be even clearer by pointing out the difference in the discussion around eq 5 that support set hsys in the inner loop is mixed between tasks whereas in metamix only the hqyq are replaced by mixing overall the approach is described well enough to understand the approach and is emperically shown to result in decent performance gains in the low task data settings for which it is intended the theoretical sections corroborate this but i found them hard to follow ### Summary:

current metalearning algorithms suffer from the requirement of a large number of tasks in the metatraining phase which may not be accessible in realworld environment this paper addresses this bottleneck introducing a crosstask interpolation in addition to the existing intratask interpolation the main idea is very simple which can be viewed as an incremental addingup to existing augmentation methods however the method is well supported by nice theoretical results which highlight the relation between task interpolation and the rademacher complexity in fact this is not a trivial extension of existing work authors did a good job in the rebuttal phase resolving most of concerns raised by reviewers leading that two of reviewers raised their score all reviewers agree to champion this paper congratulations on a nice work

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: quality of the paper the paper is quite clear on the background literature on adversarial examples physics based rendering and the core idea of generating adversarial perturbations as a function of illumination and geometric changes originality and significance the idea of using differential renderers to produce physically consistent adversarial perturbations is novel references the references in the paper given its scope is fine it is recommended to explore references to other recent papers that use simulation for performance enhancement in the context of transfer learning performance characterization eg veerasavarappu et al in arxiv wacv cvpr 2015 17 pros good paper illustrates the utility of differentiable rendering and simulations to generate adversarial examples and to use them for improving robustness cons the experimental section needs to be extended and the results are limited to simulations on cifar100 and evaluation on lab experimental data inclusion of images showing cifar100 images augmented with random lighting adversarial lighting would have been good the details of the image generation process for that experiment is vague and not reproducible docsepthe paper demonstrates a method for constructing adversarial examples by modifications or perturbations to physical parameters in the scene itselfspecifically scene lighting and object geometrysuch that images taken of that scene are able to fool a classifier it achieves this through a novel differentiable rendering engine which allows the proposed method to backpropagate gradients to the desired physical parameters also interesting in the paper is the use of spherical harmonics which restrict the algorithm to plausible lighting the method is computationally efficient and appears to work well generating plausible scenes that fool a classifier when imaged from different viewpoints overall i have a positive view of the paper however there are certain issues below that the authors should address in the rebuttal for me to remain with my score of accept especially the first one the paper has no discussion of or comparisons to the work of athalye and sutskever 2017 and zeng et al 2017 except for a brief mention in sec 2 that these methods also use differentiable renderers for adversarial attacks these works address the same problem as this papercomputing physically plausible adversarial attacksand by very similar meansbackpropagation through a rendering engine therefore it is critical that the paper clarifies its novelty over these methods and if appropriate include comparisons while the goal of finding physically plausible adversarial examples is indeed important i disagree with the claim that imagelevel attacks are primarily tools of basic research and not models of realworld security scenarios in many applications an attacker may have access to and be able to modify images after theyve been captured and prior to sending them through a classifier eg those attempting to detect transmission of spam or sensitive images i believe the paper can make its case about the importance of physical adversarial perturbations without dismissing imagelevel perturbations as entirely impractical the athalye 18 reference noted in fig 1 is missing the references section includes the reference to athalye and sutskever 17 postrebuttal thanks for addressing my questions with the new comparisons and discussions wrt the most relevant methods i believe the contributions of the paper are clearer im revising my score from 6 to 7 docsepsummary this work presents a method to generate adversary examples capable of fooling a neural network classifier szegedy et al 2013 were the first to expose the weakness of neural networks against adversarial attacks by adding a humanimperceptible noise to images to induce misclassification since then several works tackled this problem by modifying the image directly in the pixel space the normballs convention the authors argue that this leads to nonrealistic attacks and that a network would not benefit from training with these adversarial images when performing in the real world their solution and contributions are parametric normballs unlike stateoftheart methods they perform perturbations in the image formation space namely the geometry and the lighting which are indeed perturbations that could happen in real life for that they defined a differentiable renderer by making some assumptions to simplify its expression compared to solving a light transport equation the main simplifications are the direct illumination to gain computation efficiency and the distant illumination and diffuse material assumptions to represent lighting in terms of spherical harmonics as in ramamoorthi et al 2001 which require only 9 parameters to approximate lighting this allows them to analytically derivate their loss function according to the geometry and lighting and therefore generate their adversary examples via gradient descent they show that their adversary images generalize to other classifiers than the one used resnet they then show that injecting these images into the training set increase the robustness of wideresnet against real attacks these real attack images were taken by the authors in a laboratory with varying illumination strength the proposed perturbations in the image formation space simulate the real life scenario attacks the presented results show that the generated adversary images do fool the classifier used to compute the loss but also new classifiers different than the one used to compute the loss as a consequence the generated adversary images increase the robustness of the considered classifier flexibility in their cost function allows for diverse types of attacks the same modified geometry can fool a classifier in several views either into detecting the same object or detecting different false objects under different views major comments method can only compute synthetic adversary examples unlike stateoftheart the main contribution claimed by the author is that their perturbations are realistic and that it would help better increase the robustness of classifiers against real attacks however they do not give any comparison to the stateoftheart methods as is expected minor comments even if the paper is well written they are still some typos ### Summary:

the paper describes the use of differentiable physics based rendering schemes to generate adversarial perturbations that are constrained by physics of image formation the paper puts forth a fairly novel approach to tackle an interesting question however some of the claims made regarding the believability of the adversarial examples produced by existing techniques are not fully supported also the adversarial examples produced by the proposed techniques are not fully physical at least compared to how physical adversarial examples presented in some of the prior work were overall though this paper constitutes a valuable contribution

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors proposes a hierarchical bayesian approach for lifelong rl the global worldmodel posterior models the world model shared across tasks and the taskspecific model learns the dynamics within a specific task the taskspecific model achieves forward transfer by initializing from the global world model the authors use meanfield variational approximation to scale the proposed model also the authors introduce sample complexity analysis the method is evaluated on two toy tasks gridworld and box jumping and one on mujoco simulator and showed superior performance to the previous works clarity of the paper should be improved significantly the current version makes understanding hard the paper does not describe the notion of many notations eg the indices j and k in figure 1 and denotes some distributions in its shortform eg qe or qthetami without introducing its full form these are not just a few but observed broadly across the entire paper perhaps section 3 requires the most significant improvement the paper also explains the proposed method by directly explaining lines of the pseudocode without discussing the big picture or the rationale behind the design it also skipped describing the full joint distribution of the model which is essential in describing such probabilistic models the key idea of the proposed method is quite simple its like applying variational continual learning to the world model learning in the rl setting it can also be understood as bayesian metalearning but with sequential task exposure the experiment is also quite simple the first two toy tasks are quite too toyish very lowdimension and tow task complexity the last mujoco experiments show the superiority of the proposed model the algorithms eg variational continual learning bayesian meta learning and meanfield variational inference on which the proposed method is based are known to be not scalable in more complex settings like highdimensional data like image many sequential tasks eg 1000 or more tasks and the meanfield approximation has significant limitation in its expressiveness so im somewhat doubtful if this method can be an important milestone toward more realistic and complex settings the writing clarity requires a significant improvement its currently a major drawback hindering the understanding of the proposed model i understood at the later part of the paper but it was hard until reaching there the evaluation is quite simple and uses toy tasks im doubtful about the potential of the proposed model to extend to more realistic and complex settings such as image inputs docsepthis submission presents an approach for bayesian modelbased exploration in a lifelong rl setting building upon existing approaches for bayesian exploration boss and bayesian multitask modeling hipmdps the approach keeps separate models for sampling transitions and rewards for each task and each task model is drawn from a shared prior that models the distribution over tasks the method continually updates the shared model with data from all observed tasks and the taskspecific model with data from the current task to achieve backward transfer the approach replaces the task model with the shared model whenever the task model has not been sufficiently trained on a particular stateaction pair strengths 1 the overall idea of modelbased lifelong rl is very relevant particularly since lifelong rl precisely seeks to reduce sample complexity 2 the highlevel idea of replacing the task model with the world model whenever the task model is uncertain is intuitively appealing 3 the use of the task model permits deriving reduced sample complexity bounds thanks to the bayesian formulation weaknesses 1 the low performance of the agent in the more complex mujoco evaluations makes the results unconvincing 2 the technical approach is very closely tied to existing works in particular hipmdps and variants and boss 3 there are no comparisons to existing modelbased lifelong rl methods arguments the primary contribution of this work is the introduction of one of the first approaches for modelbased lifelong rl since one of the key desiderata of lifelong rl is to decrease the amount of experience needed to learn new tasks forward transfer using modelbased techniques which are inherently more data efficient than modelfree techniques seems to be a promising direction however the proposed method itself is fairly incremental in particular it heavily hinges on boss as an exploration technique and hipmdps as a multitask modelbased model while the use of these models permits relatively simple adaptations of the proofs of boss to demonstrate decreased sample complexity which is a nice plus it does make the work contain very little technical insights in particular when adapting a multitask method hipmdps into a lifelong method i would have expected to see considerable effort in designing a technique for updating the shared model with knowledge from new tasks however the authors simply train this shared model in a multitask fashion with data from all tasks this is not only costly in terms of memory footprint requiring to store vast amounts of data for each task but is also computationally inefficient since the multitask training step is executed iteratively for each task on2 cost the one piece of technical insight offered in the submission that i found interesting was the idea of replacing the task model with the world model whenever the task model is not yet well trained unfortunately the approach itself is very heuristic relying on thresholds on the uncertainty across sampled models and the heuristic choices are not discussed in detail or validated empirically eg via ablative tests or sensitivity analysis on the thresholds i intuit that the method is quite sensitive to these thresholds if they are too high the task model will almost always be used even if its uncertain if they are too low the world model will be used too frequently and task performance will likely degrade by reverting to the average world model would it not be possible to do some sort of soft combination of the task and world model weighted by the uncertainty instead of a hard selection of one or the other however my main concerns with the submission lie on the empirical evaluation the first major concern is that all the agents evaluated on mujoco domains achieve very low performance as compared to the results in the papers cited in the submission for the experimental design of these tasks mendez et al and wang et al while it is clear that the reduced performance stems from the choice of using fewer environment interactions which itself is a nice choice given the use of modelbased techniques this does raise questions about the conclusions drawn from these results in particular how useful or informative are forward and backward transfer if the agent hasnt really learned any meaningful behaviors it would be useful to include videos of the learned behaviors to assess whether the transfer results are in any way significant from a behavioral perspective or if theyre simply minor reward increases across poor behaviors my second major concern is the choice of baselines for these evaluations on one hand the authors chose to only compare against modelfree lifelong rl techniques in this setting it should certainly be expected that the modelbased approach outperforms those baselines simply by the nature of the underlying techniques this is not novel insight yet the authors claim these as general improvements over existing lifelong rl methods which seems like a stretch instead the authors should have considered existing modelbased lifelong rl methods eg 1 for a more applestoapples comparison note that 1 is a taskagnostic method so this would require some adaptation to handle the setting where the agent is given access to task indicators on the other hand the authors make claims about forward transfer but in the mujoco domains there is no comparison to a singletask or notransfer baseline like used in the boxjumping task such a baseline is critical for assessing whether the approach is actually achieving transfer across tasks since improvements wrt singletask training are precisely what demonstrate transfer as one additional comment regarding the evaluations there is no information about the implementation details of any of the baselines including their model architectures and hyperparameters how were these chosen to guarantee a fair comparison additional feedback the following points are provided as feedback to hopefully help better shape the submitted manuscript but did not impact my recommendation in a major way intro i wonder if the example of different houses and toothbrushes matches the hipmdp formulation introduced immediately after the intro is fairly clear and describes the solution approach well id suggest including an example that more closely matches the hipmdp formulation since this is the formulation adopted throughout the paper sec 4 the ideas seem to be very closely related to the original hipmdp papers especially the bnn extension of killian et al 2017 the notation for the bnn needs a fair bit of work the authors never explain what the particles are are these the sars tuples sampled from the sequences given by the combination of cem and the bnn this becomes increasingly relevant in 41 where the authors define their approach to backward transfer my understanding is that aleatory variability is modeled as the bnns internal variance whereas the epistemic uncertainty is measured as the variance in the mu sigma output by the bnn across sampled particles is this understanding correct sec 5 the girdworld evaluation shows nothing about forgettingbackward transfer boxjumping why no comparison to a singletask variant of the solution this is required to assess forward transfer also no backward transfer measure mujoco again no singletask learner so its unclear if theres forward transfer plus the rewards are very low so it seems that even vblrl is not solving the tasks how useful are these results then even though vblrl is the best its not really achieving meaningful behaviors i disagree with the claim that the model cannot suffer from forgetting since certainly the wrong choice of threshold for backward transfer could lead to forgetting how was this hyperparameter chosen typos sec 2 second paragraph the task facing a single agent the agent facing a single task 1 nagabandi et al deep online learning via metalearning continual adaptation for modelbased rl iclr 2019 unfortunately i recommend the rejection of this work while i agree with the premise of the submission that modelbased lifelong rl is a relevant area of research with potential implications on realworld applications of lifelong rl the submission as it stands appears to not be ready for publication on the technical side the approach seems to add just a few incremental changes to multitask hipmdps to adapt them to the lifelong setting this on its own is perhaps relatively minor since the novelty comes from adapting it to a new problem setting however such technically incremental contributions should generally be accompanied by strong empirical evaluations which is not the case in this work in particular the low overall performance of all agents on mujoco domains suggests that none of the agents are learning to achieve meaningful behaviors which raises questions about the conclusions reached by the authors moreover the authors should have compared at least qualitatively but ideally also empirically to existing work in lifelong modelbased rl on the flip side the submission does include an interesting insight of replacing the taskspecific model with the shared model whenever the task model is uncertain docsepthe paper deals with the problem of lifelong rl also referred to as meatrl where an agent attempts to solve a sequence of tasks in order to facilitate the solution of a novel task the framework follows that of baxter 2000 albeit that paper deals with supervised learning and has been widely studied in recent years the basic assumption is that the tasks are drawn from an underlying taskdistribution and each task an mdp is stochastically selected from a taskspecific distribution the authors work with a bayesian framework assuming a hierarchical distribution of the two levels and learn the two levels separately this framework has the advantage of providing both estimates and uncertainty estimates for the discrete case they present a sample complexity analysis and suggest a variational approach to practical learning finally experiments are provided supporting the utility of the approach the formal framework is that of hiddenparameter mdps hipmdps from doshivelez 2016 and each mdp is modeled based on a transition and a reward model based on a hidden parameter as more tasks are encountered the posterior over world models sharpens and being used to learn new tasks is expected to facilitate learning the learning of each new task is as in boss and takes place by sampling from the learned mdp distribution creating a mixed mdp and using standard modelbased approaches to solve these the main theoretical contribution suggested in the paper is a pacmdp of theorem 1 for a single task this theorem is based on lemma 2 for which a full proof is not provided in the main text or appendix so its veracity cannot be verified moreover it is based on the assumption that the posterior is consistent which i believe is what needs to be shown in a metalearning setup and cannot be assumed the form of the bound is also strange as it depends on delta rather than on ln1delta as in previous bounds eg strehl 2006 and asmuth 2009 and its dependence on gamma and epsilon is also worse than previous bounds as far as i understand this is a bound for the single instance setting rather than for metalearning following the theoretical part the authors develop a variational approach based on probabilistic networks that model the posterior distribution as far as i am aware their variational approach is rather standard although the authors do not refer to previous work also their use of modelpredictive control is common in current ml applications but again this is not mentioned or discussed eg the length of the future horizon and how is it selected the authors conclude by presenting numerical simulations for a gridworld and some mujoco problems for continuous control while the method compares well to simple baselines it is hard to assess performance relative to more recent work such as liu et al taming maml efficient unbiased metareinforcement learning and the other baselines measured in it the paper is phrased within a sequential approach to metalearning that has been widely studied within the supervised learning community eg baxter 2000 pentina and lambert a pacbayesian bound for lifelong learning 2014 and much later work with explicit performance bounds it would be nice to acknowledge these roots the present approach is plausible and combines previous work such as hipmdps boss variational bayes in a sensible manner however i do not find that the level on innovation in this combination of approaches suffices for publication at iclr nor did i find the theoretical or experimental results of sufficient interest see comments above following rebuttal following the authors response and my response to their rebuttal i have lowered my assigned grade due to my dissatisfaction with their replies which served to enhance my existing concerns about the paper ### Summary:

the topic of this paper is timely and important however ultimately the reviewers remained unconvinced that this paper provides a sufficiently clear and sufficiently significant advance to lifelong rl as an additional note the setting under investigation here is not the full lifelong learning setting eg several of the challenges outlined by schaul et al 1 are not treated and this work is instead situated in a somewhat typical multitask setting with substantlal structure that is not bad but it would be good if this is reflected clearly in all the statements and eg in the title of the work the authors are encouraged to carefully take the provided feedback and see how they can use it to improve their work this is an important research direction it was just felt the current submission was not quite ready for publication yet 1 httpsarxivorgabs181107004

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a clever and sensible approach to using the structure learned by the auxiliary variational method to accelerate randomwalk mcmc the idea is to learn a lowdimensional latent space that explains much of the variation in the original parameter space then do randomwalk sampling in that space while also updating a state variable in the original state which is necessary to ensure correctness i like this idea and think the paper merits acceptance although there are some important unanswered questions for example how does the method work on higherdimensional target distributions i would think it would be hard for a lowdimensional auxiliary space to have high mutual information with a much higherdimensional space in principle neural networks can do all sorts of crazy things but phenomena like vaes with lowdimensional latent spaces generating blurry samples make me suspect that auxiliary dimension should be important how does the method work with hierarchical models heavytailed models etc rings mogs and flat logistic regressions are already pretty easy targets is it really so valuable to not need gradients highquality automatic differentiation systems are widely available and variational inference on discrete parameters with neural nets remains a pretty hard problem in general some other comments its probably worth citing ranganath et al 2015 hierarchical variational models who combine the auxiliary variational method with modern stochastic vi also i wonder if there are connections to approximate bayesian computation abc i think you could prove the validity of the procedure in section 21 more succinctly by interpreting it as alternating a gibbs sampling update for a with a metropolishastings update for x if we treat a as an auxiliary variable such that pa x tilde qa x px a propto px tilde qa x then the equation 2 is the correct mh acceptance probability for the proposal tilde qa x aa tilde qx a alternating between this proposal and a gibbs update for a yields the mixture proposal in section 21 its also possibly worth noting that this procedure will have a strictly lower acceptance rate than the ideal procedure of using the marginal tilde qxx as a mh proposal directly unfortunately that marginal density usually cant be computed which makes this ideal procedure impractical it might be interesting to try to say something about how large this gap is for the proposed method we choose not to investigate burnin since avs is initialized by the variational distribution and therefore has negligible if any burnin time this claim seems unjustified to me its only true insofar as the variational distribution is an excellent approximation to the posterior in which case why use mcmc at all its easy to find examples where an mcmc chain initialized with a sample from a variational distribution takes quite a while to burn indocsepin my opinion the paper contains very interesting novel ideas however some parts needs a future clarification and the stateoftheart must be improved first of all sections 231 or 232 can be improved and clarified for instance i believe you can create a unique section with title choice of proposal density and then schematically describe each proposal from the simplest to the more sophisticated one at the beginning of section 2 please devote more sentence to explain why extending the space and apply the variational inference is good for finding a suitable good proposal density related to section 2 themixture proposal mcmc contribution the authors should discuss in the introduction and also in the related works section the multiple try metropolis schemes with correlated candidates where for instance a path of candidates is generated and one of them is selected and tested with mhtype acceptance probability in a proper way this is more general that your scheme but very related please see qin zs liu js 2001 multipoint metropolis method with application to hybrid monte carlo journal of computational physics 172 827840 l martino v p del olmo j read a multipoint metropolis scheme with generic weight functions statistics and probability letters volume 82 issue 7 pages 14451453 2012 l martino a review of multiple try mcmc algorithms for signal processing digital signal processing volume 75 pages 134152 2018 related again with the stateoftheart description the references regarding adaptive mixture metropolis methods are completely missed if i have properly understood you also adapt a mixture via variational inference please in section 4 consider the different works that considers an adapting mixture proposal for a metropolistype algorithm p giordani and r kohn adaptive independent metropolishastings by fast estimation of mixtures of normals journal of computational and graphical statistics vol 19 no 2 pp 243259 september 2010 tran mn m k pitt and r kohn adaptive metropolishastings sampling using reversible dependent mixture proposals statistics and computing 26 121 2014 d luengo l martino fully adaptive gaussian mixture metropolishastings algorithm ieee international conference on acoustics speech and signal processing icassp vancouver canada 2013 roberts g o and j s rosenthal 2009 examples of adaptive mcmc journal of computational and graphical statistics 18 349367 docsepthis paper proposes an auxiliary variable mcmc scheme involving variational inference for efficient mcmc given a target distribution px the authors introduce an auxiliary variable a and learn conditional distributions pax and qax by minimizing the kl divergence between pxpax and qaqxa with qa something simple the authors use gaussian a mh proposal step involves simulating x givea the current mcmc sample x from pax taking a step in aspace and then returning back to the x space using qxa the authors show how to calculate the acceptance probability i think the idea is nice and useful im surprised people havent thought of this before though i think the paper presents this in a less clear way as an extension of ideas from agakov and barbers auxiliary variational method while this is correct and perhaps more general in my mind it slightly obscures the main idea as well as the strong ties with variational autoencoders express a complex distribution as a learnt transformation of a simple distribution this is the actual approach taken in the experiments the motivation of the approach is that the nonlinear encoding network can transform the complex px into a simpler qa for this reason i think an important baseline is the independent mh sampler from equation 8 i think this essentially uses a trained vae generative model as a proposal distribution the authors talk about how producing independent proposals can be suboptimal yet it seems to me that if the encoder and decoder neural networks are powerful enough this should do a good job i think excluding this baseline hurts the paper a bit the proof of correctness while correct is a bit unclear can perhaps be simplified if you view the mcmc algorithm as operating on an augmented space xax with stationary distribution pxqaxqxa writing writing q for tildeq this clearly has the right distribution over x each mcmc iteration starts with x and proceeds as follow 1 given x sample a and x from qax and qxa 2 make a deterministic proposal on the augmented space to swap xx the acceptance probability is now equation 2 3 discard ax in figure 4 the authors use hmc as an improved mcmc algorithm yet this is not an algorithm that deals with multimodality well more useful would be to include some tempering algorithm like serial or parallel tempering while i like the idea i unfortunately dont think the experiments are very convincing and the authors barely discuss their results other than mixture of gaussians hmc which involves no training appears to be superior with some tempering i expect it to outperform the proposed method for the mog case table 2 left since hmc involves no training does this mean that taking training time into account hmc is 56 orders of magnitude more efficient lke i mentioned earlier these results need more discussion it would also help to provide absolute training and run times so the reader can better understand whether the proposed method of anice is better figure 3 why dont the authors also plot the histogram of values in the auxiliary space pa it would be interesting to see how gaussian this is this is what variational inference is trying to achieve also does figure 3a mean that conditioned on x pax is basically a delta function this would suggest that the encoder is basically learning a deterministic transformation to a simpler lowdimensional space there is some work in this direction in the statistics literature eg variable transformation to obtain geometric ergodicity in the randomwalk metropolis algorithm the authors some refers to the distribution of ax as qax sometimes in section 21 and sometimes as pax which is a bit confusing figure 2 the labels are wrong ### Summary:

the reviewers all argued for acceptance citing the novelty and potential of the work as strengths they all found the experiments a little underwhelming and asked for more exciting empirical evaluation the authors have addressed this somewhat by including multimodal experiments in the discussion period the paper would be more impactful if the authors could demonstrate significant improvements on really challenging problems where mcmc is currently prohibitively expensive such as improving over hmc for highly parameterized deep neural networks overall however this is a very nice paper and warrants acceptance to the conference

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper applies two existing techniques derived from adversarial example literature and msgan to improve the quality and diversity of generated samples by gan the former idea from goodellow et al 2014b is used to shift a latent vector z originally sampled from the gaussian which is different from the original adversarial attack paper that transforms the input image the direction to move the latent vector is calculated by using ifgsm with a standard loss for the generator proposed in the vanilla gan paper the experiments show that this technique can improve the quality of generated samples furthermore this paper also consider how to improve the diversity of generated samples by transforming the latent vectors before putting them into the generator network the approach for that is based on msgans idea that mines hard latent vectors for the discriminator the authors combine these two techniques and achieved better results compared to dcgan wgan wgangp and sngan models on several relatively small scale datasets such as cifar10 stl10 etc this paper introduces their motivation convincingly with some easily comprehensible figures however the experiments to show the effectiveness of the proposed method are basically performed only with small scale datasets so that it makes difficult to figure out how complex manifold in the target space can be handled by this approach in particular it is unclear how it can scale to the imagenet dataset the discussion to compare the proposed method with other work seems to be not enough because there are some more research that shed light on the importance of latent vector transformation for example the series of stylegan work is continuously improving the performance by precisely analyzing the relationship between the latent space and the pixel space they also firstly adopted to transform the latent vector z to another latent space w using a mapping network that should be discussed as one of the possible approach to transform the latent vector ie using a trainable function instead of ifgsm some figures to show the effectiveness of the proposed method seems difficult to interpret as the captions claim eg figure 5 it was difficult to find the clear difference between the results of antidiverse and original and the diverse results look worse than other two in terms of the quality if it is intended because the figure tries to show the increase of diversity but it still unclear that the diverse row in the figure 5 has higher diversity compared to other two the paper is well organized and easy to follow the authors claims but the qualitative comparison results seems difficult to be interpret as the captions state and from the perspective of transformation of the latent vector z there can be more discussion in the comparison with other methods such as stylegan that transform z before putting it into the generator docsepthe paper looks at the problem of improving the generative quality of gans the paper makes improvement along two dimensions a the paper adjust the samples distribution of gans using adversarial attacks ifgsm and thus effectively samples from a potentially multimodal distribution b the paper improves the diversity of generated samples using using adversarial attacks on the modeseeking objective of mao et al 2019 the paper is generally well written and quite easy to understand it does however mix preliminary work ifgsm with the proposed contribution in 32 the paper is able to improve vanilla gan models using the presented objectives the paper does fully compare to prior work yes table 8 highlights some of the similarities to prior work but the main evaluation does not compare to alternative approaches that consider the interplay between gans and adversarial attacks or latent space exploration in order to see the efficacy of the presented method the paper should experimentally compare to the majority of methods highlighted in appendix c the paper would be a lot stronger if it could show that the design choices made here are better than other attacks ie perturbations on the generated or real images instead of latent features etc the visual quality of the presented examples is somewhat underwhelming does the presented method work on larger gan architectures such as stylegan2 or biggan does the presented method actually address the issues highlighted in fig 3 the paper explores an interesting idea of adding adversarial robustness into gan training to improve latent distribution sampling and diversification unfortunately the paper falls a bit short in the experimental validation of the approach and comparison to prior approaches post rebuttal the rebuttal makes a good case for their final algorithm using additional results however i still do not see what the paper adds on top of baselines or how the problem setup in figure 3 interpolation artifacts is actually addressed the rebuttal mentions some experimental evidence that seems to indicate latentspace sampling can helps however i would need to see these results in an actual paper submission for review to feel comfortable about accepting it as is the paper seems interesting but not ready for publication docsepthis work proposed a sample shifting method in gan formulated as adding intermediate latent space to generated pixel space such method is based on observation of continuous mapping limit image quality in pixel space is not as continuous as latent space limited latent space will incur mode collapse thus poor image diversity the main contributions are a new optimization problem as sampling method to improve image generation by quality and diversity propose to use ifgsm optimization method to achieve this sampling optimization problem the experiment showed improvement on public dataset of stl10 cifar10 pros 1 the paper is well organized with clear subtitles and clear logic flow 2 ablation study on various baseline gan architecture dcgan wgan wgangp sngan is conducted to show generalizability of such sampling method cons 1 compare with baseline method msgan 1 why it only compare the div with msgan 2 improvement over baseline method msgan is very limited 2 if generator trained with better quality regularization the latent space after mapping should have better continuity comparison like fig 3 after training would be needed to prove that some minor issues 1 the paper is a bit redundant on algorithms and figures for example it seems lengthy to include both algorithm 1 and 2 in the main paper 2 fig 1 its not obvious what the latent space did in this figure the paper is overall well written and the idea is very clear and elegant meanwhile i have some doubt on the sufficiency of experiment to show the improvement from quality also the improvement over baseline method msgan seems minor for me expect the rebuttal to clear my doubt docsepthe authors demonstrate that the generator in a gan is a continuous function two latent codes that are close in the latent space are mapped to two images that are close in the pixel space however the quality of the generated images is not preserved as quality is not a continuous function in pixel space to address this issue the authors propose to transform the original latent codes and demonstrate that it results in better generation quality and diversity 1 related works have not been cited for example the following paper performs a similar optimization technique with a different objective a yan wu jeff donahue david balduzzi karen simonyan timothy lillicrap logan latent optimisation for generative adversarial networks httpsarxivorgpdf191200953pdf b httpsarxivorgabs200502435 c httpsarxivorgabs180903627 please do a thorough literature review to incorporate any such missing work 2 the authors propose five methods viz i advlatganz ii advlatganqua iii advlatganqua iv advlatgandiv and v advlatgandiv each method either focuses on improving quality or aims to enhance diversity it would be interesting to see what happens when the proposed objectives in equation 5 and equation 8 are combined does the hybrid method outperform both advlatganqua and advlatgandiv 3 another limitation of the work is that they compute fid and jsd fid although a widely used metric is not able to quantify the quality and diversity as it is a unidimensional score therefore it would be nice to quantitatively verify the claims of enhancement in quality and diversity in advlatganqua and advlatgandiv respectively comparing other metrics such as precisionrecall or densitycoverage will be more meaningful towards such goals a densitycoverage muhammad ferjad naeem seong joon oh youngjung uh yunjey choi and jaejun yoo reliable fidelity and diversity metrics for generative models in international conference on machine learning 2020 httpsarxivorgabs200209797 b precisionrecall mehdi s m sajjadi olivier bachem mario lucic olivier bousquet sylvain gelly assessing generative models via precision and recall in nips 2018 httpsarxivorgabs180600035 4 the number of baselines used in the experiment section is too less for the cifar10 case 5 it would be good have experiments on largescale datasets such as ffhq 6 i wonder if fgsm is the only method this can be applied with or any other can be used as well if so how does the method depend on the adversarial training method employed 7 i am not sure why msgan was chosen as the baseline for regularization 8 there is no theoretical justification on why should the proposed method work there is some empirical evidence but there would be better to have some theoretical backing on why this method should aid in avoiding modecollapse please refer to the above comments ### Summary:

to improve the generative adversarial nets the paper proposes to add an implicit transformation of the gaussian latent variables before the topdown generator to further obtain better generations with respect to quality and diversity this paper introduces targeted latent transforms into a bilevel optimization of gan experiments are conducted to verify the effectiveness of the proposed method the paper is highly motivated and wellwritten but the experiment part still needs to be strengthened because the goal of the paper is to improve the gan training comprehensive and thorough evaluation of the proposed method is necessary after the first round of review in addition to the clarification issue and missing reference issue two reviewers point out that the method is only tested in smallscale datasets and suggest authors evaluate the performance of the proposed method in more complex datasets two reviewers point out that the experimental validation and comparison to prior approaches are insufficient during the rebuttal the authors provide extra experiment results to partially address some issues however most of the major concerns from other reviewers such as i how are the performance of the method in large scale datasets that have complex latent space manifolds ii nonconvincing performance gain and unclear problem setup still remain after an internal discussion ac agrees with all reviewers that the current paper is not ready for publication thus recommending rejecting the paper ac urges the authors to improve their paper by taking into account all the suggestions provided by the reviewers and then resubmit it to the next venue

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors propose a layerwise pruning method that formulates the problem of eliminating neurons as a weakly submodular optimization problem for which the wellknown greedy algorithm gives an approximation guarantee they illustrate the practical performance of three different variants of their strategy when extended to prune the entire network on a variety of tasks originality the idea of formulating the pruning problem to take advantage of weak submodularity is novel to me although it does build somewhat crucially on existing work quality the technical and experimental results seem to be wellexecuted to the best of my assessment one can always add more competitors and try on a wider variety of architectures but i found the selected experiments to be illustrative and compelling clarity the exposition was clear overall though i found figure 1 tough to read even after significant zooming significance while it may be more expensive than some other approaches the cost of this procedure is typically only born once so i can see this as being a very useful tool in practice and may spur pruning research in novel directions some limitations were discussed though i dont see the focus on the data limited regime as a limitation of the methodology necessarily or am i missing something important docsepthis paper propose a network pruning approach via sub modular optimization the proposed method uses a greedy fashion to layerwisely select neuron that improves the performance most this paper shows that the returned solution given by the greedy algorithm is able to well approximate the optimal solution of a np hard problem to reduce the cost a computationsaving approach is also proposed empirical results shows that the approach is able to achieve good performance when the available training data at pruning is small overall i find this paper quite interesting especially its performance when there is limited number of training data at pruning the theoretical guarantee is also stronger and does not require stronger assumptions however i do have some concerns 1 it seems the proposed method in general is very similar to ye at al both methods are greedy forward selection and the different seems subtle could you give more discussion in terms of methodology 2 following up q1 it seems that an main improvement over ye at al is to reduce the computation cost however 1 also propose a new technical to reduce the cost and i was wondering how does your method compares with 1 3 what would maxsle k fs looks like i understand that it is np hard but it would be interesting to show how this quantities looks like 4 can we empirically verify the gammauk as this is an important quantity 1 greedy optimization provably wins the lottery logarithmic number of winning tickets is enough yes docsepthe paper proposes a neural network node pruning method and shows that the objective is essentially a form of weaklysubmodular function optimization therefore the pruning of a single layer can be solved using the greedy algorithm with a theoretical guarantee the paper also shows that using a limited number of data the proposed method is able to achieve the best performance compared to baselines strengths 1 the paper draws a very intriguing and solid connection between neural network pruning and submodular optimization more specifically the weaklysubmodular optimization factor is closely related to the activation matrix 2 the paper studies the problem in a comprehensive manner including pruning of regular regions of neurons strategies of pruning multiple layers and speedup tricks for the submodular optimization 3 the proposed method empirically achieves the best performance compared to baselines on some network structures under a limited number of samples weaknesses 1 the proposed approach has relatively high computational complexity it seems that scaling would be a problem for scenarios such as larger network structures or utilizing a large number of data samples for pruning 2 given the limitation of the complexity i guess that the empirical experiments could be hardly extended to more complex datasets eg ones with larger image dimensions or larger network structures 3 the proposed framework only works for one layer for pruning of multiple layers the paper proposes some heuristics yes docsepgoal effective and efficient structured pruning of a pretrained nnnetwork if only small amount of unlabeled training data is available contributions 1 the authors propose a new technique called principled dataefficient structured pruning that alters the existing reweighting method mariet and sra 2015 unlike mariet and sra2015 submodular optimization they formulate the subset selection problem of structured pruning as a weakly submodular maximization problem and solve it approximately by greedy search extended pruning pruning of regular regions of neurons eg channels and three strategies for pruning of multliple layers limited number of training data cca 1 of the original training data with no labels and oneshot pruning without finetuning 2 theoretical justification of the method and its performance in the supplementary material 3 experimental evaluation of a solid scope with promising results strengths significance and quality 1 the problem of effective structured pruning of a pretrained nnnetwork in the presented limiteddata regime is important most of the existing structured pruning techniques require greater amount of training data and finetuning to work well the proposed method or even its particular parts appears to be a valuable contribution with this respect 2 the experimental evaluation of a solid scope offers promising outstanding and stable results 3 theoretical performance guarantee 4 the authors try to be fair in their comparisons with concurrent techniques eg the application of reweighting various parameter settings and analyze also the weaknesses of their method clarity 1 the submission is relatively clearly written and easy to read except the theoretical parts and the figures if there was enough place i would prefer to move more experimental results from the supplementary into the main paper the figures eg figure 1 are small and less comprehensible it would maybe help to scale the graphs differently to highlight the variants of the proposed method or to change the used colors novelty 1 the novelty of the contribution is slightly limited the proposed method is based on previous work by mariet and sra2015 the original method is altered using the principles of submodular optimization to be advantageous in the new context of limiteddata 2 the related work is cited and addressed adequately ok the authors try to analyze both strengths and limitations of the paper in the experimental part ### Summary:

the paper proposes a dataefficient structured pruning method that for a given layer finds neuronschannels to prune with corresponding new weights for the next layer that minimize the change in the next layers input induced by pruning this selection problem is formulated as a weakly submodular maximization problem thus it can be provably approximated using the greedy algorithm the proposed solution is interesting and practical as it requires limitednumber of training data and no labels the reviewers found the authors response convincing however the authors are strongly encouraged to incorporate the clarifications provided in the rebuttal into the final version

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposed to relax fixed table structures by introducing a transferable tabular transformer transtab for tables they basically convert each row into a feature vector and then apply stacked transformers for feature encoding there are several advantages of this encoding 1 it can deal with the tables that have different number of columns 2 it is easier to transfer the knowledge learned from different columns they conduct experiments on one clinical dataset and several public datasets under four different settings supervised learning feature incremental learning transfer learning and zeroshot learning the empirical results show that the proposed approach outperform the baselines in the literature they also showed that in the zeroshot learning scenario they can almost match the performance of pretraining plus finetuning originality the main idea in this paper is a good combination of several ideas proposed in the literature with modifications and adaptations it worked and yield promising results on several datasets quality the proposed approach is technically sound and the experimental results showed that it outperformed several strong baselines for tabular data prediction although the results are impressive i have several comments one of the main advantages advertised in the paper is that the proposed method could easily extend to feature incremental learning pretrainingfinetuning and zeroshot inference in the paper existing works only cover vanilla supervised learning and fixedtable pretraining due to the fixedcolumn assumption in my point of view this is overclaiming not all existing works only cover vanilla supervised learning for example those transformerbased architectures like tabtrans fttrans can be easily adapted to those settings the zeroshot performance in table 5 seems surprising to me how do you split the table into three distinct sets do you do random split and how many random seeds have you tried i would imagine a split that during the training the model mostly sees categorical and binary features while during test it mainly sees numerical features in this way i dont think the model is able to do zeroshot transfer moreover can you try a setting that you manually control the number of categorical binary and numerical feature in both training and testing and see how does the model generalize in addition to the quantitative results i would also like to see some qualitative analysis of the transferability of the model what does the data look like and why the model is able to do the transfer how does the selfsupervised pretraining affect the performance i would like to see an ablation study where you only training the model with the direct supervision signals this could help understand how much of the improvement is from the architecture design and how much improvement is from the selfsupervised pretraining clarity in general this paper is wellorganized and easy to follow i think section 24 could be better explained whats the definition of vik in line 133 how do you compute psi in equation 4 significance this paper achieved strong results across a range of different datasets although the experiments are not comprehensive enough for the readers to understand every aspect of the system i think it still sets a strong baseline and a good reference for the future work in this direction this paper has sufficiently addressed the limitations docsepthis paper presents a tabular learning framework that covers transfer learning across tables zeroshot inference feature incremental learning pretraining and finetuning this approach does not assume that columns in the table are fixed and work even with variable column tables the authors propose two contrastive learningbased pretraining approaches by vertically partitioning the tables this pretraining approach is feasible since the columns can vary across tables making selfsupervised and supervised pretraining possible the transformer model proposed performs significantly better in all the claimed settings transfer learning across tables zeroshot inference feature incremental learning pretraining and finetuning in addition the authors also introduce clinical trial mortality prediction tabular dataset pros the proposed contrastive learning methods are computationally cheaper this variable column approach is really useful when the tables have too many columns and encoding them will be difficult in current existing transformers for tabular data eg tabert cons setting for feature incremental learning and transfer learning seems very similar dividing the dataset into three sets containing an equal number of columns and first training on set 12 then train on set 3 vs the transfer learning setting in the paper line 214215 is confusing incomplete in feature incremental learning no comparisons on how the performance on set1 after training on set1set2 set1 set2 set1set2 after training on set1set2set3 will the performance on previous sets decrease if yes how to mitigate that na docsepthis paper focuses on the transferability of tabular data classification methods it proposes three novel settings to evaluate the model transferability in terms of columns column overlapping column increment and zeroshot it also proposed a novel method combining selfsupervised and supervised pretraining strength three novel settings to evaluate the model transferability on tabular data classification transferability is an important research topic a novel method based on selfsupervised pretraining for tabular data classification which is more accurate and transferable weakness incorrect claim in line 109 e is not contextualized to get contextualized embedding the input embeddings should interact with each other but not simply concatenization feature incremental learning setting is unclear no baseline results eg vime and scarf for zeroshot setting this paper assumes tables are matrixlike and column types are given which hiders its transferability many papers in the nlp community have explored to process tables under a more flexible setting wang et al robust controlled tabletotext generation with structureaware equivariance learninghttpsarxivorgpdf220503972 naacl 2022 yang et al tableformer robust transformer modeling for tabletext encodinghttpsarxivorgpdf220300274pdf acl 2022 wang et al retrieving complex tables with multigranular graph representation learninghttpsarxivorgpdf210501736 sigir 2021 it would be meaningful to compare the methods focusing on numerical tables with those focusing on text tables ### Summary:

this work introduces and evaluates a general scheme to featureize tabular data and methods for selfsupervised pretraining over the same with a focus is on learning transferable representations reviewers were unanimous that the approach proposed constitutes a flexible practical approach that borrows and brings together existing sota techniques some questions about the specific settings concerned in the evaluation and distinctions between them were sufficiently addressed during the response period empirical results show consistent gains over the baselines on the tasks considered an additional suggestion one might naively anticipate that transfer learning for tables is not particularly promising given the very different semantics two arbitrary tables might have however the scenarios considered here involve settings in which transfer seems a priori reasonable i might suggest the authors address this upfront and explicitly outline the conditions under which transfer learning for tables is anticipated to work and what assumptions are necessary for such cases and where it is not

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a new method called gentd for offpolicy evaluation of gvfs the algorithm can further estimate multiple intercorrelated gvfs at once under some reasonable causal assumptions the contributions are mainly theoretical convergence proofs some empirical evaluation on a small mdp is provided strengths overall the paper tackles an interesting problem identifies a shortcoming of previous algorithms and proposes a solution with theoretical guarantees i found it overall pretty clear weaknesses scalability i have some concerns about the scalability of the method and whether it could extent beyond the linear regime in particular alg 1 requires some projections step that may not make sense in the nonlinear regime could authors comment on whether they think it is an issue experiments the experimental validation is weak in my opinion i understand this is a theory paper and the main contribution is the algorithm and its guarantees however i believe it is also in the authors interest to showcase that the algorithm performs well on environments more complex in particular gendice which this work builds upon had some empirical evaluation on nontrivial gridworlds as well as a simple control halfcheetah environment as far as i know methods based on minmax problems dualdice etc are known to be quite unstable so i think it is important that you showcase the robustness of your algorithm limitations are mentioned above beyond the questions on the theory the limitations appear most on the practical side in the paper the empirical evaluation may be lacking and thus making it harder to convince the community that this algorithm would be a safe and efficient one more broadly it is not clear to me how this algorithm would be extended to the nonlinear setting docsepthe problem formulation this paper is concerned with is the general value function gvf approximation task where the algorithms task is to estimate a vector which is defined as an expectation of some algebraic manipulation of the signals received this vector is named a gvf on this task the authors mainly compared their approach with another existing method called gradient temporal difference gtd the main observation of this paper is that the goal of gtd is to minimize the empirical mean squared projected bellman error mspbe whereas a more legit goal would be the expected meansquared projected bellman error which they name as the mean squared projected general bellman error mspgbe to account for the shift from the empirical datadependent mean to the expected mean they applied an existing density ratio estimation method strengths originality the motivation is legit quality the logic chain works through it is clear what they are doing clarity the story is easy to read i didnt check any proof weakness significance 1 seems ab where a gtd b density ratio estimation 2 more precisely i put this as a weakness because personally i dont quite get the logic why learning density ratio would be more accurate the authors put as a counterexample where gtd fails for complete fphi however if for the same example the density ratio is not learned very well why would gentd work i guess assumptions count for limitations a better limitation from my point of view would be to specify for which interesting classes of rl tasks the algorithm gentd wouldnt work docsepthe paper considers evaluating multiple interrelated general value functions using offline data a generalized td learning algorithm is developed and its theoretical properties are analyzied in detail the strengths and weaknesses of the paper are given in the next section the contributions of the paper are mainly theoretical and include 1 establishment of the contraction property for both forward and backward value functions 2 development of a generalized td learning algorithm to overcome the limitations of previous baseline methods 3 convergence analysis of the proposed algorithm 4 identification of sufficient conditions under which the proposed algorithm would converge strengths 1 the paper evaluates both forward and backward general value functions despite the richness of the literature on offpolicy evaluation these general value functions are less studied in that sense the paper targets an interesting research problem 2 the proposed method considers multiple interrelated general value functions jointly rather than on a casebycase basis 3 a generalized td learning algorithm is proposed to address the limitations of the gradient td methods 4 convergence of the model parameter and the estimated value functions are investigated in detail some comments 1 there is a typo in the title of the pdf file in addition there is a question mark in the checklist 2 the linearity assumption is quite strong the algorithm might perform poorly in nonlinear systems with highdimensional state information 3 it remains unclear to me if the variance of the reward to go mentioned on l23 can be represented in the form of the general value function in particular the variance of the cumulative reward would involve the interaction term that measures the covariance of the rewards at different time points 4 can you allow bj to be unobservable the gradient of the value function mentioned on l26 does not seem to be an observable quantity it might be more useful to cover cases where bj needs to be estimated from the data as well 5 the literature on offpolicy evaluation with a general scalar value function is not thoroughly reviewed in addition it was mentioned on p2 l92 that these methods are not directly comparable however i do not agree with this argument suppose the initial stateaction distribution concentrates on a particular stateaction pair then the value function is reduced to the stateaction value combined with a kerneltype estimator existing ope methods such as marginal importance sampling double robust estimation double reinforcement learning can be potentially applied to this setting it remains unclear to me whether the proposed estimator is better 6 the numerical study is oversimplified it considers a simple toy example with 7 states and 2 actions more extensive simulations based on eg openai gym environments are needed to test the empirical performance of the proposed algorithm the main limitation includes the linearity assumption as well as a lack of extensive empirical studies the former was partly discussed in the discussion where nonlinear function approximation was mentioned docsepthe paper proposes a new offpolicy algorithm to evaluate forward and backward general value functions that have the property of causal filtering they show that the existing algorithm gtd fails in this for example failing to compute the ground truth gvf and propose an algorithm to overcome its shortcomings thanks to the authors for putting in the effort in doing this work strengths i think this is an important area to research gvfs are relatively underresearched and its important to see this type of work the 1 use of the concrete example on line 140 was helpful to communicate the idea of gvfs 2 the comparisons between gentd and gtd was also nice weaknesses given that the concept of gvfs is unique and somewhat niche i think the authors should be clearer on what gvfs exactly are see suggestions below the experimental section seems rather limited the cited paper on gvfs general value function networks 2021 seems to have a pretty detailed experimental section it would be interesting to see how the proposed algorithm scales to harder domains no they do not address it but this seems to be more fundamental work with no direct societal impact ### Summary:

the paper proposes a new algorithm called gentd for the estimation of multiple general value functions predictive and retrospective from offpolicy data the paper shows convergence guarantees for this algorithm to the ground truth for a certain class of general value functions with causal filtering the initial reviews were mixed on the positive side the reviewers found the writing to be clear overall found the studied problem important and appreciated the theoretical results on the negative side several reviewers voiced concerns regarding the experimental evaluation other concerns are the limitation of the linear setting and possible extensions to the nonlinear setting as well as the significance specifically whether this work is merely a combination gtd and density ratio estimation the authors response could alleviate these concerns further clarifying the contributions of the paper as well as adding additional experimental results after the discussion with the authors all reviewers view the paper positively and the ac agrees all in all this paper is recommended to be accepted

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the major result of this paper is to provide a general framework for analyzing compressed gradient descent an algorithm that has unbiased gradient and has bounded variance with respect to a shift vector this general framework allow one to recover several existing algorithm and provide some improvement in particular it improves the previous rate of max kappa1fracomegan omega to kappa1 fracomegan there are no major weakness of this paper provided i do not miss important literature one minor issue i can think of is that the algorithm only works for strong convex function and smooth which is relative narrow if there are simple adaptations to other setting as usual in optimization literature please explicitly point out in the paper the writing in general is good with a few minor issue 1 page 5 our first approach is based on the celebrated diana it is unclear to me why diana is a celebrated one please consider rewording 2 there is no explanation on the parameter omega how large it could be in practical and how people view it in literature docsep1 the paper widely covers several distributed communication compression algorithms and combines the existing work with the proposed shift compressor it provides a new aspect of analyzing compression algorithms 2 this paper has a clear structure with sufficient discussion of related work and several comparisons see detailed comments 1 the experimental results seem a little bit weak the authors just provided the classical ridge regression optimization problem which is quite poor to show the outperformance of the proposed methods although the authors mentioned that the benefits have been clearly demonstrated by other related work it is better to show results on the common deep learning baselines in this paper 2 i am also a bit concerned on the novelty of the shift compressor in particular i feel this is quite similar to the ef21 compressor proposed in ef21 a new simpler theoretically better and practically faster error feedback advances in neural information processing systems 34 2021 of course this paper provides a general view of such compressors yet it still seems highly related and the intuition seems quite similar the authors might want to claim the differences with the above work 3 the summary in the contribution part claimed about improved rates yet the authors mentioned in the following the results can have the same complexity as compressed gradient methods it seems from this claim the shifted compressor maintains the convergence rate instead of really improving it 4 the authors can explain more about the shift compressor and the metaalgorithm the eq3 shows that it subtracts a shift h compresses and then adds back the shift h it involves two more steps adding more computational costs but it is not clear about the advantage of such a shifted compressor through the theoretical analysis 5 it seems that the choice of shifts is crucial for the proposed shift compressor however one may not know which shift is optimal 6 the theory is all about convex cases i wonder if the authors could extend it into nonconvex cases also it seems to only work under gradient descent can it be extended to the stochastic gradient case these would largely improve the contribution of the proposed work minior there is a typo in the right plot of figure 1 the first legend of rdiana should be s 8 docsep1 the idea of shifted compression itself is neat and clean and could be easily applied to a lot of existing distributed optimization algorithms 2 the theoretical analysis shows that applying shifted compressors to some existing distributed optimization algorithms such as gdci can result in better convergence 3 the experiments on ridge regression and logistic regression show good performance compared to diana 1 it is unclear why using diana in randdiana to compress the communication of the shifts actually i cannot even understand how diana is used in randdiana since randdiana seems simply a random masking 2 my main concern is that the experiments are too simple both the optimization problems ridge regression logistic regression and the datasets synthetic w2a are very simple and small for modern computation hardwares these problems could be easily and quickly solved on a singlenode cpu machine which could hardly used to justify the results for distributed optimization algorithms distributed training is only necessary when the model and the datasets are both extremely large 3 the experiments only show relative error vs number of bits for distributed training what the users really care is whether the overall training time could be reduced however the wallclock time for the training are not reported 4 the uncompressed baseline is not reported in the experiments 1 in the experiments is the algorithm called diana the diana itself or algorithm 1 with diana on the shifts 2 how is diana used in randdiana i cannot see any connection between diana and randdiana since randdiana seems simply a random masking in the communication 3 for distributed training what the users really care is whether the overall training time could be reduced i strongly recommend to report the wallclock time for the training and compare it with the baseline including uncompressed training baseline 3 i strongly recommend to report the uncompressed baseline in the experiments so that the reader can see the gap between the compressed training and the uncompressed training ### Summary:

meta review this paper studies a distributedfederated optimization setting with a focus on communication as the bottleneck specifically they focus on the case where compressed noisy estimates of the gradient are being used in a suitable formalization the papers most significant contributions are theoretical first they provide a framework which generalizes several extant algorithms for such settings namely access to an unbiased gradient and bounded variance with respect to a shift vector second they provide a convergence rate analysis for their framework along with ways to pick optimal shifts which gives an improved algorithm overall the paper makes nice theoretical contributions though several reviewers noted the empirical evaluation is somewhat restricted and the settings apply to the convex setting only which might be restrictive for some practical applications

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this work focuses on the update regression problem for structured prediction the authors explore two standard structured prediction approaches graphbased and transitionbased they conduct a set of experiments and introduce a noveal approach to mitigate the update regression problem edit after discussion with the authors i still disagree with authors but they made a good point in our discussion so i upgraded my grades i strongly recommend the authors to soften some claim made in the paper refer that in some cases kl div technique for kd can be used and give proper citations about how one could do that strenghts this work focuses on an important and understudied problem experimental results are interesting weaknesses unfortunately this paper has many weaknesses first and importantly the main contribution of this paper section 53 is not well motivated and the explanation of the contribution itself is really handwavy second explanation of the different baseline approaches is also too handwavy making the overall paper difficult to understand and not selfcontained on the motivations the authors argue that previous approaches cannot be applied to structured prediction i strongly disagree with this l 227 the output distributiion of global prediction is often intractable in the nonprojective dependency parsing case the distribution can be computed using the matrix tree theorem 1 2 3 if we restrict the problem to projective trees this can be computed via dynamic programming 4 5 for the transition based model approximated approaches have been explored in the litterature 6 l 222 the instantiations of kd are usually based on a loss function computing the crossentropy of output distributions however existing methods are no directly applicable to structure prediction tasks i dont understand why first the next sentence is false see previous point but also kl divergence between structured distributions has been studied in the litterature for nonprojective trees see 7 section 63 for methods based on dynamic programming see 8 l 263 furthermore it is unclear how to adapt existing sampling methods to solutions that are not formalized as sequence generation recent work has considered sampling from the nonprojective tree distributions including sampling without replacement 9 10 moreover previous work has also considered perturbandmap approaches 11 12 13 finally in the case of dynamic programming algorithms it is well known that it is possible to sample from the associated exponential familly distributions see eg 14 related work as suggested by the comment above the literature is not properly explored or cited by the authors there are similar problems in the introduction and related work section for example l 54 authors cite dozan and manning 2017 for graphbased parsers whereas the correct citations is more than 10 years older 15 l 21 for transitionbased parsers they cite ma et al 2018 better citations would be 16 17 1 structured prediction models via the matrixtree theorem koo et al 2 probabilistic models of nonprojective dependency trees smith and smith 3 on the complexity of nonprojective datadriven dependency parsing mcdonald and satta 4 semiring parsing goodman 5 differentiable dynamic programming for structured prediction and attention mensch and blondel 6 globally normalized transitionbased neural networks andor et al 7 efficient computation of expectations under spanning tree distributions zmigrod et al 8 first and secondorder expectation semirings with applications to minimumrisk training on translation forests li and eisner 9 efficient sampling of dependency structures zmigrod et al 10 unbiased and efficient sampling of dependency trees stanojevi 11 perturbandmap random fields using discrete optimization to learn and sample from energy models papandreou and yuille 12 differentiable perturbandparse semisupervised parsing with a structured variational autoencoder corro and titov 13 learning latent trees with stochastic perturbations and differentiable dynamic programming corro and titov 14 see section 1745 of machine learning a probabilistic perspective muprhy for the idea and latent template induction with gumbelcrfs fu et al for application to crflike distribution 15 nonprojective dependency parsing using spanning tree algorithms mcdonald et al 16 a classifierbased parser with linear runtime complexity sagae lavie 17 algorithms for deterministic incremental dependency parsing nivre nothing to report docsepthe paper examines model update regression in nlp structured prediction tasks model update regression is an issue appearing when a model is updated and the new model classifies part of the test examples negatively while the old model classifies them positively the novelty of the paper is that it explores the problem in structured prediction while it has been previously explored for classification the work proposes a new method for correcting the new model from the old one backwardcongruent reranking and experimentally shows that it performs better than other previously used methods such as model ensemble and knowledge distillation the work also defines a new measure related to the model update regression problem in structured prediction negative flip impact strengths studying the model update regression in relation to structured prediction is interesting and important the contributions are novel enough the paper is very wellwritten and clear the problem previous work and the proposed solution are clearly described the proposed improvements are meaningful and the experiments show that the proposed method for correction backwardcongruent reranking works better than the alternatives on the explored tasks weaknesses i did not find any major issues with this work i am suggesting some things i found that could improve it further line 120 maybe add a citation for ud 22 on the next line only the postagging dataset is cited line 182 an example could be beneficial for understanding how the global prediction is thus decomposed into a sequence of nextword predictions line 217 from the description it is not really clear how checkpoint averaging and prediction ensembling differ table 5 it is not clear what is the difference between rows 5 and 8 for dropoutp03 na docsepthe paper studied a problem called model update regression in the field of structured prediction for nlp which means a new model that achieves higher scores may perform worse than its baselines on some cases the authors proposed an approach bcr to handle this issue the basic idea is to use an old model to filter predicted candidates from the new model a trick called dropoutp sampling is also applied to improve the diversity and quality of produced candidates the experiments had been done on dependency parsing and conversational semantic parsing which were aimed to show the effectiveness of bcr pros 1 the writing is good and clear 2 the problem is well defined and the solution is straightforward cons 1 syntactic parsing is indeed very classical for computational linguistics but only stands for a tiny part of tasks in the field of structured prediction some other tasks like ner and pos tagging are far more common and have much more applications in the nlp industry plus a lot of space in the paper is used for introducing syntactic parsing which causes a bar even for researchers from nlp and is not necessary for its future applications to common scenarios therefore the authors should investigate more representative tasks in structured prediction such that the formulation is more clear and the work has a broad impact on the whole community 2 knowledge distillation for structured prediction is a mature field in nlp please at least see this work structurelevel knowledge distillation for multilingual sequence labeling and check the recent literature hence the authors shouldnt say like existing knowledge distillationbased solutions cannot be directly applied to structured prediction models in the paper and more solid experiments are expected for comparisons 3 the problem mentioned in the paper is formulated in a quite intuitive manner which might lead to many explanations that cant be proved false for example from my view the problem may be a cause of randomness in the premise that neural networks cant fit the whole dataset which may explain why the ensemble model works therefore i believe a theoretical foundation is expected besides my concerns raised in strengths and weaknesses i think the authors should do more experiments to empirically confirm the problem and describe it in a more acceptable way i have seen that the authors took a lot of space to show the performance numbers of their models which i believe should not be the focus of this paper docsepthis paper tackles the problem of model update regression when a model with better aggregate performance is deployed it may make mistakes that a previous model didnt make how can we minimize these regressions specifically this paper is the first to look at this problem in a structured prediction setting on both dependency parsing and on a conversational dialogue semantic parsing task the paper evaluates new models to see how many examples they regress on the paper describes several techniques to mitigate regression including a new one backwardcongruent reranking that reranks the new models output using the old model this is combined with a form of dropout at test time to yield diverse samples the paper shows that bcr achieves good task accuracy while minimizing the number of model regressions strengths the problem setting in this paper is novel while a somewhat niche problem i am convinced of the core argument that this could be useful with some reservations see below under weaknesses the exploration of new models is quite thorough and its nice to see the contrasts between some different types of model updates including changing the mode paradigm the bcr technique is simple and i could see it being widely adopted for this task the dropoutp sampling technique is interesting and a novel way of getting diverse samples from these kinds of generation models weaknesses the main conceptual weakness i see in this paper is the notion of a regression as it ties to model confidence it seems to me that if you take a model achieving 90 accuracy and perturb its weights slightly it may still get 90 accuracy but make different predictions many of the flipped predictions will be those where the 2ndbest option had nearly the same probability as the 1best option that is cases where the confidence was low entropy of the label distribution was high its not clear to me in these cases that the first model is getting things right and the second is getting them wrong more that the model had high uncertainty and it broke one way correct for some examples and another way incorrect for other examples considering that bcrs performance is always lower than that of the ensemble it seems to me that whats happening here is that this is really an ensemble that weights more heavily towards the earlier model we arent ensembling the two models symmetrically but by reranking outputs from the second using the first were really only making changes where the second model is significantly more confident and doesnt even return what the first model initially preferred resulting in a smaller performance gain but also fewer changes so basically this is a clever way of combining the models but mostly has the effect of preferring the first i also wonder about the nfrnfi metrics as they relate to the motivation of this paper the idea of a system changing and then breaking ux or downstream modules that were depending on certain things working is a problem that resonates with me however do nfi and nfr really correlate with this motivation again many of the differences seem like they could most heavily depend on examples that were basically halfworking to begin with which were probably not consistent features a user was very attached to as a result while this paper explores a different point in the design space of how to ensemble these two models im not sure what it presents is really groundbreaking in terms of results there is little discussion of limitations and societal impact but i dont see this as a big problem for this paper ### Summary:

this is a good paper with a topic that is very important in practical scenarios but does not have many off the shelf solutions and i find that this paper makes an attempt to this end in addition i am happy to see the discussion with the reviewers most of whom suggest acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper introduces a new model for performing multilabel classification which is particularly wellsuited to situations where there are elements of structure hierarchy mutual exclusion in the label space the fundamental idea is to use hyperplanes in hyperbolic space as the decision boundaries for label assignment the main benefit of hyperbolic space over euclidean space is that there are infinitely many hyperplanes in hyperbolic space which are nonparallel and do not intersect thus allowing for the regions corresponding to label assignment to capture hierarchical and exclusionary patterns the previous sota model used axisaligned hyperrectangles or boxes and the authors argue that such a constrained geometric region is not in alignment with the natural decision boundaries provided by neural network architectures whereas using the hyperplanes in hyperbolic space are more amenable to the output of such encoders a thorough analysis on 12 datasets along with additional ablation studies are provided in support of these claims originality the method is novel and the reasons for this approach are wellmotivated quality the submission is technically sound the experimental results while only a minor improvement quantitatively are statistically significant one caveat which will be brought up in the questions is that it is not necessarily clear that the full benefit comes from the use of hyperbolic hyperplanes but rather the injection of the mutual exclusion constraints which other models could also potentially easily exploit still even if the model is only onpar with sota results when taking this into account the underlying idea is sound clarity the submission is mostly clear but there are a number of improvements to the presentation i would recommend in the questions section even so i feel i would be able to reproduce the model from the description provided in the paper significance the task is of interest to a number of researchers and more broadly the idea of the incorporation of structure in hyperbolic space would be of interest to the community of researchers working on such representations the authors mention that a limitation of the work is that it currently only considers implication and exclusion and they suggest logical equivalence la wedge lb iff lc as another potential useful constraint to include however i think that more can be said on this point the fact that the current model does not include such a constraint is not merely that it has yet to be implemented there is a representational limitation in that the intersection of two balls is not generally speaking another ball and therefore the existing model does not lend itself to easily encoding this constraint on the other hand models which use box regions can very easily incorporate such a constraint as the intersection of two boxes is also a box docsepthis paper proposes to model hierarchical structure as an embedding inference using poincare balls hierarchical inclusion and exclusion are used to construct training losses in poincare space and experiments show the proposed model generally outperforms boxbased baselines across multiple datasets 1 the proposed poincare hyperplane has some nice attributes on inclusion and exclusion the construction of the training loss is intuitive and sensemaking 2 experiments on hierarchical multilabel classification suggest promising results over boxbased baselines the amount of novelty over the neurips 17 paper is unclear this paper has a substantial amount of analysis which is a bonus to have but the technical crux is on the model docsepthis study explores a structured multilabel prediction problem to this end the authors propose to convert logical constraints into soft geometric constraints in the hyperbolic embedding space where the hyperplanes are viewed as convex areas with insideness and disjointness of these regions representing logical linkages implication and exclusion extensive tests on 12 multilabel classification problems demonstrate the models capacity to boost performance strengths this study presents a novel translation that converts logical constraints into soft geometric constraints in the hyperbolic embedding space besides there is clear geometric intution where the implication is modeled by the geometric insideness while the mutual exclusion is modeled by the geometric disjointness weakness the experiment lacks some case studies that they can faithfully show that the obtained results well reflect the initial motivation as mentioned by the author other logical constraints can exist in these datasets and the authors do not currently consider these relationships docsepthis paper introduces a method for multilabel classification when the class labels have known dependency structures namely implication and mutual exclusion hyperbolic geometry is employed to jointly learn parameters for an encoder that embeds points in a poincare ball and poincare hyperplanes for class labels if the embedded point lies on one side inside of the poincare hyperplane it is predicted to have that class label due to the curvature of the space the poincare hyperplanes can be contained completely inside one another overlap somewhat or nonoverlapping the method presented in this work presents a joint training objective to not only correctly classify the examples but also encourage known constraints to be satisfied by the learned poincare hyperplanes for each class implication complete containment and mutual exclusion nonoverlapping the method is compared on standard benchmark datasets against reasonable baselines strengths paper is overall wellwritten motivation is clear figures 1 2 make the method very clear and easy to understand experimental evaluation is adequate and convincing with several reasonable ablations weaknesses it seems like the method is narrowly applicable it only seems to beat the best baseline methods when constraints are available how often are these constraints actually available in practice as presented only two types of logical constraints can be softly enforced by the objective these constraints are arguably the most common types of constraints for these problems though the claim that their method uses less dimensionality than baselines is somewhat misleading see appendix g of patel et al the authors dont compare their method to the best performing baseline mbm without explicit constraint modeling the authors have adequately addressed the limitations of their work the potential negative societal impact of their work is not addressed but their work has no more potential negative societal impact than any other paper submitted to neurips ### Summary:

the reviews of this paper are uniformly positive the novelty is the handling of exclusion edges which expands on previous work on the negative side the improvements seem small and do not solidly establish the value the value of the hyperbolic hyperplanes but the reviewers liked the paper and i recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper is an extension of 1 the task in 1 is language modeling while this paper is doing machine translation with the similar idea the authors propose a nonparametric method for machine translation via a knearestneighbor knn classifier specifically it predicts tokens with a knn classifier over examples cached in a socalled datastore and this method can be applied to any pretrained neural machine translation model without further training the experiments show that it improves results across a range of settings indomain outofdomain and multilingual evaluations strengths the method is simple and can be applied to pretrained neural machine translation model without further training the experimental results across a range of settings are effective weaknesses although the method is simple and does not add trainable parameters it add the computational cost the authors mentioned the computational cost briefly but there are no detailed experiments it would be good to see the authors add more analysis on the computational cost for example how it varies with k technical novelty over 1 seems to be incremental where a large portion of the work is essentially regarding machine translation as a language modeling and applying the method in 1 to machine translation 1 khandelwal urvashi et al generalization through memorization nearest neighbor language models arxiv preprint arxiv191100172 2019 docsepthis paper describes a nearestneighbor enhancement to nmt where internal tokenlevel context representations are used to index into a large data store to find relevant source target prefix pairs since the index representation is taken from a presoftmax representation in the decoder network no additional training of the nmt model is required the authors show a diverse range of strong results from improvements using a data store over the models own training data to improvement from using a collection of domainspecific corpora not present during training used for domain adaptation to language specific collections to improve capacity of multilingual models they are also able to show by example how the model makes mt more interpretable this is a very strong paper its wellwritten and easy to read the method is very novel to mt and the results are great the method isnt practical right now decoding is two orders of magnitude slower but its very interesting and thoughtprovoking i can imagine it influencing a lot of work even if the actual method doesnt see a lot of use the only complaint that i could imagine raising against this paper is that the method is not particularly novel in light of recent work on nearestneighbor language modeling but in this day and age with so many papers available i think its actually very important to make these incremental stops in neighboring fields to make the connections explicitly clear all the great experiments on multilingual mt and domain adaptation also help a lot to their credit the authors provide a concise section discussing the changes that needed to be made for the conversion to conditional language modeling mt small concerns the expd in figure 1 is missing a negative expd table 1 what does the bolding indicate it looks like statistical significance but if so please be clear about what test was useddocsepsummary this submission introduces the knnmt approach for neural machine translation which incorporates the memorizecatching spirit with the nearest neighbor classifier on a large datastore when generating the decoding sentences together with the neural machine translation model for similarity search no additional parameters are needed but the inference cost increases the authors conduct experiments are different settings the single language pair translation the multilingual machine translation and the domain adaption translation results show that knnmt can easily improve translation performances by searching out related test sentences with nontrivial scores comments generally speaking the submission is okay and the proposed approach has no big flaws however i feel hard to make this submission to be accepted the main reasons or concerns are 1 it is clear that this submission is a direct and straightforward extension of the previously published iclr2020 paper knnlm as the authors also clearly stated in the abstraction therefore in terms of the contributions and differences they are quite limited the technique is almost the same except the key is added with the source language sentence the presentation of this paper is also similar to knnlm the direct extension of the knn approach from language model to neural machine translation makes me feel hard to recommend and this makes much more like a technique report of the method extension 2 to say about the approach i acknowledge that this method is effective as the authors have done with multiple experiments however the computation cost is also high the authors also discussed this in section 3 it is hard for realtime systems to afford the increased inference cost as this approach made the improved results with a little increased cost are okay but too much is not a good choice though the authors mentioned there is a tradeoff and i also acknowledge this but it is still not clear what is a good tradeoff 3 also this method highly depends on the scale of the dataset also the similarity between training and test dataset if i understand correctly this assumption can hold for high resource translations but for low resource translation this would be limited this is another drawback of these searchbased algorithms minor question what is the effect if lambda is varied therefore shortly speaking i feel this paper is straightforward to extend from the previous paper indeed this is the future work and the answer from the review comments of previous work this concerns me a lot for another one in iclr2021 update i thank the authors to give responses to my points especially the discussion about novelty but i still feel the success of knn for nmt is similar for lm thats why a lot of works study on nmt are also work on lm since this knn method only targets at the decoder side same as lm model therefore i still feel not novel enough docsepthis work presents an approach to exploits at decoding time a very large translation memory to improve nmt an extensive evaluation is performed along with some detailed analysis on the important parameters strengths the idea is very simple very easy to understand and intuitive can be added to existing pretrained nmt model many interesting applications domain adaptation multilingual model specialization for instance are presented and are mainly the reason why i think paper can be accepted for publication the paper is easy to read and wellwritten weaknesses exploiting a translation memory at test time is not novel exploitation of billions of tokens is rather impressive but in my opinion making this possible is more an engineering problem the approach is described within one page the remainder of the paper is about evaluation and analysis for iclr the paper lacks of substance the improvements over sota englishgerman are very small considering that billions of tokens are exploited and the high decoding cost the experiments presented in this paper are not reproducible since unpublishable data are exploited to train the system eg ccmatrix computational cost at testtime is extremely high as expected this is probably why nobody tried it before i do not see how it could be used for realworld applications focusing on reducing the computational cost would greatly improve the paper questionssuggestions we also provide scores from aharoni goldberg 2020 did you check that these scores are comparable with yours it is unclear in the paper whether they also used sacrebleu insert the sacrebleu signature in a footnote in your paper to help future work reusing your scores i recommend to add the decoding time in the tables and a description of the hardware used since the major issue of the proposed approach is its computational cost adding the decoding time would probably encourage future work to try to improve it ### Summary:

this paper extends past work on knnaugmentation for language modeling to the task of machine translation a classic parametric nmt model is augmented with knn retrieval from an external datastore decoderinternal tokenlevel representations are used to index and retrieve relevant contexts source target prefix that weighin during the final probability calculation for the next target word results are extremely positive across a range of mt setups including both indomain evaluation and domain transfer reviews are thorough but quite divergent there is general agreement that the proposed approach is reasonable wellmotivated and clearly described and further that experimental results are both solid and relatively extensive however the strongest criticism concerns the papers relationship with past work in terms of ml novelty everyone agrees including the paper itself that the proposed methodology is a relatively simple extension of past work on nonconditional language modeling however two of the four reviewers strongly feel that in light of the potentially prohibitive decoding costs the positive experimental results are not sufficient to make this paper relevant to an iclr audience given the lack of ml novelty in contrast another reviewer strongly takes an opposite standpoint rather that the results will be extremely impactful to the mt subcommunity at iclr since they are unexpected ie that a nonparametric model might compete with highlytuned nmt systems and very positive across a range of domains and settings ie indomain outofdomain multilingual further that the approach has substantial novelty in the context of mt where parametric models are the norm and that it might inspire substantial future work eg on efficient decoding techniques and further nonparametric techniques given that it so drastically breaks the current mt mold the final reviewer shares the concern of the former two about novelty but is swayed by the experimental results and potential uses for the model given knn augmentation is possible without further training and therefore votes for a marginal accept after thorough wellreasoned and wellintentioned discussion between all four reviewers the reviews land just barely in favor of acceptance but with substantial divide after considering the paper reviews rebuttal and discussion i am swayed by the argument that a these experimental results are largely unexpected b they are both extremely positive and offer a new tradeoff between test and train compute in mt and c that the paper may therefore inspire substantial discussion and followup work in the community thus i lean in favor of acceptance overall

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors demonstrate a method to perform unsupervised amortized optimization of game solvers that seemingly leads to sharp speedups they also design an architecture that respects the equivariances of the problem and perform ablations demonstrating that this is useful strengths the technique seems useful and i could imagine it being helpful in marl settings that the objective is unsupervised ie you dont need the actual optimal strategy is very neat the invariant architectures should be useful for other attempts at this problem the main weaknesses are in the writing of the paper i highlight some problems below the superscripts in line 133 come out of nowhere for example why does hatsigma have the l1 norm as a superscript im not sure what these superscripts mean on a first read someone from an adjacent field might not know what an nxn game is i think itd be worth explaining the citation links are broken and link back to the title instead of to the actual citation are table 1 and 2 actually included in this paper i see them in the supplement are those the same tables youre referring to in the paper body on figure 4 its not clear what left and right mean in figure 4 caption its worth pointing out as you do in the text that the arms correspond to worst mean best the term mecce is introduced but not defined anywhere in the main paper the stack function in equation 12 does not appear to be defined in the main text the equivariant architectures section was quite hard to follow it is unclear what the underlying logic behind each of the transformations is i would love to see a more expanded version of this section in the appendix or with the extra page if the paper is accepted equivariant is misspelled on line 193 figure 4 is somewhat hard to read where the bars overlap you might consider using different colors and making the arm sizes of one of the bars different than the other yes docsepthis paper aims to create a method for training nns to solve for ne ce and cce across a set of games with a fixed action space for each player this is achieved through considering the dual formulation of the lp resulting from each of these equilibria concepts and preforming gradient decent on the nn given random games in with that action space size so that the strategies the network produce satisfy the constraints of the lp strengths the strengths of this paper are the novel to my knowledge idea to train solvers of games in the dual lp space rather than the primal this is an interesting approach which could be promising and should be explored the idea to train one solver for all games of a given shape rather than solving each game onbyone equivariant networks and payoff invariances which are shown to improve performance weaknesses the main weaknesses i see are the motivation that other approaches in the space cannot solve for ce seems weak since it seems easy to adapt psro or cfrm to such a class the scaling argument seems weak as the approach of training on all games of a give size seems much much more difficult computationally than solving a particular game in that class if the argument is that this method is scalable then i would expect experiments that run on much larger than 8 by 8 games more broadly as this paper is taking the approach of solving all n by n games at once but most of the payoff matrices in this space are ones which we dont use in practice i would expect an evaluation on a transfer set of games which we do care about to show that it works to solve them correctly otherwise it is difficult to evaluate the method as it could have low error on average but not low error on the problems we care about in the same vein since we usually care about getting the ce for a particular game i would be useful to compare to finding the ce for that particular game directly rather than solving all games of that shape first minor comments figure 4 is hard to understand given that the difference between the gray and black error bars are never described the appeal to occams razor on line 93 is very suspect it appears this is alluding to something specific which should be cited but regardless of what the citation is there has to be a miscommunication somewhere because there maximum entropy isnt well justified by occams razor in some sense they could be seen to be the opposite as occams razor is often phrased as preferring the simplest solution and maximum entropy is the mostrandom solution under some measure and thus the leastcompressible under that measure which could be seen as the most complicated solution regardless none of this is necessary because any method of choosing between equilibria is fine for the purposes of this paper the limitations were adequately addressed except to the extent mentioned above docsepgames are a useful formalism in machine learning there are multiple notions of solution to a game this paper focuses on correlated equilibria ce and coarse correlated equilibria cce given the payoffs of a game the set of all ce resp cce form a polytope with number of constraints exponential in the number of players and actions thus solving for a ce cce can be computationally intractable this paper proposes to use a neural network to predict approximate ce resp cce given the payoffs of all players as well as some auxiliary data the authors propose several innovations to make the proposed neural equilibrium solver more efficient and flexible several choices of secondary criterion used to select a particular ce resp cce from within the polytope of all ce resp cce a permutationinvariant network architecture capable of exploiting symmetries in the game eg exchangeability of players permutations of a players action set focusing on solving the dual problem which has fewer optimization variables as compared to the primary problem strengths predicting computing nececce is certainly a worthy topic i appreciated the use of strong duality for convex optimization experiments are thorough weaknesses overlooks certain important aspects of the literature i found it hard to follow how the proposed network is actually trained scalability of proposed approach to even moderately sized games is unclear edit changed score to 7 after rebuttal these are adequately addressed ### Summary:

this paper introduces an neural network based equilibrium solver which utilizes a special equivariant neural network architecture to approximately predict nes ces and cces of normalform games experiments show the effectiveness of the proposed methods across multiple dataset all reviewers support the acceptance of this paper while i agree on the merit of this paper worth acceptance id also recommend authors to revise a bit in the final version regarding to the theoretical complexity of finding equilibrium 1 in line 18 solving for an equilibrium of a game can be computationally complex 9 8 in fact the cited intractable results only apply to finding nash in multiplayer generalsum games finding cecce can be always done by lp which is tractable and can be guaranteed to finish in polynomial time 2 this paper emphasize that prior methods may take an nondeterministic time to converge while this method proposed in this paper gives determinism however it appears to be the methods proposed in this paper is not provided with guarantees to converge in certain time thus without determinism either its better if the authors can clarify or modify corresponding arguments

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper shows how to extend the widelyused expected improvement heuristic from into the contextual bandits setting to create a new basic type of contextual bandit algorithm they propose two novel algorithms and propose a method for choosing an improvement threshold for controlling the explorationexploitation cutoff which provably achieves an tilde osqrtt regret rate even in settings with adaptive adversaries the paper then shows that the proposed methods have strong empirical performance strengths 1 paper extends the expected improvement heuristic into contextual bandits building a connection with the best arm identification and bayesian optimization literature providing a novel and significant result 2 paper provides proofs for the results and is able to get competitive regret rates for linear contextual bandits and neural bandits 3 paper shows experimental evidence for the value of the method 4 paper is clearly written and contextualizes expected improvement in the broader literature and clearly shows how the analysis of the linear and neural cases differ weaknesses 1 minor i wish the paper explained a bit more how the algorithms are able to work even with an adaptive adversary the paper discusses the limitations of using a gaussian distribution for the reward model as well the fact that their usage of the ntk kernel restricts the neural network classes that they can use the paper does not address potential negative societal impact of this work docsepthe authors propose a novel contextual bandit algorithm based on the expected improvement ei and study the corresponding regret analysis the proposed algorithm contains a modified element from ei for mab by suggesting a hybrid of ei with pure exploitation the paper adds different insights to the body of literature in that ei is an understudied technique to handle the tradeoff between exploration and exploitation in contextual bandits they propose two novel eibased algorithms for this problem one for linear payoff and deep neural networks the authors provide numerical experiments strength the paper adds different insights to the body of literature in that ei is an understudied technique to handle the tradeoff between exploration and exploitation in contextual bandits ei can be viewed as a variation of ts and in that respect the proposed algorithm improves the upper bound of ts by sqrtd weakness explanations for modifying ei by mixing with pure exploitation are not convincing why should one use the proposed algorithm over linucb since it carries extra sqrtlog t yes docsepthis paper applies the expected improvement ei principle to contextual bandits which contrasts with the more popular approaches of upperconfidence bound ucb and thompson sampling ts for adversarially chosen contexts but realizable conditional mean rewards ei is practically applicable with both linear and neural network predictors in the linear case the proposed technique essentially achieves informationtheoretic lower bounds given subgaussian residuals in the neural case the network tangent kernel can be used to characterize the regret matching analogous results for neuralucb and neuralts strengths include the novelty of ei in this setting and the connection to ntk theory weaknesses the lack of clear motivation for ei is it just another way to do contextual bandits that nobody has done yet if so this paper is likely to be ignored by practitioners is it statistically distinct from ucbts approaches ie works better in theory no everybody is achieving the same regret bounds essentially in practice maybe but thats where the experiments are too limited for a strong conclusion one advantage of using older datasets is you can provide exhaustive comparative study as in httpsarxivorgabs180204064 is it computationally distinct from ucbts approaches ie cheaper to compute or scales better to larger action sets if so you could exhibit on a modern dataset such as amazon3m from httpsarxivorgabs210207800 i believe this work would benefit from spending more time better differentiating ei before presenting to the general community otherwise its potential impact will be limited this reviewer is satisfied update after author discussion period authors have addressed concerns raising score docsepthe expected improvement ei is a classic approach to select the action in bayesian optimization bo this paper extends ei to the contextual bandit setting including the linear setting and nonlinear setting based on the fact the analysis of ei is not well studied this paper provides the regret analysis of ei in linear contextual bandit and neural contextual bandit moreover authors show good empirical results proposed algorithm enecccdejhvddktidjggvrltrghcivbtjdnlekdkejtc weakness 1 the overall proposed two algorithms linei and neuralei look very similar to ts to me take the linei as an example in a round rt is the same for every arm so the selection criterion can be considered as emu sim nxtop mu which is the expected version of lints just add the expectation to the ts theoretically we only need to take the expectation over the normal distribution based on ts so i doubt the novelty of the proposed algorithms and their analysis ### Summary:

this paper proposes and analyzes algorithms based on expected improvement for the contextual bandit setting and proves that the resulting algorithm can attain od sqrtt regret in the linear bandit setting the result improves over linear ts all the reviewers agree that the modified linei algorithm and its analysis are novel and important to the community i agree with the reviewers and recommend accepting the paper for the final version of the paper it would be helpful to add more details on why the pure ei strategy does not work and add the scaling with d experiments to the main paper the response to rev rpmu if there is space it would also be helpful to add a proof sketch for the linei algorithm and distinguish it from the lints analysis which is more standard and known in the community response to rev kpae

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper studies the dynamic monopolistic pricing problem under markdown constraints which requires pricing decisions to be nonincreasing the decision maker does not know the form of the demand function that stays constant over time receives bandit feedback for decisions made and aims to minimize cumulative regret compared to the maximum achievable revenue under the optimal price the paper first proposes a categorization for demand functions called markdown dimensionality to describe the complexity of demand functions in the context of markdown pricing then the paper presents dynamic pricing algorithms for each category respectively as well as matching regret lower bounds demonstrating the proposed algorithms are tight strength the paper is wellwritten and to the best of my knowledge the key contributions regarding 1 defining markdown dimensions 2 developing algorithms to achieve near optimal regret for each markdown dimension regime ie 0 finite and infinite markdown dimensions and 3 presenting regret lower bounds for each regime are novel in my opinion the paper presents a valuable framework to characterize the hardness of learning and dynamic monopolistic pricing under monotone constraints and may lead to interesting research directions i also think the paper positions itself well compared to existing papers for nonconstrained dynamic pricing as well as existing work on dynamic pricing with markdown constraints the paper also presented a clear illustration for technical definitions such as the markdown dimension via concrete examples and also for the proposed algorithms weakness in my opinion the main weakness of the paper is that individual algorithms are presented for different demand markdowndimension regimes meaning that the decision maker would need to know whether the supposedly unknown demand functions markdown dimension is 0 finite or infinite in order for a decision maker to deploy the corresponding algorithm also in the finite nonzero markdown dimension regime the proposed algorithm 2 requires the decision maker to know the underlying markdown dimension of demand the paper would be much stronger if it can either propose a single best of all worlds algorithm that can achieve optimal regret under demands with any markdown dimensions while being agnostic to the regime or analyze how the algorithms proposed in the papers would perform if the regime is misspecified this suggestion may be beyond the scope of the paper but perhaps it would be helpful to run some simulations to shed light on relevant aspects na docsepthe paper deals with learning pricing with markdown constraints that is that subsequent prices must be non decreasing the authors investigate this problem in the case where the demand is a parametric function of the price with known form but unknown values of the parameters in this case a tradeoff must be found between exploring to accurately learn the parameters of the model and losing a lot due to exploration and the markdown constraint the main contributions of the paper are i the introduction of markdown dimension which quantifies the how difficult it is to learn the parameters of the model from data ii an algorithm that balances exploration with good performance under the concept of pessimism in the face of uncertainty and iii an algorithm that matches the regret lower bound when the markdown dimension of the problem is infinity in which case no algorithms with sublinear regret exist strengths 1 the analysis is nice and rigorous 2 from a mathematical point of view introduction of the markdown dimension to characterize the difficulty of the problem is an intersting idea 3 also the result that knowledge of the demand model can lead to improvements in the regret and the algorithms to achieve this can be a good addition to the related literature of pricing with markdown constraints weaknesses 1 the main weakness is that th setting is not convincingly motivated since the markdown constraint is motivated by pricing problems in markets of some sort i am not sure it is reasonable to assume that the form of demand essentially how the market will react to the price is known there is no concrete example where a parametric form of the demand model is reasonableaccurate and no numerical results to illustrate the performance of the proposed algorithms vs standard algorithms in practical problem settings no potential negative societal impact foreseen docsepthe paper considers the monotone markdown price constraint in the dynamic pricing problem where the demand has a parametric form in price the paper introduces a concept the markdown dimension d which measures the complexity of the parametric family when the dimension d0 1 le d infty d infty the paper proposes algorithms achieving the regret olog2 t otdd1 ot2s13s1 here s measures the degree of smoothness of revenue function at the optimal price also the matching lower bounds are provided for each case originality its interesting to consider the monotonicity constraint in the parametric function the paper shows some new results which are different from the nonparametric case however i find the definition of markdown dimension d a little unintuitive and it is hard to see if the concept can be applied to other applications quality its technically sound except for algorithm 1 and theorem 1 see questions clarity the paper is written clearly there are three main results in the paper first for the simple case when the dimension d0 the paper shows the minimax regret bound olog2 t which is different from the unconstraint pricing case olog t the result is not surprising but still adds to the literature in this case theres a onetoone mapping from the parameter to the realized demand every price is informative and theres no tradeoff between learning and earning my major concern is whether the prices in the algorithm are actually decreasing see questions for details second when d 1 the paper shows the minimax regret bound otdd1 which are new results i think thats the major contribution of the paper the learning and earning tradeoff shows up in the hyperparameter h ie the smallest magnitude that the price decreases between consecutive periods setting hot1d1 the regret will be otdd1 my question is how to connect the parametric result with the existing nonparametric result see questions for details third when the function is nonparametric the paper assumes a higher order of smoothness and proposes an algorithm algorithm 3 i think the contribution of this part is marginal because the generalization from lipschitz continuous to higherorder smoothness is explored by the literature na docsepthis work studies a singleproduct pricing problem under a monotonicmarkdown price constraint in this work the authors introduce a markdown dimension d that indicates the hardness of a demand curve to be learnd from customers feedback for each of the following cases d0 dinmathbbz dinfty the authors propose a pricing algorithm with provable expected regret guarantees specifically these bounds are optimal for s2 as the authors also prove matching lower bounds in conclusion this work improves existing results in related literatures and introduce a new method of determining the hardness of a pricing problem strengths 1 the problem setting and constraints are practical customers are indeed more sensitive to price raising than discounting 2 the new concept on markdown dimensionality is new in the field of pricing research also it helps determine the hardness of pricing problems 3 most of the regret upper bounds of the algorithms this paper proposes are proved to be optimal up to log or loglog factors by the authors weaknesses 1 the writing and organization of this paper is not good in specific there are many obvious typos and syntax errors even in the most important definitions for instance see definition 10 i really suspect that the authors made a mistake in this definition especially c all their applications of this definition point at a smoothness ie exists c0 such that cleq rscdot0 based on this definition a sensitivity can only be used to lower bound the price perturbation instead of upperbounding it eg for s2 it is somewhat a strong convexity 2 many key definitions and statements are ambiguous or misleading for example in definition 8 is there a definition of parameterization and how does theta relate to the following conditions in 2 notice that phi1py is not necessary a constant unless f is didentifiable also what does it in 1 refer to 3 for key definitions propositions and assumptions there are lack of explanations or insights that help the readers to understand for example in definition 2 i had spent a long time until i realized that p is the lowest best price and p r is an argmax oracle in contrast there are redundant explanations on trivial facts for example the matrixform equation under assmption 5 is not necessary and it can be clearly described just by two equations d sumj0dthetaj pj and theta vp1y 4 the authors did not show the markdown dimension of a variety of common distributions except for the 0dimensional ones in other words there exists no example on dgeq1 and dinfty which qualifies the application of most of their theoretical results 5 this is very important to get the authors notice notice that the authors added more contents in their main pages in the supplementary materials this is unfair to the other authors and their submissions as this actually breaks the 9pagelimit rule although it is in the supplementary materials the authors should separate their main pages with the proof details of upper and lower bounds and put them into an appendix besides the format of this paper seems like a preprint instead of a submission as an option in the latex template from my point of view this paper has strong technical contributions but the authors convey these contributions in a careless way not sure if it is suitable for publishing in neurips ill tentatively give a 6 in advocate of their theoretical results and see what the other reviewers would say no discussions found in this paper i recommend the authors to discuss the limitations of their work at the end of the main pages there seems no negative social impact or ethic issue and i also encourage the authors to discuss them in their appendix docsepthis paper considers infinitearm mab with markdown constraints since existing results have shown that t34 is optimal under minimal assumptions the authors turn to study how to further improve the regret bound by assuming additional assumptions for the demand functions to this end they introduce a general complexity notion called markdown dimension using this notion they not only show that a better regret bound is possible under certain complexities but there still exists a separation between mab with constraints and without markdown constraints strengths a comprehensive study of mab with markdown constraints matching upper and lower bounds weaknesses no experimental results writing needs improvement yes the authors adequately addressed the limitations ### Summary:

this paper focuses on an interesting problem dynamic pricing the paper brings conceptual new ideas markdown dimension and associated algorithms i have 2 concerns 1 it would be better if the algorithms were adaptive to the aforementioned dimension 2 it would have been better if the authors had followed the instructions and especially not updated the rebuttal as the revised version point 1 would be future work while point 2 is ok since the pdf can still be found in the submission files as a consequence i recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper addresses an issue of transformers that sometimes they fail to find solutions that are easily expressible by attention patterns the issue is justified to be the same as the problem of learning useful control flow the authors propose two modifications namely adding a copy gate functionality and a geometric attention module which facilitates focusing on local useful operations the resulting method achieves near perfect accuracy on the considered benchmarks for length generalization simple arithmetic tasks and computational depth generalization main strengths 1 the paper is well written and does a great job in introducing the problem and revealing the flaws of the universal transformer in achieving good performance in the described tasks as well the authors intuition of the properties of a good solution 2 the main components of the proposed method are explained in sufficient details to help reproducing the proposed method 3 the proposed benchmarks and datasets the empirical approach and chosen hyperparameters are provided and discussed in details 4 the paper is well positioned with regards to the related work main weaknesses 5 it is not clear if the the considered benchmarks cover all required aspects of task generalization or the generalization is only valid for tasks that are to some extent similar to the considered experiments the authors should further explain which aspects if any are missing and are not addressed in this work 6 it is not clear if the considered assumptions are always necessarily and correct the authors should address the following questions in the paper either in form of justified explanations or if required with ablation studies 61 is there any task which would benefit or require settings that are not covered by the considered settings described in section 2 62 regarding point 2 in section 2 what if the data dependency graph was too long that memory complexity would not practically allow to use such a depth in other words to what extent the proposed depth is necessary 63 regarding point 3 in section 2 could the gating function result in a shortcutcollapse in optimization considering a far more complex task that is generally addressed by transformers could reveal such issues 64 regarding the final point in section 2 could a task would prefer nonlocal operations to local ones does the performance of the proposed method degrade in that situation 7 some previous works for example on the listops task consider sequences that are orders of magnitude longer than the ones considered in this paper a couple of examples are 1 2 it is not clear if the claim that previous results did not achieve the perfect accuracy is wellsupported it seems like that to be fair the authors should have considered some of the sota methods and adapt their hyperparameters for these tasks with limited sequence length before testing how they would perform minor points 8 in section b2 the set of values or ranges over which the hyperparameters are searched should also be mentioned 9 first line of page 14 sample sampled references 1 modeling hierarchical structures with continuous recursive neural networks by chowdhury jr and caragea c arxiv210606038v1 2 nystr omformer a nystr ombased algorithm for approximating selfattention by xiong et al arxiv210203902v3 the paper is well organized and covers the background knowledge required to follow the discussions it motivates the goal provides justifications for the choices made in proposed methods follows a thorough empirical approach and achieves near perfect results in the considered benchmarks there are a few points of concerns that need to be cleared before i can fully support the submission regardless i see many of the properties of a good research project and so i lean towards accepting the paper at this stage i look forward to authors feedback on my concerns before i finalize my decision edit i thank the authors for responding to my comments in details after reviewing their response and the changes made in the paper many of my concerns are resolved therefore i change my recommendation to accept this paper docsepthis paper proposes two modifications to provide additional inductive bias to the attention mechanism in the transformer architecture the first modification adds a copy mechanism to simulate a noop at a given transformer layer and the second modification is an attention mechanism that is biased towards attending to local context both of these modifications are motivated as being useful for algorithmic tasks like compositional table lookup and arithmetic from experiments that are mostly concerned with some kind of lengthdepth generalization we see very significant improvements overall i enjoyed reading this work the writing is clear and to the point and the approach itself is very well motivated see questions for more and simple to implement without too many tunable hyperparameters and as such the experiments are cleanly setup and do suggest improved improved generalization from the analysis of the attention maps we can see that the method is doing exactly what it is supposed to as well that is copying previous values until other intermediates have been computed paying more attention to local hidden states etc based on these strengths i recommend that this paper be accepted to the conference so now let me focus on some weaknesses suggestions questions overall positioning firstly i think the paper should probably make it more clear that its only focusing on a very specific notion of systematicity that has to do with length depth generalization and not other more traditional notions like generalizing to new compositions which isnt really something that is evaluated like sqoop from bahdanau 2019 etc evaluation secondly while not a strict requirement there is no evaluation on language tasks pseudo language tasks like scan there is a length generalization benchmark within scan itself and it would be good to know how this method does on that analysis in figure2 bottom what does it is unclear what the y axis is isnt the copy gate just a single number for each time step for each layer if so i wouldve expected the figure to just be a single number for each time step for the various layers so i dont understand what the grid signifies overall i think based on the results i recommend that the paper be accepted into the main conference the problem is well motivated the comparisons are fair and the results compelling though there is some scope for improvement that i highlight in the weakness section of the main review docsep the authors propose transformer control flow tcf a set of improvements to the universal transformer dehghani et al iclr 2019 they show that for three compositional problems tcf allows trained models to generalize to longer sequences a common problem of many transformer implementations as in the universal transformer ut the encoder consists of one shared transformer layer self attention fully connected network which is iterated through a fixed number of times by feeding the output of each iteration back into the input of the shared layer however whereas the ut uses a sequence to sequence model tcf is an encoderonly architecture which decodes the last element in the output sequence as the final result two new features are introduced a gating mechanism that allows the model to skip a layer the input is then copied to the output on the basis of the selfattention output a weighting system for the outputs of attention heads which favors shortrange attention ie tokens close to the one currently considered and can be trained to be biased towards a certain direction before or after the current token experiments are conducted over three tasks predicting the output of sequences of permutations of 8 elements in prefix or postfix notation predicting the result of of additions and multiplications modulo 10 in infix notation predicting the result of operations on lists of small integers in prefix notation for each task tcf is shown to be capable of extrapolation to larger problems ie longer sequences than those seen at training the paper is very clearly written and proposes an interesting solution to an important question the tasks chosen are meaningful and the experimental results suggest that the proposed architecture can solve the extrapolation problem the technical aspects are precisely documented which makes the research easy to reproduce my main concerns are related to the experimental comparisons and the impact of certain design choices such as the use of a fixed model depth at training and test time the use of the last word in the encoder representation as the basis for model output and the absence of the adaptive computation time act in the universal transformer implementation that serves as the main baseline this makes it difficult to judge the impact of the two improvements suggested copygate and geometric attention and the benefits of the new architecture compared to an encoderonly stateoftheart version of the universal transformer with relative positional embedding and act i believe improving this part of the experimental design and discussion would greatly reinforce the paper below are my concerns and questions split into four themes computational and model depth at the beginning of section two the authors argue that four properties are needed for network to extrapolate to larger problems shared layers depth of the computational graph step skipping shortrange attention i would disagree with the second point for two reasons first computational depth is a relative notion in an arithmetic task i can choose to represent modular addition as one operation or two addition and modulo or even three digit addition carry propagation modulo on the other hand some linear algebra packages define add and mul as a single operation there is no doubt that network depth should somehow increase with complexity but defining it from computational depth seems unpractical second since you use shared layers model depth can be varied without having to retrain specifically model depth could be adjusted to the complexity of the training examples and then increased at inference to fit the complexity of the test set using the maximum depth in both train and test sets provided it can be defined from computational trees is not necessary and might not even be beneficial in a recent paper httpsarxivorgabs210604537 schwarzschild et al have shown using different architectures and testing on different tasks that adding iterations during inference could help models extrapolate to larger problems it would be interesting to test this on tcf and baselines copygating and variable depth the original universal transformers paper proposes a copygating mechanism which uses the adaptive computation time act mechanism graves 2016 at the token level the gating works differently than in tcf all gates begin closed and once opened remain so however i believe this universal transformer plus tokenlevel act gating is the correct baseline for tcf can such a comparison be provided this is all the more important as gating has a large impact on performance for the three problems considered actgating has another merit it adaptively controls the depth of the universal transformer which goes on iterating until all gates are open this means that the model can adjust to longer sequences by iterating for more ponder time this would provide an adaptive solution to the depth adjustment problem discussed in the previous section do you think an adaptive control for the number of iterationslayers could be implemented encoderonly output and geometric attention the original universal transformer is a sequence to sequence model when decoding a solution all the output sequence of the encoder is attended to in your implementation only the last element in the output is taken into account as you observe in the results discussion of section 31 this creates problems at test time because the position of the last word changes as sequence length increases it also complicates training since the output position depends on the sequence length which varies in all the problems you propose as you show this can be alleviated by relative positional embeddings and directional encodings which can be used to force the result to move right as the computation proceeds it also seems to be the main justification of geometric attention which seems to bring very little when used alone cf table 1 but could the original problem variable output position be eliminated what would happen if the output position had a fixed positional embedding this could be done in many ways reading the output from the first position instead of the last since the transformer is bidirectional this should have no adverse effect or from some other fixed value eg the fifth output word or enumerating positions so that the last token has a fixed embedding eg counting backward or from both ends to the center another question is the use of a single output element to decode the solution would the model be improved by using a longer part of the output sequence eg the n first output words with n the minimal size of output sequences or shorter output padded to this size an alternative and in my opinion much better solution would be to use a special attentionbased layer for the decoder an attention plus a linear layer working from the output sequence of the encoder this amounts to a minimal seq2seq model with one nonshared crossattentiononly layer in the decoder this would eliminate the variable output position problem and allow the full output sequence to be taken into account while decoding i believe these alternative decoders need to be tested without them it is hard to assess the importance of relative positions and geometric attention compositional table lookup the backward case unless their architectures are bidirectional the backward task is very unfair on lstm and dnc which are causal models to solve the backward task they would need to memorize all the tables before seeing the value to be operated on an impossible task given their capacity transformers on the other hand are bidirectional their bad performance on the iid backward case comes as a surprise and the unusually large error on this observation suggests an experimental problem do you have explanations about this high standard deviation of experimental results on the test data the fact that relative positional embeddings seem to improve the forward but not the backward case might be due to the choice of the last term in the output as the result to be decoded would for instance the results be inverted if the first output were chosen instead or some fixed middle position overall i am not certain this backward case helps the argument about the efficiency of tcf what it demonstrates i think is that causal models like the lstm need their inputs to be presented in a particular order which is no new news tcf appears to be a promising architecture and the experimental results seem good however some design and experimental choices make them difficult to compare with the original universal transformers in particular the choice of a fixed depth at train and test time might be an unnecessary constraint the proposed gatedcopy operation should be compared with the actbased copy described in the ut paper and the use of the last element in the output sequence as the basis for decoding might unduly increase the necessity of relative positions and shortrange attention ie the geometric attention and directional encoding recommended by the authors hence my note of 6 which i would gladly increase if additional experimental results are provided edit thank you very much for the detailed response i have raised my note to 8 ### Summary:

this work proposes a novel transformer control flow model and achieves nearperfect accuracy on length generalization simple arithmetic tasks and computational depth generalization all reviewers give positive scores ae agrees that this work is very interesting and has many potentials it would be exciting if the author could extend this framework to more challenging tasks eg visual reasoning 1 2 given the novelty of the proposed model ac recommends accepting this paper 1 clevr a diagnostic dataset for compositional language and elementary visual reasoning iccv 2017 2 ptr a benchmark for partbased conceptual relational and physical reasoning neurips 21

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this dataset includes daily living action data collected by two cameras and several imus from 51 strokeimpaired patients and 20 healthy subjects different from previous human action datasets this dataset focuses on shortduration action functional primitives recognition including reach reposition transport stabilization and idle the labeling requires a significant amount of effort from trained annotators the motivation is to facilitate the rehabilitation process after stroke that requires repetition of subsecond actions in addition a new action sequence identification method is presented that can outperform other approaches and the method is validated on the presented dataset and other previous video datasets this dataset is different from other existing datasets and focuses on actions in short duration this new way of action labeling provide a different view of behaviors that can be helpful for rehabilitation after stroke with this largescale dataset a new actionrecognition benchmark is presented using several previous segmentationbased approaches and newly developed sequencetosequence models inspired by speech recognition the result also shows that the model trained on impaired patients can also be applied to healthy subjects but the opposite is not working and this action recognition is shown to be useful for quantifying does by counting these shortduration actions functional primitives the paper presents a dataset including video data and motion data from imus however the motivation is not clear what is the advantage of adding imu sensor data setting up two cameras is very easy but attaching imus to a human body is not very easy it is also useful to show whether adding multiple imus can affect action execution from figure 1 it is clear that tow imus are attached to human hands and this setup may have some effect on impaired patients the paper states that it introduces a multimodal dataset but the benchmark is done respectively and the paper doesnt show the advantage of the multimodal data docsepin this paper authors introduce a new multimodal dataset called strokerehab it includes inertial measurements and video data of several daily activities performed by 51 stroke impared patients and 20 healthy subjects sequences are labeled at a high temporal resolution for annotating elemental shortduration actions in addition a novel approach dedicated to high resolution action identification is proposed experiments on the proposed dataset as well as existing ones suggest that the proposed approach is suitable for the task in comparison to stateoftheart the proposed dataset is interesting and seems challenging compared to existing ones in particular for the task of subsecond action identification as it is multimodal i think it could be beneficial for the research community in both the field of computer vision and time series analysis in addition the datasets also propose additional challenges like distributional shift and quantification of rehabilitation dose that are very interesting moreover the proposed approach for high resolution action identification sounds good and promising the fact that authors simultaneously propose a new dataset and a novel approach can also be seen as a weakness indeed it results in partial information for both parts while the supplementary material is significant and provides useful information the necessity to make backandforth between the paper and the appendix does not facilitate the reading and the whole understanding it would maybe be a good idea to focus the paper on the dataset with a benchmark using existing methods as it is the scope of this track and let the novel approach for a further paper docsepthis article proposes a dataset for identifying short duration actions for physical rehabilitation that contains both video and imu data the dataset includes data from strokeimpaired patients and healthy subjects performing activities of daily living and labels at a high temporal resolution along with the dataset the article proposes a novel sequencetosequence approach to identify actions and presents the perfomance on this dataset and other datasets of two versions of algorithm from their proposed approach and compared with stateofthe art algorithms the dataset proposed contains data from a large number of patients the dataset has also been annotated by several people following a precise protocol with a good interannotator agreement score the algorithms proposed are contrasted against state of the art algorithms on both the proposed dataset and other datasets the annotators training and annotation protocol is very precise but i have doubts about its adequacy the process needs to be justified the training seems to enable all annotators to acquire the same definition of the actions and the labeling process however i have concerns this lengthy process also erases the interannotator variability the coders could end up emulating the experts annotation instead of labeling by his own judgement and this would erase interannotator variability the labeling by experts would be more interesting than by coders even with a smaller number of labelers the approaches proposed lack a detailed description in the article a highlevel explanation and motivation are given but the final algorithms themselves are not described in the article only in the supplementary material the dataset aims at segmenting elementary actions that are quite similar however the data acquisition uses wearable sensors that are placed on the hands they and the straps can be bulky and change the natural motion of the subjects this change can have an impact especially on similar movements has this impact been evaluated docsepthis paper describes the strokerehab dataset for action sequence recognition the dataset consists of subsecond 3372 trials 120891 functional primitives in 71 subjects data was recorded with 9 imus on the upper extremities and featurized using the imu acceleration quaternion output and joint angle representation from a proprietary algorithm video data was also recorded from two views as well the data was labeled in a highly granular manner under supervision of a domainexpert using this dataset the authors tested different approaches for action sequence recognition finding sequencetosequence models outperformed segmentation based models for this data as well as others in the field overall i was impressed by the granularity of action labeling in the paper and the novelty of the application to the community i think the authors make interesting and likely valid conclusions about the utility of action segmentation vs seq2seq approaches for action labeling for short primitives which speaks to the potential for the reuse of the dataset in the community so i am open to accept this manuscript however i have reservations below that may affect this decision while i was impressed by the annotation density i do have multiple questions that i would like to be resolved before acceptance the paper contextualizes its results well in the field and is well described while i believe the dataset is largely sound i have multiple comments that i would like to see clarified and resolved data collection were the video and sensor data synchronized can you comment on how similar the video and sensor data were from subject to subject and day to day knowledge of domain differences across video recordings eg shifts of perspective will affect their generalizability raw imu data will depend on the subject size rehabilitation applications i was confused about how aer was chosen as the metric of choice as opposed to tprfdr of actions or more general taskbased quantities eg effector velocity the description is cursory and it is important to communicate for nondomain experts it also affects the conclusions as segmentation models were more performant on the basis of tpr is the accuracy of these models sufficient to reach desired capacity for physical therapy applications much of the scope of this application track is so domainagnostic experts and it is not clear what aer is satisfactory for applications related to the above what is the interhuman reliability in the aer l295 detailed countbased results are provided in appendix d i am not sure what this refers to domain shift why is there asymmetry in the transfer of models in table 1 why does stroke data generalize to health but not vice versa one trivial reason is that because there are more users in the stroke dataset they may cover domains better eg contain participants of a greater range of sizes and shapes and video conditions improving the machine learning transfer were the comparisons in table 1 balanced can you comment on how labels were made across domains that is were different definitions based eg on kinematics made for labeling stroke primitives given that they may be overtly different kinematically what is the quality of action recognition models on examples from this dataset eg given individual presegmented videos is the issue with the seqseq models and segmentation models that actions are hard to recognize or that they are hard to segment table 3 why dont confusion matricies sum up to one rowwise or columnwise to 1 also it should be clarified whether the matricies are normalized rowwise or columnwise docsepa new dataset called strokerehab provides action information for multiple patients rehabilitating from strokes as well as healthy subjects performing similar actions the dataset contains information from multiple modalities sensors and video data the paper also proposes a new algorithm to improve action recognition on this dataset 1 the motivation of the work is clear such a dataset can help the medical community in multiple ways and design better rehabilitation programs in the future 2 while the absolute number of identities present in the dataset patients and healthy subjects is small i believe the dataset is sufficiently large in the medical domain 3 the authors manually label the data and crosscheck the quality of labels 4 the authors first show that naively training the current stateoftheart approaches on this dataset leads to inferior results and proposes a new approach to tackle the task 1 i did not find figure 6 in the paper i believe it will be the figure which has the architectural definition the figure is in the supplementary material and should be mentioned properly in the main paper i believe this is an important figure and thus should be included in the main paper 2 i believe transformers can also be tried instead of lstmbased architectures data scarcity might be a valid reason not to use transformers which are known to be data hungry but this should be clearly mentioned and discussed in the paper 3 while a large amount of sensor data is collected the information provided in the paper regarding this is less i believe more information like where the sensors are placed can be provided docsepthe authors introduce a largescale actionrecognition dataset called strokerehab the contributions of this paper are summarized as follows 1 this dataset provides a benchmark for subsecond action identification 2 the dataset consists of 3372 trials of rehabilitation activities and 120891 functional primitives 3 it provides a benchmark for generalization in the presence of realistic distributional shift 4 it can be used for quantifying dose by counting functional primitives 1 this dataset consists of a considerable amount of shortduration elemental actions which required more manual effort to label and more time for training 2 this dataset contains both video and sensor data which can leverage both modalities to perform the task of sequence estimation 3 this dataset provides a benchmark for generalization in the presence of realistic distributional shift and datadriven quantification of rehabilitation dose 1 it seems not novel to make a dataset for shortduration action identification since there are many similar practices 2 there are some writing mistakes eg in table 2 on both hs should be on both hs sp 3 the experiment part is relatively insufficient the author should supplement the elaboration of the experimental settings such as the selection of the backbone of the baseline models used the implementation details of training and testing etc 4 lacking experiments to verify that this dataset can improve the performance of identifying more elemental motions at high temporal resolution the models trained on this dataset should be tested on the existing datasets and the real scenes and it would be better to supply the visualized results 5 the authors spend a little too much space on the action sequence identification algorithm but the proposed dataset is the theme to highlight the content of the dataset the structure of the article should be adjusted appropriately ### Summary:

this submission receives reviews from 6 different reviews most reviewers 56 appreciate the contribution of the new dataset they acknowledge that the problem setup is interesting and the dataset may be useful for different research communities computer vision time series analysis medical on the other hand reviewer 38gn concerns about the novelty of the proposed dataset ac reads all reviews and comments and discussions and is convinced that the proposed dataset will provide a useful benchmark for research thus ac recommends to accept this submission as a poster ac recommends the authors to incorporate all suggestions from reviewers for the final camera version

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors propose tesseract an aggregation scheme that is robust to the directed deviation attack proposed in fang et al 2020 pros a the defense is based on an interesting observation that for a sufficiently small learning rate as the model approaches optima in the benign setting a large number of gradients do not flip their direction with a large magnitude and if such a behavior is observed it is indicative of an attack the paper defines a flip score for every received gradient update and uses it to identify and either reward or penalize the update b the history of rewards and penalties for a client is maintained as a reputation score normalized reputation scores are then used to compute the global model for the next round algorithm 1 c in addition to defending against the directed deviation attack the paper also proposes two adaptive attacks adaptivekrum and adaptivetrim in which colluding attackers knowing the parameters of tesseract adjust their attack vectors to escape the cutoff flip scores d the paper provides a convergence analysis sketch and compares the performance of tesseract with a set of byzantine resilient defenses krum bulyan trimmed mean and median etc weaknesses 1 the paper proposes a defense against a specific form of attack and does not provide any guarantees or justification about why it should generalize against other more powerful forms of model poisoning attacks on pg 2 the paper argues without justification that other attacks are less damaging and essentially weaker than the attack in fang et al however one can argue that while the directed deviation attack is designed to prevent model convergence there are model poisoning attacks that not only insert targeted backdoors but also allow the model to converge bhagoji et al 2019 httpsarxivorgpdf181112470pdf bagdasaryan et al 2020 httpsarxivorgpdf180700459pdf these attacks are thus more powerful and can also maintain stealth 2 while the authors provide two very interesting adaptive attacks it is hard to generalize the strength of defense without any formal guarantees in other words can there be no other adaptive attack that can bypass this defense for example can an attacker target the reputation update mechanism over time 3 typically for reasons of privacy the gradient updates are usually encrypted before being sent to the server secure aggregation scheme by bonawitz et al 2017 httpseprintiacrorg2017281pdf tesseract in its current form will be difficult to implement with such schemes 4 in fig 2 both subfigures are labeled a and there is an inconsistency between the subfigure caption and the image caption figure caption describes a as an aggregation of benign updates and subfigure caption describes a as an aggregation of malicious updates finally the flip scores of the figuresyaxes are very different but what is confusing is that there are both benign and malicious clients in both please see the detailed comments above docsepthis submission with the title tesseract gradient flip score to secure federated learning against model poisoning attacks discusses defenses against data poisoning in federated learning the authors propose a novel defense against the recently popularized attack tesseract gradient flip score to secure federated learning against model poisoning attacks by yang et al this attack reduces model availability by sending malicious updates from compromised client that maximize sign flips in the global model gradient this defense then proposes a measure of change in gradient direction that can be evaluated for each local update and used to dynamically downweight clients with a large number of flips in direction this submission presents a reasonable and timely defense against a strong attack against data poisoning in federated learning which is nice the design of the defense is wellexecuted and the inclusion of a dynamic reputation is a good addition to its robustness i do have a few comments mostly regarding the part of the paper concerning adaptive attacks the submission discussses an adaptive attack against this defense but i would like to understand and see more discussion on why this is a strong adaptive attack specifically it is currently not clear to me that this is a strong adaptive attack it seems that the attacker could mount a stronger adaptive attack by keeping track of its own reputation and send local updates are optimized under an additional linear constraint that includes its own reputation it would be great if the authors could clarify my understanding in this matter how does this defense perform compared to the other considered defenses when the number of malicious clients is unknown for example assuming cmax is fixed to m5 but the actually number of malicious clients is decreased from m5 down to 1 this would be especially interesting to compare to defense algorithms that require no knowledge of cmax and some minor comments and questions gm and lm are used in the text before the variables are formally explained we halve the reputation score of every client if any of the values grows so large that the softmax operation causes an overflow i am surprised that this could even happen given the sizes of m nr and mu page 7 here to how the effect on diverse datasets here to show the effect on diverse datasets we find empirically result not shown that faba degrades fast it would be better if the authors could include this result in their appendix or clarify the sentence i think a partial answer to this statement is contained in fig3b the caption of fig2 says a shows the results where only benign updates were aggregated using fedsgd and b shows the case where only malicious updates were aggregated but the figure headings say the opposite for cifar10 and shakespeare only two clients are malicious is the attack too strong to be mitigated if more malicious clients exist in summary i think this is a decent submission i have some questions the central one is the strength of the adaptive attack which i would like to discuss with the authors and i am open to changing my evaluation accordingly docsepthis paper studied a very important topic in the field of federated learning how to efficiently resist untargeted model poisoning attacks in order to defend against such a poisoning attack the authors developed tesseract an aggregation algorithm that assigns reputation scores to participating clients based on their behavior in the training phase and weights the clients contribution extensive case studies have verified the effectiveness of the algorithm in particular the experimental results show that tesseract provides robustness against even a whitebox version of the attack the strengths and weaknesses of this paper are summarized as follows strengths the problem studied in this paper is important and needs to be solved in federated learning good writing weaknesses need to include more related work that is highly important need more justifications about the novelty claims need to add some experiments under noniid settings need to unify the attack paradigm insufficient theoretical analysis comments 1 the following important references are missing 1 kang j xiong z niyato d et al incentive mechanism for reliable federated learning a joint optimization approach to combining reputation and contract theoryj ieee internet of things journal 2019 66 1070010714 2 awan s luo b li f contra defending against poisoning attacks in federated learningceuropean symposium on research in computer security springer cham 2021 455475 3 zhang j wu y pan r incentive mechanism for horizontal federated learning based on reputation and reverse auctioncproceedings of the web conference 2021 2021 947956 2 the untargeted model poisoning attacks ie fullkrum attack and fulltrim attack designed in this paper are vague it would be better if the authors could formally define these attacks second the authors need to explain how these attacks in this paper differ from 4 4 minghong fang xiaoyu cao jinyuan jia and neil zhenqiang gong local model poisoning attacks to byzantinerobust federated learning in 29th usenix security symposium usenix security 20 boston ma august 2020 3 the reputationbased techniques used in this paper are quite common and nonsurprising a lot of previous work eg 13 has used reputationbased techniques to mitigate the poisoning attacks therefore readers may think that the authors just applied the reputationbased methods in federated learning the authors need to provide more details to justify the novelty of this paper 4 there are some grammatical errors and inappropriate formula symbols in the context for example k12m should be k12ldotsm 5 the editorial quality of this paper is not always satisfactory it contains quite a lot of inconsistentnonprecise descriptions as also reflected in the above comments 6 theorem 1 lacks rigorous proof and complete theoretical analysis it would be better if the author could give complete proof of theorem 1 7 the number of attackers has always been a very important hyperparameter or factor in model poisoning attacks therefore it would be better if the authors could conduct more case studies to explore the influence of the number of attackers on different defense algorithms 8 in addition noniid data seems also to affect the gradient direction or value of the client therefore the authors need to add some experiments to illustrate the effectiveness of the proposed algorithm under noniid settings 9 in fact the poisoning attack defended by the baselines chosen by the authors is different from the attacks designed in this paper then it would be better if the authors tested the proposed defense method on the poisoning attack involved in the baseline schemes 10 in general the authors need to add more theoretical analysis and verification experiments for now the authors need to add more theoretical analysis and verification experiments the reason is that there are still unfair comparisons in the comparative experiments in this paper if the author can address the reviewers comments i will consider giving it a score of 6 docsepthe paper tackles the problem of adversarial attacks in federated learning settings the main proposal is a defensive technique to address the byzantine generals problem in federated learning how to ensure that the general ml model is not affected by poisonous attempts made by corrupted clients the proposed technique is experimentally validated on four datasets outperforms previous defensive methods and the evaluation also considers adaptive adversaries with increasing degrees of knowledge overall the presentation of the paper is very good the quality of the english text is good figures are appropriate tables require some editing the topic addressed by the manuscript is trendy and inline with iclrs scope the references should be improved the contribution is significant strengths adaptive adversary trendy subject federated learning evaluation on multiple datasets technically sound weaknesses unclear assumptions and threat model problem or feature space attacks lack of a concrete usecase tradeoff i enjoyed reading the paper in contrast to many research efforts on adversarial ml this paper makes many security assumptions that set it apart with respect to the existing body of literature i also praise the the consideration of attackers with varying strength and the different datasets all these points make me lean to recommend acceptance nonetheless there are some issues that the authors could solve to further improve their paper let me elaborate on the abovementioned weaknesses starting from the most significant ones assumptions and threat model this is probably the only true problem of the paper which should be absolutely rectified i was not fully able to understand the assumptions made by tesseract does it work only against the directed deviation attack proposed by fang et al or does it also protect against different attacks in general section 22 threat model is not very comprehensive the authors should better expand this section by clearly pointing out all the assumptions and requirements of the proposed method this is especially true because the fang et al attack was proposed in 2020 and some of its assumptions are not yet wellknown specifically this statement is suspicious we assume a fullknowledge whitebox attack where the attackers have access to the current benign gradients does it mean that tesseract only works under this assumption ie the attacker knows and exploits the current benign gradients this is a rather unrealistic assumption i understand the willingness to work against worst case scenarios yet if such worst case scenarios are not realistic in the first place then what is the purpose of the proposed mechanism what benefit is there in protecting against an attack that will never happen in the first place i invite the authors to restructure this section by using the common taxonomies adopted in adversarial ml papers i problem or feature space attacks the authors perform their experiments on four wellknown datasets mnist cifar shakespeare femnist for each dataset a different deep ml model is targeted three of these datasets are of images whereas shakespeare contains text data there are different ways to create adversarial examples depending on the space where the perturbation is applied as far as i am aware the adversarial examples considered in this paper to perform the poisoned updates are created in the feature space it would be a lot more interesting if at least one evaluation included adversarial examples generated in the problem space aor at the very least considered samples generated by physically realizable adversarial perturbations b i acknowledge that the method should work even in these circumstances as the proposed tesseract defense is agnostic of the process used to apply the perturbation however considering the strong relationship with real security that permeates the paper i believe that a more convincing usecase would dramatically improve the quality of the paper this is also motivated by the current stateoftheart after almost a decade of adversarial attacks more recent efforts are leaning towards evaluation that consider more realistic circumstances where the attacker is constrained by the limitations of the real world this is even more true in distributed system scenarios such as network intrusion detection systems which bear a strong relationship with federated learning eg c d e f as such i invite the authors to perform an additional proofofconcept experiment where they consider adversaries with constrained capabilities this is also motivated by the fact that some perturbations may yield different effects when created in the problem space as shown in a tradeoff a common problem in adversarial ml countermeasures is that they may degrade baseline performance g h hence i am interested in knowing how the proposed method responds when there are no malicious clients even if the baseline performance does not decrease what is the overhead of the proposed method for instance in table 2 the authors report some results for attacknone which i assume represent the accuracy when no attack takes place however all the rows of these experiments namely fedsgd tesseract faba foolsgold fltrust consider hardening fl techniques for instance on mnist the proposed tesseract has an accuracy of 9252 when no attack takes placethe best among all other defences despite being appreciable i am interested in knowing the performance when no defense is applied surely the test accuracy in a fully trusted fl setting should be superior than 9252 hence i ask what is the cost of tesseract lack of a concrete usecase i believe that the paper could be further improved with a concrete usecase where the authors explain stepbystep how a single or multiple attacker can compromise a federated learning system and how the proposed method can help in solving such problem hence i request the description of a concrete usecase explaining the abstract scenario reported in figure 1 such usecase can be at the basis of the constrained attack that i invite the authors to perform in my problem space perturbations suggestion some additional issues in the introduction the authors state to counter this threat a set of approaches has been developed for countering byzantine clients in fl i believe that byzantine clients is a wrong term what is countered by tesseract are not byzantine clients but unloyal clients that are against the byzantine clients at least by referring to the wellknown problem of the byzantine generals which should agree on a method to reach consensus in the presence of unloyal generals the caption of figure 1 has a typo c out of m clients maybe be malicious in figure the gradient lmc1 is out of place in section 2 the authors state our simulation of federated learning consists of m clients each with its own local data but the same model architecture and sgd optimizer out of which c are malicious as shown in figure 1 is there a minimum amount of m figure 1 appears before figure 2 but in the text it is referenced after figure 2 putting figure 2 so early on is very confusing the flip score is a measure introduced for the first time in this paper as such any reader would be thrown off by such graphs before reading the paper meaning that the findings of figure 2 are difficult to interpretduring the introduction as the flip score has not been defined yet as such such graphs are ultimately meaningless i have to trust the authors that they correspond to interesting observations and fair experiments which is not scientific the presentation and notation in the flipscore page 5 is very ugly and difficult to follow section 5 should be merged in section 6 wrt table 2 the authors state we see that tesseract is the winner or 2nd place finisher in 7 of the 12 cells benign two attacks 4 datasets this should be better highlighted i only see three bold values for tesseract in table 2 wrt table 2 the authors state we have not shown the test loss curve for krum aggregation because of the large loss values i invite the authors to report such values in table 2 because the different formats of the three subtables none fullkrum fulltrim make this table very hard to interpret external references a intriguing properties of adversarial ml attacks in the problem space 2020 ieee symposium on security and privacy sp ieee 2020 b improving robustness of ml classifiers against realizable evasion attacks using conserved features 28th usenix security symposium usenix security 19 2019 c modeling realistic adversarial attacks against network intrusion detection systems acm digital threats research and practice 2021 d constrained concealment attacks against reconstructionbased anomaly detectors in industrial control systems acm annual computer security applications conference 2020 e conaml constrained adversarial machine learning for cyberphysical systems proceedings of the 2021 acm asia conference on computer and communications security 2021 f resilient networked ac microgrids under unbounded cyber attacks ieee transactions on smart grid 115 2020 37853794 g adversarial example defense ensembles of weak defenses are not strong 11th usenix workshop on offensive technologies woot 17 2017 h deep reinforcement adversarial learning against botnet evasion attacks ieee transactions on network and service management 174 2020 19751987 i wild patterns ten years after the rise of adversarial machine learning pattern recognition 84 2018 317331 the paper tackles a very interesting problem and the many security considerations as well as the experiments on various datasets and comparisons with existing defenses are commendable some issues unclear threat model flexbile perturbations in the feature space still prevent me from recommending complete acceptance more clarifications are necessary and by adding some more realistic experiments i believe that the paper could be turned into a significant submission of iclr i am recommending a 5 but my score can be easily increased to 6 by addressing the many clarifications expressed in my review further experiments and the concrete usecase would further increase my score after rebuttal score increased to 6 which i will increase additionally to 8 if the authors are willing to support the claim that tesseract is a secure by design defense further update score increased to 8 and i stand by my decision unless other reviewers point out that the claim of tesseract being securebydesign is flawed ### Summary:

the paper presents a defense against the gradient sign flip attacks on federated learning the proposed method is novel technically sound and well evaluated the crucial issue of the paper is however that this defense is specific to gradientflip attacks the authors show the robustness of their method against whitebox attacks adhering to this threat model and claim that an adaptive whitebox attacker with access to all internals of tesseract including dynamically determined threshold parameters cannot bypass its defense the latter statement does not seem to be well justified and following the extensive discussion of the paper the reviewers were still not convinced that the proposed method is secure by its design the ac therefore feel that the specific arguments of the paper should be revised or the claim of robustness further substantiated in order for the paper to be accepted furthermore as a comment related to ethical consideration the ac remarks that the papers acronym tesseract is used by an open source ocr software httpstesseractocrgithubio as well as in a recent paper pendlebury et al tesseract eliminating experimental bias in malware classification across space and time usenix security 2019 all of the above mentioned reservations essentially add up to a major revision recommendation which given the decision logic of aclr translates into the rejection option

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper presents algorithms for optimization using signsgd when the access is restricted to a zero order oracle only and provide detailed analysis and convergence rates they also run optimization experiments on synthetic data additionally they demonstrate superiority of the algorithm in the number of oracle calls for black box adversarial attacks for mnist and cifar10 the provided algorithm has optimal iteration complexity from a theoretical viewpoint the paper was overall very well written and sufficient experiment were presented the math also seems correct however i think they should have explained the motivation for the need of developing such an algorithm better section 3 can be improved i think this is an important paper because it provides a guaranteed algorithm for zero order signgradient descent however the ideas and the estimators are not novel they show applicability of standard gradient estimators for zero order oracles for signsgd algorithm docsepthe authors proposed a zeroorder version of the recent signsgd algorithm by replacing the stochastic gradient with a usual function difference estimate similar convergence rates as signsgd were obtained with an additional sqrtd factor which is typical in zeroorder methods three typical gradient estimates based on function values were discussed overall the obtained results are relatively straightforward combination of signsgd with existing zeroorder techniques quality the technical part of this paper seems to be solid the experiments on the other hand are quite ambiguous first off why do you choose that peculiar least squares binary classification problem on page 7 is assumption a2 satisfied for this problem why not use logistic regression the experimental results are also strange why would zosignsgd converge faster than zosgd or any other zo variant shouldnt they enjoy similar rates of convergence why would taking the sign make the algorithm converge faster note that the original motivation for signsgd is not for faster convergence but less communication for the second set of experiment how do you apply zosgd to generate adversarial examples again why do we expect zosignsgd to perform better than zosgd clarity this paper is mostly wellwritten but the authors at times largely overclaim their contributions or exaggerate the technical challenges page 2 2nd line the authors claim that our analysis removes the impractical assumption of b ot but in the later examples page 6 top they require q ot how is this any different than b ot even worse the former case also require b n ie there is no stochasity at all assumption a2 how crucial is this assumption for obtaining the convergence results note that not many functions have lipschitz continuous bounded gradients logistic regression is an example page 4 top zosignsgd has no restriction on the minibatch size b the rates at the end of page 5 suggests otherwise if we want the bound to go to 0 due to the term sqrtdb page 4 top the last two technical challenges do not make sense once we replace f by fmu these difficulties go away immediately and it is wellknown how to relate fmu with f originality the originality seems to be limited contrary to what the authors claimed i found the established results to be relatively straightforward combination of signsgd and existing zeroorder techniques can the authors elaborate on what additional difficulties they need to overcome in order to extend existing zeroorder results to the signsgd case significance the proposed zeroorder version of signsgd may potentially be significant in applications where gradient information is not available and yet distributed optimization is needed this however is not demonstrated in the paper as the authors never considered distributed optimization added after author response i appreciate the authors effort in trying to make their contributions precise and appropriate the connection between zosignsgd and adversarial examples is further elaborated which i agree is an interesting and potentially fruitful direction i commend the authors for supplying further experiments to explain the pros and cons of the proposed algorithms many of the concerns in my original review were largely alleviateaddressed as such i have raised my original evaluationdocsepin this paper the authors studied zeroth order sign sgd sign sgd is commonly used in adversarial example generation compared to sign sgd zerothorder sign sgd does not require the knowledge of the magnitude of the gradient which makes it suitable to optimize blackbox systems the authors studied the convergence rate of zerothorder sign sgd and showed that under common assumptions zeroorder sign sgd achieves osqrtdt convergence rate which is slower than sign sgd by a factor of sqrtd however sign sgd requires an unrealisitcally large minibatch size which zerothorder sign sgd does not the authors demonstrated the performance of zerothorder sign sgd in numerical experiments overall this is a well written paper the convergence property of the zerothorder sign sgd is sufficiently studied the proposal seems to be useful in real world tasks weaknesses 1 out of curiosity can we improve the convergence rate of the zerothorder sign sgd if we assume the minibatch size is of order ot this could help us better compare zerothorder sign sgd and sign sgd 2 figure 2 is too small to be legible also it seems that the adversarial examples generated by zerothorder sign sgd have higher distortion than those found by zerothorder sgd on cifar10 dataset is it true if so it would be beneficial to have a qualitative explanation of such behavior ### Summary:

this is a solid paper that proposes and analyzes a sound approach to zero order optimization covering a variants of a simple base algorithm after resolving some issues during the response period the reviewers concluded with a unanimous recommendation of acceptance some concerns regarding the necessity for such algorithms persisted but the connection to adversarial examples provides an interesting motivation

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper focuses on designing more effective ways for contrastive learning the author claims that stronger augmentations are beneficial for better representation learning different from directly applying the stronger augmentations to minimize the contrastive loss the author proposes to minimize the distribution divergence between the weakly and strongly augmented images the experimental evaluations are conducted on imagenet classification and related downstream tasks and the results are promising clarity 1 the method is very simple and straightforward my main concern is the experimental comparisons as we all know contrastive learning algorithms like moco and simclr benefits from longer training epochs a lot for example training with 800 epochs is much better than with 400 epochs thus i think the comparisons in table 2 are not convincing from algorithm 1 we can find that the equivalent batch size of the proposed clsa method is two times as classical moco method thus i would prefer to check the results of clsa at epochs 100 and 400 for fair comparisons 2 what is the value of the balancing coefficient it would be nice if some ablation results are provideddocsep this paper proposes the better utilization of strong data augmentations for contrastive loss functions in unsupervised learning in moco set up typically weaker augmentations such as color jittering cropping is applied to construct positive pairs from the same image in this study by proposing a modified objective the authors leverage stronger data augmentations to construct more challenging positives and negatives pairs to improve the quality of the representations the paper delivers a novel objective together with leveraging existing strong augmentations to improve downstream performance the authors can find my questionsconcerns listed below 1 the paper is overall wellwritten however it is disappointing to see many typos grammar mistakes throughout the paper some examples are in thus we proposed the clsa contrastive learning with stronger augementations to train an unsupervised representation the contrastive learning hadsell et al 2006 is a popular selfsupervised idea 2 in section 31 the authors mention that the keys in the memory bank is managed with first in first out method is it not supposed to be first in last out i would like to see some clarification on this 3 the numerator in equation 3 should be zi vs zi not zk 4 the authors claim that in he et al an input image is resized and cropped to 224224 pixels it should be an image is first cropped from an input image and resized to 224x224 pixels 5 in the experiments section the authors list other methods including moco simclr mocov2 byol and compare to what they propose as a baseline it would be nice to directly use the stronger augmentations in mocov2 objective and perform comparison to their method throughout the paper the authors claim that strong augmentations hurt the learned representations due to distorted images it would be meaningful to show this experimentally as well 6 the authors explain that they choose a strong transformation randomly from the given 14 transformations and repeat it 5 times to strongly augment an image is the sampling done without replacement in other words do the authors choose 5 unique transformations with the corresponding magnitude and apply those transformations to a single image 7 i like how the authors point the similarity of their objective to knowledge distillation in this case strong augmentations are assigned probability of being a positive pair from the positive pair constructed with weak augmentations it helps to understand the full picture for the proposed method 8 finally i think the figure 3 is confusing rather than being helpful both weak and strong augmentations go to the memory bank and it looks like two distributions come out of nowhere in the figure it would be more clear to point out that there is distribution of the representations from the strong augmentations and weak augmentations and they supervise the assignment for strong augmentations given predictions on the weak augmentationsdocsepthis paper presents a method to incorporate stronger augmentations into the visual representation contrastive learning framework specifically three correlated views of an image are first generated by using two weak and one strong augmentation operations on the same image then the networks are trained to maximize the agreement between the two weak views and also to minimize the distribution divergence between a weak view and the strong view the method is evaluated on several visual tasks including classification transfer learning and object detection with the standard evaluation protocol for selfsupervised learning and the results are promising pros 1 this paper is wellstructured and easytofollow 2 the idea of utilizing strong augmentations for contrastive learning is interesting and novel to me and the results are promising 3 the proposed framework seems general which might be easily incorporated into the existing contrastive learning frameworks cons 1 the motivation about using stronger augmentations is not well justified specifically the authors propose to use stronger augmentations based on two reasons 1 stronger augmentations can expose some novel useful patterns 2 the effectiveness of stronger augmentations is proved in the semisupervised learning and supervised learning field however no related papers are provided to support the first point while the papers cubuk et al 2018 qi et al 2019 wang et al 2019 that are cited to support the second point do not explicitly make relevant conclusions chen et al 2020a even demonstrate that when training supervised models stronger color augmentation hurts their performance i would like to see a more comprehensive review of related works to clarify the motivation 2 in addition some important ablation studies are missing in the experiment eg how does the performance change as the magnitude or usage times of stronger augmentations changes 3 the proposed ddm loss seems general for different contrastive learning frameworks i would like to see if it still works when applied to other frameworks eg simclr infomin overall given the novelty and strong results of the proposed framework i remain positive towards this paper i will be happy to increase my rating if my concerns are addressed in the rebuttal period docsepsummary this work investigate the recent popular direction of unsupervised representation learning using contrastive loss between augmented images authors propose to minimize the divergence between the distributions of strongly augmented vs weakly augmented images the method reaches competitive performance in recognition and object detection strengths the main idea is well motivated that strong augmentation reveal useful cues in visual representation learning but has not been successfully exploited in unsupervised learning the proposed solution is novel within contrastive learning to my best knowledge results are extremely strong concerns the divergence between the two conditional distributions can be a moving target since they are trained jointly it is not clear if this is will result in stable learning for the unsupervised setting and what effect that may have on the performance and quality of the representations evaluation only focus on final result and lacks analysis of the proposed method especially when compared to recent paper of similar nature published in top conferences for example strong augmentation is a focus of this paper but there are no ablation regarding the augmentations is the performance sensitive to the choice of strong augmentation the paper could also use some more theoretical analysis to address some of the weaknesses stated above recommendation i like the proposed idea it is novel and interesting and seems to achieve good results however the lack of both theoretical and empirical analysis beyond results on performance raises many questions as a result i am on the fence but leaning towards accept ### Summary:

this paper improves mocobased contrastive learning frameworks by enabling stronger views via an additional divergence loss to the standard weaker views three reviewers suggested acceptance and one did rejection positive reviewers found the proposed method is novel and shows promising empirical results however as pointed out by the negative reviewer the paper should have clarified about computational overheads of the method compared to the baseline moco in several aspects eg their effective batch sizes or training costs for the readers better understanding as the concern was not fully resolved during the discussion phase ac is a bit toward for reject ac thinks the paper would be stronger if the authors include more ablations and the respective discussions regarding this point eg clsamulti and single vs mocov2 under the same training time both at early epochs 200 as reported in the author response and longer epochs after convergence 1000 and even more

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper presents a mixedsize cnn training scheme using several different input image sizes for one single model training the authors assume the training budget represented as si2bidi ie spatial sample size the number of batched distinct samples and the duplicates for each distinct sample to be a fixed constant during training step i under such an assumption two mixedsize training scenarios are considered one for training acceleration and the other for improved model generalization ability the authors additionally use stepwise image size sampling gradient smoothing and persize bn calibration to enhance the model performance under the above two mixedsize training scenarios experimental validation is performed on cifar and imagenet datasets using diverse cnn structures mixedsize training is a critical problem and the methods proposed in this paper are interesting my main concerns to this paper are as follows critical related works and comparison are missing mixedsize training of cnns for image classification are not new here are some recent works however they are missed by the authors resolution adaptive networks for efficient inference in cvpr 2020 resolution switchable networks for runtime efficient image recognition in eccv 2020 mutualnet adaptive convnet via mutual learning from network width and resolution in eccv 2020 besides as described by the authors neurips 2019 paper fixing the traintest resolution discrepancy also considers how to enhance the performance of cnn models when applying them with different input image sizes but a performance comparison is missing to show the advantages of this paper thorough discussion and performance comparison with the above works are necessary taking resnet50 trained on imagenet as an instance i notice that the proposed method shows obviously worse accuracy compared to some of these works another critical baseline is also missing in page 8 we note that for original fixedsize regimes this calibration procedure resulted with degraded results and so we report accuracy without calibration for these models to my understanding this is somewhat weird why bn calibration does not work on the other image sizes when the model is trained with a fixed image size it is not clear furthermore in neurips 2019 paper fixing the traintest resolution discrepancy this line of methods work pretty well such a bn calibration should server as another baseline for more fair comparison regarding b design how about the wallclock training cost in hours instead of the number of iterationsepochs how about the performance of applying scale the learning rate linearly to train the baseline model how about model transfer ability only image classification tasks are considered in the experiments how about the performance of the trained backbone models when transferring them in the downstream tasks such as object detection and semantic segmentation can performance gain be transferred others is there any principled way regarding the size sampling strategy the current strategy is based on manual setting which limits its use in real applications i suggest the authors to also provide precise accuracy numbers etc regarding some figures eg figure 1 figure 4 shown in the paper generally i am on the fence to this paper i encourage the authors to address the questions i raised above docsep summary the paper proposes to use different image resolutions during the training of a deep neural network image classifier and varying the batch size or number of data augmented versions of the images keeping the computational cost per step roughly constant the authors apply this approach to several architectures and three datasets and show that they can achieve or improve the same accuracy as the baselines but much faster or achieve better results with the same computational budget pros the authors conducted their experiments using three different datasets cifar10 cifar100 and imagenet and six different architectures resnet44 resnet50 wideresnet densenet and efficientnet the proposed approach outperforms the baselines consistently across the 8 pairs of dataset architecture that they have studied in addition mixsize can be easily implemented the authors also provide a pytorch implementation the authors investigate different tricks to apply during training when using mixsize to stabilize training or achieving better results for instance they compared randomly sampling the size from a distribution as they propose versus increasing the image resolution as training progresses and showed that randomly sampling yields slightly better results figure 3a seems to suggest that mixsize yields more robust classifiers under a wide range of image sizes the area under the mixed s144 curve seems to be larger than the area under the fixed s224 however further experimentation is needed to confirm this since the area of the mixed s208 seems closer to fixed s224 and in any case the maximum image size was capped around 415 cons on of the claimed contributions is faster training vs high accuracy we show that reducing the average image size at train ing leads to a tradeoff between the time required to train the model and its final accuracy however i would not consider this a novel contribution since the tradeoff between speed and accuracy is wellknown in fact the authors cite huang et al 2018 and szegedy et al 2016 which already showed this efficientnet is another wellknown architecture that takes advantage of this fact in the intro the authors claim touvron et al 2019 demonstrated that networks trained on specific image resolutions perform poorly on other image sizes at evaluation time as confirmed in figure 1 this is inaccurate since touvron et al 2019 actually show that slightly increasing the test resolution improves accuracy due to the discrepancy in object sizes introduced by data augmentation cropping in fact figure 1 shows the same effect model trained with 224 res achieves best results with 284 eval image size the statement in the introduction is again contradicted at the end of the first paragraph in section 21 the authors do not report any statistical significance metric some datasets have very close results so its hard to tell whether the improvements are statistically significant or not poor captions in figures and tables for instance the difference between solid lines and dashed lines is only explained at the very last figure in the appending figure 7 also the caption of figure 3 reads test accuracy on validation set which is ambiguous is it a typo or is it that the authors report the results on the 50k validation set of imagenet and use some smaller subset of the training set as validation reasons for score although the proposed approach is simple and consistently improves the baseline results im not convinced that the originality and significance of the work is enough for it to be accepted regarding originality there is a plethora of works exploring the tradeoffs between image size and accuracy the most similar works are howard 2018 and karras et al 2017 which increase the image size through training its not clear that random sampling offers a much better result judging from figure 8 in appending e if one compares the accuracy of smalllarge strategy at 125k steps possibly before the last increase in size regarding significance if one restricts the analysis to the best architectures in each dataset the increase in accuracy does not seem to be very large cifar10 9816 9832 amoebanet imagenet 7632 7653 efficientnetb0 cifar100 shows a larger improvement but the authors did not use amoebanet which worked best in cifar10 for some unknown reason the fact that no statistical significance metrics are reported does not help to discern whether the improvements are meaningful or notdocsepthis paper proposes to increase training costs to compensate for the reduced costs from multiscale cnn training by either increasing batch size and therefore lowering the number of iterations per epoch or increasing the number of augmented versions duplicates of the same samples within a batch the former allows for smaller total training costs than conventional singlescale training while the latter maintains the total training costs but improves the final performance several training improvement methods are introduced to improve the multiscale training papers strengths the paper is quite wellwritten code and models are provided for reproducibility gradient smoothing is a nice way to mitigate the variability of gradient statistics resulted from different input sizes as far as i know this is quite novel particularly in the context of multiscale training papers weaknesses multiscale training is a common practice in many computer vision tasks especially in object detection less common in image classification this paper also does multiscale training but only introduces some minor improvements that are neither breakthroughs nor that they provide any interesting insight bag of freebies for training object detection neural networks arxiv yolo9000 better faster stronger cvpr 2017 mmdetection open mmlab detection toolbox and benchmark arxiv for stepwise size sampling it seems like that conclusion to use this variant of sampling is heuristically chosen and totally ignores the existing practice in other computer vision tasks one of the straightforward ways to do multiscale training in object detection is to select different input sizes even for the images within the same batch by padding zeros for the smaller images alternatively one could sample different input sizes for different gpu batches all images within a gpu share the same size while doing multiplegpu training the three training improvements stepwise size sampling gradient smoothing and batchnorm calibration are what separate this paper from prior work but they are not extensively evaluated some of them are briefly evaluated in the appendix and some of their effects are just briefly mentioned in the method section they ought to appear in the experimental section of the main paper gradient smoothing seems like a nice idea but it is unclear how important it is given that there is only one figure fig7 showing its impact on the performance this paper strives to increase the number of batch samples given a fixed budget of computational and time resources for per iteration step i wonder why this should be limited to the cost of an iteration step but not the entire training cost from all epochsiterations it makes more sense to measure the cost for the entire training process which accurately tells how much is spent to train the model until convergence in sec 51 the size ratios for different datasets are carefully chosen based on crossvalidation this makes it hard to directly apply mixsize to other datasets or settings without going through this step it also adds additional computations which defeat the purpose of increasing batch size to maintain the same training cost as conventional singlescale training using separate batchnorm statistics for multiscale inputsfeatures has been explored in the following papers published at least few months before iclr deadline they should be cited and compared against mixsize learning to learn parameterized classification networks for scalable input images eccv 2020 stochastic downsampling for costadjustable inference and improved regularization in convolutional networks cvpr 2018 overall this paper shows good performance and has some good ideas eg gradient smoothing for improving multiscale training but it fails to give more emphasis to or do a deeper dive into the potentially novel aspects of the work the current performance improvements may come from doing just trivial multiscale training which was already widelyexplored in prior workdocsepthe paper proposes the use of mixed image sizes during training they argue empirically that such an approach improves generalization for both fixed image size eg 224 in imagenet as well as for variable image size the proposed training algorithm maintains the same computational budget at each step by either changing the batch size or by using moreless augmentation they show that adjusting the batch size leads to a faster training but using augmentation leads to a better test accuracy hence a tradeoff however in order for their proposed method to work the authors also propose modifications to standard training procedures ie smoothing the gradient adjusting the batchnorm layers but without carrying out an ablation study that shows the impact withwithout each of these steps my particular concern is in the use of gradient smoothing if i understand it correctly this is not very different from using momentum which is known to reduce the variance and improve generalization however the authors use gradient smoothing only in their proposed method and do not use it in the baseline method why not it is possible that the reported improvements eg for fixed image size come solely from this step the other concern is when sampling the image size per step the authors propose distributions that seem odd in their experiments eg why is p06 for size 128 in imagenet which is much larger than others and why not uniform in cifar10 it is important to know if the results are sensitive to the choice of the distribution to make sure that the benefit is not due to random chance also if this distribution needs to be finetuned then the discussion about improving the training time would be meaningless the last issue is the robustness to different image sizes figure 3a shows that if the average image size during training is small the network will perform better for small images but not for large images conversely if the average image size during training is large it will perform better for large images but not for small images if the concern here is around using mixedimage sizes at inference time then the red curve in figure 3a shows that a fixed image size is reasonably robust if one knows in advance that the average image size would be smaller than 224 one can train with a fixed image size that is smaller one minor last remark feel free to ignore regarding the motivation the authors study the correlation between the full gradients for the same image with different sizes on the one hand and for different images with the same size on the other hand they conclude that the first case different sizes shows a stronger correlation which is true according to table 2 but this statement omits the fact that most correlations were low anyway for example for partially trained network it is 008 vs 002 i do not think that one can use such figures to conclude that smaller image gradients can be used as an estimate to the full image gradients the improvement in test accuracy is very promising but i believe some ablation is needed to identify exactly where this improvement comes from and whether it can be obtained using simpler approach eg smoothing the gradient alone or using augmentation alone etc ### Summary:

this work proposes to train networks with mixed image sizes to allow for faster inference and also for robustness the reviewers found the paper was wellwritten and appreciated that the code was available for reproducibility however the paper does not sufficiently compare to related methods the authors should resubmit once the comparisons suggested by the reviewers have been added to the paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: background for the paper semidefinite programs sdps form a popular class of convex programs but suffer from large input size in most useful settings to circumvent this issue of size burer and monteiro proposed expressing the problem variable as xyytop for some yinmathbbrntimes p and instead of sdp solving the following proxy problem which we refer to as bm beginalign minyinmathbbrntimes p yytop inmathcalccbullet yytop endalign where mathcalc is the constraint set for the original sdp with m constraints the benefit conferred by this reduction is that bm uses onp memory whereas sdp uses on2 bm clearly lies in a lower rank space than sdp and therefore it shouldnt necessarily be the case that solving bm solves sdp however it turns out that when p in the size of y above satisfies p osqrtm then the solution set of bm also contains that o sdp following the celebrated result of barvinok and pataki independently obtained that there exists a ranksqrtm solution to sdp in opposition to the memory saving an immediate disadvantage of the formulation of bm is its nonconvexity standard convex optimization techniques therefore fail to provide convergence guarantees there has been a flurry of recent work boumalvoroninskibandeira bhojanapalliboumaljainnetrapalli cifuentes and cifuentesmoitra that provide polynomialtime guarantees for the bm formulations of various classes of sdps in the smoothed analysis setting what problem this paper studies in the line of work mentioned above smoothed analysis is central to the theorem statements in other words all the guarantees provided by those papers exclude a set of cost matrices the reason for this is that those papers prove their guarantees by applying a small perturbation to the cost matrices and showing that the probability of the algorithm ending up in a set of spurious critical points matrices that are secondorder stationary points for bm but do not correspond to solutions to the original sdp is vanishingly small the question posed by this paper is if such an exclusion is necessary the paper then provides an affirmative answer to this question by an explicit construction of such an sdp it uses tools from riemannian calculus to substantiate a point being a secondorder stationary point while its corresponding point being suboptimal for the original sdp based on my current understanding of the papers contribution my preliminary review is rejection this is because my understanding is that this problem has been solved by bhojanapalliboumaljainnetrapalli httpproceedingsmlrpressv75bhojanapalli18abhojanapalli18apdf see the paragraphs after corollary 4 and also the formal statement in theorem 5 could the authors please explain to me how their result differs from this one its highly likely that im misunderstanding the contribution in which case i want to learn what im missing and ill of course update my score accordingly i look forward to reading the authors response after the rebuttal its clear that i am missing the key contribution here i am going to try to figure this out but for now will change my confidence score since i am no longer confident of my original assessment i hope i can better understand this works contribution but if that doesnt happen soon enough ill request the ac to ignore my input please see above docsepthis work studies the burermonteiro method a nonconvex method for solving semidefinite programs sdps that have only equality constraints and whose solution is a n times n psd matrix of rank p the solution to the burermonteiro method is known to coincide with the original sdp when the rank is above the barvinokpataki bound p gtrsim sqrt2n although the burermonteiro problem is noncovex it is known to be solvable in polynomial time in a smoothed analysis setting when p gtrsim sqrt2n and it is known to be solvable in polynomial time for worstcase instances when p n2 the main result of this paper constructs worstcase instances for the maxcut sdp for all ranks sqrt2n lesssim p leq n2 whose corresponding burermonteiro problem has spurious local minima and hence gradientdescent type approaches fail to solve this problem this suggests that the use of beyondworst case analysis frameworks is necessary for proving the efficiency of the burermonteiro method strengths significance the burermonteiro has attracted interest in recent years and it has remained open whether or not a smoothed analysis setting was necessary for polynomial time algorithms above the barvinokpataki bound qualityoriginality the proof of existence of local minima relies on a notion of strictly pseudopsd matrices and a careful analysis of riemannian gradient descent in my view these ideas are interesting and the technical level is moderately high clarity the paper is wellwritten and organized weaknesses i do not see any major weaknesses i just have a quibble in the next bullet about how the contribution of this paper is phrased clarity i find the phrase the burermonteiro method fails to be confusing its not clear what it means to fail since solving the burermonteiro optimization problem solves the original sdp in a certain regime the real problem is that the burermonteiro solution may not be efficiently computable this paper gives evidence of computational hardness by demonstrating the presence of spurious local minima in worstcase instances but it does not strictly rule out some other clever polynomial time algorithm from solving the maxcut burermonteiro problem minor comments 1 i think it would improve the clarity of this paper to first introduce the burermonteiro method in general ie for general sdps with linear equality constraints and rank p solutions and then specialize to the maxcut problem 2 the preliminaries are introduced abruptly and if the paper is read linearly it is not clear initially why some of them are needed either i a highlevel sketch of the proof of theorem 1 before the preliminary section or ii a short sentence for each preliminary describing why it is needed eg we use riemannian gradient descent to prove existence of local minima could help with this 3 the notation p gtrsim sqrt2n might be confused with p omega sqrt2 n although the latter is not what is meant here 1 the authors do not foresee negative societal impacts and i agree with this assessment docsepthis paper studies the landscape of the burermonteiro optimization problem corresponding to the classical goemanswilliamson sdp relaxation of maxcut it was known that for generic edge weights the burermonteiro optimization over n x p matrices has no spurious secondorder critical points once the rank constraint exceeds the barvinokpataki bound pp12 n however it was unknown whether there exist particular edge weight matrices for which spurious secondorder critical points or local maximizers can exist this work provides an explicit construction of a class of signed edge weight matrices for which the bmoptimization of size 2p x p has a spurious local maximizer by zeropadding this construction extends also to the bmoptimization of size n x p for any n 2p the construction is to posit a specific matrix y i i in r2p x p as the candidate for the spurious local maximizer and to then characterize those edge weight matrices for which this y is a firstorder critical point secondorder critical point and spurious local maximizer respectively the characterizations of firstorder and secondorder criticality are simpler and based upon explicit forms of the riemannian gradient and hessian however as the hessian is rankdeficient for p n2 and there is an entire submanifold of local minimizers containing y a more involved geometric argument that explicitly shows convergence of riemannian gradient descent to this submanifold is used to argue that y is a local minimizer experiments are provided in the appendix that explore the basin of attraction of this local minimizer i think this is an interesting paper and would be supportive of its publication in neurips the result is a bit limited in scope in that it studies only the specific goemanswilliamson sdp and constructs only a specific type of local optimum for bmoptimization of this sdp however i find the identification of this particular type of local optimizer y the axial position matrix to be insightful and it is nice that the construction works all the way to the sharp threshold p n2 the analysis to show that it is a local optimizer rather than just a secondorder critical point is also nontrivial due to the rank degeneracy of the hessian and i think it develops an interesting proof idea overall i think the paper is insightful wellwritten and resolves a question whose answer was previously unknown in this literature yes docseplarge sdps are costly to solve via interior point methods therefore there is great interest in techniques that are much faster and achieve nearly optimal results burermonteiro bm is one such method it seeks a sdp solution a n times n matrix x which has rank k and parameterizes xvtv where v is k times n v is then optimized and the sdp constraints then become restrictions on the rows of v the present paper studies the bm method for the wellknown sdp relaxation of maxcut specifically the main question is whether local maxima of bm correspond to global maxima of the sdp the focus is on the regime kk12n in this regime there exist rank k solutions that are optimal for the full rank sdp however standard bm is only guaranteed to find local maxima the question then becomes is it always the case that a local maximum for bm is a global maximum also and thus a global optimum for the sdp quite simply this paper shows that the answer is no in general for a large range of k up to n2 this is shown by an explicit construction and nicely complements a result by boumal voroninski and bandeira that showed that the answer is yes for generic instances of the maxcut sdp the present paper also shows that there bad local maxima are stable for gradient ascent the main strength of the present paper is that it in some sense finishes the landscape analysis of bm for the maxcut sdp this is an interesting result that has strong connections with important recent work the construction in the proof is nice and original the analysis of how the objective function behaves around the bad local minimum is also interesting and uses the machinery of riemannian optimization as well as probabilistic arguments the main weakness is that in some sense we expect local optimization methods to do quite well as secondorder local minima of burermonteiro are nearly optimal for the sdp this is shown in mei et al solving sdps for synchronization and maxcut problems via the grothendieck inequality colt 2017 indeed a rankk local maximum achieves the sdp maximum up to a 1o1k error whenever the sdp instance comes from a weighted graph with nonnegative weights on the edges for kgeq sqrt2n as considered here this means that the rankk solutions are close to the full rank ones since maxcut involves a rounding with an approximation factor anyway one could argue that the small extra error from met et al is not very substantial still to be clear i think this is a strong paper that in a way finishes an important line of research on how benign bm is as a sdp solution method see the weakness above my only comment is that the contribution by mei et al should be discussed ### Summary:

the burermonteiro method is widely used for solving large scale semidefinite programs it works based on replacing an n times n positive semidefinite matrix x with y yt where y is n times p this has the benefit that it is more space efficient to store y than x but it transforms a convex optimization problem into a nonconvex one above the barvinokpataki bound an analogue of the notion of a basic feasible solution for semidefinite rather than linear programming we are at least guaranteed that there is a lowrank optimal solution but does the nonconvex problem have spurious critical points recent works have studied the critical points under a smoothed analysis model and shown that the burermonteiro method works almost down to the barvinokpataki bound the main contribution of this paper is to complete the analysis of the landscape by showing that without smoothing even for the maxcut sdp there are spurious critical points even for p n2 one reviewer had doubts about the relationship to the work of bhojanapalliboumaljainnetrapalli but i found the author reply to be convincing that the setting and techniques are fundamentally different the other reviewers were uniformly positive this is a nice contribution to the literature on the burermonteiro method as a comment to the authors i would suggest elaborating on the connection to the work of mei et al i agree that showing the global and local optima are close in objective value can be somewhat orthogonal to showing that the sdp recovers eg some underlying clustering in community detection this provides a further justification why it is important to understand the loss landscape and not just bound the suboptimality of any locally optimal solution indeed from what i remember of the mei et al paper the locally optimal solutions do not get nontrivial performance for the associated community detection problem so i think investigating this further and explaining it would be helpful since these are subtle distinctions

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes an adaptive unsupervised domain adaptation based on a variational particle filter bayesian posterior on an encoder which is then used to form predictions on a nonstationary time changing task the particle filter is run with respect to a continuoustime differential equation and some theoretical results are proposed to associate the particle weights with the parameterspace transition distribution used to form one of the terms of the training objective some comparative experimental results are presented pros this paper attempts to address many difficult questions including an efficient prediction of the bayesian parameter posterior do it in a nonstationary environment albeit assuming it is stationary which is weird and doing it with neural networks and solving differential equations to capture the trajectory of the posterior comparative results although not against all the relevant baselines seem impressive cons the overall system seems very complicated and i cannot say that i was able to get an overall satisfying grasp of it i have some concerns about the proof of theorem 1 i have some concerns that the posterior is a unimodal gaussian over parameters in terms of related work and baselines it seems that this paper has a blind spot wrt the family of conditional neural processes architectures which also propose an efficient way to directly generate samples from the parameter posterior conditioned on the past training data there is a bit of a contradiction between the uda setup where new tasks can appear at any time and the assumption of stationarity of pthetat assumed for thm 1 a much more transparent analysis of different computational costs is needed computational costs are not analyzed results do not compare against the family of neural processes which have similar properties docsepthe paper proposes an approach to unsupervised domain adaptation by formulate predictive modeling as a continuoustime bayesian filtering problem the works introduces extrapolative continuoustime bayesian neural networks with a particle filtering differential equation at its core that only rely on historical data for inference and models the change of importance weights the proposed model is trained in a framework that combines the minmax optimization of unsupervised domain adaptation and the elbo of vae to apply the model to nonstationary streaming data an additional temporal domain invariant loss term was used to encourage the model to generalize to unseen data experiment results show strong performance of the proposed ecbnn against other unsupervised domain adaptation models strength treating the timeevolving nn parameters as a continuoustime stochastic dynamic system and extending bayesian neural networks to continuoustime domains is a novel approach to unsupervised domain adaptation i also appreciate the proposal of pfde it is an original contribution to the reviewers best knowledge with strong motivation for efficient multistep ahead inference the experiment settings considered by the paper are comprehensive and diverse weakness a minor concern i have about the paper is the organization of the method section the traditional particle filtering method introduced in the background section is not part of the original contribution of the work and may be better incorporated in the related works section the author address the limitations of the work but not discusses negative social impact but i think it might not be required for this work which is a primarily methodology paper docsepthe paper describes a lowlatency unsupervised domain adaptation uda framework for nonstationary streaming data to this end the method includes a bayesian neural network which produces a temporally evolving parameterization to the feature extractor acting as a meta network with temporally changing output it includes furthermore a discriminator allowing adversarial domain adaptation training thirdly a predictive module is fed by the invariant representation the bayesian inference is tackled via variational inference relying on a continuous time particle filtering idea domain adaption on nonstationary stream in real time is unquestionably a problem with both theoretical and practical significance the described framework clearly novel and the description of the method is clear despite the clarity of the description the paper is quite dense as the method is quite complex it takes 5 or 6 equations to describe its loss function alone this level of complexity requires further justification ablation studies are entirely missing from the paper the question inevitably appears in the reader does this complexity really necessary it is not entirely clear why bayesian approach is necessary it is easy to imagine that it helps it is just far from being trivial that it does what are the weak points of an evolving point estimate approach versus an evolving bayesian approach for estimating the encoder parameters the problem setup could be described with a bit more clarity as it is not always entirely clear what is the main difference between the source and target domain in the growing circles dataset for example fig 2a shows that the source corresponds to a green wedge in the input space and the target is the other part fig 2b shows that the source and the target is temporally separated 0 39th frame and 5089th frames respectively is it both l210212 describes an unusual solution the prior is used more like a target where the kl term in the variational lower bound is used at training time to force the estimated parameters to follow this target this use seems against the meaning of prior in bayesian framework the authors describe that their method is only capable to work with continuously evolving systems at the moment and they mention handling discrete dynamics as a future work it is not entirely clear how large datasets can be handled the method is quite complex the evaluation time computational complexity seems quite favorable table 2 the training time complexity can be interesting as well ### Summary:

paper combines the use of a particle filtering differential equation newly proposed in this paper for sampling posterior parameters of a bayesian neural network with unsupervised domain adaptation and achieves strong results on tasks demonstrated reviewers found the method novel and the problem important several questions were raised about the motivation of using bayesian neural networks about the utility of combining several of the pieces of the loss function proposed about the computational cost but the authors provided satisfactory answers to the questions and provided ablations showing the utility of the different components of the loss function one downside pointed out was that the method is quite dense and involves several parts that are intertwined i hope the authors will revise their submission taking into account the points raised by the reviewers

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper analyzes whether low and midlevel feature information is clearly represented in early cnn layers but not as clearly represented in later cnn layers a property that is present in early and late vision processing systems in mice in particular the low and midlevel features of luminosity contrast orientation and corners are investigated additionally the paper shows that the intrinsic dimension of representations is relatively low in early cnn layers but much higher in later cnn layers overall the paper is reasonably written and effectively conveys what the authors did however the originality and significance of the paper are in my opinion not very high while specifically looking at luminosity contrast orientation and corner information is nice the idea that early feature information is prominent in early cnn layers and is lost in later layers is not new nor is the idea that the complexity of layer representations increases from early to late cnn layers one extremely relevant paper on the topic is gl and van gerven 2015 which showed that information present in early human visual areas is prominent in early cnn layers but not in later cnn layers that paper also showed that the kolmogorov complexity of layer representations increases with depth the analysis of mutual information between layer representations and labels is not new either 35 the comparison of cnns to mouse data is also not new cadena et al 2019 cadena et al 2019 found that cnns were a poor model of the visual system of mice if the submitted paper could address and present evidence for why their results contradict the findings of cadena et al 2019 it would strengthen the paper the authors should add a discussion on the limitations of their work to the conclusions and discussion section one limitation that potentially could be discussed is that high level statistical properties can be shared by systems that process information differently docsepin this work the authors have studied the similarity in the emergence of certain statistical patterns across neural activity recorded from the rat ventral visual stream and a trained randomly initialized deep convolutional network optimized to perform largescale object recognition on imagenet the authors present 3 interesting observations 1 similar trend of intrinsic dimensionality of object representations across the rat ventral stream and pretrained deep convolutional networks 2 observed the pruning of lowlevel and midlevel information which followed their distillation in early layers of the rat ventral visual stream and vgg16 these effects being a result of training and not merely a byproduct of the hierarchical processing architecture 3 object categorization decoding accuracy closely tracked the increase of objectspecific information across the vgg16 hierarchy strengths there has been an explosion of recent studies exploring the similarity between biological and machine visual perception via representation similarity analysis and direct comparison of neural representations on a common stimulus set this work on the other hand presents a less commonly studied yet important complementary direction of observing commonly emergent statistical trends in the representations of biological and artificial neural networks and highlights the change in these trends as a function of training while the current study is restricted to comparing object recognition trends of deep networks with the rat ventral stream the general methodology presented can be extended to primate analyses once the technology to record primate data across the ventral visual stream for a considerably large set of images is available the authors provide adequate mathematical background in the methods section to clearly follow the key results presented in the subsequent sections in comparing the statistical trends between rat ventral stream and vgg16 representations transparent disclosure of which neurons were discarded from the analyses and the criteria for discarding them poor selectivity to the stimulus features being analyzed in section 23 i found the paper to be very wellwritten and organized with clearly labeled plots for the most part along with detailed captions describing the key results are very helpful weaknesses while the current analyses are interesting and sufficient to convey the key observations made by the authors a highlevel comment would be to relate the observed statistical trends with those obtained by the rsa studies measuring braincnn similarity for eg it appears that a related finding is shown in httpsarxivorgabs180700053 in fig 5 wherein there exists a correspondence between the decoding accuracy of successive layers of a pretrained deep neural network with successive areas of the primate ventral visual stream nit some of the plots may be hard to read due to the minor difference in the color or linestyle of the various markers eg the authors must please check if fig 4s coloring scheme is accessible to colorblind readers it was difficult for me to initially identify the solid vs dashed lines in fig 5 as they look quite similar here similar to fig 3 i would add plot titles orientation and contrast to fig 4a and 4b for improved readability the authors could potentially discuss more the limitations of the current work and highlight areas of improvementextension that could be useful to inspire future work as mentioned in the weaknesses i would like the authors to please discuss limitations and provide directions for extending the current work docsepthe paper compares internal representations of what could be called the rats ventral visual stream and the vgg16 network trained on imagenet the authors find that the intrinsic dimensionality id of rat ventral stream and vgg16 follow a similar pattern with id first increasing and then decreasing again in addition they estimate the mutual information between some lowlevel image features and internal representations and also find similar trends strengths understanding similarities and differences of real and artificial neural networks is an important topic paper is clearly written and easy to follow finding that id follows a similar pattern in brain ann might potentially be interesting weaknesses analysis way to restrictive considering only one fairly old cnn architecture increasing mi values in fig 35 are implausible data processing inequality unclear how the analysis advances either ml or neuroscience yes docsepthe paper investigates a parallel between two processing systems 1 a deep convolutional neural network vgg16 and 2 the rat visual cortex more precisely the paper considers previous results obtained on one system and attempts to replicate them on the other system these results are related to the processing of information along each system to investigate which information is represented in each layer of each system the paper considers a number of metrics computed on the intermediate representations intrinsic dimensionality mutual information with an image metric computed on the input average luminosity contrast orientation couple of orientations decoding accuracy by considering the trend of these metrics over the layers of each system the paper demonstrates a parallel between the two systems the results also describe the difference in representations between a pretrained cnn and a network with random weights clarity the paper is very clearly written in all aspects exposition of the problem relation with the literature proposed method results and discussion it is well structured and the figures are of top quality the results seem sufficiently described to be reproducible additionally sharing the code to reproduce all the figures is appreciated not tested originality the investigation is relatively original a large number of paper have investigated mappings between artificial and biological neural networks particularly cnn and the visual cortex in humans monkeys rats and more using methods such as encoding models or representation similarity analysis this paper follows this line of work but it is original in that it investigates a number of manually computed metrics these metrics have a somewhat limited complexity but they can be computed over different layers to investigate explicitly how the information is processed in each system quality the experiments are sound seem well executed and the results are reasonable the paper is structured around the replication of figure 1 abcd in the other system figure 1a vs figure 2 the hunchback profile in the visual cortex is not as neat as in the cnn but the results are somewhat consistent the consistency assumes we ignore roughly layers 916 figure 1b vs figure 3ab the decreasing trends are consistent but only if we ignore layers 13 figure 1d vs figure 3cd here the trends are not as consistent as stated in the visual cortex the orientation representation decreases with depth and the corner representation increases with depth in the cnn the orientation trend is only decreasing if we ignore layers 35 and the corner trend is only increasing if we ignore layers 516 figure 1c vs figure 5 the normalized mutual information mostly increases in the last layers say 716 overall the trend consistency between the visual cortex and the cnn is a bit overstated the trends are only consistent if we consider a subset of layers and this subset is not the same depending on the representation considered significance the results are not particularly surprising a lot of studies have already shown a parallel between cnn and the visual cortex in mammals this new study adds up to this parallel describing it with an original approach the pretrainedvsrandom results are all unsurprising but nicely quantified in the different experiments 4 the authors briefly discuss the fact that the dataset considered is not capturing the entire visual hierarchy thus limiting the parallel with the cnn architecture 5 the authors have not discuss the choice of vgg16 compared to other existing cnn architectures ### Summary:

this paper examines the intrinsic dimensionality id of representations in the rat brain and cnns the authors show that the rat brain like the cnns studied have distinct expansioncontraction phases and that in the cnns one also observes a distillation and pruning of low and midlevel information similar to what is seen in the brain the authors also show that in the cnns highlevel object information only emerges after these steps of distillation and pruning this illustrates potentially interesting parallels between information processing in the real brain and cnns the reviewers were split on this paper the initial reviews identified issues of novelty and insight but after the author responses three out of four agreed that the work was interesting and technically sound and they found the paper wellwritten one reviewer was still concerned that the paper does not do enough to show that these similarities in id and information retention tell us anything meaningful they were also concerned that comparisons were not made across enough architectures nonetheless given the balance in the reviews and post response scores an accept decision was reached

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors present a fully differentiable yet modular pipeline for autonomous vehicle perception and control it preserves the traditional abstractions such as perception prediction planning and control but implements differentiable versions of these components so that gradients can pass all the way from task performance back to perception they demonstrate the value of this on a trajectory prediction task strengths the idea is wellmotivated and the approach seems well executed the approach preserves some of the important interpretability charateristics of traditional methods while still enabling endtoend learning using task performance to learn perceptual models is a promising area of research and this paper showcases a possible method for doing so in the case of autonomous vehicles weaknesses questions the authors should highlight to what degree the differentiable components are novel to my understanding they have mostly taken existing components and assemble them together so the contribution should be mostly considered at the system level if this is not the case the authors should clarify the cost function in 3 contains a lot of weights how sensitive is the algorithm to the selection of these weights similar question for alpha13 in the combined loss function how is the discretization of s chosen for the planner ie what is the dimension of the categorical distribution and why docseprather than building an av stack in a modular fashion this work introduces a differentiable and modular stack for prediction planning and control this enables optimization of upstream components such as prediction via backprop through planning and control strengths the method is still interpretable in the sense that it is composed of modules with specific purposes the method is able to be trained endtoend due to being fully differentiable comparison to a nondifferentiable but modular method is made clearly weakness no real world or even simulator results not differentiable to all possible parameters comparison to an endtoend neural network is not made the authors note that not all parameters are differentiable how does this system compare to an endtoend neural network that doesnt attempt to provide an interpretability how much performance is being sacrificed for interpretability can the authors provide quantitative or qualitative examples of how this interpretability is useful docsepthis paper presents an endtoend differentiable stack for autonomous driving the prediction module is a neural network and the planning and control modules are handdesigned algorithms notably the handdesigned algorithms are differentiable which allows training of the upstream prediction module for a downstream control objective by backpropagating gradients through these handdesigned algorithms the authors make planning differentiable by replacing the argmin operation with sampling from a categorical distribution and the authors make use of an offtheshelf differentiable mpc algorithm to make control differentiable the authors show openloop offline results which indicate that this kind of planningaware training of the prediction module improves performance strengths a step towards endtoend training of prediction modules while retaining the interpretability stability of handdesigned control and planning algorithms the paper is wellwritten and easy to parse weaknesses only offline openloop evaluation so it is difficult to predict what real world performance will be like i found the metrics to be somewhat hard to interpret but this could be because im not a researcher in the av space docsepthe authors tackle the task of offering a full autonomous driving software stack in contrast to classical approaches that focus on modular software containing of perception planning and control the authors present here software components that are full differentiable the authors focus on prediction planning and control modules that are made differentiable train it in a simulation environment nuscenes and then show the results in comparison to baselines the authors display that this approach in contrast to prior endtoend methods gives more modularity and interpretability strengths the paper is wellwritten and good to read the technical explanations are exceptional the paper tackles a very hot topic in the field especially for avs were we are currently seeing bottlenecks in handengineered algorithms honest opinion about the limitations of this approach it is not a full full differentiable stack the results indicate that there is value in moving from purely predictionoriented evaluation metrics towards downstream taskoriented metrics this was new to me and is a significant result especially with all the work in predictionalgorithms right now weaknesses no hardware experiments were provided no training results were provided although it is stated that the nuscens datasets is used there is nothing explained about training quality and training time no comparison to a modular software stack classical pipeline or a complete endtoend pipeline i do not understand the comparison with the baseline we compare diffstack to the same predictionplanningcontrol stack but with modules 219 trained for different objectives that capture a varying degree of planningawareness the authors claim with the title its a fullstack but in the limitations they show that this approach is only performed in openloop training and evaluation ### Summary:

this paper proposes a modular but fully differentiable stack for selfdriving cars the differentiability enables the gradient to backpropagate from task performance all the way to the prediction module it demonstrates better prediction accuracy than the traditional modular approach while preserving interpretability and reusability all the reviewers agree that the paper is well motivated written and executed the paper tackles an important problem and contains ideas that will potentially have a major impact in the field of autonomous vehicles also there are also a few areas for improvements brought up by the reviewers 1 validation with a closeloop simulation or realworld experiments would significantly improve the quality and the potential impact of this paper 2 comparison with a classical modular approach andor a noninterpretable endtoend system may reveal more benefits or limitations of this work such comparisons are worth adding and discussing the authors response and the additional experiments have sufficiently addressed the main concerns in the original reviews thus we would like to recommend accepting this paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper provides an ntk analysis for a class of residual networks with a mixture of activation functions at different layers the authors derive bounds on the minimum eigenvalue for both finite and infinite width settings they also give a generalization bound for this class of network architecture the main contribution of this work in my opinion is a nontrivial derivation of lower and upper bounds on the minimum and for residual networks with a mixture of activation functions however analysis and proof techniques are mainly taken from the wellestablished ntk literature such as huang et al 2020 oymak and soltanolkotabi 2020 similar generalization bounds are established by cao and gu 2019 and arora et al 2019a there is an interesting connection to nas that the authors observe empirically where nas always picks up relu andor leaky relu in the optimal search that coincides with the fact that relu and leakyrelu imply larger minimum eigenvalue in the ntk sense however deep neural networks in practice do not generally operate in the ntk regime in short i see this work purely as an ntk analysis rather than an unifying framework for nas i dont see how the work studies optimization and generalization of nas or how the derived results guide nas in terms of the presentation the paper is wellwritten and easy to follow one thing i would suggest is to instantiate theorem 1 with a few special cases such as the standard 2layer networks with relu activation illustrating the hermite coefficients of such cases would help the readers to compare with existing results eg oymak and soltanolkotabi 2020 na docsepthis paper extends the neural tangent kernel limit to different and mixed activations functions as well as a varying proportion of skipconnections then this paper is able to bound the minimum eigenvalue of the neural tangent kernel matrix and to link this value to the generalization capability of the model this allows to guide neural architecture search without training the authors validate their methods numerically by first showing that the derived bounds match empirical results and that using their bounds to guide nas improves nas performances originality while i am not very familiar with the field this is to my knowledge the first time that an ntk approximation was derived for various and mixed activations functions quality the paper contributions seems sounds though the empirical validation could be more convincing how well does the criterion rank different architectures clarity the paper is clearly written the title and the abstract while quite clear appear to be a little misleading i dont think this paper helps understanding nas for instance nas convergence rather than providing a criterion which can help guide nas significance while the empirical results dont show a huge improvement being able to guide nas across different activation functions and skipconnections proportions seems useful the result would be much more significant if it applied to cnn and transformers but this is an interesting progress i wish the authors had tested their approach on more challenging tasks and with more competitive activation functions the authors clearly state that a big limitation of their work is that it only works for fc networks and not more common cnn or transformers docsepthis paper provides the theoretical analysis of a fullyconnected neural network with mixed activation functions in both infinite and finite width schemes furthermore the authors provide analysis on both lower and upper bounds of minimum eigenvalues of ntk and generalization error bounds in sgd finally the authors provide empirical experiments to support their theoretical claims on nas problems strengths 1 building upon cao et al and nguyen et als proof framework the author provides the theoretical analysis of the minimal eigenvalue of ntk and generalization error on mixed activation neural networks weaknesses 1 a considerable gap exists between theoretical analysis and the actual nas problem their theoretical results only provide the minimum eigenvalue of ntk and generalization error of a fullyconnected neural network with mixed activation functions i will elaborate on this in more detail in the question section 2 nasbench201 results in table 2 are not compelling enough latest methods such as betadarts 3 or drnas 4 find the optimal architecture in nasbench201 3 general nas problems contain a larger degree of freedom in choosing the components of a neural network not only just activation functions and skip connections similar to the first point there is a considerable discrepancy between the analysis and practice the author provides a thorough theoretical analysis of the minimum eigenvalue on ntk and generalization bounds on a neural network with mixed activation functions however the analysis is only for a particular family of neural networks with mixed activation functions and selective skipconnections furthermore the neural network analysis is on the final architecture assuming that existing nas algorithms such as darts random search ws or eigennas can find the optimal architecture from their search phase overall i see the authors theoretical analysis is not closely correlated to understanding the actual nas problem furthermore the empirical performance is far lower than existing stateoftheart in nas literature therefore i give a reject 1 cao yuan and quanquan gu generalization bounds of stochastic gradient descent for wide and deep neural networks advances in neural information processing systems 32 2019 2 nguyen quynh marco mondelli and guido f montufar tight bounds on the smallest eigenvalue of the neural tangent kernel for deep relu networks international conference on machine learning pmlr 2021 3 ye peng et al bdarts betadecay regularization for differentiable architecture search proceedings of the ieeecvf conference on computer vision and pattern recognition 2022 4 chen xiangning et al drnas dirichlet neural architecture search arxiv preprint arxiv200610355 2020 5 liang hanwen et al darts improved differentiable architecture search with early stopping arxiv preprint arxiv190906035 2019 6 dong xuanyi and yi yang nasbench201 extending the scope of reproducible neural architecture search arxiv preprint arxiv200100326 2020 7 yang antoine pedro m esperana and fabio m carlucci nas evaluation is frustratingly hard arxiv preprint arxiv191212522 2019 docsepthis paper presents theoretical analyses on neural architecture search using the recent theory of ntk especially it has provided the lower and upper bounds of the minimum eigenvalue of the ntk matrix as well as the generalization bound induced by the ntk matrix for the architectures in their predefined search space finally based on these theoretical analyses this paper develops a trainingfree nas algorithm namely eigennas eigennas has shown its competitive performance in trainingfree nas which in turn also supports the theoretical analyses in this paper strengths 1 this paper has provided nontrivial theoretical analyses on nas using the recent theory of ntk which may inspire more theoretical studies on nas in the nas area 2 the eigennas inspired by their theoretical results shows competitive performance on different nas benchmarks weaknesses 1 this paper does not really study the convergence or optimization of nas as what has been claimed in its title or abstract instead it is mainly focusing on the generalization property of its restricted search space ie a small search space compared with the standard nas search space 2 the motivation for why the minimum eigenvalue of ntk needs to be studied and how it is explicitly related to the generalization bound ie theorem 3 have not been well clarified as a result the study of the minimum eigenvalue of ntk in this paper may be less related using the minimum eigenvalue of ntk to bound the generalization performance of dnn in theorem 3 will make the theoretical results in this paper more coherent 3 this paper does not provide a clear or explicit interpretation of how the theoretical results in this paper can help us understand nas shown in the title instead from my own perspective this paper has mainly developed a generalization bound for nas and then proposed a trainingfree nas algorithm ie eigennas inspired by their theoretical results and such a trainingfree nas algorithm is similar to the one in r1 using the trace norm of the ntk matrix this paper has provided a valuable discussion about limitations and future works ### Summary:

this work relates nas to the conditioning of a dnn through the ntk framework this work is well supported theoretically and empirically and making this connexion is surprising given the potential interest for the nas community i recommend accepting this paper

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: clarity the work is a clear introductionoverview of this area of research the reviewer enjoyed the connections to multiplegradient descent and clear distinctionscontrasts with previous approaches to weighting the outputs of multiple discriminators all in all the paper is quite clear in what its contributions are and how it differs from previous approaches the details and motivations of the hypervolume maximization hvm method especially as it relates to and interacts with the slack method of picking the nadir point were a bit harder to follow intuitively given the standalone information in the paper originality adapts a technique to approximate mgd called hvm miranda 2016 and applies it to multidiscriminator training in gans as far as the reviewer is aware this is a novel application of hvm to this task and well motivated under the mgd interpretation of the problem significance unclear this work in isolation appears to present an improvement over prior work in this subfield but it is not obvious that the findings in these experiments will continue to be robust in more competitive settings for instance the worst performing model on cifar10 wgangp according to the experiments run wgangp also holds near sota inception scores on cifar10 when appropriately tuned without any experimental results extending beyond toy datasets like mnist and cifar10 the reviewer is not confident whether fundamental issues with gan training are being addressed or just artifacts of small scale setups closely related previous work neyshabur 2017 scaled to 128x128 resolution on a much more difficult dataset imagenet dogs but the authors did not compare in this case quality some concerns about details of experiments see cons list and significance section for further discussion pros the work provides a clear overview of previous work on approaches using multiple discriminators the connections of this line of work to mgd and the reinterpretation of various other approaches in this framework is valuable the author provides direct comparisons to similar methods which increases confidence in the results on the experiments run the hvm method appears to be an improvement over the two previous approaches of softmax weighting and straightforward averaging for multiple discriminators cons performance of gans is highly dependent on both model size and compute expended for a given experiment see miyato 2018 for model size and training iterations and brock 2018 for batch size training multiple discriminators in this paper up to 24 significantly increases compute cost and effective model size no baselines controlling for the effects of larger models and batch sizes are done the paper lacks experiments beyond toyish tasks like mnist and cifar10 and does not do a good job comparing to the broader established literature and contextualizing its results on certain tasks such as cifar10 reporting ratios to a baseline instead of absolute values for instance the absolute inception score of the baseline dcgan needs to be reported to allow for this is the inception score of the authors dcgan implementation similar to the 6 to 65 reported in the literature figure 3 is slightly strange in that the x axis is time to best result result instead of just overall wallclock time without additional information i can not determine whether it is admissible do all models achieve their best fid scores at similar points in training why is this not just a visualization of fid score as a function of wallclock time a method which has lower variance or continues to make progress for longer than methods which begin to diverge would be unfairly represented by the current figure additional comments in section 31 eq 5 appears to be wrong the loss of the discriminator is presented in a form to be minimized so exponentiating the negative loss in the softmax weighting term as presented will do the opposite of what is desired and assign lower weight to higher loss discriminators in fig 6 fid scores computed on a set of 10k samples are shown the authors appear to draw the line for the fid score of real data at 0 but since it is being estimated with only 10k samples there will be sampling error resulting in nonzero fid score the authors should update this figure to show the boxplot for fid scores computed on random draws of 10k real samples i have only worked with fid on imagenet where fid scores for random batches of 10k samples are much higher than 0 i admit there is some chance the value is extremely low on cifar10 to make this point irrelevant however docsepthis paper studies the problem of training of generative adversarial networks employing a set of discriminators as opposed to the traditional game involving one generator against a single model specifically this paper claims two contributions 1 we offer a new perspective on multiplediscriminator gan training by framing it in the context of multiobjective optimization and draw similarities between previous research in gans variations and mgd commonly employed as a general solver for multiobjective optimization 2 we propose a new method for training multiplediscriminator gans hypervolume maximization which weighs the gradient contributions of each discriminator by its loss overall the proposed method is empirical and the authors show its performance by experiments first i want to discuss the significance of this work or this kind of work as surveyed in the paper the idea of training of generative adversarial networks employing a set of discriminators has been explored by several previous work and showed some performance improvement however this idea methods along this line is not popular in gan applications like imagetoimage translation i guess that the reason may be that the significant computational cost both in flops and memory consumption increase due to multiple discriminators destroys the benefit from the small performance improvement maybe im wrong in appendix c figure 10 the authors compares the walllock time between dcgan wgangp and multiplediscriminator and claims that the proposed approach is cheaper than wgangp however wgangp is more expensive due to its loss function involves gradients while the proposed method does not if directly compared with dcgan we can see an obvious increase in wallclock time flops in addition the additional memory consumption is hidden there which is a bigger problem in practice when the discriminators are large sngan have roughly the same computational cost and memory consumption of dcgan but inception and fid are much higher from my perspective a fair comparison is under roughly the same flops and memory consumption the paper is wellwritten the method is wellmotivated by the multiobjective optimization perspective although the presentation of the hypervolume maximization method section 32 is not clear the resulting loss function equation 10 is simple and shares the same form with other previous methods the hyperparameter eta is problematic in the new formulation the authors propose the nadir point adaption to set this parameter the authors conduct extensive experiments to compare different methods the authors emphasize that the performance is improved with more discriminators but its good to contain comparison of the computational cost flops and memory consumption at the same time there are some small questions for the experiments the reported fid is computed from a pretrained classifier that is specific to the dataset instead of the commonly used inception model i recommend the authors also measure the fid with the inception model so that we have a direct comparison with existing reported scores overall i found that this work is empirical and im not convinced by its experiments about the advantage of multiplediscriminator training due to lacking of fair computational cost comparison with singlediscriminator training docsepthe paper investigates the use of multiobjective optimization techniques in gansetups where there are multiple discriminators using multiple discriminators was proposed in durugkar et al arora et al neyshabur et al and others the twist here is to focus on the pareto front and to import multiple gradient descent and hypervolumemaximization based methods into gans the results are decent the authors find that optimizing with respect to multiple discriminators increases diversity of samples for a computational cost however just scaling up and carefully optimizing can yield extremely impressive samples httpsarxivorgabs180911096 it is unclear how the tradeoffs in optimizing against multiple discriminators stackup against bigger gans from my perspective the paper is interesting because it introduces new methods into gans from another community however the results themselves are not sufficient for publication ### Summary:

the reviewers found that paper is well written clear and that the authors did a good job placing the work in the relevant literature the proposed method for using multiple discriminators in a multiobjective setting to train gans seems interesting and compelling however all the reviewers found the paper to be on the borderline the main concern was the significance of the work in the context of existing literature specifically the reviewers did not find the experimental results significant enough to be convinced that this work presents a major advance in gan training

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors begin by discussing the discrepancy in association between normal and anomalous points and then suggest an anomaly transformer based on an anomalyattention mechanism that is further improved using a minimax technique on six different datasets empirical analysis demonstrates that the proposed method outperforms stateoftheart anomaly detection methods strength 1 the paper is well written and easy to follow 2 figure 1 and 2 are very intuitive and easy to understand 3 detailed empirical analysis weakness 1 if i am not wrong then the smap and msl dataset are from a but the author cite su et al 2019b 2 there are repetitive ve entries in references for example su et al 2019a and su et al 2019b i suggest that the authors recheck all entries in bibliography carefully 3 i think it is important to at least provide the reader with different types of methods used for anomaly detection the authors have done a good job at it but in my humble opinion literature review is still missing some important papers for example lstm based method and smap msl dataset a dimensionality reduction clustering method b spatiotemporal method using convolutional lstm c tensor decomposition based method d 4 i have several questionconcernssuggestions about the experiment section how the threshold is set can you add more details and provide a solid reason on why r05 for smd 01 for swat 1 for other dataset the author should discuss the falsepositive rate in more details inside the main text as minimizing falsealarm is really important in practical scenarios what is the reason behind selecting 3 layers for anomaly transformer channel states of hidden state dmodel is set to 512 can you provide a reason for selecting this number and also can you discuss the impact of increasing and reducing this number on performance efficiency memory etc in my humble opinion the term robustness is very loosely used in the paper i suggest the author to tone down the sentences about robustness building on the previous point the robustness of anomaly transformer is not evaluated against adversarial attacks latest research have shown that almost all anomaly detectors such as mscred fails against simple fgsm and pgd attack it would be interesting to see how the robustness of proposed method in those scenarios figure 5 and 6 are hard to understand it suggest that the author add some background shading for anomaly regions and also add the threshold line so that the reader can easily understand how your method is outperforming other methods also figure 6 need more context and detail in the main text 5 i was unable to locate the link to the code repository it is critical to validate the papers claims and one of the simplest ways to do so is to access and run the code i believe that the authors should consider making the code for their method and empirical experiments available to the reviewers and later release it publicly a hundman kyle et al detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding proceedings of the 24th acm sigkdd international conference on knowledge discovery data mining 2018 b yairi takehisa et al a datadriven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction ieee transactions on aerospace and electronic systems 533 2017 13841401 c tariq shahroz et al detecting anomalies in space using multivariate convolutional lstm with mixtures of probabilistic pca proceedings of the 25th acm sigkdd international conference on knowledge discovery data mining 2019 d shin youjin et al itad integrative tensorbased anomaly detection system for reducing false positives of satellite systems proceedings of the 29th acm international conference on information knowledge management 2020 well written good empirical analysis but in some places the paper lacks the justifications and proper reasoning experimentresults section need some better explanations docsepthis paper introduces an anomaly transformer for detecting anomalies in time series with association discrepancy this paper introduces two discrepancies the series association eg period and trend and the prior association eg the local smoothness or continuity for abnormal points these two associations have a small discrepancy and for normal points there is a large discrepancy between these two associations this is because abnormal points have a strong local association while the normal points have a global association in the model a twobranch strategy is adopted to model the two associations separately the model is trained by minimizing the reconstruction loss and maximizing the discrepancy the experimental results demonstrate the effectiveness of the proposed model strengths 1 the observation that the abnormal points have a strong local association or prior association and a weak global association is interesting 2 to better model the association discrepancy the paper proposes a novel minmax association learning strategy to avoid the gaussian prior reduction problem 3 comprehensive evaluation on a variety of anomaly detection datasets demonstrate the effectiveness of the proposed association discrepancy learning strategy weaknesses 1 whats the convergence property of the minmax strategy 2 some details are not very clear for example in equation 2 whats the shape of wl and what does the operation mean equation 6 is ambigous mathcalxhatmathcalx22 is a scalar while the assdis score after softmax is a nbyd matrix in the implementation details for the r why not set r similar to ar of the datasets as shown in table 1 in table 3 for the recon of anomaly transformer if you only use the reconstruction error then there should be no optimization strategy for the association discrepancy why the optimization strategy is minmax in figure 5 what are the labels for the yaxis for the reconstruction and association criteria are they the values of loss in general the observation that abnormal points have a large discrepancy between the prior association and the series association is very interesting a novel transformer based model is proposed to model this discrepancy experiments demonstrate the effectiveness of the proposed method however it is unclear about the convergence property for the proposed minmax strategy besides there are many ambiguous details that hinder the readability of the paper docsepsummary this paper is looking at anomalies in time series data in particular they define anomalies as ones that lie outside some learned gaussianlike distribution they propose a minimax objective function that tries to minimize and maximize the discrepancy between a gaussian distribution with a learned variance parameter and an empirical one learned directly through selfattention on the data they measure discrepancy using the symmetric kl divergence between the two distributions and also use this for their anomaly score major comments is this approach a generalization extension of change point detection that is not mentioned at all the prior association is learned as one that minimized the difference with the series association and the series association is one that is learned to maximize the difference with the prior association this will promote the series association to learn as nongaussianlike of a distribution as possible while the prior association is finding the closest gaussian distribution to the series association will this minimax strategy not lead to a degeneration where both distributions becomes extremely wide and almost uniform like the reconstruction loss will guide the seriesassociation to find the most informative associations such as the adjacent time points of anomalies why are adjacent time points of anomalies most informative is this because this window of time can be altogether be considered anomalies as opposed to noise from a single out of distribution point the anomaly score just indicated that there is anomalies or not within the n time point window in order to narrow down where the anomalies are with the time points do you need to then test smaller windows of time wouldnt smaller windows cause there to be a shortage of data samples needed for learning kl divergence is not very good at measuring differences in the tails of distributions it does not put enough importance on that area but shouldnt the anomalies be in the tails of the distribution as they are rare minor comments some of the sentences are grammatically strange in the abstract and introduction sections overall the paper has goods results and seems sufficiently interesting and novel docsepthis paper proposes a new anomaly detection approach based on a transformer architecture the main idea is to leverage selfattention to capture the temporal dependency structure of the observations in a sliding window as a measure of anomaly since anomalies can be defined as a sequence that is inconsistent with regular ones the attention matrix is expected to reflect some major aspects of anomalies the authors propose a twobranch attention architecture to handle multidimensional realvalued timeseries data where prior and seriesattention matrices are computed and utilized in a certain minmax competitive learning framework the final anomaly score is defined as the product between the reconstruction error and the kl distance between the two attention matrices update after the discussion with the authors the authors have addressed all of my immediate concerns the paper now looks very strong i recommend acceptance the key idea of using tranformers selfattention as a measure of anomalousness sounds novel it is an excellent idea the proposed architecture featuring a twobranch attention mechanism also looks novel the novelty seems undisputable to the best of my knowledge the problem is however that the paper mostly ignores almost all the existing anomaly detection approaches for time series it obviously does not make sense to use pointwise anomaly detection methods such as svdd to capture sequential anomalies many researchers in this domain may agree that the baseline model in the present context can be the vector autoregressive model which naturally realizes a particular type of selfattention in the form of the lagdependent covariance matrices you can find many works that leverage a dependency graph for anomaly or change detection id also suggest looking at the literature on timeseries segmentation another issue is that the paper lacks sound justifications for the proposed approach given much theoreticalempirical work with var or statespace models or their neural extensions we expect a much more understandable derivation of the proposed model many functions such as assdiss lack a proper definition for example i didnt understand how the probability distributions had been defined from pl and sl just writing softmax or just showing eq2 does not mean you have defined a distribution for a matrix the gaussian distribution in eq2 is defined in the entire real domain but probably you are on a regular time grid apparently some mathematical inconsistency exists thats not a problem if this were part of api documentation of a software library but as a technical paper submitted to a top machine learning conference there may be different expectations i think the main idea deserves much more careful and deep thoughts i encourage the authors to redo the text to perfect it im sure that the new version will be a great piece of work in the community updated after the discussion novel idea of using the degree of selfattention as a metric of anomalousness new approach of positional encoding based on gaussian kernels comprehensive empirical comparison with alternative methods original summary novel idea of using the degree of selfattention as a metric of anomalousness unclear and unjustified descriptions lack of the critical baseline major issue in this domain ### Summary:

the paper proposed a novel approach that leverages the discrepancies between the global series association and the local prior association for detecting anomalies in time series the authors provided detailed empirical support to motivate the above detection criterion and introduced a twobranch attention architecture for modeling the discrepancies and establishing an anomaly score all reviewers acknowledge the technical novelty of this work including the key insight of modeling anomalousness with transformers selfattention and concrete training mechanism via a minimax optimization process as well as the comprehensiveness of the empirical study meanwhile there were some concerns in the positioning of the work in particular in the clarity in connection to related work and some reviews concern the clarity of the presentation eg missing some details in experimental results and the clarity of the exposition of the training process the authors provided effective feedback during the discussion phase which helped clarify many of the above concerns all reviewers agree that the revision makes a solid paper and unanimously recommend acceptance of this work the authors are strongly encouraged to take into account the feedback from the discussion phase to further improve the clarity concerning the technical details as well as the reproducibility of the results

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper designs sublinear algorithms for hierarchical clustering in dynamic streaming model query model and mpc model over very large graphs minimum cost hierarchical partitioning is the optimization objection dasguptas cost function from the theoretical aspect this paper proved the bounds including lower and upper bound respectively for the three given models they prove a general structural result that shows that a cut sparsifier can be used to recover a 1 o1approximation to the underlying hc instance although this research problem is not novel sublinear algorithms for hierarchical clustering is one of the fundamental issue in the studies of graphs lots of applications need the sublinear algorithms especially for massive graphs the paper is well written with a good presentation it supplies enough theoretical proofs for the complexity and 1 o1approximation is a good general structural result in the three models however i have some concerns about the approach and applications 1 some recent works prove a seem better bound for the same problem the authors should compare to them for example assadi s chatziafratis v mirrokni v et al hierarchical clustering in graph streams singlepass algorithms and space lower boundscconference on learning theory pmlr 2022 46434702 2 although proofs on bounds are good evidence for showing the performance it would be better and attractive to give some practical experimental studies to show the real effectiveness in massive graphs in some cases a well theoretical complexity still be not enough for the real huge graphs minor problem although the presentation is relatively well some necessary examples are needed for the readers to follow clearly 1 some recent works prove a seem better bound for the same problem the authors should compare to them for example assadi s chatziafratis v mirrokni v et al hierarchical clustering in graph streams singlepass algorithms and space lower boundscconference on learning theory pmlr 2022 46434702 2 although proofs on bounds are good evidence for showing the performance it would be better and attractive to give some practical experimental studies to show the real effectiveness in massive graphs in some cases a well theoretical complexity still be not enough for the real huge graphs docsepthis paper considers developing resource efficient algorithms for hierarchical clustering of weighted graphs in several settings streaming graph query model mpc model following recent work which developed an objective function for hierarchical clustering dasgupta 2016 the goal is to develop algorithms with sublinear resource usage in the number of edges in each model while producing an ophiapproximation to the optimal tree for dasguptas objective function the main results of this paper are to achieve exactly this by making a key observation about the structure of the objective function the authors show that this problem reduces to efficiently constructing a cut sparsifier in some settings eg streaming using tildeon space this can be done immediately via known techniques for constructing cut sparsifiers for the other settings graph query model and the mpc model a more relaxed notion of cut sparsifier is proposed which allows for some additive error in addition to the multiplicative error and the authors give sublinear algorithms for constructing such a relaxed cut sparsifier in the remaining settings lower bounds showing near optimality of the results are also given there has been significant interest in hierarchical clustering recently and one issue that many algorithms suffer from is scalability this paper helps to address this problem in several settings from the perspective of dasguptas objective function the techniques are natural and are well explained one thing that is missing from this paper but not probably not necessary is an experimental evaluation to demonstrate the practical effectiveness of the proposed algorithms na docsephierarchical clustering over graphs ha been mathematically formalized with a natural objective function introduced by dasgupta stoc2016 unfortunately this function is hard to optimize and approximation algorithms have been proposed in the tcs literature this paper studies this problem in the regime of sublinear computational resources specifically for three models of computations streaming model sublinear space query model sublinear time mpc model sublinear communication for each model upper and lower bounds are provided the paper presents informal statements for the main results proven in appendix the results appear to be new there is a recent colt paper 5 with similar results in the streaming setting this paper is wellwritten and deals with theoretical results the motivation for these results is very far from any practical application na docsepthis paper focuses on hierarchical clustering over graphs with the objective function of dasgupta sublinear algorithms wrt the number of edges in the input graph are developed in three models of computation including the dynamic streaming model the query model and the massively parallel computation mpc model interesting matching lower bounds are provided for the upper bounded resource above specifically a 1pass semistreaming algorithm naturally follow from existing algorithm 1 and their observation in the query model combining existing methods 1437 and their observation leads to sublinear time algorithms for graphs of different sparsity for the mpc model a 2round tildeommemory algorithm and a 1round tildethetam43memory algorithm are developed based on 1 all above algorithms rely on the crucial observations that from a graph cut perspective the objective function can be well approximated in a cut sparsifier of the input graph moreover two hard instances are designed to derive time and communication lower bounds in the query and mpc models respectively finally the extensions to other related hierarchical clustering objectives such as the dissimilarity 15 are discussed strengths 1 the idea of using cut sparsifiers in improving the computational resource of graph algorithms has been widely used but using it in hierarchical clustering and providing theoretical analysis on the required resource and approximation factor is not well studied with only a few related work eg 5 the problem of hierarchical clustering is reduced to the problem of constructing cut sparsifiers with limited resource based on the observations that cut sparsifiers can approximately preserve the cost of a dendrogram comprehensive studies on 3 models of computation are performed and upper bounds are complemented with almost matching lower bounds 2 the developed lower bounds in the query and mpc models are quite interesting and need to overcome multiple challenges the authors are able to describe the highlevel overview in the main text and then refer to the full details in the appendix 3 the organization of this work is good i like the discussions in sections 4 and 5 providing context on related works and extensions to other objective functions of hierarchical clustering i browsed some contents in the appendix and the quality of writing remains good compared to the main text weaknesses 1 the computational upper bounds developed in section 2 are not very challenging and based on existing methods in the respective models the upper and lower bounds in the dynamic streaming model are straightforward 2 as a reader of a hierarchical clustering paper in neurips i would expect to see some experimental results on the developed algorithm unless the theoretical contributions are very novel and significant it would be much better if experiments on some of the algorithms had been implemented and evaluated in realworld datasets no ### Summary:

the paper presents new algorithm for hierarchical clustering in different regimes in particular they show a new algorithm for a dynamic edge streaming model for a neighbor query model and for the mpc model the paper contains both nice theory results and in the rebuttal phase the authors supported them with interesting experimental results overall we suggest to accept the paper as poster

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper considers the task of learning gans that decompose the image formation process into foreground background and mask generation and composition compared to previous methods the proposed comgan aims to avoid trivial solutions where masks do not correspond to foreground objects mainly through the network architecture instead of regularizations which often require extensive hyperparameter searches for suitable regularization strengths strengths the proposed approach simplifies the design of compositional gans compared to previous methods and demonstrates improved performance in terms of synthesis quality as well as unsupervised segmentation performance compared to similar compositional generative models like finegan 25 the supervision requirements regarding weak background supervision is further reduced finding a compositional generator architecture that is stable to train has many applications beyond gans thus the work is potentially interesting for a larger audience although there are still a few hyperparameters mask consistency loss weight binary regularization weight dimensionality of latents relative sizes of subnetworks experiments demonstrate some robustness to these parameters weaknesses the core idea and differences to previous approaches are not clearly stated the requirements for proposition 1 regarding what it means for an architecture to be similar to comgan are not stated clearly i assume the key point is that m consists of only an sigmoid layer however the formulation in l 149 seems to be the only place where this is stated and even there it remains vague and could be interpreted as containing the same layers as f and b and in addition a sigmoid layer the latter interpretation is also what fig 3 suggests albeit with one residual block less if this a shared decoder with a minimal mask decoder is the key idea of the paper it should be communicated more clearly and probably also on a higher level already in the introduction without clear restrictions on the architecture one could also think that finegan satisfies the requirements with g set to the identity the lemmas propositions and proofs seem a bit vague as they do not clearly state assumptions or define all involved quantities in l 160161 it is not clear what is meant by barxm m1gz why would the input of m equal its output intuitively i also dont see how it is clear that any change of foreground or background affects the mask since f and b do contain additional layers foreground and background could be affected by changes in those layers even though gz and hence the mask would remain unaffected no the other way that any change of the mask affects both foreground and background seems to be true motivation for dscomgan architecture is unclear why is the barxz output needed in addition to the output composited from foreground background and mask limitations and potential negative societal impact have been addressed adequately docsepthe authors point out two factors that lead a scene decomposition model to fall into trivial solutions in a mathematical analysis those are related to the vanishing gradient phenomena that goes into a mask generator to avoid these they propose a novel network architecture where features for generating decomposed scene elements are composed of the ones that used for generating the entire scene at once with this architecture they achieved the sota scores on both the mask prediction and the image quality evaluation metrics strengths the authors tackle the important problem in the scene component generation models they propose a novel architecture for robust mask generation based on the theoretical analysis on the problem the authors did a thorough ablation study to show that each element proposed are all effective the comparison of both quantitative and qualitative results with previous works imply that the proposed method is effective in boosting the scene decomposition performance weaknesses the connection between the theoretical analysis and the proposed architecture design is not well established more details are needed to understand why the proposed architecture is helpful for model to not fall into the vanishing gradient phenomena authors did not address the limitations and potential negative societal impact of their work suggestions i want to know the authors opinion on how much would it be difficult to apply this method on coarse grained datasets like imagenet docsepthis work analyses the reason for trivial solutions during mask learning in image composition gans and introduce a new model architecture comgan to solve the trivial solution issue furthermore an unsupervised object segmentation module is also involved to construct the dscomgan model dscomgan can perform both disentangled image generation and object segmentation and outperforms semisupervised and weakly supervised baselines strengths it is claimed that this work is the first to solve the trivial solution in disentangled image generation by changing the network architecture the change is simple to apply and obtained significant improvement i believe this technic can also be useful for other models and tasks both disentangled image generation and object segmentation tasks can be performed in a single framework more importantly the learning can be achieved in an unsupervised way by a carefully designed adversarial learning strategy sufficient experiments and analyses have been done to demonstrate the effectiveness of the proposed method weaknesses my main concerns are about the description the description in subsection 31 is not that readable i suggest improving the description with a more intuitive illustration to point out the key contribution how to avoid vanishing gradient from the network architecture perspective besides some symbols seem not consistent do the variable f in equation 9 and the variable f in figure 3 represent the same thing what does the variable mean the description of the mask distribution alignment could also be improved if i understand correctly the proposed segmentation networks s does not need any pairedunpaired segmentation data for dm barxm is regarded as the real and hatxm and xm are regarded as the fake the learning process is not clear does dscomgan need to be trained in two stages
 besides the overall objective is missing and the loss term lbinary is introduced in the experiment section instead of the method section the limitations and potential negative societal impact have been well described ### Summary:

the paper proposes a compositional gan model with a novel network architecture that solves the vanishing gradient problem underlying trivial solutions the proposed model achieves strong results on image disentanglement and unsupervised segmentation tasks the rebuttals by the authors have successfully addressed most of the concerns of the reviewers all the reviewers are positive about this paper reviewer tkuws main concerns regarding the evaluation and the clarity of the method were addressed the reviewer raised the rating reviewer 19kw felt positive about section 31 in the revised version and the additional empirical results regarding the gradient values observed during a training stage as given in figure 5 the reviewer also updated the initial rating reviewer rwmns concerns have also been addressed the reviewer appreciates the additional detailed theoretical analysis on the problem

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this work addresses finegrained entity typing by leveraging semantic relations of the mention with other words in the same sentence specifically the authors showed how to use hypernym relations and verbargument relations for the first they used wikidata to train a string match based candidate extraction and bertbased verification model for the second they used an existing srl system to extract relations between the verb and the mention then the two system each produce a prediction that is then combined with the base model through a gating network the proposed method significantly improves over baselines and they performed ablations studies to show the help from hypernym and verbargument strength 1 the proposed approach is well motivated and described clearly 2 the advantage of the proposed modules hr and vr is validated through ablation studies weakness 1 the proposed method for combining leveraging different semantic relations is an ad hoc ensemble of separate systems and each system has some other dependencies extra data for example wikidata or external trained model for example allennlp srl which introduces more complexity in training 2 it would help to show some examples to demonstrate the advantages of hr and vr for example what kind of sentences do they help and what kind of sentences do they hurt questions since the model combines three systems i was wondering if the accuracy would drop comparing to no hr or no vr on sentences where there is no hypernym or no verbargument structure detected in other words would adding hr or vr hurt performance on sentences where they only output zero vector docsepthe paper shows that semantic relations associated with mentions can be used to improve finegrained entity typing the whole model contains three parts 1 base fet model 2 hypernym relation model 3 verbargument relation model experimental results show that the integrated semantic relation information improves the final performance the comparisons are extensive the submission is well suited to the akbc conferencedocsepthe paper describes an approach that models linguistic features extracted from the entity context and applies them to the finegrained entity type fet prediction task experiments show that incorporating models for hypernym relation detection and semantic role labelling improve the performance i would like to see more motivation for the fet task in the introduction it is not clear why explicit type modelling is required for the downstream tasks there are many papers that report increase in performance on the nlp tasks such as question answering from incorporating these and other linguistic features that should be mentioned in the related work eg 1 fabian hommel philipp cimiano matthias orlikowski matthias hartung extending neural question answering with linguistic input features semdeepijcai 2019 3139 2 daniel khashabi tushar khot ashish sabharwal dan roth question answering as global reasoning over semantic abstractions aaai 2018 19051914 semantic role labelling should be illustrated with examples and clearly motivated for the fet task it is interesting to see dataset statistics with respect to the extracted features eg how many hypernym mentions where detected how many arguments for each of the roles in each of the datasets were extracted error analysis is missing how many errors are propagated from the previous stages the hypernyms extracted by our approach are of high precision what is the precision of hypernym extraction gating network architecture is not clearly specified is it the mlp with two fully connected layers formula 3 suggests a linear combination of vectors but the description above does not correspond to this formula abstract should contain more details on the datasets and results we conduct experiments on two commonly used datasets the results show that our approach successfully improves finegrained typing performance ### Summary:

the paper make use of semantic relations hypernym and verbargument to obtain the state of the art performance in entity typing especially compared to strong baselines such as bert the paper presents an interesting message that linguistic features could be still important among the age of endtoend methods it is also clear that entity typing is crucial for constructing knowledge bases making the paper quite appropriate for the proceedings of akbc

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper addresses the problem of predictive modelling with missing input features the authors formulate the problem as a latent variable model and in addition to the standard variational lower bound elbo propose to use a variational upper bound based on cubo dieng at al 2017 modified by an exponential divergence to solve the mc estimation of cubo they further propose surrogate parametrization to reduce the variance in the gradients the experimental evaluation over standard regression uci datasets with randomly dropped features shows marginal improvements over existing baselines pros cons predictive modelling over inputs with missing features is an important problem arising often in many application domains this paper contributes this somewhat underexplored field the rather low documented performance benefits over simpler baselines do not justify the use of the complex model combining 5 networks proposed here as opposed to simpler vae or cvae variants the method and the various bounds introduced are mathematically intriguing well motivated and potentially useful in followup research however the paper is difficult to follow and at places the reader is left guessing what the authors meant this should be improved concretely 1 last para of section 2 the optimization of negative l is relatively difficult why what makes it difficult 2 last para of section 2 have been no equivalent of vi what about cubo and its variants pick up from in your work these have some specific flaws for which they do not qualify here 3 before equation 3 exponential divergence you mean the bregman exponential divergence a citation to help the reader 4 pu z theta in equations 11 and 12 seem to use the same parameters theta though for 11 u y tildex and for 12 it is u tildex is this in practice the same network with two outputs 5 but then in equation 15 these use different ztheta and zpsi samples how is this designed and trained in practice 6 page 5 clarify notation for and explain the gain function what is the intuition purpose for it 7 def 1 effective parameters are those with gradient zero ie the set of parameters inducing tight variational approximation how zero gradient achieves this in a complex nonconvex problem ie cant this be a local nontight extremum 8 page 7 dvae dvae you say these are mnar and mcar model variants as in collier at al 2020 can you clarify how these translate into your rather more complex model formulation and what specifically changes in the loss especially the eubo part further questions for clarificationdiscussion 1 why do you condition y and x on m in equation 1 these are the complete x data so they should not depend on the masking so that py x m py x or is this not true or is it the y that depends on m or is it rather the m which depends on x as in some values being more likely to be masked 2 you introduce two latent variable models in equations 7 and 8 my understanding is that the latent z is shared as 8 is just a marginalization of 7 over y you then formulate to approximate posteriors qz y tildex and qz tildex the first learned through elbo maximization the 2nd through eubo minimization there is currently no link between the two approximate posteriors would it make sense to somehow link them sorry i dont know how and may not be possible or not easily 3 you used the bayes rule to decompose the predictive conditional log likelihood into two terms in equation 4 of which one you are bounding from the bottom elbo and the oher from top eubo what is the effect on the predictive conditional py x is it somehow sandwiched or not really due to splitting and modelling the two nonconditional log likelihoods separately minor text problems typos 1 this first proposition in in page 6 is numbered 2 not 1 confusing the paper contributes to practically very important yet relatively little explored area of research that of predictive modelling with missing data the proposed method is rather complex composed of multiple steps adding onto each other to solve a problem arising in the previous steps these are all well motivated however overall the current presentation of the method is difficult to follow and should be improved to help the reader see main review moreover the documented performance benefits seem to be rather little to justify the use of such a complex method over simpler baseline these two lack of clarity low performance given the complexity of method are for me the reasons not to consider the paper for this conference docsepthe paper considers the problem of prediction with missing incomplete features the authors propose a class of generative models that includes missingness of features and develop a discriminative learning algorithm that maximises the conditional posterior loglikelihood of the training data approximately experiments show that the method is competitive compared with existing approaches and in particular with approaches based on vaes the abinitio generative model class proposed by the authors for handling predictions with missing features is convincing it has the advantage that the involved distributions are simple factorising however at the price of introducing latent variables to learn it discriminatively requires to maximise a difference of concave functions the first term is lower bounded by elbo as in vaes the second term requires a tractable upper bound the authors develop a novel upper bound starting from alpharenyi divergence that admits a stochastic gradient estimator they further introduce a data dependent surrogate reparametrisation in order to achieve an estimator with low variance the technical part of the paper is concisely written and correct the authors prove that the transformation used for the reparametrisation preserves the effective parameter subset ie the subset of parameter combinations for which the overall bound is tight this is indeed a desirable property but is in my view not sufficient the reason is that this effective subset can be very small and cover a subset of simple models only moreover there is no guarantee that the respective gap will become small during learning the experimental section first analyses the learning properties of the method in an ablation study the authors then show competitiveness of their method by comparing it with existing approaches on a subset of tasks taken from the uci machine learning repository the description of the experiments is clear and reproducible the experiments are however not fully convincing wrt the scalability of the approach all networks used for the model and bound construction have only one fully connected hidden layer this seems to be sufficient for the considered tasks from the uci repository however this would be not sufficient eg for image classification tasks where the involved networks are usually deep cnns further comments you mention earlier works ghahramani jordan 1994 smola et al 2005 noting that their applicability is restricted to exponential families please explain whether these approaches are are not applicable for the model class analysed in your work as i understand it the models pyxzm considered by you are exponential families but of course after marginalising over z the resulting mixture model pyxm is not any more it remains however unclear to me whether a dca difference of convex functions algorithm for learning pyxzm can be somehow generalised for learning pyxm i would suggest to drop the data instance superscript earlier in the text eg starting from subsection 22 the latest this would in my view improve readability and reduce clutter the conceptual part ie the model and the proposed learning approach are in my view concise and sufficiently novel this outweighs the missing scalability analysis in the experimental part i would however expect the authors to clearly address the raised conceptual questions docsepthe authors propose a new method dig for discriminative tasks with missing input features it uses latent variable models to marginalize out the label and the missing part of the features given the latent variable in order to compute the objective the conditional log likelihood of the label given the corrupted features as this objective is intractable the paper builds a conditional evidence lower bound celbo that can be unbiasedly approximated using monte carlo samples celbo consists of the regular elbo as the lower bound for the log joint probability of the label and the observed features and an evidence upper bound eubo that bounds the log marginal probability of the observed features the derivation of eubo involves the alpharenyi divergence and the exponential divergence the stochastic celbo contains a density ratio that can lead to large variance in the stochastic gradients during optimization so the authors propose a surrogate parameterization to bound the gradient norm experiments on real datasets justify the effectiveness of the variational approximations to stabilize the optimization when compared with vae cvae and mice the dig algorithm shows better or comparable predictive performance and robustness against feature corruption strengths the paper is overall clearly written the issue it tries to solve discriminative tasks with missing input features has great impact for a wide range of practical machine learning problems in real life technically the paper has quite some novelty including the creation of a rigorous lower bound to the true objective using recent advances in the variational inference area and designed an effective surrogate parameterization to stabilize the optimization weaknesses and questions 1 sec 31 more detailed explanation of the exponential divergence would be beneficial is there a reference for it what role does fboldsymbolu xi play if it can be any realvalued function why was it chosen to be a gaussian pdf as shown in the appendix 2 sec 32 which standard automatic differentiation library was used the submission mentioned both the reparameterization trick and the reinforce trick which one was actually used in the experiments 3 sec 33 i dont quite understand how this part works all i can see that in eq17 the problematic ratio term is multiplied by g which is always smaller than 1 and nonincreasing based on figure 3 but why g was defined in that math format what does vee mean why does g represent the ratio before and after the transform equation between proposition 2 and 3 4 sec 41 missing value processes mcar and mnar what do they mean are they different ways to decide what values are missing in the feature and thus leading to different versions of a dataset if so shouldnt we also add cvae simple and mice could you give more explanation for the last sentence of section 4 saying dvae is more robust than dvae 5 size of the test datasets based on table 3 the datasets are all quite small ranging from 353 to 10k data points and we further split these points into training and test which makes the training sets even smaller in the appendix its said the minibatch size is 521 what if the entire training set is smaller than 512 how long did the algorithm take to run on yearpred is the algorithm able to be easily extended to larger datasets 6 how does dig works compared with other more recent imputation baselines such as miwae mattei frellsen 2019 and gain yoon et al 2018 given the strengths of the paper listed above i would recommend acceptance for this paper if the authors can figure out a clear feedback for the questions i summarized when reading the paper docsepthis paper proposes a new method for learning with missing data compared with previous approaches the authors choose to perform discriminative learning with generative modeling so as to borrow the benefits from these two types of methods to optimize with the underlying intractable loss function the authors start from the traditional variational lower bound elbo and one upper bound cubo from a previous work the chidivergence lower bound 1 and derives a lower bound for the original loss function to solve the issue with the estimation bias as well as the potential huge variance the authors change the divergence function in cubo as well as add the surrogate parameterization so that the monte carlo estimation of the loss can be unbiased and potentially with smaller variances experiment results show the proposed methods run stably and perform comparably or better compared to baseline methods references 1 adji bousso dieng dustin tran rajesh ranganath john paisley and david blei variational inference via upper bound minimization in advances in neural information processing systems pp 27322741 2017 strengths 1 the idea of learning missing data using discriminative learning together with generative modeling is interesting as mentioned in the paper performing such kind of learning will resulting a loss function as a subtraction on two integrals with respect to the latent variables which makes it harder to derive a lower bound compared to the traditional variational inference cases to solve the term being subtracted the authors found a upper bound that could be estimated in an unbiased way with monte carlo methods 2 the exponential function in the first version of celbo will potentially has a bigger variance when estimated using monte carlo to solve this issue the authors propose adding a regularization to the loss function while remain the optimal solution unchanged under the zero gap case this also helps a lot in making training more stable as shown in the experiments weakness 1 the topic of this paper is to focus on missing data however this paper does not put enough efforts on learning various cases of data missing patterns as in the experiments the authors only test the case where the data are missing completely at random mcar which may not be the most common case in reality mnar case might be a more interesting situation to study with the proposed method is mostly focusing on solving the variational upper bound while overlooks modeling the missing patterns suggest the authors could add some experiments with mnar data also it would be better if the authors could add some modeling part on the missing pattern into the loss for example let the mask m to depends on x y z this will make the proposed model more useful in practice 2 from proposition 2 we know that the surrogate parameterization can make the optimal solution of celbo remains under the zero gap case however it is nearly impossible to reach the zero gap case in reality since it is unlikely to select variational distributions ie qcdots to perfectly estimate the model posterior distributions what about the suboptimal cases is the celbosp optimal solution close to the celbo optimal solution in a small gap but not zero gap case i understand this might not be easy but it will be better if the authors could add some theoretical analysis on this 3 the performance metrics in table 1 is not showing the proposed methods can outperform the baselines with big gaps meaning that the proposed methods is not much better compared to previous approaches empirically however i think there are many ways that the authors could try to improve the performances for example the authors could try a different divergence function a better way to add the regularization etc the author propose an interesting discriminative learning approach with generative modeling to solve the missing data modeling problem by extending the traditional variational lower bound elbo with a novel and stable upper bound that can be estimated without bias with monte carlo estimation it is better if the author can study more on the missing data pattern both empirically and theoretically and the optimal solution preservation under suboptimal cases also the empirical performance still has some space to improve ### Summary:

while generative model can be used to input data this work propose to a novel discriminative learning approach to optimize this data imputation phase by deriving a discriminative version of the traditional variational lower bound elbo the resulting bound can be estimated without bias with monte carlo estimation leads to a practical approach leading to encouraging experimental performances the reviewers recognised the novelty and suggest that this approach given its novelty and wide applicability could be considered for an oral presentation

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper studies an online deep equilibrium models deq method for regularization by denoising red the proposed oder incorporated randomized processing of measurements the oder algorithm aims to bypass the high computationalmemory complexity of deq led by the high dimensionality of measurement space the introduced online backward pass is demonstrated to lead to a more scalable and flexible deq framework for inverse problems based on standard assumptions in the analysis of fixedpoint and sgd the authors also have given some analysis of the oders convergence the authors have also conducted experiments to analyze the behaviour of oder and demonstrate its effectiveness as an accelerated deqred the proposed oder is a useful extension to deq and red strengths 1 the studied problem is interesting and worth devoting to and the author has done significant work 2 the analysis and experiments are extensive and can support the claims 3 the paper is well written and easy to follow weakness 1 online processing of measurements and its analysis tricks are not new 2 some experimental settings and studies on hyperparameters are not well discussed see the points in questions my initial rating of the paper is weak accept i am open to raising my score if the authors can address my concern during the rebuttal suggestions there are other relevant works that are worth mentioning as far as i know in addition to the denoiser prior the below dl paradigms also incorporate the knowledge of data acquisition to solve inverse imaging problems which have been studied for the purpose of learning generative prior 1 neumann inversion 2 or equivariance prior 3 to a broader view its necessary to add some review or comparison with these related paradigms 1 bora a price e dimakis a g 2018 ambientgan generative models from lossy measurements in international conference on learning representations 2 gilton d ongie g willett r 2019 neumann networks for linear inverse problems in imaging ieee transactions on computational imaging 6 328343 3 chen d tachella j davies m e 2021 equivariant imaging learning beyond the range space in proceedings of the ieeecvf international conference on computer vision pp 43794388 docsepedit score raised by 1 point based on author revisions this work proposes oder an online learning method for deep equilibrium learning for regularization by denoising deq is a recently proposed framework for memory efficient learning of an infinitedepth unrolled network as implicitly defined by a fixed point of an operator red is a specific type of iterative algorithm that can incorporate learned priors and has the fixed point operator structure the authors therefore apply stochastic gradient descent across the measurement direction for each input in the training set and derive the corresponding update equations for gradientbased learning they show that their approach is able to get similar quality reconstructions with reduced memory and training time compared to fullfledged deqred strengths the paper is wellorganized and presentation is clear the work nicely connects online learning with deq with specific application to red there is a freelunch result both memory and time to train are reduced without any sacrifice in image quality for this reason this work is exciting the work is also supported by theoretical results and is demonstrated for a several different applications weakness as the authors state onlinelearning for red is not a new concept and both are special cases of running sgd across the measurement direction eg coils in mri which is not novel on its own for example 1 ong f zhu x cheng jy et al extreme mri largescale volumetric dynamic imaging from continuous nongated acquisitions magn reson med 2020 84 1763 1780 httpsdoiorg101002mrm28235 the ct experiment had only one subject in the test set which seems prone to overfitting i dont think that section 6 sufficiently describes limitations of the work could the authors discuss limitations of the approach for example the required theoretical assumptions implementation considerations etc another example is the relative improvement of oder over red unfold or red denoising as both of these alterntives can also be implemented with online learning how will the training time compare docsepthis paper introduces an online deep equilibrium learning approach for largescale inverse imaging problems the method is built upon the recently proposed deep equilibrium architecture that unrolled an optimization algorithm eg steepest gradient decent for regularization by denoising into a neural network with a potential infinite number of depth iterations this paper goes beyond that by presenting an online variant to enable largescale inverse imaging applications where the full evaluation of data consistency term is quite expensive it therefore demonstrates superior performance on three dataintensive inverse imaging tasks its reconstruction quality is comparable to the full batch solution while is 23x faster suggesting the benefits of the proposed method the major strength of this work lies at the strong empirical evidence on three practical largescale inverse imaging problems plus the paper is nicely written and easy to follow the theoretical convergence of the algorithm is also rigorously analyzed nevertheless the technical novelty of this work is rather incremental the methodology here is mostly credited to a which first connected the deep equilibrium model and pnpred method the nontrivial technical parts thus can only be attributed to the online version as well as theoretical analysis of convergence but none of them could be viewed as significant contributions unfortunately virtually the deltas here are well known in the pnp literatures eg online version of pnp and red algorithms are proposed in b and c respectively even both of them have already established the theoretical convergence consequently given the pros and cons on balance i feel this is a very borderline paper and i vote for borderline accept tentatively i raise my score because of the originality of the main theory highlighted in the rebuttal a deep equilibrium architectures for inverse problems in imaging tci 2021 b an online plugandplay algorithm for regularized image reconstruction tci 2019 c block coordinate regularization by denoising neurips 2019 na docsepin this paper to address the issue that the training of deep equilibrium models deq can still be a significant computational and memory challenge in applications that require processing a large number of sensor measurements the authors propose online deep equilibrium learning for regularization by denoising oder for inverse problems that adopts stochastic processing of measurements within an implicit neural network experiments on three applications demonstrate the potential improvements in trainingtesting complexity this paper solves dataintensive imaging inverse problems and owns some strengths 1 the main advantages of the presented technique are the reduced execution time wrt the main baseline red deq and the lower memory consumption of the measurement 2 the paper is overall well structured and written 3 the provided references are comprehensive and adequate 4 the work seems correct since the proposed approach lays its foundations on robust literature papers and the authors theoretically analyse oder regarding its convergence and ability to approximate the traditional deq approach however there are some issues that i would like to highlight 1 noveltyoriginalitycontribution although integrating the online processing into the deq framework is overall a novel attempt for solving inverse imaging problems using pnpred operators it is a straight borrow from other literature and stitching i worry that the contribution of the paper is limited since the only improvement to deq and ref35 is the addition of online processing this seems like a small and straightforward contribution to preexisting works and hardly motivates the publication of a new paper 2 significance this paper specifically addresses issues in imaging applications that require processing a large number of sensor measurements which may limit its impact 3 experiment in figure 3 tables 2 and 3 the ssim and snr of the proposed approach are not better than red deq in experiments on ct and mri images so the only real contribution is the reduced execution time yes ### Summary:

the paper proposes a learning method specifically a deep equilibrium learning approach for regularization by denoising a plugandplay method for solving inverse problems after the rebuttal all reviewers support acceptance of the paper the reviewers find the paper to be well written the problem to be interesting and the claims to be well supported reviewer hjnn both empirically reviewer udgc and through theory reviewer a7f5 finds the work particularly exciting since both memory and training time are reduced without sacrificing image quality based on my own reading and the unanimous support of the reviewers i recommend acceptance of the paper a nice contribution

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a new knowledge distillation framework for directed graphical models based on the reparameterization trick the new distillation framework overcomes the intractable marginals in marginalized distillation and error accumulation in local distillation empirically the new distillation framework surpasses the baselines on deep generative model compression vae continual learning and discriminative dgms compression i think the proposed distillation framework is novel and the entire paper is wellorganized however i have the following concernsquestions 1 the title claims for a unified kd framework for deep dgms however the proposed distillation loss only applies to dgms where the latent variable z has a reparameterization form i believe the author should highlight this limitation of their framework 2 in equation 3 the expected kl divergence is computed over pphiyjx i am wondering if the following three ways will improve the performance of local distillation though they are no longer equivalent to equation 2 my intuition is that for the original objective equation 3 the conditional kl divergence is computed with respect to the teachers distribution pphiyjx however at inference time the conditional distribution of the student model is computed based on its own distribution pthetay jx this makes the training objective does not consistent with the test objective mathcallkd mathbbepdataxleftmathbbepthetayjx klpphiyjyj x pthetayjyj x right mathcallkd mathbbepdatax left mathbbepthetayjx kl pthetayjyj x pphiyjyj x right mathcallkd mathbbepdatax leftsumj 1lambdacdot mathbbepphiyjx klpphiyjyj x pthetayjyj x lambdacdotmathbbepthetayjx klpphiyjyj x pthetayjyj x right for some lambda in 0 1 3 can you explain more on why do you remove epsiloni in equation 6 i dont understand why such a change will better penalize the dissimilarity of latent variables zi 4 can you provide some qualitative comparisons between the samples from the distilled dgms on the celeba dataset overall i like the solution proposed by this paper to address the issues in marginalized distillation and local distillation however i believe the authors did not fully investigate the failure reasons for local distillation the argument in the current paper is kind of vague and not wellsupported i believe a more rigorous analysis and carefully designed experiments are needed to illustrate the argument eg as i mentioned above i will increase my rating if the authors can provide more convincing results docsepthe authors propose a method for knowledge distillation specifically for deep directed graphical models the authors compare their method with marginalization methods which integrates the latent variables out and the factorized local method which distills knowledge between teacher and student on each factor they validate their approach on continual learning model compression and discriminative learning writing the paper is relatively readable the main idea came across clear but it can enjoy thorough editing definitely in the experiment section method the papers main claim is that the local method can result in the accumulation of errors however the second term in eq 7 somewhat does the same in fact one can view the proposed method as a combination of the marginalization and local methods together i am not convinced by the idea of the semiauxiliary graph that yields the loss function in eq 34 the example in fig 1 is too simple for example when we factor out the zs the resulting graph on the observed variables is no longer dag consider y1 z y2 if z is factored out then y1 and y2 are connected with undirected edge how does eq 3 handle such a case the advantage of the method is mostly for dgms with continuous latent variables for discrete latent variables the local distillation is used im not sure how well this scales to models with the large number of discrete latent variables one limitation is that the teacher and students should have the same architecture can it be extended to cases when the architectures are not necessarily the same experiment the results in the experiment section are not convincing for example the difference between the performances of different methods is within the same standard error especially for the local method and the proposed method the choice of some of the tasks is questionable for the knowledge of dgm distillation for example why is continual learning a good task to show that the knowledge distillation method working the argument made in the paper is that the delta of forgetting the previous task is small which is acceptable for continual learning however it is not clear why continual learning is a good task to show knowledge distillation in figure 5 it is not clear why the authors claim that the proposed method produces better results for example why c is better than e this experiment is very qualitative and subjective none of the experiments except hm really motivate the knowledge distillation for dgm vae is a very simple dgm and there is no real structure in the graphical model the authors could have simulated data from a hierarchical graphical model and used a complex teacher to learn that model then applied their method to show their approach can recover the true model from the teacher for example see 1 for examples such simulation a simple mixture model i strongly recommend the authors look into the metrics introduced in 2 several metrics and experiments presented in that paper can be adopted or adapted for the dgm knowledge distillation references 1 httpsarxivorgpdf160306277pdf 2 httpsarxivorgpdf210605945pdf the paper is relatively clear the experiments are not wellchosen and the results are not convincing the method section does not explore the idea of knowledge distillation for dgm deeply there are questions about the generalizability and scalability of the proposed method docsepthis paper proposes to use the reparameterization trick to convert the latent variables in dgms to deterministic variables in the context of kd it then proposes a surrogate distillation loss and latent distillation loss and evaluates the performance of the proposed method in three applications experimental results confirm the effectiveness of the proposed model strengths the paper proposes a knowledge distillation framework for deep dgms on various applications weaknesses after reading the paper several times i still dont see the significant novelty of this paper a the paper converts each hidden random variable to a deterministic variable via the reparameterization trick this is a wellknown technique the vae for example uses this technique during training although the paper argues that we do not primarily use reparameterization trick for model training rather we leverage it to convert the latent variables z in dgms to deterministic variables so that we can effectively distill knowledge from a compact form of dgm but isnt this very straightforward i dont see any big difference between using the reparameterization trick during training and kd the authors should provide a discussion on this b i dont see a difference between equations 4 and 2 when applying to vae because during vae training the sampling over the auxiliary random variables epsilon is implicitly included even though we just apply equation 2 3 equations 5 and 6 look very intuitive and straightforward i am more interested in knowing what theoretical guarantee we can have when using these losses c for experimental evaluation could you compare your model with more stateoftheart kd baselines eg figures 4 and 5 and table 1 i am mainly concerned about the novelty and clarity of this paper at the current stage i dont recommend the paper for acceptance docsepthe authors propose an unified knowledge distillation technique for general deep directed graphical models they use the reparameterization trick on the intermediate latent variables of the original dgm network and the student network this converts the networks to a compact semiauxiliary form then they use a surrogate distillation loss combined with latent loss to reduce the error accumulations over the chain of random variables they discuss the similarity of their technique with others and demonstrate its performance for 3 applications pros 1 the authors do a good job of giving basic preliminaries and of ruling out the nave marginalized distillation and local distillation approaches 2 after the compact dgm reductions for both the teacher and student networks each target variable has direct dependence on the input x and prior yis this is a neat approach 3 proposition 31 looks correct to me when kl divergence is chosen concerns questions 1 just verifying the reduction to a compact semiauxiliary form is a novel contribution of this work correct fig 1c 1d 3e 2 how do we get the correct choice of deterministic transformation g for the reparameterization trick of the original teacher network pphi is a neural network so i am curious how to get g without loss of accuracy algo1 lines 1112 please give a detailed example 3 the chain error accumulation is reduced as the number of layers reduces in the semiauxiliary form right pg5 para2 or is there any other rationale to it 4 i wonder how the performance is with only using lsd in eq7 lambda0 setting 5 i am a bit confused about the vae compression expts each layer is considered a latent variable z i presume in this case what is the student network chosen i might have missed it also curious to know how well the proposed lsd loss works with lambda0 setting kindly clarify this expt the work is good and the paper is well written i feel the contribution of the work is not that novel my confidence in evaluation will increase once the authors address the concerns raised above ### Summary:

the paper proposes a framework for distilling deep directed graphical models where the teacher and student models have the same number of latent variables z the key idea is to reparameterize both models in terms of standardized random variables epsilon with fixed distributions and train the student to match the conditional distributions of the observed variablestargets given the values of the standardized rvs epsilon the approach aims to avoid error compounding that affects the local distillation approach where the student is trained to match conditional distributions of the teacher model without the above reparameterization to deal with discrete latent variables and vanishing gradients the authors augment the target matching loss with the latent distillation loss that matches the local distribution for each zi given the standardized variables epsilon it depends on positives the paper tackles an important problem the idea of using reparameterization for distillation in this way makes a lot of sense for continuous latent variables and could be impactful the experiments provide some evidence in support of the idea negatives there are considerable issues with the clarity of writing for example it is really not clear how and why the method is supposed to work for discrete latent variables the explanation provided by the authors in their response to the reviewers was helpful but still not clear enough the fact that the teacher and student models need to have the same number of latent variables and perhaps even the same structure is a big limitation of the method given the claim of its generality and thus needs to be clearly acknowledged and discussed for example the method cannot be used to train a student model with fewer latent variables than the teacher which seems like a very common use case the experimental evaluation is extensive but insufficient in large part due to the evaluation metrics given that vaes are trained by maximizing the elbo and distilled by minimizing a sum of kls it makes sense to also evaluate them based on the elbo rather than solely on the fid is done in the paper the vrnn experiment would be much more informative if it included a quantitative evaluation eg based on elbo in summary the paper has considerable potential but needs to be substantially improved before being published

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary the authors propose a novel combination of vaes and flow models where the decoder is modelled through a conditional flow taking as input a local representation of the size of the input image and a global representation output by the encoder the authors evaluate the proposed method on density estimation quality of generations and linear probing on a variety of datasets and show improvement over state of the art great conceptually simple method that seems to work quite well in practice for this class of models the linear probing experiment is quite convincing in justifying the use of global and local characterizations of the learned representations so are the interpolations could be improved its not clear to what extent each of the proposed refinements to glow reorganization different splits finegrained multiscale architecture improves glows performance the authors propose a novel combination of known methods evaluate it extensively and show considerable improvements over current state of the art a clear accept docsep summary this paper aims to improve a normalizing flow generative model in particular glow by conditioning the flow on global information of the image in the form of a latent vector learned with the vae framework this latent vector is injected at the scale and bias terms of the affine coupling layers of the flow inspired by what stylegan does at the batchnorm layers unfortunately critical aspects of the method remain unclear or unspecified to the best of my understanding the paper lacks a clear explanation of the complete pipeline used for training the method and the final objective function for evaluation sampling and likelihood computation procedures are not completely specified either pros the general ideas of the paper are well motivated combining the advantages of explicit likelihood and latent variable generative models is in my opinion an extremely interesting research direction the authors propose architectural improvements to glow and craft a conditional version that can effectively incorporate additional information to the flow the authors model achieves improved or competitive density estimation sampling and downstream performance across various datasets compared to the stateoftheart some degree of local and global properties disentanglement is demonstrated in the qualitative results showing the proposed direction is a promising one in that regard cons the main drawback is in my view the presentation of the method the method part of the paper section 2 mixes theoretical justification with architecture details and fails to clearly explain the full pipeline and training objective this made it very difficult if not impossible to analyze it and draw conclusions the second half of the paper is dedicated to experimental results yet the sampling procedure and likelihood computation are not clearly explained either i think the submission would have been much stronger if the authors clearly explained the whole method and dedicated more of the paper to a careful analysis and justification of the design decisions and of some of the claims eg avoiding posterior collapse globallocal disentanglement going into detail by reading the abstract i get the impression that the vae framework is used and the normalizing flow is used to model the generative distribution pxz yet the abstract also claims to only use a plain loglikelihood objective as in explicit likelihood models instead of the vae elbo alternatively i thought the latent code was learned with a separate vae but the introduction states that the generative flow is embedded in the vae framework to model the decoder assuming that the vae framework is used with a conditional normalizing flow for decoder section 2 introduces more confusion when the authors state we feed z as a conditional input to a flowbased decoder which transforms x into the representation nu with the same dimension this is confusing since typically the decoder input should be a lowdimensional representation but here it seems to be the image as well so the concept of decoding seems ill placed moreover if this is the case what would be the point of the reconstruction term in the elbo if invertibility guarantees perfect reconstruction shouldnt the flow output nu be a stochastic latent code as in vaes is a prior density regularization imposed on nu as well finally another option would be that everything is trained with the negative loglikelihood cost of the normalizing flow this would be consistent with the claim in the abstract that only a plain loglikelihood is utilized but in that case what is the justification for using a stochastic encoder if it is not regularized how can it be guaranteed that z will not be ignored what is the role of z during sampling does the likelihood computation involve z or just nu i apologize for writing my internal thought process but i also wanted to convey that even if the method was clarified to be some of the options i described or another one many of the design decisions would still require extra justification and analysis other comments although figure 1 shows capturing of some global color properties looking at the interpolations in figure 5 there seems to be little variability wrt z so maybe the claims of learning decoupled and disentangled representations require more justification about the initialization of the weights of the last linear layer with zeroes section 21 wouldnt this create a null output at thus a null backward gradient in the first iteration even if a nonzero bias was used wouldnt an unbreakable symmetry condition be produced after rebuttal i thank the authors for their multiple clarifications and apologize for my initial misunderstanding i understand now that the flow model is used to compute pxz as a function of x and z maybe the decoder terminology is a bit confusing here but this is quite a nice idea overall it would have been nice to see multiple samples from pxz for a fixed z to evaluate the expressiveness of the model im raising my score to acceptance ps some typos remain in the revised version eg varnishes tne docsepthe paper introduces a mixture of flows with the specific intent to disentangle global and local representations to improve visual quality of samples architectures are borrowed from stylegan literature the contributions are mainly architectural and empirical the demonstrates improved visual quality over other normalizing flow methods strengths the paper introduces a straightforward latent variable model for flows which is designed in such a way that training on nll gives good sample quality which is measured in fid even further the model also performs quite well on the nll objective itself since the focus of normalizing flow literature has been mainly on nll i think this paper is a nice complement to existing literature weaknesses the paper never puts the equations from the background section together in the final objective it would be helpful to have an equation representing the final objective with some of the relevant variables ie log px with variables x z and v the paper does not introduce many novel theory or methods this is not really a problem but the paper can be better connected to existing work and clarified in this respect the model the authors propose is very reminiscent of infinite mixtures of flows as outlined by papamakarios et al normalizing flows for probabilistic modeling and inference page 32 an example would be continuously index flows by cornish et al note their method was introduced with a different intend in a different manner so i think it would only make the paper better by citing these methods conditioning on a context variable in flow layers is not new see kingma et al improved variational inference with inverse autoregressive flow and lu et al structured output learning with conditional generative flows this is not really a problem but again this should be clarified how much added computation is required by the encoder model plus fcnet in eq 7 compared to the glowrefined model on which the proposed method is based perhaps the naming compressing encoder is not particularly useful it implies a direct connection to actual image compression which is as far as i understand not the case other than that this seems like a fairly standard vae encoder other than the size difference between x and z docseppros i like the idea of conditional generative flows where a lowdimensional embedding captures highlevel features and a larger embedding latent space captures local representations use a compression encoder to enforce informative lowdimensional embeddings good experimental results cons can you really say that the lowdimensional embedding z is capturing global variables to me global variables in a generative model drives non iid samples hence correlating samples with each other for instance see the paper diane bouchacourt ryota tomioka and sebastian nowozin multilevel variational autoencoder learning disentangled representations from grouped observations in thirtysecond aaai con ference on artificial intelligence 2018 the author should clearly explain the training cost function at the end they are combining a flow generative model typically trained using ml with an encoder vae like posterior approximation i guess they optimize some soft of elbo but it is not clear to me yet overall i like the paper and would like to see it accepted ### Summary:

the paper proposes a hybrid vaenormalizingflow for extracting local and global representations of images while the reviewers found the model itself to be conceptually simple and straightforward all were convinced by the empirical evaluation that indeed interesting representation learning is going on resulting in a unanimous vote to accept

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposes a few tricks to improve the stability of bert fineturning which include a standard adam optimizer with bias correction the top bert layers reinitiation and longer training it provides extensive study on the glue benchmark showing how important these tricks are for small tasks such as rtemrpc which have less 1k training samples the paper is well written and provides an insightful analysis although it provides several useful tips for practitioners it lacks novelty for example the adam bias correction is from the original adam paper also pointed it out by 2 and training longer helping the performance is also observed by 1 gradient clipping may also help stabilize the training and it will be great to have a discussion as well at last does these approaches help large tasks such as mnliqqp it will be great to have a few settings experiments on small or large tasks 1 nakkiran et al deep double descent where bigger models and more data hurt 2 mosbach et al on the stability of finetuning bert misconceptions explanations and strong baselines docsepthe paper focuses on instability issues in bert finetuning on small datasets they list three factors which leads to instability and provide simple fixes for each 1 lack of bias correction term in bertadam fix was to use standard adam 2 using all pretrained layers for finetuning reinitializing the last few layers before finetuning 3 training for a predefined number of epochs train for a large number of epochs the fixes proposed reduces the variance in the results and in most cases also improves performance they also show that several proposed solutions to fix training instability lose their impact when the aforementioned fixes are incorporated overall i like the paper the observation about reinitializing top layers of bert was interesting and counter intuitive to me and i think this will be the most important contribution of the paper although not directly related to bert this paper httpsarxivorgpdf180400247pdf also suggests training for longer epochs this paper should be cited here the tasks considered in the original bert paper had large datasets so i think the 23 epoch suggestion was tuned to those the result about bertadam being unstable in low data settings was a nice contribution i feel this algorithm was also suggested considering the large datasets considered in the bert paper docsep summary this paper investigates finetuning bert for fewsample datasets notably the authors find debiasing omission in bertadam they find original debiased adam is better than bertadam besides they also find reinitializing top layers can speed up learning and achieve better performance these two findings are interesting another finding finetuning bert for longer is incremental to some extend strengths the two findings mentioned above are notable the authors conduct extensive experiments to support their claims weaknesses and questions table 1 shows the results of reinit but does not show reinit how many top layers for each task i suggest the authors can investigate debiased adam and reinit on the datasets with enough samples like mnli or qnli if they can achieve slight improvement or at least do not degrade the performance we can just conveniently use the same finetuning method for most datasets lack of explaining the meaning of int task docseplarge language models lm architectures such as bert xlnet etc are not generally trained from scratch but rather used as pretrained models among all bert is one of the most widely used ones and its use on downstream tasks mainly consists on a stage of finetuning where the new layers added are trained and the rest of parameters of the network are left unfrozen and hence are adjusted slightly to better fit the new task however this step of finetuning bert is known to be quite unstable and depends on a large set of factors especially the initialization since the final performance on these downstream tasks can vary notably different approaches has been proposed to circumvent this but still the most common solution consists simply on choosing the best performing model from a few random initialisation using the validation set summary in the current paper the authors aim at tackling the aforementioned problem in a more grounded way they investigate deeper the possible causes for these instabilities and propose methods to counteract these pitfalls hence they propose three simple yet effective approaches to stabilise the training and ensure better performances a modified optimiser the use of randomly initialised top layers and more training steps they provide a large collection of results compare all these solutions to previous works and discuss differences and similarities thanks to the analyses carried out the current paper results in an exhaustive study on how safely finetune bert and the different factors that are to be taken into account when making use of these models strong and weak points i would like to start with the weakest point of the paper it actually does not present anything clearly novel nor innovative or groundbreaking all the solutions proposed are inspired by previous approaches or are just slight modifications of existing methods but this does not mean the paper is not valuable as i do believe it is the instability while finetuning large lms on downstream tasks is a well known problem but yet it has not been tackle exhaustively and i do believe there does not exist clear guidelines andor modifications that enable easily circumventing a critical weakness of these models but i consider this paper succeeds at precisely this important task thanks to the extended and exhaustive study it presents and how it proposes three simple modifications that seem to solve this pitfall on most scenarios besides the paper is quite well written and presents in a clear manner the problems with the models some intuition about the cause of those issues and then the solutions to overcome them all the solutions are sufficiently justified and are intuitive and simple the latter instead of being a weak point for this precise problem it is more an advantage as will allow an effortless adoption its improved performance is ensured thanks to the large set of benchmarks on various datasets the authors have compiled on the current manuscript this is indeed another strong point as all the solutions proposed are also tested under different conditions with more or less training steps and different numbers of top layers randomly initialised decision and key reasons i believe the paper is ready to be accepted overall it is an interesting and useful paper that will help many nlp researchers and endusers of bert finetune better models obtain improved performances and therefore start from a better baseline for their endeavours and all this with just some simple and intuitive modifications and guidelines all the proposed methods and suggestions are not drawn from a few bunch of tests but rather from a large collection of simulations for different and varied datasets with disparate starting conditions and run over a fair amount of random initialisations therefore i believe the authors have taken their time and simulation time to ensure that the presented results are robust and consistent which is something to remark also questions and additional evidence although i believe the paper is nicely written and compiles all the required results and tests i would appreciate if the authors could comment further on the following points i do believe there is a reason for not performing biascorrection on bertadam and therefore introducing it back might be affecting bert training and finetuning in some specific i guess negative way could the authors comment on this or their understanding on why the correction was removed for bertadam in figure 4 you suggest that with 5 to 10 random trials the bias correction will achieve good results however observing the plots for all the datasets we realise that indeed that number of random trials may benefit more the noncorrected version as in most of the datasets the performance is either higher or at least comparable and although the variance is larger we might still ensure at least a similar result could you comment on this would not be the corrected version a better option when no random initialisations are envisaged for the reinit when just training for 3 epochs it surprises me that indeed we could train the last 6 layers with just this reduced amount of data and training steps and more surprisingly according to figures 1416 is that the weights for these last 6 layers are the first to stabilise even though they started from scratch and they are supposed to be critical for the downstream tasks could you comment on this i guess my understanding is wrong and i would appreciate therefore some further insights also on the reinit scheme you mention that the number of layers to reinitialize depends on the task could you in any case offer here a general rule of thumb extra feedback finally i would like to conclude listing some small typos and errors i could spot in the manuscript page 7 after results the reference to the table is wrong page 8 table 2 i believe the result for the rte int task is mistype i guess it should be something around 718 page 14 section e effect of reinit the reference to the figure the caption for all figures 14 to 17 is wrong as it should read finetuning these are the ones i could find but it is not an exhaustive list in any case i would like to highlight the quality of the present manuscript in terms of clearness and writing ### Summary:

this paper addresses some of the welldocumented instabilities that can arise from finetuning bert on a dataset with few samples through a thorough investigation they highlight various bizarre behaviors that have a negative impact on stability first that bert inexplicably uses an unusual variant of adam that in fact harms behavior and second that people tend to undertrain bert on some downstream tasks separately they find that reinitializing some of the final layers in bert can be helpful since finetuning bert has become such a common way to attack nlp problems these practical recommendations will be quite welcome to the community these findings address issues raised by recent work so the paper is timely and relevant the paper has through empirical analysis and is clear to read there is a concurrent iclr submission with similar findings and this paper stands on its own reviewers all agreed that this paper should be published

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: authors assessed how their adaptive temporal fusion network performs on public datasets such as something v12 kinetics etc the contribution of this paper is in proposing an approach to automatically determine which channels to keep reuse or skip per layer and per target instance that can result in efficient action recognition strengths the proposed method is modelagnostic making it easy to use as a plugin operation for other network architectures reusing history features when necessary to make the network capable for strong temporal modeling concerns the paper has examined the temporal fusion module on bninception and resnet models while more recent models evaluation is missing while the policy network is defined as two fc layers and a relu it is not clear why the authors chose this architecture and how they have tuned it in section 3 using 2dcnn for action recognition a citation to one of the recent works in modeling the temporal causality is missing asghariesfeden sadjad mario sznaier and octavia camps dynamic motion representation for human action recognition in the ieee winter conference on applications of computer vision pp 557566 2020docsep summary the paper presented an adaptive inference model for efficient action recognition in videos the core of the model is the dynamic gating of feature channels that controls the fusion between two frame features whereby the gating is conditioned on the input video and helps to reduce the computational cost at runtime the proposed model was evaluated on several video action datasets and compared against a number of existing deep models the results demonstrated a good efficiencyaccuracy tradeoff for the proposed model pros the paper has a novel idea adaptive temporal feature fusion and addresses an important problem in vision efficient action recognition solid experiments on multiple datasets the analysis of the learned policy is quite interesting wellwritten paper cons limited technical novelty the idea of building adaptive inference models with a policy network for video classification has been previously explored by wu et al meng et al and others eg skip part of the model select a subset of frames choose the input resolution to the model the main technical component of the model is also very similar to the channel gating network hua et al the key innovation seems to be the perspective of modeling temporal feature fusion for adaptive inference this is probably best considered as in parallel to previous approaches for adaptive video recognition the technical components thus look less exciting lack of comparison to other adaptive inference models temporal fusion schemes there isnt a real comparison between the proposed method and recent works on adaptive inference video recognition eg wu et al meng et al the benefit of model temporal feature fusion a main contribution of the paper thus remains unclear with respect to other design choices eg input resolution or frame selection id suggest some experiments that compare to those work another important experiment is to contrast the proposed method with other temporal feature fusion schemes eg lstm tsm for example tsm a handcrafted feature fusion module seems to have less number of parameters slightly higher flops and comparable accuracy table 3 if that is the case the contribution of the proposed adaptive fusion scheme is much weakened minor comments it is not totally clear to me how the flops of the proposed model are computed as the proposed model will have a different flop conditioned on the input video were the reported flops averaged across the dataset i was not able to find a description in the paper it will be great if the authors can report some runtime performance eg wall time to achieve the theoretic flops the proposed model will rely on filter rearrangement on the fly and sparse convolution kernels both can be less efficient on certain devices eg gpus justification for score all in all a good paper my main concern is the missing link comparison to previous works on adaptive video recognition if this concern can be addressed i am happy to raise my rating docsep general this paper proposes an adaptive temporal fusion network called adafuse for action recognition which adaptively removes temporal redundancy and reuses past features for accuracy and efficiency i listed the pros and cons i found in the paper below as well as some questions to clarify some of the details pros 1 the idea of learning a decision policy to dynamically determine whether channelwise features at time t are calculated normally reused from t1 or skipped is interesting and reasonable 1 the experimental results show that the proposed method achieves good accuracy with reasonable computational budget 1 the ablation study in table 4 reveals that the performance is greatly affected by the policy and it is important to fuse the futures from different frames to captures the temporal dependencies cons 1 the propsoed method is not compared with some of the recent methods such as 13 4 is optional because the publication date is very close to the iclr 2021 submission deadline especially for jester and minikinetics dataset the proposed method is compared with only tsn which is old and weak as baseline as it does not incorporate the temporal information 1 in table 3 it seems that the proposed method achieves good accuracy but i am afraid that it is just because of the strong base network tsm merely adding adafuse to tsm indeed saves some computation but degrades the performance as described in the paper the proposed remedy indeed slightly improves the accuracy but it requires much more parameters compared to the vanilla tsm overall i find it benefitial to use the proposed method on top of simple base networks such as tsn but the benefit of using the proposed method on top of strong base networks such as tsm may be marginal combined with the point 1 above i am not well convinced of the effectiveness of the proposed method 1 some of the important details are not clear i would appreciate if the authors could answer the questions i listed below questions 1 is it necessary to use gumbel softmax i think there are two kinds of tricks involved in gumbel softmax one is a trick for sampling from a categorical distribution and the other is a trick for making the opperation differentiable in my understanding which may be wrong the required characteristic for the present method is the latter one and the sampling from the categorical distribution is not necessarily required in this case i think simply using q instead of logr g in equation 7 is enough 1 related to the point above please clarify the type of output hard or soft of the policy net the sentence after equation 2 says the output is integer values 0 1 or 2 while the sentence before equation 7 says it is a realvalued vector 1 suppose pti 1 reuse and pt1i 1 reuse again in this case is yti copied from yt2i or is the feature map of ith channel at time t1 calculated on the fly for reusing at time t in other words if the policies for a channel is reuse n consecutive times does the method take the feature from n frames before other comments 1 figure 1 may be incorrect or misleading i think pt the output of the policy net should go to the 2d conv block otherwise the block never knows which channel to compute at time t and which channel to reuse or skip 1 sudhakaran gateshift networks for video action recognition cvpr 2020 2 martinez action recognition with spatialtemporal discriminative filter banks iccv 2019 3 jiang stm spatiotemporal and motion encoding for action recognitionstm spatiotemporal and motion encoding for action recognition iccv 2019 4 kwon motionsqueeze neural motion feature learning for video understanding eccv 2020docsepin this work the authors introduce an adafuse network for efficiency action recognition in videos specifically they design a policy net to decide which channels should be kept reused or skipped according to the input features of two adjacent frames strength 1 the paper is written well and the organization is ok 2 the idea of adaptive temporal fusion is somehow novel and interesting weakness 1 how to save computation i understand the general idea of saving computation if some channels are reused or skipped however in the training phase the policy net would produce the realvalue vector by eq 7 instead of the onehot vector in other words the keep entry for each channel is always used during training then i guess computation saving is not claimed for training it is for testing right how to do test the policy net produces the realvalue vector and then you make it as onehot vector for saving computation 2 missing sota compared with this paper many recent approaches can achieve a competitive computation with better accuracy it significantly reduces the potential value of this paper jiang et al stm spatiotemporal and motion encoding for action recognition iccv 2019 li et al tea temporal excitation and aggregation for action recognition cvpr 2020 sudhakaran et al gateshift networks for video action recognition cvpr2020 liu et al teinet towards an efficient architecture for video recognition aaai 2020 3 please correct the abstract the experiments are performed on minikinetics rather than kinetics i indeed suggest that it would be better to perform the proposed method on kinetics to further show the effectiveness ### Summary:

this paper presents a model for video action recognition the reviewers appreciated the development of a novel dynamic fusion method that examines channels from feature maps for use in temporal modeling after reading the authors responses the reviewers converged on an accept rating the solid empirical results and analysis the fact that is is a plugin method that could be used in other models and the clear exposition were deemed to be positives as such this paper is accepted to iclr 2021

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper modifies the gan objective by defining the true and fake labels in terms of both the training sample and a newly introduced random variable s the intuition is that by progressively changing the definition of s and its effect on the label we can prevent the discriminator network from immediately learning to separate the two classes the paper doesnt give any strong theoretical support for this intuition and it i found it a bit surprising that the discriminator doesnt immediately learn the one extra bit of information introduced by every new level of augmentation however the results do seem to show that this augmentation has a beneficial effect on two different architectures in different data scenarios although the increase is not uniform over all settings the approach presented in this paper is motivated primarily as a method of increasing stability of training but this is not directly investigated figure 3 and table 2 both suggest that the augmentation does nothing to reduce variance between runs there is also no direct comparison to other methods of weakening the discriminator although these are mentioned in the related work i think the paper would be much improved by a thorough investigation of the methods effect on training stability to go along with the current set of evaluationsdocsepthis paper proposes a new trick to improve the stability of gans in particular the authors try to tackle the vanishing gradient problem in gans when the discriminator becomes to strong and is able to perfectly separate the distribution early in training resulting in almost zero gradient for the generator the authors propose to increase the difficulty of the task during training to avoid the discriminator to become too strong the paper is quite well written and clear however there is several unsupported claims see below a lot of work has been proposed to regularize the discriminator its not clear how different this approach is to adding noise to the input or adding dropout to the discriminator pros the experimental section is quite thorough and the results seem overall good the paper is quite clear cons there is a major mistake in the derivation of the proposed method in eq 6 7 c is not an equivalence minimizing the kl divergence is not the same as minimizing the jensenshannon divergence the only thing we have is that klpq 0 jsdpq 0 pq the same kind of mistake is made for d note that the kldivergence can also be approximated with a gan see 1 since the equivalence between 6 and 7 doesnt hold the equation 11 doesnt hold either the authors say that the discriminator can detect the class of a sample by using checksum the checksum is quite easy for a neural networks to learn so i dont really see how the method proposed actually increase the difficulty of the task for the discriminator especially if the last layer of the discriminator learns to perform a checksum and the discriminator architecture has residual connections then it should be straightforward for the discriminator to solve the new task given it can already solve the previous task so im not sure the method would still works if we use resnet architecture for the discriminator i believe the approach is really similar to adding noise to the input i think the method should be compared to this kind of baseline indeed the method seems almost equivalent to resetting some of the weights of the first layer of the discriminator when the discriminator becomes too strong so i think it should also be compared to other regularization such as dropout noise on the discriminator the authors claim that their method doesnt just memorize the true data distribution its not clear to me why this should be the case and this is neither shown theoretically or empirically i encourage the author to think about some way to support this claim the authors states that adding highdimensional noise introduces significant variance in the parameter estimation which slows down training can the author give some references to support that statement according to the author regularizing the discriminator with the gradient penalty depends on the model distribution which changes during training and thus results in increased runtime while i agree that computing the gradient penalty slightly increase the runtime because we need to compute some second order derivatives i dont see how these increase of runtime is due to change in the model distribution the authors should clarify what they mean others it would be very interesting to study when does the level number increase and what happens when it increase also what is the final number of level at the end of training conclusion the idea has some major flaws that need to be fixed i believe the idea has similar effect to adding dropout on the first layer of the discriminator i dont think the paper should be accepted unless those major concerns are resolved references 1 nowozin s cseke b tomioka r 2016 fgan training generative neural samplers using variational divergence minimization nipsdocsepauthors argue that the main issue with stability in gans is due to the discriminator becoming too powerful too quickly to address this issue they propose to make the task progressively more difficult instead of providing only the samples to the discriminator an additional processed bitstring is provided the idea is that the bitstring in combination with the sample determines whether the sample should be considered true or fake this in turn requires the decision boundary of the discriminator to become more complicated for increasing lengths of the bitstring in a limited set of experiments the authors show that the proposed approach can improve the fid scores pro a simple idea to make the problem progressively more difficult the writing is relatively easy to follow standardized experimental setup con ablation study of the training tricks is missing 1 how does the proposed approach perform when no progressive scheduling is used 2 how does it perform without the linear model for increasing p 3 how does the learning rate of g impact the quality does one need all of these tricks arguably if one includes the fidkid to modify the learning rates in the competing approaches one could find a good setup which yields improved results this is my major issue with this approach clarity can be improved several pages of theory can really be summarized into learning the joint distribution implies that the marginals are also correctly learned similar to alibigan this would leave much more space to perform necessary ablation studies comparison to 1 is missing in that model it seems that the same effect can be achieved and strongly improves the fid namely they introduce a model in which observed samples pass through a lens before being revealed to the discriminator thus balancing the generator and discriminator by gradually revealing more detailed features can you provide more convincing arguments that the strength of the discriminator is a major factor we should be fixing in some approaches such as wasserstein gan we should train the discriminator to optimality in each round why is the proposed approach more practical then approaches such as 2 1 httpproceedingsmlrpressv80sajjadi18ahtml 2 httpsarxivorgabs170608500 ### Summary:

the submission hypothesizes that in typical gan training the discriminator is too strong too fast and thus suggests a modification by which they gradually increases the task difficulty of the discriminator this is done by introducing effectively a new random variable which has an effect on the label and which prevents the discriminator from solving its task too quickly there was a healthy amount of backandforth between the authors and the reviewers which allowed for a number of important clarifications to be made esp with regards to proofs comparison with baselines etc my judgment of this paper is that it provides a neat way to overcome a particular difficulty of training gans but that there is a lot of confusion about the similarities of lack thereof with various potentially simpler alternatives such as input dropout adding noise to the input etc i was sometimes confused by the author response as well they at once suggest that the proposed method reduces overfitting of the discriminator but also state that we believe our method does not even try to regularize the discriminator because of all this the significance of this work is unclear and thus i do not recommend acceptance

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper takes the negativefree contrastive learning methods as an example to explore the disentanglement property of the selfsupervised methods experimentally to address the limitations of existing disentangled metrics in highdimensional representation models the author proposes a new decoupling metricmed based on mutual information experiments on realworld datasets and highdimensional representation space demonstrate this metrics superiority and applicability strengths 1 existing work on disentangled representation learning is limited to the generative model this paper empirically studies the disentanglement property of negativefree contrastive learning which is an exploratory work 2 this paper proposes a new metric medtopk med which extends the decoupling metric to highdimensional space 3 the paper is well organized so the reader can get the gist of the article the authors clearly describe the proposed method in addition the experimental results show the effectiveness of the proposed method weaknesses 1 the author designed a version of medtopk med to evaluate the disentanglement section 54 analyzes the effect of dimension on top2 med however the effect of dimension on med is unclear in the upper part of table 1 the authors provide results in 1000dimensional representation space however the paper does not directly show results in lowerdimensional spaces no additional pca required just set the projection dimension such as 100 or 200 2 the author found that contrastive learning without negatives learned a welldisentangled subspace of latent representation from experiments it is not yet known how this subspace and the subspace learned by disentangled representation learning perform on downstream tasks such as classification tasks the authors have not addressed the limitations and potential negative societal impact of their work docsepthis paper provides experiments on disentanglement for negativefree contrastive learning the results indicate that current metrics have limitations on this setup as a result the authors present a novel metric for evaluating disentanglement in the proposed scenario strengths investigating and expanding current metrics for quantifying disentanglement can address an important issue in deep learning the assessment of current issues on disentangled metrics is done properly they assess disentanglement on realdatasets rather than synthetic validation of the new metric with qualitative results on generative factors weaknesses missing comparison with other recently proposed metrics httpsopenreviewnetpdfidhjgk0h4ywr httpsopenreviewnetpdfidebidjbynyj8 httpsarxivorgabs210603375 the work validates the proposed metric empirically however would be also interesting to investigate the theoretical justification of this method docsepthe paper tries to empirically study the disentanglement of negative sample free contrastive learning methods eg byol simsiam the authors find that the existing disentanglement metric does not fit with the disentanglement of highdimension feature representation space thus the authors propose a new running timeefficient metric named mutual information based entroy disentanglement and med for short the authors evaluate the new metric on some popular synthetic datasets and a realworld dataset celeba and argue that negative sample free contrastive learning methods can learn a welldisentangled subset of representation strength 1 the paper points out previous methods drawback and propose a timeefficient metric that is designed for highdimension space 2 the author report various ablation studies to show the properties and effectiveness of the new metric ie uniqueness of factorrepresentation correspondence and influence from manipulating factors weakness 1 the main weakness is that the authors do not theoretically prove the new metric is sound the major message of this paper is that the previous disentanglement metrics are not good enough and the proposed new metric can fix these problems however the paper only showed the drawback of previous metrics and for the new metric the authors only empirically show it may work eg figure 4 why should we believe the new metric is better than other methods why the value of med can be regarded as a measure of disentanglement property from my perspective the best way to prove a proposed metric work is theoretical analysis ie 1 and the paper misses that part i recommend the authors consider a highdimension linear case and prove that med can beat some other metrics or prove some property of med 2 also in section 32 the highlevel intuition of mde is not clear the metric has three parts equation 1 2 3 and it is quite complicated i did not get a clear insight ie rij used in rhoi and si why should we combine them in the way in equation 3 the last paragraph of section 32 should be longer and provide more explanation 3 the authors argue that contrastive learning can learn a welldisentangled subset of representation without negative samples the conclusion here is weak there are many further important questions that the paper does not give answers to why the model can learn a welldisentangled subset of representation without negative samples what is the disentanglement difference between methods with and without negative samples is there any highlevel intuition or further suggestion about this conclusion more discussion is needed here 1 kornblith simon et al similarity of neural network representations revisited international conference on machine learning pmlr 2019 limitations are the theoretical analysis mentioned above docsepthis paper empirically studied the disentanglement property of selfsupervised methods such as moco barlowtwins and byol besides the authors validated the disagreement of current disentanglement metrics for the models with highdimensional latent space the authors proposed a new metric based on mutual information to measure highdimensional representations massive experiments conducted on synthetic datasets and a realworld dataset showed that negativefree contrastive methods can learn a disentangled subset of representation contribution 1 this work studied the disentanglement properties of negativefree contrastive models for the first time 2 this work proposed a new metric for highdimensional models and a selection strategy to pick a disentangled subset of representation strength 1 the experiments were comprehensive and massive to cover popular conventional disentanglement methods and negativefree contrastive models 2 this paper aims to address the disentanglement measurement of highdimensional representations and bring negativefree contrastive learning into disentanglement learning weakness 1 line 34 latent representation this latent representation this 2 comparing metrics for a model in a subfigure may better show the disagreement or agreement of metrics in figure 4 3 we know orientation is hard to be disentangled but the authors still need to show all experimental results without selection 4 what is manipulating a factor in section 43 getting a set of images by traversing one factor could you color the latent index with the factor you picked i cannot see the existence of a welldisentangled subset for orientation the authors did not show the superiority of the proposed metric they need to find some cases where all metrics fail to measure the disentanglement except the proposed metric ### Summary:

there was a consensus among reviewers that this paper should be accepted the key convincing arguments that this paper studies a novel setting how to measure the disentanglement in highdimensional spaces for this the authors perform extensive experiments and come up with a novel metric the reviewers further felt that concerns raised in the initial reviews were subsequently addressed in the author rebuttal

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: overall i vote for rejecting the core idea of the new algorithm looks interesting but this paper does not provide convincing evidence theoretically and empirically pros 1 this paper introduces a new variant of double qlearning to reduce the overestimation risk and reduce variance 2 in part of the experiments the new algorithm demonstrates better performance than previous ones cons 1 this paper does not provides enough insightful intuition and theoretical guarantees on the design of the new algorithm there should be more explanation and evidence to help reader understand why for instance the definition of qvalue in equation 8 makes sense 2 this paper is not wellwritten there are missing references links typos and missing definitions of some notations for example in the second paragraph of introduction lillicrap et al is just text and not linked to a paper in the references and there are many other cases like this typos can be seen in many places as well such as in the first paragraph of section 33 it should be twofold instead of two folder in the last paragraph of section 5 categories should be categorised moreover in the convergence analysis the meaning of many notations are not explained at all eg alphat the author only says we provide sketch of proof which borrows heavily from the proof of convergence of double qlearning and td3 but without the definitions of the notations the completeness of the paper is greatly undermined docsepthe paper proposes a method that modifies double qlearning by eliminating a linearly correlated part of one q i am not familiar with the proof of double qlearning and td3 and thus find the proof of this paper hard to read as it omits the majority of proof by claiming it is similar to the proof of the aforementioned two algorithms to name some of the part that confused me while reading what is the definition of fqt and ct in 14 why does a small delta2 exist in 16 why does deltat converge to zero as claimed in the line after 16 why is the randomness of st1 not mentioned in the subscripts of es in 5 and 9 etc therefore i suggest the authors write a thorough proof and put it in the appendix to make the convergence analysis readable i am also curious how the decorrelation term helps to improve the convergence in the analysis as it is the main contribution of this paper besides double qlearning and td are mostly used in function approximations i wonder if the analysis can extend to some simple case of parameterized q functions eg linear approximations the experiment part looks good to me as it compares d2q with several sota algorithms and get satisfying resultsdocsepsummary the paper suggests an improvement over doubleq learning by applying the control variates technique to the target q in the form of q1 beta q2 eq2 eqn 8 to minimize the variance it suggests minimizing the correlation between q1 and q2 in addition it applies the td3 trick the resulting algorithm d2q outperforms ddpg and competes with td3 recommendation i hope i havent misunderstood this paper but ive found neither the theory nor the experiment convincing therefore i recommend a rejection strengths the proposed algorithm is simple and straightforward to use weaknesses 1 theory a minimizing the variance of eqn 8 requires maximizing the correlation between q1 and q2 if they are independent whats the point of including q2 check out httpsenwikipediaorgwikicontrolvariates b eq2 is the average over all possible runs its unclear how its calculated maybe run a few identical rl experiments with different random seeds just to get eq2 feels wasteful to me c why would minimizing the squared cosine between last layer feature vectors lead to minimum correlation if the feature for q2 is obtained from that of q1 through a deterministic 90 rotation wouldnt that result in a zero cosine but really strong correlation d why is it ok to ignore varq1 while computing beta no theory is given here 2 experiments in fig 1 3 d2q sometimes outperforms and sometimes underperforms td3 because these two algorithms are so similar i cant tell whether the comparison is statistically significant other feedbacks please address questions raised above perform additional experiments to make the paper more convincing docsepthe proposed decorrelated double qlearning algorithm combines a few techniques to improve the performance of modelfree rl including control variates for reducing variance decorrelated regularization for reducing bias and a technique from td3 for stabilizing learning overall the ideas of this work are interesting and bring some insights for tackling the overestimation issue of qlearning empirically the proposed method shows some improvements over the existing ones however a few major concerns are as follows the theoretical analysis of convergence seems handwaving and confuses me for example does the analysis only apply to the tabular case the authors dont seem to state this explicitly how does eq 17 follow from eq 9 are we missing the gradient of the decorrelated regularization term all experimental results are about reward vs iteration curves which are not convincing or insightful enough for example is there empirical evidence showing that the proposed algorithm does learn two decorrelated critics the structure of section 3 may need some adjustment in particular in the current version the formal definition of the correlation term sec 32 and the description of the full algorithm itself sec 33 appear after the convergence analysis of the algorithm sec 31 which looks weird based on the above comments i think substantial improvements are needed for publication of this work ### Summary:

this paper investigates some variants of the double qlearning algorithm and develops theoretical guarantees in particular it focuses on how to reduce the correlation between the two trajectories employed in the double qlearning strategy in the hope of rigorously addressing the overestimation bias issue that arises due to the max operator in qlearning however the reviewers point out that the proofs are hard to parse and often handwaving with important details omitted the experimental results are also not convincing enough     

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper highlights important variables impacting the effective robustness er of a pretrained finetuned model the authors identify that increasing model size dataset size and example difficulty improves the er of a pretrained finetuned model the experiments suggest that the zeroshot component of clip plays a significant role in the high value of er clip achieves the investigation of er on dominance probability shows that models with high er have high dominance probability the authors also present a negative result showing that several reasonable approaches to maintaining high er while finetuning fail strengths the paper is very clearly written and has a thorough experimental section validating the authors claims the authors have a thorough selection of experiments that validate their claims weaknesses one weakness of this paper is that the authors do not properly define finetuning while its meaning is implicit finetuning is a key concept in this paper so having a clear definition of the term seems necessary this is especially true when considering multiple finetuning steps such as when finetuning a finetuned model bitm21k on cifar10 the authors use a pretrained or randomly initialized model on a large dataset finetune on a smaller dataset and measure ood accuracy on an analogous dataset to the finetuned dataset it would help if the authors give some examples of when such a training procedure would be useful usually finetuning is carried out on the distribution that the model is going to be evaluated on an analysis of the relation between the finetuning dataset and the ood test set would be useful right now the relationship is alluded to based on natural distribution shifts but its not clear how this might generalize to other types of distribution shifts overall this paper is very thorough the authors set out to investigate the role finetuning has on ood robustness and they successfully identify several key variables to consider there are many experiments in the main paper as well as in the appendix that validate their claim this work will be very valuable to the community as it provides some insight into what variables lead to ood robustness for pretrained finetuned models docsepin the manuscript entitled the evolution of outofdistribution robustness throughout finetuning the authors present an empirical investigation of model exhibiting a property known as effective robustness in particular their focus is on how effective robustness changes during fine tuning and on the characteristics of these models apologies to the authors for what may sound like a rather glib judgement of this submission and for which i acknowledge through the confidence scores below that my opinion is not absolute as i do not work directly in the space of image classification but the results and conclusions of this paper seem remarkably obvious namely that models that have been pretrained on a large collection of different datasets tend to lose their strengths at predicting out of distribution as they are progressively finetuned towards predicting a specific type of data and that when these models are performing in the effective robustness mode the types of in sample problems they find easy alt hard are different to those that models trained on the dataset at hand find easy alt hard the perils of overfitting to a particular training set are well known and strategies to avoid this and improve generalisation are a major component of ongoing work in the machine learning see eg roger grosses comp sci lecture notes httpswwwcstorontoedurgrossecoursescsc3212018readingsl0920generalizationpdf to change my mind on this point would require additional discussion by the authors to connect this work to general principles of machine learning and establish the novelty of the insights reached from these numerical experiments that said my many years in research have taught me that sometimes results that seem to me to be remarkably obvious are actually not so for the general audience and that simple examples demonstrating such principles can actually have a large impact and generate huge citation indices i mean this genuinely not trying to be cynical here so for that reason i would respect the decision of other reviewers and the aes if this paper was in fact recommended for the conference series certainly having a reference to point to for eg the fact that selfdriving cars probably shouldnt spend too much time refining their algorithms to overfit to a commuters every day journey to work this being inevitably at the expense of performance when heshe wants to take a drive in the countryside could actually be very useful conclusions seem obvious to this reviewer but willing to consider other opinions docsep the paper conducts an empirical study into an interesting problem of robustness of deep models on out of distribution data the paper finds that the pretrained models exhibit better effective robustness during training which will disappear upon convergence of the same models summary the paper conducts an empirical study into an interesting problem of robustness of deep models on out of distribution data the paper finds that the pretrained models exhibit better effective robustness during training which will disappear upon convergence of the same models pros the paper is wellwritten easy to understand and follow along most significant of all is that this paper has an extensive study of various initializations with pretrained models for vision problems the breadth of explorations such as pretrained models er during training data set size example difficulty cons are there proper bounds for the er values what would it really mean to have higher value lower value etc can you briefly explain er 0 for cifar10 figure 3a at the end of training exactly the point at which any of the models are having the corresponding best accuracies on in set the only difference at that point is the accuracy of various models is different which is already known and wellstudied in literature similar for imagenet it is visible at low accuracy and the trends are visible as the accuracy gets better similar to cifar the question is why should anyone care if the er is high in the middle of training at low accuracy this is not welljustified in the current version of the paper also the reasons for the peaks in er during training are not justified why are they intriguing is it because the pretrained models change significantly to the downstream tasks or something else the random initializations dont fluctuate that much why not investigate these observations in detail in figure 4b why further finetune only the bitm1k model what happens if you further finetune all the models this experiment is not a fair comparison not all models see the same amount of data again in figure 4c and the corresponding appendix why would anyone use a low accuracy classifier when one knows it will perform bad on the hard to classify examples in that case er is not even a thing to worry in the first place accuracy becomes the first concern fine at least the ones that the classifier can classify there is better robustness but not entirely convincing though this paper relies heavily on taori et al 2020 which seem to have a number of unresolved concerns most important of all is that the paper is a bit short on novelty however the empirical study in itself is interesting show the same findings hold for at least one more domain for example nlp overall the paper has breadth in the number of experiments and the directions that it explores without enough depth and justifications to a majority of findings overall the paper has breadth in the number of experiments and the directions that it explores without enough depth and justifications to a majority of findings also the paper lacks novelty or detailed analysis of the proposed concepts i would give it a score of 4 docsepin this paper the authors conduct a thorough empirical investigation of effective robustness during finetuning and have several observations 1 models pretrained on larger datasets in the middle of finetuning as well as zeroshot pretrained models exhibit high amounts of effective robustness but the effective robustness vanishes at convergence 2 the effective robustness increases with the larger size more diversity and higher example difficulty of the dataset 3 models that have effective robustness make different predictions than standard models and are able to correctly classify examples that no standard models get right besides they discuss several potential solutions to mitigate the problem of vanishing of effective robustness during finetuning but find that none of them are able to maintain high effective robustness at high indistribution accuracy i think this paper has the following strengths 1 i think identifying models that have effective robustness and understanding their properties is an important and interesting problem this paper has some empirical observations under this direction 2 enough details are included for the experiments 3 overall the paper is wellwritten and the related work is properly discussed however i think this paper has the following weaknesses 1 my major concern is that the contribution is not very significant the authors have some empirical observations but those observations are not very useful and dont help us understand the problem better for the models in the middle of finetuning although they exhibit a high amount of effective robustness the accuracy of those models on the indistribution dataset is not high and thus such kinds of models may not be useful also when the finetuning converges the models have high accuracy on the indistribution dataset but dont have effective robustness thus the models obtained via finetuning dont have clear advantages over previous models besides although the authors discuss several strategies for scaling effective robustness to the highaccuracy regime to improve the outofdistribution accuracy none of those methods work so such a discussion may not be useful 2 they only have some empirical observations but dont have analysis for them for example they only show that the effective robustness generally increases throughout finetuning peaks and then gradually disappears towards the end of finetuning empirically but dont analyze or explain why such a phenomenon exists it is unclear whether such a phenomenon is general or it just exists on some datasets it seems figure 3 in the paper shows that such a phenomenon doesnt exist on imagenetr and objectnet when using imagenet as indistribution 3 some claims are not well supported by results for example the authors claim that the pretrained models in the middle of finetuning as well as zeroshot pretrained models represent an entire class of models that exhibit high amounts of effective robustness i think this claim may not be true there might be other training methods that could lead to better effective robustness and also high accuracy the authors only explore the methods of finetuning and zeroshot evaluation thus it is hard to claim that they represent an entire class of models that exhibit high amounts of effective robustness the claim that the models with effective robustness make different predictions than standard models and are able to correctly classify examples that no standard models get right is also not well supported by the results they only select 48 of images that none of the testbed models get correct and show that the model that has effective robustness with the best indistribution performance gets 10 of these examples correct i think such results cannot support the claim that the models with effective robustness are able to correctly classify examples that no standard models get right since only 10 of these examples are predicted correctly by the model that has effective robustness with the best indistribution performance also it seems these results could not demonstrate that the models with effective robustness have prediction diversity 4 some observations may already be known to the community for example the observation that the effective robustness increases with the larger size and more diversity of the dataset seems obvious i think this paper doesnt make enough contributions and the claims are not well supported by results also they dont provide analysis for the observations and the observations may not be helpful in understanding the problem thus i think this paper is not ready for publication post rebuttal after reading the rebuttal i think my major concerns still remain the contributions are not very significant and the findings may not be useful i still think that the models studied in this paper are not enough to represent all models that exhibit er the authors need to explore other kinds of models that have er and also have high accuracy thus i keep my original rating and think the paper is not ready for publication ### Summary:

thank you for your submission to neurips the reviewers are quite split on this paper but some remain substantially negative even after discussion im a bit more optimistic about the paper the observed increase then decrease in er during finetuning does strike me as a fundamentally interesting phenomenon and i believe that papers that present such phenomena can be valuable contributions even without more fundamental explanations of the observations my recommendation therefore ultimately rests largely on the fact that i think as is honestly evidenced by the reviews to a large degree the presentation and contextualization of these results can be substantially improved in a future revision of the paper specifically the fact that several reviewers found the results obvious andor not sufficiently substantiated suggests that the basic premises here are still failing to land i would strongly suggest revisions that clarified these points in a resubmission

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the practical approximation of integrals is one of the most important practical mathematical problems only a handful of viable methods exists for that problem and the authors contribute a true improvement over the stateoftheart the paper is well written an a pleasure to read the formal presentation is very good and it is easy to follow the proofs and the authors reasoning an implementation is available the limitations are not discussed is the applicability limited eg by assumptions on k00 or the positive definiteness of p what about the methods complexity is it more computationally demanding than standard bq the experiments have rather small scale i would love to see results on nontoy data what are the computational limits of this method nonbq stochastic quadrature methods are not discussed mentioned honestly i am a fan of this work the idea is brilliant and it even works in practice the proposed method works with any shiftinvariant kernel which simplifies the usage in practice when compared to standard bq after explaining nicely the intuition behind the approach the authors present an upper bound on the quadrature error followed by a careful empirical evaluation of their approach on smallscale benchmark data traditional monte carlo techniques as well as standard bq deliver far worse results than the proposed method especially when data is scarce one question in eq11 it is unclear why we see sigma in the normalizing constant when expxmu2 has variance 1 docsep very well written the authors use both the uniform and the gaussian measures in the experiments section the gaussian case can be compared directly with bq that uses rbf kernels the uniform measure works well there is no competing method it would be good to have some comparison of computational time mention if there are any additional steps that increase the computational cost of gbq wrt bq also in terms of the dimensionality of the input space please clarify and ideally add more through experiments wrt dimensionality for a high enough dimension edit the authors have provided a computational complexity analysis in the rebuttal i encourage the authors to include the in the paper since it gives a full picture of this i suggest to change the colour scheme in figure 1 red and orange can be easily confused in the text the authors say that the matern kernel should work well with the disjoint polynomial but then in the figure you can see it has the highest error in the small sample size regime please clarify docsep novelty the proposed generalized bq discovers a new class of tractable bq problems when both kernels as well as density functions have their representations as rffs and it allows the bq to be applied with more expressive kernels writing this paper is overall wellwritten it has provided enough background to understand the proposed methods and it is easy to follow discussion on the runtime complexity is missing in this work while the complexity of the proposed algorithm might be the main issue from equation 13 it seems that the computation of the mean estimation is quadratic in the number of fourier random features moreover in the empirical evaluation the number of features are 100 and 300 in the 1d and 2d experiments respectively meaning that it requires a nontrivial number of features to get decent results i think the authors should also include the comparison of runtime in their experiments for a fair comparison and to better illustrate the efficiency of the proposed approach also only examples in 1d and 2d are presented while the previous bq paper such as 1 has their algorithm run on cases with dimensions being 5 or 15 where the scalability of the algorithms would be better illustrated than the low dimensional cases 1 kandasamy kirthevasan et al bayesian active learning for posterior estimation aaai 15 can the authors confirm what is the runtime complexity of generalized bq and whether scalability is an issue in sec 52 it mentions that the size of training data n ranges from 10 to 1000 while it is unclear what values does n exactly take it is unclear for which n it gives the results in table 1 2 and also it seems that not all the results for n are presented suggestion on definition 1 i think the authors should put only the definition of generalized bq for any density and separate the cases when the measures are gaussian or uniform as propositionstheorems instead of including everything in the definition typo before eq 11 the limit should be r infty instead of r infty docsepi think the key idea of the paper is really cool random fourier feature expansions are a general tool to approximate shiftinvariant kernels and the kernel means needed for bayesian quadrature can be easily estimated for rff kernel approximations when the relevant measure like the kernel is positive definite and it is easy to sample from the fourier transformed version of that measure i also like that there is some error analysis though i have not checked all details sample efficient highdimensional integration is intractable without a lot of smoothness or some other special assumption but the selling point in the 1d and 2d experiments seems to be that the method can deal with nonsmooth integrands i am skeptical that this scales to higher dimensions in lowdimensional spaces conventional numerical quadrature methods gauss quadrature and adaptive quadrature rules are quite efficient in spaces of moderately high dimension tensorproduct quadrature becomes infeasible though sparse grid methods are sometimes still attractive but techniques like bayesian quadrature and quasimonte carlo methods still have a strong edge over the competition in truly highdimensional spaces both function approximation and quadrature suffer from the curse of dimensionality unless the regularity of the function of interest or sometimes some other measure of function complexity goes up concurrently with the dimension this is part of the great appeal of bayesian quadrature with a squared exponential radial basis function implicit in the choice of kernel is the idea that the integrand is smooth enough for this to be a sensible way of doing things the proposed technique extends the set of kernels that can be used for modeling the integrand but does not seem as useful for extending the set of measures that can be treated additional flexibility in the kernel allowing choice of something less regular may help with modeling nonsmooth integrands in lowdimensional spaces but it does not seem as likely to help get around the need for a lot of samples in similar situations in highdimensional spaces i could potentially be convinced that im wrong about this but it would require more than 1d and 2d test problems a 510 dimensional test problem would be fine to make the point it would be helpful to introduce the underlying measure earlier i think eg writing integral fx px dx at the outset rather than just writing integral fx and leaving the measure implicit in equation 10 please use either the equal sign or mapsto but not both i was very confused around 11 in the statement px is approximately qx what is px if it is a gaussian how is qx an approximation also the normalization constant involves the determinant of a covariance matrix but the covariance matrix does not appear elsewhere in the expression is sigma i in this example lemma 1 seems to be about estimating the integrand not estimating the integral though the text indicates the latter it was a little unclear to me what measures were being used for the standard bayesian quadrature and qmc methods in the experiment i can guess that bq was with respect to a gaussian measure and qmc with respect to uniform on a box but neither of those is clear the box transform is as good for quasirandom uniform variates as it is for standard prngs ### Summary:

meta review while there are mixed reviews for the paper concerns largely relate to computational complexity which seem well addressed by the authors in their responses the broader coverage of the literature in the paper is not great for example its surprising that very well known work about spectral kernels such as spectral mixture kernels httpproceedingsmlrpressv28wilson13pdf is discussed nowhere in the text this should definitely be corrected in a final version as well as a careful accounting of reviewer concerns

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary the paper studies the kernel ridge regression and provides close form characterization on the loss curve in the finite sample regime of m propto dr where m is the sample size d is the input dimension and r geq 1 is arbitrary integer the paper also proves the results hold broadly to the neural tangent kernel ntk of onelayer convolutional net model the paper studies the classical kernel ridge regression under the assumption that input data draws from ddimensional sphere and certain isotropicindependence assumption of higher order fourier coefficients while previous work of mei and montanari 2021 considers mostly of linear regime ie m propto d the paper extends the result to m propto dr to arbitrary integer r result the paper provides a close form characterization of the population loss it is hard to describe in nonmathematical way the close form characterization consists of bias and variance terms where both terms are certain integral of marchenkopastur distribution limiting spectral distribution for random matrix and exhibits the double descent phenomenon bias always decay but variance first goes up and then down method the method extends from the previous work of mei and montanari 2021 they use legendre polynomial decomposition to the kernel and by standard concentration argue one only needs to look at the rth order decomposition then perform standard biasvariance decomposition and use random matrix theory to argue about the spectral some technical complications come out along the proof strength the paper provides close form characterization of loss curve for kernel regression and offers some insight on the double descent phenomenon the technique follows largely from mei and montanari 2021 but also turns out to be very interesting and nontrivial to me weakness there are no significant weaknesses but the writing could be significantly improved see below 1 line 72 it is better to use xtop instead of xt for transpose this applies to other matrixvector transpose 2 line 46 extending these result extending these results 3 line 40 large text corpora can contain trillions of tokens wheares i dont quite get it the input dimension of language model might not be that large but the entire models could still be quite large 4 eq 5 what is delta0t 5 eq 11 typo need to add transpose for the variance and covariance 6 figure 3 might be good to make bias and variance more clear 7 line 190 would be good to make a separate paragraph for low frequencies and critical frequencies 8 the experiments are all simulations that should be emphasized somewhere 9 the reference format seems wrong 10 typo line 258 ykmathbf x should be the summation over jind docsepthe authors aim to theoretically analyze the relationship between number of samples m and test error learning curve in the case of kernel regression for dot product kernels when the m sim dr where d is the dimensionality of data and r is a natural number their analysis shows that when the input distribution and labeling function follow some regulatory conditions under some asymptotics a closed form formula precisely characterizes the learning curve for the mentioned set of kernels and their experiments support this claim moreover they asymptotically derive a closedform distribution for the gram matrix of some dot product kernels which seems valuable for future works although the work indeed does have some limitations mentioned in greater detail in the limitations section both on the theoretical and experimental aspects i find the contribution to be impactful in the direction of understandingcharacterizing generalization error in modern machine learning strengths the theoretical results of the work are sound and fills a gap in the study of learning curves for the case of nonlinear relationship between the number of samples and dimensionality of the data the predicted distribution for the spectral distribution of the mentioned dot product kernels seems very accurate and can potentially benefit future work the conducted experiments although with some limitations strongly support the theoretical results weaknesses some parts of the text are hard to follow for instance the notation section brings up the whole decomposition of the function of the dot product kernel without motivating it this kind of introduction to some topic without motivating it beforehands happens a lot in the paper and for me was generally confusing moreover sometimes some equations or results are being referred to before being obtained or described for instance in line 171 theres a reference to eq 29 which is undefined at this point for the reader likewise chib and chiv are introduced without much motivation various assumptions are not justified nor analyzed and their importance could be better explained as an example the assumption of having according decomposable labeling function could be better explained in a sense that how close is it to the practical datasets that one might expect at least for some toy examples i believe the writing could enjoy more description about motivations intuitions the why of different assumptions and the relativity of them how often do they hold in practical settings why do we need them and what goes wrong if we dont have them instead of some extra formulas in the main text such that the main text would give an overall intuition about the problem and the approach to solve it along with the derived theorems and the proof sketchs could be delayed to the appendix instead however this is just my opinion although the experiments strongly support the theoretical results the range of values used for m r d seem a bit limited to me in particular there are some asymptotics in the theoretical results high frequency and low frequency decompositions of the kernel value that converge or diverge asymptotically as d to infty that i expected to result in very noisy experimental results specially for the range of tested values but surprisingly this doesnt happen in the provided experiments i would be curious to know why this is the case moreover experiments involving noise and ridge regularization could also benefit the paper i believe more insights could be provided based on the solid theoretical results that are achieved having a closed formula for determining the precise learning curve in the mentioned context unlocks a lot of analyses for instance how does r in m sim dr affect the number of descents in the curve or the slope of the curve how does the labeling function or the data affect the slope andor the number of descents the dependence on the spectral gap is mentioned but when does one expect large or small spectral gap some more detailed concerns and questions what is the definition of a fat or tall matrix why would one expect the coefficients of the legendre polynomials to be nonzero up to a large index in the decomposition of h and why does it have to be independent of d could you please shed more light on the decomposition of the labeling function and the specific assumptions like the fact that hat fk should be isotropic how likely is it that practical datasets follow such decompositions i have mentioned unaddressed limitations as part of the weaknesses docsepthe authors explicitly characterize the learning curve for kernel ridge regression under poly scaling regimes m sim dr both theoretically and empirically despite the strict restrictions on the distribution and the setting this interesting multipledescent behavior in the samplewise learning curve could help the understanding of neural networks strength theoretical results seem to be solid did not check step by step though experiments serve as good illustration of the key ideas weakness 1 minor readability could be improved by adding more details could be placed at the beginning of the appendix in a separate subsection 1 explicit definitions of asymptotic notations eg sim ocdot 2 background on decomposition via spherical harmonics moreover are there commonly seen examples for the kernel function that eq 1 is satisfied 2 majorthe contributionimpact of the current finding should be more carefully articulated instead of several lines in the intro lines 5456 also a better summary of the existing works on both linearnonlinear scaling regimes would be better what is the connection in terms of the empiricaltheoretical behavior technique in obtaining the theoretical results etc 3 majormore experiments would be better i would not say d60 is very large though i am not expert in this field since the goal is to demonstrate the asymptotic theoretical findings using numerical evidence i would expect more extensive results in terms of settings such as dimensionality distribution parameters etc for example with larger dimension would you observe better empirical scaling to be precise the authors state peak appears at m approx drr then i would expect experiments indicating as d rightarrow infty the different between mpeak and drr shrinks to zero yes as the authors pointed out in section 7 this works only focus on kernel ridge regression with uniformly distributed data the generalization of the results is a very important topic the impact of this work beyond the interesting phenomena should be more carefully explored i do not see what is the potential negative societal impact docsepthis paper characterizes the prediction error for kernel ridge regression with dotproduct kernels different from the previous work that focus on the linear scaling regime sample size m propto data dimension d this work focuses on the higherorder scaling regimes mpropto dr to establish this theoretical result the author first study the limiting distribution mp distribution of the spectral density of the gram matrix under this scaling regime theorem 1 with the help of this theorem the author can express the testing error in terms of the mp distribution theorem 2 experimental results match their theoretical result extensions to convolutional kernels are also included originality main theoretical result theorem 2 itself gives us an understanding of the learning curve of kernel regression for higherorder scaling regimes extending previous analysis in literature the intermediate theoretical result theorem 1 and supporting proof are also interesting from my perspective the key step is to view kernel function as harmonic series and then study the empirical spectral distribution of the nontrivial term from this series previous literature tao 2012 only gives similar result for the case r1 but the auhor shows that it holds for all degrees qualityclarity presentations of notations theorem statements and proofs are crystally clear theoretical results are highly nontrivial experimental results match their theory surprisingly well significance their theory on learning curves applies for dotproduct kernel and nngpnt kernel with onelayer convolution which covers many scenarios in machine learning literature weakness 1 the author commented that it is promising to extend the theory for deep convolutional kernels but due to the complicated structure of this type of kernel it is left for future work i agree on this point but i am looking forward to seeing some updates on deep convolutional kernels in the future 2 as the author have commented in section 7 the strong assumption on distributions of input data and the focus on kernel regression setting makes this work less popular however i believe similar technique can be used to handle more general settings which can be finished in the future see weakness part in previous section docsepderive the precise generalization error of kernel ridge regression in the polynomial regime nthetadr for dotproduct kernels on unit sphere the analysis generalizes mei et al 2021 to the case where r takes integer value the key observation is that the degreer decomposition of the kernel matrix has a marchenkopastur spectrum which together with a random label function assumption gives a simple description of the generalization error similar to the case of linear regression theoretical predictions align with empirical findings the generalization error of kernel methods in high dimensions is an important research problem this submission considers the challenging polynomial scaling setting and completes the picture in mei et al 2021 by covering the rinmathbbn case the asymptotic formulae provide a precise description of the multiple descent risk curve without manipulating the spectrum of the input matrix i feel that this is an interesting submission that is relevant to the neurips community on the other hand the current results are also a bit limited for the following reasons 1 the analysis is based on the decomposition of kernel matrix into convenient orthogonal bases which seems to work only for restricted input data eg sphere or hypercube it is not clear if the similar results can be shown for general settings 2 to reduce the bias term to the stieltjes transform of the decomposed kernel the highdegree components of the label function is assumed to be random and isotropic the authors should highlight this limitation in the abstract introduction minor the reference list for precise learning curves gaussian equivalence principle is incomplete please update the citations and include more relevant papers eg see related work section of loureiro et al 2021 loureiro et al 2021 learning curves of generic features maps for realistic datasets with a teacherstudent model na ### Summary:

the paper studies kernel ridge regression and characterizes its performance theoretically which are interesting and highly relevant to machine learning venues the paper is technically sound and the authors have done a good job in the rebuttal period the paper is worth publishing in neurips

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper presents a new method for contrastive why class x and not class y as opposed to why class x explanations of classification networks the main idea is to use the attributions of other classes weighted by the softmax of their logits the methodss applicability as an extension of existing explainability methods is shown as well as its computational efficiency while using information from all of the classes ir only requires a single backward pass originality while the extension of existing methods is very simple it has not been used before in the context of explainability to the best of my knowledge related work is properly cited and the main contributions are well separated from previous work quality all the claims are well supported theoretically and the authors provide convincing experimental evaluation the work is selfcontained but proposes directions for future work clarity the paper is well oeganized easy to follow and written in good english main ideas are explained clearly information about details of finetuning required to reproduce the reported numbers is missing line 269 finetuned correspondingly significance the method is a simple extension of existing methods limited to a specific task yet novel and well supported both theoretically and experimentally the method relies on softmax weights so is not directly applicable to ie multilabel classification limitations are properly addressed in the conclusions section docsepin this paper the authors argued for the need of classcontrastive explanations which not only explain why an input is classified into a particular class but also why the input is not classified as others to obtain classcontrastive explanations the authors proposed the weighted contrast explanation scheme which is similar to the previous mean contrast explanation scheme except for the weights assigned to the nontarget classes namely in mean contrast explanation scheme all nontarget classes have the same weight whereas in the proposed weighted contrast explanation scheme the weight for a particular nontarget class depends on the softmax normalized score for that nontarget class against the scores for all nontarget classes the authors showed that their proposed weighted contrastive explanation scheme is equivalent to standard explanation applied to the softmax normalized output of the model thereby simplifying the implementation of their explanation scheme finally they applied their weighted contrastive method to various backpropagation explanation techniques including gradient input x gradient integrated gradient gradcam and linear approximation la and showed quantitatively that their weighted contrastive explanation scheme can find input regions that are better associated with targetclass probabilities and also qualitatively that their weighted contrastive explanation scheme when applied to gradcam and la provides more informative explanations strengths the authors have provided very solid theoretical reasoning for where the current explanation techniques fall short and why we need classcontrastive explanations the authors have provided an easy way to obtain classcontrastive explanations namely applying standard techniques not to the logits unnormalized class scores but to the predicted probabilities after softmax normalization the quantitative measurement of changes in predicted probabilities by perturbingblurringmasking pixels or input regions identified as important does show that the proposed classcontrastive explanation scheme is superior in finding input regions that are relevant for a target class weaknesses i find it difficult to understand equations 3 and 4 in particular why is the weighted sum of standard gradcamla explanations for all classes where the weight for a class is the derivative of the target class probability with respect to the unnormalized score of that class approximately equal to the weighted contrastive explanation please show your proof i also find it difficult to see how integrated gradient with zero baselines is equivalent to the input x gradient method please show your proof a minor issue regarding related work protopnet chen et al 2019 only provides similar examples but not contrastive examples in addition protopnet does not require additional annotations in fact protopnet and a number of other works does not belong to the posthoc explanation family the authors have adequately addressed the limitation that their contrastive explanation method is only applicable to techniques where attributions heatmaps are involved docsepthe authors propose a method weighted contrast for explaining dnn classifier predictions rather than focusing on features that change the predicted probability of a given target class the authors method focuses on features that are important for one class and not others strengths 1 as shown in previous work and by the authors socalled contrastive explanations can better capture features that discriminate between classes for a given model as compared to nonconstrastive explanations 2 the method proposed by the authors is an extension of gradientbased explanation methods making it faster than some previously proposed contrastive explanation methods weaknesses listed in order of importance to my score 1 lack of baselines many recent works have focused on developing contrastive explanation methods although the authors do discuss some of these in their literature review they do not compare their proposed method against any of these previous works in their experiments instead the previously proposed methods are dismissed as being against occams razor while i acknowledge the authors point that their proposed method may be computationally cheaper than previously proposed methods its necessary to understand whether this speed comes at the expense of explanation quality for example with noncontrastive explanations simpler gradientbased approaches have been shown to produce worse quality explanations than the axiomatically justified but computationally more intensive integrated gradients or shap methods without any comparisons against previously proposed methods it is difficult to assess the significance of the authors results 2 noveltyadditional discussion on the choice of backpropagating with respect to p or y the authors show that a weighted contrastive explanation of logits y is equivalent to a standard explanation of softmaxsigmoid outputs p however to my knowledge backpropagating with respect to p is already standard practice for some previously proposed methods see eg the official tensorflow integrated gradients tutorial at httpswwwtensorfloworgtutorialsinterpretabilityintegratedgradients given this could the authors clarify what exactly the contribution of this work is is it just the theoretical perspective suggesting that one should always backpropagate with respect to softmaxsigmoid outputs as opposed to logits or something more moreover for other methods eg input x gradientslrp it is specifically recommended not to backpropagate with respect to softmaxsigmoid outputs see 1 sections 2425 could the authors comment on this 3 presentation of experimental results in addition to point 1 i found it difficult to assess the authors experimental results due to issues with figure design specifically for the bar charts in figure 3 the two darkercolored bars are superimposed on the lightercolored one for each group of bars making the chart difficult to read moreover i found figure 4 hard to evaluate without the labels of the most and secondmost likely classes 4 writing clarity i found it difficult to understand the authors point in parts of the manuscript due to issues with writing clarity specifically section 3 felt quite rushed and i had to read the section multiple times to connect the propositions with the preceding text 1 not just a black box learning important features through propagating activation differences httpsarxivorgabs160501713 the authors briefly discussed the limitations of their work in the conclusion section i would strongly recommend the authors expand the limitations section and specifically discuss how backpropagating with respect to softmaxsigmoid outputs may lead to issues with some previously proposed methods see weaknesses point 3 the authors do not discuss potential negative societal impact for this work which i think is fine i dont see any obvious potential for negative impacts ### Summary:

reviewers expressed overwhelmingly positive opinions about the simple easily implementable and at the same time innovative procedure proposed in the paper for obtaining gradientbased classcontrastive explanations appreciation also transpired for the significance of this work in clarifying some technical points in the gradientbased xai literature and the potential for future work that the paper opens up one of the main criticisms raised in the reviews the lack of comparisons against other contrastive explanation methods has been addressed satisfactorily with additional experiments and discussions in the rebuttals the most important remaining criticism was a doubt on the merits of one of the key technical points in the paper regarding whether gradientbased attributions should be computed according to the softmax outputs or logits reviewers pointed out that computing attributions with respect to softmax outputs instead of logits is already common practice in the field reviewers expressed strongly the opinion that it would be appropriate to characterizing and clarify this situation as it could be potentially misleading and indeed counterproductive even for the paper to denote attribution methods with respect to logits as standard while its instead the case that some implementation of gradientbased attribution methods already attribute with respect to the softmax output albeit inconsistently in conclusion the reviewing panel voted for accepting the paper under the condition that the cameraready version of the paper explicitly clarify the distinction between the two approaches and discuss the implication of choosing one of the other without however referring to attribution with respect to logits as standard but merely pointing out that until now the distinction has been vague and implementations inconsistent from this technical standpoint reviewers ask that the contribution of the paper should then be explicitly characterized as clarifying the distinction between logits and softmax attributions rather than as the proposal of a new procedure in opposition to an already established standard this is already perceived as a strong contribution to the community as phrasing it specifically as indicated would help elucidate the state of affairs in the literature and make the community aware of this outstanding blindspot

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this papers tackles the problem of machine unlearning that is removing training data upon request from a model as if the model is trained only with the retained data the proposed approach pcmu trains a model with randomly smoothed quantized gradient analogous to certified robustness the paper presents some certified radius of the proposed method against to pertubation of the gradient unlike previous machine unlearning approaches pcmu directly trains a model that is robust to data removal so it does not require an additional unlearning stage experimental results show that pcmu can achieve a more similar error rate with retraining than competitive models strength some theoretical results are provided the proposed method is faster than previous methods and it can matches the accuracy better with the retraining approach weaknesses im not an expert on machine unlearning but from my perspective rather than a machine unlearning method the proposed approach looks more like a differential privacy work which trains a model without utilizing too specific features from individual users so the trained model automatically satisfies removal requirements i wonder whether it counts as machine unlearning the writing is somewhat vague there is not a description or pseudocode of how the training and unlearning are performed making the paper difficult to follow there are not apparent further limitations or potential negative societal impacts other than those i raised for weaknesses and questions docsepthis paper presents a novel certified machine unlearning framework that targets the issue in the expensive computational cost of training and unlearning the authors analogize certified robustness on classification against adversarial attacks to certified machine unlearning on gradient quantization against data removals the randomized gradient smoothing and quantization techniques are developed to guarantee that the learnt model shares the same gradients and parameters and has the same performance with the naive unlearning model retrained on only the remembered data with only the cost of simultaneous training and unlearning the theoretical analyses validate the effectiveness of certified machine unlearning in terms of the certified radius and the certified budget of data removals overall the studied problem is interesting and practically important the experimental results look promising strengths 1 existing machine unlearning methods separate the unlearning process into two sequential operations of training and unlearning which leads to nontrivial computation cost when training complex models over large datasets in addition these methods often sequentially address multiple unlearning requests one by one to improve the unlearning efficiency this work trains and unlearns the model simultaneously 2 the authors propose a randomized gradient smoothing and quantization technique to directly train an unlearning model in advance with fast convergence and certified unlearning guarantees the framework is able to resolve the requests of data removal in a timely and costefficient manner 3 the proposed method provides a general machine unlearning framework the proposed framework is important for privacycritical applications that usually require nearzero tolerance of data leaking such as financial and health data analyses 4 this work theoretically analyzes and understands the certified radius regarding the data change before and after data removals and the certified budget of data removals in machine unlearning extensive experimental results on different benchmark datasets have been conducted to validate the efficacy of the developed prompt certified machine unlearning algorithms weaknesses 1 the paper provides descriptions of the benchmark image classification datasets and learning models in the paper and appendix it would be nice to include the description of the data removals for how to separate the datasets into the forgotten data and the remembered data for each benchmark dataset 2 it would be interesting to see the results about different smoothing strategies exploited in certified machine unlearning problems such as laplacian and uniform smoothing it is unclear of the potential negative societal impacts of the results such as security privacy and fairness issues etc docsepthe authors propose a novel certified machine unlearning algorithm to improve the unlearning efficiency for complex models on largescale data first the authors present an analytic framework to connect randomized smoothing for certified robustness on classification to randomized smoothing for certified machine unlearning on gradient quantization second the paper develops a prompt certified machine unlearning model for producing the effective certificates of data removals based on randomized data smoothing and gradient quantization finally the authors propose a practical framework of randomized gradient smoothing and quantization for producing the high confidence certificates in an efficient manner the proposed pcmu method brings three significant benefits simultaneously conduct the training and unlearning for improving the unlearning efficiency onetime training for responding multiple machine unlearning requests at a time and no need to know the forgotten data before the unlearning strengths the authors study an important research problem ie prompt machine unlearning which is important to improve the unlearning efficiency for complex models on largescale data and to provide the timely response to a series of machine unlearning requests there are few prior works to conduct this problem the motivation for proposing randomized gradient smoothing and quantization techniques is clearly explained the method and the claim are correct and sound the authors provide enough methodology description and theoretical analysis to explain their proposed pcmu model the paper conducts the theoretical analysis to derive the certified radius regarding the data change the certified budget of data removals and the correlation between two types of certified radii in two frameworks this work integrates the certifying and training of machine unlearning into a unied framework for further enhancing the unlearning performance the convergence analysis is conducted to demonstrate the effectiveness and efficiency of the prompt certified machine unlearning algorithm the paper provides comprehensive extensive evaluation on three real datasets to demonstrate the superior performance of the proposed techniques against a number of sota baselines the experiment results look promising weaknesses there are several typos and the paper would benet of a careful proofread for example which are used to derive the certified budget b about r in p2 b the certified budget of data removal in p5 removals and notice that the accuracy and error on test data by our pcmu keeps unchanged in p8 keep it would be nice to move the related work section to the paper for readers to better understand and appreciate the technical contributions of this paper compared with existing studies instead of the appendix in the experiments tables 12 and relevant texts lie in different pages which decreases the papers readability i suggest the authors to update the paper layout to get more clear presentation none ### Summary:

this paper proposes an algorithm for simultaneous learning and unlearning without the knowledge of the datapoints that will be forgotten this reduces the computational cost associated with unlearning in a unified fashion both experimental and theoretical results are interesting and the paper would be a great addition to neurips

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper proposed a modelfree hrl method which is combined with unsupervised learning methods including abnormality discovery and clustering for subgoal discovery in all this paper studies a very important problem in rl and is easy to follow the technique is sound although the novelty is not that significant combining existing techniques it showed good results on montezuma revenge which is considered as a very challenging problem for primitive action based rl although the results are impressive i still have some doubt about the generalizability of the method it might be helpful to improve its significance if more diversified domains can be tested the paper can be strengthen by providing some ablation test for example is performance under different k for kmeans also some important details seems missing for example the data used for kmeans it is mentioned that the input to the controller is four consecutive frame of size 84x84 so the input data dimension is more than 10k i guess some further dimensionality reduction technique has to be applied in order to run kmeans effectively regarding the comparisons the proposed method is only compared with one primitive action based method it might be better to include results from other hrl methods such as kulkarni et al is the curve based on the mean of different runs it might be useful to include an errorbar to show the statistical significance docsepsummary the authors propose an hrl system which learns subgoals based on unsupervised analysis of recent trajectories the subgoals are found via anomalyoutlier detection in this case states with a very high reward and the clustering together of states that are very similar the system is evaluated on the 4rooms task and on the atari game montezumas revenge the paper cites relevant work and provides a nice explanation of subgoalbased hrl the paper is for the most part wellwritten and easy to follow the experiments are unfortunately not making a very convincing case for the general applicability of the the methods while the system does not employ a model of the environment kmeans clustering based on distances seems to be particularly wellsuited for the two environments investigated in the paper it is known that the 4rooms experiment is much easier to solve with subgoals that correspond to the rooms themselves i can only conclude from this experiment that kmeans can find those subgoals given the right number 4 of clusters and injecting the knowledge that distances in gridworlds correlate well with transition probabilities similarly the use of distancebased clustering seems wellsuited for games with different rooms like montezumas revenge but that might not generalize to many other games the anomaly detection subgoal discovery is interesting as a method to speedup learning but it still requires these potentially sparse high reward states to be found first for tasks with sparse rewards it does make sense to set high reward states as potential subgoals instead of waiting for value to propagate that said the reward for the lower level policy is only less sparse in the sense that wasting time gets punished with a negative reward subgoal discovery based on rewards should probably also take the ability of the current policy to obtain those rewards into account like some other methods for subgoal discovery do see for example florensa et al 2018 the authors mention that the subgoals were manually chosen by kulkarni et al 2016 instead of learned in an unsupervised way but i dont think that the visual object detection method employed there is that much more problem specific like kulkarni et al 2016 the authors compare their method with dqn mnih et al 2015 but it was already known that that baseline cannot solve the task at all and a lot more results on montezumas revenge have been published since then a more insightful baseline would have been to compare with at least some other hrl methods that are able to learn the task to some extend like perhaps feudal networks vezhnevets et al 2017 looking at the graph in the feudal networks paper for comparison the results in this paper seem to be on par with the lstm baseline there but it is hard to compare this on the basis of the number of episodes did the reward go up further after running the experiment longer since the results are not that spectacular and a comparison with prior work is lacking the main contributions of the paper are more conceptual i think that it is interesting to think more carefully about how sparse reward states and state similarities can be used more efficiently but the ideas in the paper are not original or theoretically founded enough to have a lot of impact without the company of stronger empirical results extra reference carlos florensa david held xinyang geng pieter abbeel 2017 automatic goal generation for reinforcement learning agents arxiv preprint arxiv170506366 docsepthis paper proposes an unsupervised method for subgoal discovery and shows how to combine it with a modelfree hierarchical reinforcement learning approach the main idea behind the subgoal discovery approach is to first build up a buffer of interesting states using ideas from anomaly detection the states in the buffer are then clustered and the centroids are taken to be the subgoal states clarity i found the paper somewhat difficult to follow the main issue is that the details of the algorithm are scattered throughout the paper with algorithm 1 describing the method only at a very high level for example how does the algorithm determine that an agent has reached a goal its not clear from the algorithm box some important details are also left out the section on montezumas revenge mentioned that the goal set was initialized using a custom edge detection algorithm what was the algorithm also what exactly is being clustered observations or network activations and using what similarity measure i cant find it anywhere in the paper omissions like this make the method completely unreproducible novelty the idea of using clustering to discover goals in reinforcement learning is quite old and the paper does a poor job of citing the most relevant prior work for example there is no mention of dynamic abstraction in reinforcement learning via clustering by mannor et al or of learning options in reinforcement learning by stolle and precup which uses bottleneck states as goals the particular instantiation of clustering interesting states used in this paper does seem to be new but it is important to do a better job of citing relevant prior work and the overall novelty is still somewhat limited significance i was not convinced that there are significant ideas or lessons to be taken away from this paper the main motivation was to improve scalability of rl and hrl to large state spaces but the experiments are on the four rooms domain and the first room of montezumas revenge which is not particularly large scale existing hrl approaches eb feudal networks from vezhnevets et al have been shown to work on a much wider range of domains further its not clear how this method could address scalability issues repeated clustering could become expensive and its not clear how the number of clusters affects the approach as the complexity of the task increases i would have liked to see some experiments showing how the performance changes for different numbers of clusters because setting the number of clusters to 4 in the four rooms task is a clear use of prior knowledge about the task overall quality the proposed approach is based on a number of heuristics and is potentially brittle given that there are no ablation experiments looking at how different choices number of clustersgoals how outliers are selected etc im not sure what to take away from this paper there are just too many seemingly arbitrary choices and moving parts that are not evaluated separately minor comments can you back up the first sentence of the abstract alphagoalphazero do well on the game of go which has 10170 valid states first sentence of introduction how can the rl problem have a scaling problem some rl methods might but i dont understand what it means for a problem to have scaling issues please check your usage of cite and citep some citations are in the wrong format the qlearning loss in section 2 is wrong the parameters of the target rgamma max q are held fixed in qlearning ### Summary:

pros good results on montezuma cons moderate novelty questionable generalization lack of ablations and analysis lack of stronger baselines no rebuttal the reviewers agree that the paper should be rejected in its current form and the authors have not bothered revising it to take into account the detailed reviews

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: this paper is a theoretic investigation on how to learn a manifold with a scorebased generative model sgm sgm uses an approximated score of the distribution in the middle of the reverse diffusion this score estimation is only an approximation by a neural network and this paper provides the study on error bound for this approximation also this paper analyzes the impact of the limited sample population in learning the score function another story line is the limitation of diffusion in t because the diffusion step needs to be infinite to make the diffused distribution be a standard normal distribution this becomes infeasible in actual implementation so there should be discrepancy between the final timestep distribution and the prior distribution this discrepancy is analyzed and its bound is suggested strength 1 this paper provides a theoretic discussion on many mathematical assumptions suggested by sgms 2 this paper clearly shows the necessary and sufficient assumptions to make the sgm identify the lowdimensional manifold weakness 1 this paper is very difficult to understand because of its eccentric structure i partly understand the authors effort because this paper is dedicated to the theoretic analyses however this paper is really needs a restructuring to draw more attention from possible audience 1 assumptions are referred before their appearances 2 the motivation of theorem 1 and 2 need to be further provided by formally throwing a research question 3 need a further clear statement on error bounds assumption 2 shows the characteristics of ext but i expected the explicit error bound for e 2 the score approximation error will grow as we bring the diffused distribution to the standard normal distribution ie tt this is briefly mentioned in line 206211 and i think that authors might produce further discussions and plain explanation on the interaction of score approximation quality and closeness between the prior and the diffused distributions i think that this paper would need further toy example experiments and plain explanations to secure audience who may use diffusion models without enough knowledge on authors discussions background docsepthis paper is a theoretic paper that solidifies the welldefinedness of sample measure and proves the equivalence of the sample measure and the learnt measure under assumption 2 textbfstrengths until now there is no guarantee that the reverse diffusion is welldefined at t0 if pdata is embedded in a lowdimensional manifold this paper is the first to prove that we can safely solve the reverse diffusion up to t0 when we use the previously suggested diffusion strategies ve vp cld on top of that this paper shows the welldefinedness of the generative measure if assumption 2 holds theorem 2 additionally proves that the sample measure and the trained measure have identical support textbfweaknesses it would have been much novel work if the author proved that assumption 2 is the necessary and sufficient condition in theorem 2 theorem 2 only proves one direction and it is difficult to conclude that uniform integrability is the key diagnosis of indicating the overfitting of the trained network it would be much better to move the proof of theorems to the appendix and put more explanations and experiments in particular this paper significantly lacks the empirical validation of their claims although i believe the claims are novel the onepoint lesson is missing to the community of diffusion models what would be the concrete truth observed from the highdimensional experiment textbfnotes this is not a weakness but i should note that theorem 1ii is nothing but theorem 1 of song21maximum in theorem 1 of song21maximum they used the data processing inequality for kl divergence but the data processing inequality is well known for its fdivergence extension and theorem 1ii is merely a straightforward generalization of the work of song21maximum with all this nevertheless proving theorem 1i is a significant improvement so i value theorem 1 again this is not a weak point but i think it would be preferable to describe the meaning of equivalent measure for the general audience a measure mu is equivalent to nu if textsuppmutextsuppnu and this can be framed in an everyday language as support matching force the general audience will much more value this paper if theorem 2 is wellunderstood not a weak point but it seems that assumption 2 is akin to the assumption of theorem 1 of bortoli21diffusion theorem 1 of bortoli21diffusion explicitly bounds the total variation distance between the data distribution and the generative distribution under the uniformly bounded score estimation under such previous research it would be very interesting to compare theorem 1 of bortoli21diffusion and the authors theorem 2 to get a generalizable model the authors claim that assumption 2 should be violated kim22soft introduced unbounded ncsn and unbounded ddpm as mathbfsthetaxtetat with limtrightarrow 0etatinfty instead of ncsn and ddpm that parameterize the score estimation as mathbfsthetaxtt to enable the score network to successfully estimate the unbounded score function but it seems that ncsn and ddpm are more appropriate to violate assumption 2 from their network design it could be an interesting topic to investigate the network architectures with respect to the generalization power song21maximum song yang et al maximum likelihood training of scorebased diffusion models advances in neural information processing systems 34 2021 14151428 bortoli21diffusion de bortoli valentin et al diffusion schrdinger bridge with applications to scorebased generative modeling advances in neural information processing systems 34 2021 1769517709 kim22soft kim dongjun et al soft truncation a universal training technique of scorebased diffusion model for high precision score estimation international conference on machine learning 2022 i am a practitioner in the community of diffusion models in my perspective a paper in this venue is better to contain intuitive illustrations and related explanations in the current form the submitted manuscript is flawless in its solidness and i believe this is good work but i doubt if this version could be valued in this field simply because it is difficult to understand the contents for the audience please remember that the expected audience of this paper is mostly the practitioners and most of them are shortlessonseekers for instance what is the meaning of uniformly integrable martingale neurips is not annals of mathematics and sufficient interpretation of mathematical concepts is required with the kind and satisfactory illustrations and insights practitioners could find the true value of this paper docsepthe paper considers the approximation errors in the sampling process of scorebased models specifically the errors are in two folds the estimation error of neural networks and the approximation of priors the authors study the terminal distribution of the reverse sde under such errors they first show that the terminal measure is absolutely continuous wrt the data measure if we have an imperfect prior next they show that the terminal distribution has the same support as the data distribution under mild assumptions of the estimation error toy experiments demonstrate the validity of the theoretical results  strengths the problem of approximation errors in score models is interesting and the theoretical understanding is lacking the paper takes a step in this direction and investigates the terminal distribution of reverse sampling with the existence of various sources of errors the paper verifies the correctness of the main results on toy experiments and examines popular sdes to see whether they fulfill the assumptions  questions before going into the concrete questions i would like to double check some terms and the overall proof ideas with the authors please correct me if im wrong 1 in line 71 its distribution is equivalent to the distribution of does the equivalence here only mean that the two distributions have the same support 2 the general proof idea of theorem 2 to my understanding the paper uses the girsanov theorem with zt as the ratio fracmusamplemudatazt the uniformly integrable martingale assumption is to ensure the boundness of zt together the result manifests itself weaknesses the main result theorem 2 in the paper is straightforward its a direct implication of girsanov theorem  the results seem tangent to the actual scorebased generative models for the image scorebased generative models the terminal tduring sampling is set to 1e3sim1e5 due to discontinuity at t0 critically the support of ptx t0 is mathbbrn and the results trivially hold in this case in addition i ran some quick experiments on cifar10 by adding 1 to the predicted score the paper also does similar things and the final results are noisy samples with very large pixel values due to the limited impact of the theoretical results and the gap to practical models i think the paper has a lot of room for improvement the authors have adequately addressed the potential negative societal impact of their work docsepthis is a pure theoretical study of the score based generative model the authors discover the conditions under which the sample distribution is equivalent to the data distribution 1 the theorems provided here could help researchers better understand those score based generative models which is significant to the whole community 2 the assumptions proposed here can guide future design of score based models 3 as a pure theoretical work the authors provide detailed proofs which is helpful but the lack of experimental verification could make it hard to digest although i would not penalize the authors for this since numerical verification on high dimension is a challenge on itself no societal impace for a theoretical work ### Summary:

this paper presents a theoretical analysis of scorebased generative models sgms diffusion models specifically the paper theoretically studies the effect of approximations used by sgm 1 approximating pt by prior and 2 approximating logpt by a neural network which currently lacks a solid understanding the paper presents conditions that assures sgms can sample from the underlying data manifold and also analyzes conditions under which an sgm memorizes the training data the latter relates to understanding the generalization properties of sgms besides technical discussions and clarifications during the rebuttal period the authors overhauled the introduction section and also added some experiments with cifar10 dataset to support their theory both of which were requested by the reviewers to enhance the paper reviewers were satisfied with the responses and the improvements in the revision in concordance with them i believe the paper provides a solid theoretical contribution to our understanding of sgms and recommend accept

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper studies the robustness of knn and rnn to poisoning attacks they try to achieve certified bounds on the robustness of rnn and knn algorithms against poisoning attacks they claim to achieve certified upper bounds on the effect of poisoning attacks on overall accuracy the techniques used are very simple their first theorem which is about the robustness of individual test examples states that if the number of poison points are smaller than half the distance between the number of neighbors with label that has highest vote and the number neighbors with label that has second highest vote then poisoning is not effective then they try to extend this result to overall accuracy of scheme their theorem 3 tries to achieve such a bound but i think this theorem is not correct i mentioned the issue with this theorem in the comments bellow then they have some experiments that uses this theorem to achieve certified bounds for the case of mnist and cifar10 their experimental results show that they can achieve better certified bounds compared to some previous certified defense papers but their results are extremely dependent on the correctness of theorem 3 and it is necessary for authors to rewrite the proof and theorem itself even if the authors fix their theorem i still dont find the theoretical contribution of this paper significant but their experiments and certified bounds could be interesting enough for paper to be accepted comments to authors i think there is a problem in the proof of theorem 3 it is mentioned that ais are different but bis are not necessarily different and they could all be the same label then the attacker can potentially flip all of them to bi in page 4 it is mentioned that for breaking the ties in knn we can use ranking of training examples how would that exactly work the claim on being the first certified poisoning defense on overall accuracy is not correct the work of steinhardt et al 2017 also studies provable defenses for overall accuracy it is crucial for this paper to compare their result with steinhart et al 2017 page 1 worsecase worstcase page 2 page 3 the formal definition o sdtre is inconsistent with what is described in text page 4 the notation is not very clear sl is the number of votes for class l and for instance x it is better to use something like slx similarly ai and bi are better to be aix and bix docsepthis paper studies to train a certifiable robust model against data poisoning attacks using nearest neighbors the paper studies the voting mechanism in the nearest neighbor models and presents a relationship between the poisoning instances and the difference between the majority votes and the second majority votes such a relationship will result in a guarantee on the lower bound of a training models accuracy which is referred to as certified accuracy ca the theoretical results are neat the experiments are conducted on mnist and cifar and results show better ca than previous approaches of dpa and bagging my main concern is the limitation of the applicable machine learning models which seems restricted to only knn and rnn models they may not yield the best performance on most interesting tasks for example from fig 2 and 3 we can see that even when poisoning size e0 the accuracy which should be identical to ca is far below the sota on the corresponding mnist and cifar tasks also the lower bound is established with respect to sa sb e ke therefore to be able to handle larger poisoning size e one has to employ a larger k in the knn case or larger r in the rnn case such choices of hyperparameter typically hinder the accuracy on the clean dataset it is not clear how such a restriction can be mitigated in a practical setup due to the above concern im borderline on work docsepsummary first the paper identifies knearest neighbor knn and radius nearest neighbor rnn to be naturally effective baseline certified defenses against data poisoning attack it is easy to see that knn and rnn are resistant to poison attacks since to flip the prediction of a test example one would need to insertdelete enough examples to change the majority vote second the paper proposes a joint certificate that further improves certified accuracy for rnn specifically it uses the fact that for any given poison removal budget it can only decrease the vote for a single label even though the idea is simple the experimental result is quite impressive significantly outperforming the previous more sophisticated certified defense methods strength the approach is easy to implement and understand despite its simplicity the approach performs significantly better compared to previous methods this should be the new standard baseline for all certified poisoned defense papers weakness the technical novelty is not very strong since it is obvious that knn is naturally robust to poisoning the proposed joint certification helps compensate for the technical deficiencies however i expect more ways to improve this lower bound than what is presented here for example another natural way to improve the joint certification is to consider how the added poison cannot influence two test examples concurrently when they are far enough apart even though joint certification can improve certified accuracy in practice individual certification may be more important compared to joint certification since users of the system probably want certifications as individual queries come in since knnrnn are not used as frequently in practical settings the proposed solution may not be as useful to systems that require the use of neural networks however i am also aware that none of the existing defenses work well enough for any practical setting recommendation even though the technical novelty is limited i recommend acceptance due to the simplicity of the approach and its impressive performance compared to previous methods i think this paper will become a new standard baseline for future certified poisoned defense papers update after reading reviewer2s comment i realize that there is literature proving much stronger results that i was unaware of i still think these results should be used as a standard baseline for certified poisoning defense but due to the lack of novelty i have to downgrade my score docsepthe paper studies robustness of knn and rnn against data poisoning attacks the main message of the paper is that knn and rnn are automatically resilient against attacks furthermore by grouping test examples based their predicted labels data points with different predictions are grouped together and then better certification guarantee can be derived experimental results demonstrate that knn and rnn are indeed selfrobust against data poisoning attacks the theoretical angle proposed in this paper is interesting however i have a highlevel question regarding the paper is the goal of the paper simply providing theoretical robustness guarantee of knn i do not see any new defense mechanisms developed in this paper the grouping idea is only intended to prove the theoretical results if so i feel like contribution of the paper is not significant enough besides that how does the result in this paper compare to the following one i hope the authors could clarify the concern analyzing the robustness of nearest neighbors to adversarial examples secondly i think definition 1 is not explained well does modification addition and deletion counts equally as a single operation the authors seem to point toward that modification is equivalent to onetime addition plus ontime deletion this makes me wondering if modification counts as two operations furthermore does the training data allow repetitions of any data point for example if the attacker simply adds the same point that exists in the clean training data multiple times does that count as poisoning in definition 1 the dataset is considered a set thus repetitions will be absorbed as a single item which leads to dd however repetitions definitely matter for knn and could be potentially exploited by the attacker therefore i hope the authors could provide a more clear definition of the defining poisoning size in the beginning of section 31 a data point is certifiably correct only when the predicted label stays unchanged before and after attack and it matches the true label i am wondering does this requirement rule out the test examples that are originally misclassified there are indeed cases where due to the attack some test examples become correctly classified while originally they are not in this case i am not sure how to interpret the robustness because the attacker is conversely helping the knn this makes wonder if certification is a correct way to define robustness throughout the paper there is no discussion regarding the groundtruth underlying data distribution which i think is important for the definition of robustness ideally a robust classifier should maintain high accuracy over the underlying data distribution even with attack there is little reason to care too much about the certification for a particular data point since that point appears with probability 0 i wonder if the authors can discuss this point the theoretical results in this paper are very interesting but it lacks a nice and intuitive explanation before the proof for example why grouping can give us a better certification rate what is the intuition behind that the explanation is not given enough space in this paper i only see a few sentences before theorem 3 and that does not support my understanding a lot in figure 1 i sort of see why multiple data points cannot be jointly certified in my understanding it is because the point 0 and 2 are somewhat far from each other thus the attacker cannot place enough poisoning points within the overlapping neighborhood of the two points such that their labels are changed simultaneously however if they are close enough then the attacker can modify some neighbors of point 2 while adding new points to the overlapping area which leads to a successful attack therefore it is not easy to distill the distinction between individual certification and joint certification from this example this example also does not help understanding of later theorems i am wondering if the authors could design a more informative example in the experiments i think it will be helpful to also draw the theoretical lower bound for the individual and joint certification so that readers can see whether the analysis is tight or not apart from that what is the data poisoning attack algorithm used to evaluate the robustness overall i believe the paper is not written clearly and there is a huge space for improvement docsepexistence of much stronger results i dont get why majority voting is claimed to be the stateoftheart technique if im understanding it correctly the majority vote technique can only handle a number of corruption points up to ok k being the number of voters furthermore since the voters more or less split the dataset in order to maintain the accuracy of each voter the number of voters cant be too large and is usually o1 therefore this majority vote approach can only handle o1 number of corruption more specifically for the case of knn the certified accuracy theorem 2 becomes vacuous as soon as the number of corrupted points e becomes greater than k on the other hand there are techniques developed from the robust statistics community that can be robust against an epsilonfraction of corruption points that is if there are a total of n training points it allows epsilon n number of points to be corrupted for example sever 1 a recently developed robust supervised learning algorithm guarantees osqrtepsilon generalization error under epsilonfraction of arbitrary corruption such a guarantee is much stronger than the ones majority voting approaches are able to achieve thus im having trouble appreciating the contribution of this paper given the existence of much stronger results relevance to the field while prior approaches like dpa also suffers from the same weaktrivial guarantee they are at least metaalgorithms that allows one to plug in any base learners depending on the application the method developed in this paper however only works on knn and lets be honest not many modern ml applications use knn with the slightest chance so i dont see much empirical value nor any significant theoretical contribution 1 ilias diakonikolas gautam kamath daniel m kane jerry li jacob steinhardt alistair stewart sever a robust metaalgorithm for stochastic optimization ### Summary:

some reviewers expressed concerns on soundness of the theory in the paper specifically theorem 3 does not seem to be correct there are other concerns such as the significance of the theoretical contributions little empirical value and existence of much stronger results unfortunately the authors did not provide responses to the concerns raised by the reviewers

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors propose a new ml force field based on gnns that uses higherorder equivariant messages previous methods like dimenet and gemnet use higherorder messages triplets or quadruplets but they enumerated the various triplets quadruplets which made them computationally expensive and limited the order of interactions in the present work the authors use a tensor product formulation to make the framework efficient moreover the authors empirically show that only 2 mp layers are needed to get good performance with their model which makes it quite fast so3 equivariance is achieved using spherical harmonics representations similar to previous methods the authors also perform scaling laws analysis as well as extrapolation to ood data and show that their model performs well strengths the authors present a new mpnn using higher order messages the network is equivariant which is a desirable property for its data efficiency the model performs well on a variety of small molecular datasets the model is very fast thanks to the tensor formulation of higher order messages and the fact that it only requires 2 layers efficiency for ml force fields is very useful in practice for running long md simulations or performing structure relaxations scaling laws analysis shows desirable scaling behavior the model works well for ood data weaknesses in section 4 the paper claims that the higher order features bietaklm can be interpreted as a complete basis is there a reference proof for this the paper does not mention any experiments dont compare against newer methods like gemnet that also use higher order message passing seems adequate docsepthis work proposes a new equivariant mpnn model named mace for force field prediction in particular by combining equivariant message passing with efficient manybody messages mace achieves both stateoftheart performance on several benchmarks and considerable computational efficiency strengths the proposed method achieves stateoftheart performance on several benchmarks the proposed method is faster to train and inference than previous models which is meaningful for modeling some macromolecular systems the analysis of the impact of manybody messages on learning curves is interesting which may inspire future works in balancing message correlation orders and network depth while designing architectures weaknesses the connection between tensor product and the standard manybody expansion is unclear to me the authors should provide clearer background and technical details while presenting this core module the novelty of the proposed method is not well clarified as mentioned in related works lin99109 both the theories and implementation strategies of the multiace framework have been put forward by previous works there is no clear clarification of nontrivial modification in the proposed method making it difficult to justify the technical contribution of the paper the description of evaluation benchmarks is unclear for example the meaning of rmd17 is not well explained the claim about receptive fields is misleading in line 220 of section 52 the authors claim that by decoupling the increase in correlation order of the messages from the number of message passing iterations mace only requires two layers resulting in a much smaller receptive field there is no doubt that a small receptive field will make the model more parallelisable however the big receptive field also captures more global longrange interactions than the small receptive field which usually leads to better empirical performance see figure 1 in this paper it seems that the authors think the correlation order of body messages is more important than the size of the receptive field so the negative impact of small receptive fields is ignored but there is a lack of convincing discussion about this point correct me if i misunderstand something limitations are well discussed docsepthe paper describes a message passing nn approach that efficiently handles manybody interactions where classical methods typically handle 2 body interactions the method is based on a standard steerableequivariant edge embedding and aggretation leading to equivariant atomic environment embeddings these pernode embeddings are then pulled through some higher order cg tensor product like an equivariant polynomial by which the pairwise features start interacting with each other and effectively a higher body order is achieved notably this interaction takes place on the node level not over all possible many body combinations which makes the method highly efficient the paper then systematically shows what components of equivariant architectures are important spherical harmonic degree or max body order or both to a large degree it is the max body order and spherical harmonic degree helps but to a lesser extend the paper further shows that with highbody order the number of layers can be greatly reduced to 2 whilst reaching state of the art strengths the paper is very well written literature review is coherent and the storyline is clear despite though material the paper does an excellent job in presenting the method in a concise precise and comprehendible way experiments are systematically setup as to allow to specifically test individual components and in order to draw appropriate conclusions weakness i found it hard to assess the impact of some of the ablation studies in particular those pertaining to md17 i wonder if anything can be set about how well these results generalize to large datasets and molecules of larger size i am wondering how representative is the aspirin molecule for the large amount of challenging problems in computational chemistry the work could still benefit from a more in depth discussion on handling higherorder interactions eg via simplicial neural networks or via dimenetlike methods it is clear that these approaches are different is there anything to say about what difference is decisive eg eq 10 is based on products small notes i do not see the point of the mlp in equation 13 all are linear layers except for the last one why the last one is apparently a one layer mlp is the only difference then an activation function otherwise a onelayer mlp a linear layer experiments are very well organized and conclusions are appropriate it is however slightly unclear how well this method generalizes to other types of problems in computational chemistry involving more or large molecules ### Summary:

this paper proposes a novel equivariant message passing network for modeling atomistic systems based on the popular atomic cluster expansion formalism the method relies on a clever factorization of higher order terms into products of twobody terms this allows mace to be fast while also taking into account manybody effects experimental results seem strong with intriguing scaling properties neural network potentials for atomistic systems is a rapidly growing subfield and this seems like a great contribution all reviewers supported acceptance noting the strong experimental performance the fast training and inference speed and demonstrated scaling with dataset size

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: by restricting can also be viewed as expanding to holomorphic neural networks the authors are able to use a contour integral in the complex plane for finite beta to evaluate a differential used in equilibrium propagation ep that is only valid as an approximation of the backprop gradient in the limit beta to 0 the authors interpret this use of the contour integral as a time integral of an oscillating teaching signal the authors show that this holomorphic ep surpasses ep in performance especially in the presence of noise the authors incorporate a cool idea to convert a differential to a contour integral in the complex plane and then interpret this integral as a time integral of an oscillating teaching signal they also show that allowing for finite beta enables robustness to noise unlike ep which is only vaild in the lim beta to 0 however the authors do not provide any pointers as to how this may be implemented in biology except mentioning that there may be connections to theta neurons and phasor networks still this is an interesting idea that possibly deserves to be disseminated to stimulate further progress in the search for bioplausible backprop disclaimer i have not verified any proofs derivations yes docsepequilibrium propagation is a promising framework to learn neural networks in a biologically plausible way however it is sensitive to noise which makes it hard to scale to large machine learning tasks and requires two phases limiting its interpretability as a biologically plausible learning framework the authors solve those two problems by introducing a new variant of equilibrium propagation that relies on complex analysis and holomorphic functions and derive an online unbiased version of the corresponding learning rule they show that this new algorithm is more robust to noise than traditional equilibrium propagation and scales to large vision classification tasks such as imagenet significant theoretical advances on equilibrium propagation careful empirical study of the impact of the different parameters n beta on the quality of the gradient approximation strong empirical results robustness to noise scale to imagenet while being arguably more plausible than previous equilibrium propagation versions the paper is overall very well written weakness a detailed description of the algorithm is lacking along with an interpretation in terms of neural dynamics i found the discussion of the limitations of the paper adequate docsepthe authors present holomorphic equilibrium propagation holomorphic ep an extension of equilibrium propagation for networks with holomorphic activation functions which estimates the gradient of the loss using an approximation of the complex cauchy integral this avoids the need for an infinitesimal teaching signal making the method more robust to noise strengths i think the authors have presented a beautiful extension of equilibrium propagation for energybased models with holomorphic activation functions i found their presentation to be clear and as far as i can tell their work is original though i am not an expert in this area the problem of solving the credit assignment problem using local learning rules is an important challenge in neuroscience and i believe the authors have put forth an interesting and elegant solution weaknesses despite the elegance of the authors solution it is not clear if the results here are actually useful for understanding neural computation which is in part the motivation for studying local learning rules however i think it is reasonable to leave such considerations for future work as stated above it is not clear to what extend the method of holomorphic equilibrium propagation is useful for understanding neural computation the authors address this to some degree in the discussion but not in detail i think this is fine for this paper but it is an important potential limitation docsepthe authors begin by showing that the classic equilibrium propagation can be extended to the case where the network layers are holomorphic dynamical systems the essential result which follows is that the gradient with respect to parameters can be transformed to a contour integral on a circle around zero which is equivalent to the first fourier coefficient of the derivative of the nudged energy function with respect to network parameters the central claim is that this equivalence eliminates the requirement that the nudge size go to zero the authors define a straightforward numerical approximation to the gradients equivalent to discrete fourier transform and show that the bias of this approximation goes to 0 as the sample rate of the fourier transform goes to infinity the authors toy experiment shows that holomorphic ep is capable of learning and that the cosine similarity of the approximated gradients is significantly higher than with classic ep the authors theoretical investigations also reveal that holomorphic ep can be adapted to an online learning algorithm assuming that the time scale of the layer dynamics is much less than the time scale of the oscillations computing the fourier transform which is in turn much less than the time scale of the weight updates the authors validate this result with the mnist task and show that the online approximation of the gradients are still significantly more accurate than the classic ep algorithm they also compare classic homlomorphic and online homlomorphic ep on the mnist task and claim that holomorphic ep has greater robustness to noise finally the authors validate their algorithm by adapting a cnn architecture to be holomorphic and showing that the holomorphic ep training yields virtually the same results as bp training on a standard cnn the paper presents a very novel idea which greatly increases the power of a local learning rule using the elegance of complex analysis the proof of theorem 1 in the main text could be presented more clearly it seemed as though the authors were implying that the logic of the complex chain rule needed to be applied to theta as well as beta the proof in the appendix is much more clear i think that one thing the authors could do to improve presentation in the main text would be to denote sthetabeta instead of sbeta the derivation of the weight gradients presented was only with respect to a meansquared error loss however for the computer vision tasks i would assume cross entropy loss would be required was an equivalent version of eq 8 derived for cross entropy anywhere eq 8 seemed out of place since the main tasks are classification if i understood correctly eq 8 is the basis for the derivation of the online learning algorithm is the use of mse in the online learning essential or inconsequential it would be strange if the online training of mnist used mse was the jax autodiff engine used to compute the gradient of the nudged energy function with respect to parameters in the case where mse was not used the toy task was not adequately explained in the main text what exactly are the inputs and targets the way it sounded was that the network is given noise as input and is supposed to output a single value this is very trivial even for a toy task were there multiple gaussians was the task to classify which samples came from which distribution how many classes were there another minor clarity issue is the labelling of figures 2d and 3c versus 4a and s6 why did the authors choose to present 1 cos similarity instead of just the similarity itself this made the plots look very surprising at first overall this is a very interesting paper which i would very strongly accept on the condition that its clarity issues are fixed the authors adequately addressed potential societal impact and some of the limitations of their work however only in the final appendix did i find a note that holomorphic ep was much slower at learning than bp especially since the authors mention edge and iot devices in their discussion i think a discussion of computational cost should also be moved to the discussion just how much longer should we expect holomorphic ep to take to train and evaluate was it only for imagenet and why would that be the case it was not made clear whether or not 64 bits was required for each real and imaginary part or for the entire complex number this is an important factor which would certainly affect performance ### Summary:

equilibrium propagation is a biologically plausible form of backpropagation based learning where the true gradient of an energy based model is computed for infinitesimal perturbations in this innovative work authors extend ep using complex analysis that links contour integrals for finite perturbations with the oscillatory dynamics in time this not only allows better gradient estimates but also applications to related theories of learning in neuroscience as well as neuromorphic engineering it represents a significant advance that opens new doors

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the authors present the split poisson gamma spg distribution an extension of the poissongamma distribution to model a discrete nonstationary stochastic process spg has an analytical posterior allowing accurate prediction after the model parameters have been inferred a single time the authors apply the spg to model tumor mutation rates and show that model parameters can be accurately inferred from highdimensional epigenetic data this is achieved through a combination of cnns gps and mle the results are promising in detecting tumor drivers such as genes regulatory structures and basepairs the paper is well written with motivation described and prior literature being discussed figure 1 is well laid out to drive home the point comments section 2 are all the distributions univariate if not a table giving dimensions will be helpful what is the dimension of the covariates etar it would also help if a plate diagram of the generative model is given why was cnn chosen for dimensionality reduction were other non neural architectures used for dimensionality reduction what is the significance of 735 epigenetic tracks how valid is the assumption that events are distributed independently in the mutation space i think this is too restrictive but at the same time simplifies the problem a discussion of this simplification will be essential for the medicalbiology audience title of paper has causes title on iclr webpage has cusses please correct if possible docsepsummary the paper extends poissongamma models for nonstationary sequences in a manner that allows partitioning the counts according to a binomial model to account for multiple resolutions this generalisation is motivated well with a biological application of practical relevance and the proposed method is particularly strong in enabling linear computational scaling required for analysis of large genome data reasons for score i am leaning towards rejection in the current form the contribution itself is worth publishing and the method is likely to be valuable for the application but the presentation would need to be improved especially regarding the gpcnn part see the detailed feedback to better communicate the technical contributions for the iclr audience conditional on improved presentation i would be leaning towards acceptance detailed feedback the proposed split poissongamma spg process seems reasonable but in technical terms is a fairly straightforward hierarchical structure and can be constructed using standard properties of poissongamma it is well motivated by the resulting efficient inference and this specific application and may find uses in other applications as well but does not provide a very clear theoretical contribution that would open immediate followup research directions for more general modelling questions my main problem with the paper concerns the structure of the presentation while the authors motivate the model well and provide very clear illustrations for the application the method sections are disconnected and i had trouble following the technical contributions in particular the connection between section 2 spg and the technical algorithm required for using it section 33 is unclear to me it seems the gpcnn part is an integral part of the overall solution and a contribution in itself and in fact an important one the spg alone is not quite sufficient as theoretical contribution it provides a concrete algorithm using spg and is general but now the description is provided only after talking about specific data and looks more like a minor technical detail with no proper theoretical justification for me the paper would be more natural if section 33 and possibly some other parts of section 3 would be described after section 2 as description of how spg is used in practice and if they would use shared notation and terminology this would make the practical approach easier to follow and the contributions more clear the empirical experiments and illustrations for the application are well carried out and serve as good demonstration of the method however a reader uninterested or uneducated in this specific application will have some trouble figuring out how well the method works this could be improved by complementing the results with clear artificial data of slightly simpler nature modifications after discussion increased score by one as the presentation in the revised version has clearly improved along the lines requested in the original reviewdocsepshort summary this paper introduces a splitpoissongamma model to capture discretetime integervalued stochastic processes at multiple scales although it seems to be simple and incremental compared with poisson gamma models this novel method may has some impacts in modeling mutation rates and identifying genomic elements that drive tumor emergence thus i vote for weak acceptance quality the technique contribution appears to be simple incremental but could be useful in real applications of detecting cancerrelated mutations clarity the most parts of the paper is clearly written nonetheless i find the paper is improvable by clearly describing the input data in sec2 originality to my knowledge it is the first attempt to develop splitpoisson gamma model for discretetime integervalued stochastic processes at multiple scales significance it appears to be an incremental contribution in the family of poisson gamma models i think the splitpoisson gamma model is a sensible and useful method in detecting cancerrelated mutation rates pros a useful and very sensible model for detecting cancer related mutation rates cons a clear description of the input data is missing in the beginning of sec2 i think it could be better if the authors can position the splitpoisson gamma model among the closely related poisson gamma models to clearly distinguish the main differences and why the novel model outperforms the others in addition to estimating mur and sigmar2 how to place hyper priors over these parameters and perform map estimations i agree the detection of cancercausing mutations is a significant application of the multiresolution modeling of discrete stochastic processes nonetheless i would suggest the authors to discuss the broad applications of the splitpoisson gamma spg model for a venue like iclr comments nb notations are used in eq67 but are not defined for negative binomial yet you miss a pr for lambdar in eq9 can you present the complete procedure to perform parameter inference in the supplement in c1 of pp14 it should be nbmialphar 1pi thetar1 ### Summary:

reviewers agree that the paper excels in providing a principle pipeline that combines cnns and gps with a poissongamma distribution to provide a generic approach for multiresolution modelling of tumour mutation rates as a whole such combination of techniques addresses a key challenge in computational biology that also scales to large datasets

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: summary clusterformer is the latest proposal for enabling transformers to deal with long input sequences such sequences are particularly problematic for problems like question answering qa or summarization where the context can be arbitrarily long and effectively openended when the setup includes a context retrieval component eg as in openqa clusterformer combines local information by encoding sequence chunks separately with a sliding window then injects clustering layers that use kmeans to compute centroids to cluster hidden states and capture global information the approach yields stateoftheart and topofleaderboard results on natural questions long answers this is great solid work showing how clustering can be designed implemented and used successfully to capture long distance dependencies in sparsified selfattention models this is a concrete and useful contribution in itself for the large community working on this type of architecture and related problems at the same time the approach involves quite a bit of complexity which makes one wonder if the baselines could be more competitive given a comparable amount of fine tuning at the same time competitive solutions of different nature generative are being proposed that pose a concrete challenge to this type of architecture which are not evaluated but should be at least discussed pros solid proof of concept and reference to successfully implementing clustering in sparse attention strong empirical results particularly the natural questions leaderboard for long answers impressive amount of technical work also with respect to reproducing results with other systems notwithstanding the amount of work in this area literature review and comparison seems adequate but i might have missed something some qualitative analysis which could be extended and articulated in particular it would be interesting to understand where the long distance information helps eg vs the sliding window approach and particularly vs lsh cons one of the arguments for the paper is that it is not clear if related methods like reformer can generalize to long sequences however in the evaluated implementation table 2 lsh is not that much worse than kmeans in fact even just the sliding window alone seems surprisingly competitive on all qa tasks while being much simpler i find the authors effort to compare with all these related methods truly commendable it seems natural to wonder how much more finetuning has gone into clusterformer compared to the simpler baselines given its additional complexity it would be important to discuss this aspect in more detail given the recent work of generative readers httpsarxivorgabs200511401 and particularly izacard grave fid httpsarxivorgpdf200701282pdf it seems unclear that direct encoding is the only or the best option for dealing with long sequences at least for qa in particular fid seems attractive due to its simplicity and capacity about twice as much as clusterformer it seems the authors should discuss this work it would be ideal at some point to compare directly by evaluating on openqanq or by other means detailed feedback pleas define x from xtimes d right below eq1 num tokens in context scaler valuescalar value it would be great to explain eq2 step by step for clarity what is the effect of the overlapping content size ml and in general of parameters l and m in particular could this affect positively the performance of the simpler sliding window model why using cluster layers at only 2 fixed depths how does this parameter affect results the max length is constrained to 5k 10k test due to memory constraints can this be improved how how long did it take to train the leaderboard nq entry system unclear what table 2 evaluates on eg for nq is this on the dev set or a fraction of it docsepthe paper proposes clusterformer to address the problem of quadratic compute requirements of the attention mechanism in a transformer model to this end this paper proposes to combine local attention to promote local consistency and proposes kmeans clustering to gather global information for every token the paper establishes strong results on the long form question answering task of natural questions in an extractive setup with it getting the leaderboard position ahead of etclarge while the idea in the paper is natural and the results on nq are strong unfortunately the idea in the paper is not new and has already been introduced in the work efficient contentbased sparse attention with routing transformers 1 2 which the authors fail to cite or credit therefore i recommend rejection references 1 httpsopenreviewnetforumidb1gjs6etdr 2 httpsarxivorgabs200305997 docsepthe paper describes a method to handle long documents for question answering most existing approaches use a sliding window approach without communication between different sliding windows instead they propose an approach that clusters individual vectors and allows communication attention among the locations in the same cluster i am not sure about the intuition behind this why would communicate between similar vectors more efficient than dissimilar or randomly chosen vectors would the performance improve if you use a better clustering algorithm the authors do not provide much intuition on this either i have a concern about comparison with locality sensitive hashing the number of buckets used in locality sensitive hashing was 64 and its clear that having more clusters help and the comparison between c64 cluster former and locality sensitive hashing is marginal less than one point on all measures i am not sure the results are strong enough to support that clustering is better than random assignments for a valid comparison they should report the results with localitysensitive hashing and 512 buckets the paper evaluates on three qa datasets as long as experiments on perplexity for language modeling and shows promising performances some clarifying questions 1 could you specify a bit more on how do classify the mean values of the first hidden state of all the chunked sequences to identify whether the question has short long answers or not 2 im a bit confused with the experimental set up for nq whats the numbers in table 2 is it on the dev set and the numbers on table 3 are on the test set please make it clear 3 would this work on a really lengthy qa dataset such as narrativeqa 4 from table 2 it seems the more the number of clusters the better the performance why do you stop at 512 is this have something to do with computational efficiency docsepsummary this paper introduces the clusterformer a transformer architecture that scales gracefully to long sequences by restricting pairwise attention by the cluster assignments of the hidden states of input tokens the paper presents strong empirical results on question answering benchmark datasets outperform stateoftheart approaches as well as strong baselines introduced by the authors summary of review strong empirical results on question answering datasets interesting datadriven efficient transformer model further clarification on relationship to related work needed experimental results would be stronger with more analysis of the proposed method strengths the all pairs self attention component of transformers limits their scalability to long sequences this paper presents a model that is reduces the complexity by grouping related tokens into clusters such that selfattention is applied only within each cluster in particular a long sequence is first encoded using a sliding window style approach then these sliding window representations are clustered and the resulting cluster memberships determine the sparsity for the remaining layers of the transformer the approach appears to work quite well on question answering datasets for which the approach achieves stateoftheart results on three datasets the paper is well written and the presentation is very clear weaknesses relationship to related work the proposed approach appears to share many similarities to the routing transformer roy et al 2020 while both approaches from this year i think that it would be important to present the similarities and differences of the two approaches ie sliding windows way kmeans centers are updated etc clearly in this paper other related though more distinct ideas are used in the inducing point based variant of set transformers lee et al 2019 empirical analysis of scaling to long sequences i think the presentation of the paper would be improved if the authors demonstrated just how much computation is saved by using these sparse clusterbased attention layers it would also improve the presentation to compare the efficiency of the proposed approach to other methods at varying input length sizes similarly it would be interesting to show the performance of the proposed approach compared to baselines for varying maximum sequence lengths it would further be interesting to investigate the cluster centers discovered by the method what they represent and how they change over time this would be particularly important to analyze how the model picks up information across long sequences ie showing that clusters are not made up of tokens from the same sliding window details of kmeans apologies if ive missed this but is anything done to ensure that the cluster sizes produced by kmeans are relatively balanced the skew of these sizes will directly impact the scalability of the method further while it is implied by the methodtext it would be nice to describe how the gradient is calculated for this hard cluster assignment aurko roy mohammad saffar ashish vaswani david grangier efficient contentbased sparse attention with routing transformers first posted march 2020 httpsarxivorgabs200305997 juho lee yoonho lee jungtaek kim adam r kosiorek seungjin choi yee whye the set transformer a framework for attentionbased permutationinvariant neural networks icml 2019 httpproceedingsmlrpressv97lee19dlee19dpdf questions for the authors please see questions in the details of kmeans section ### Summary:

the paper attempts to make transformers more scalable for longer sequences in this regards authors propose a clusteringbased attention mechanism where only tokens attends to other tokens in the same cluster this design reduces memory requirements and allows more information mixing than simple local windows using the proposed approach new stateoftheart performance is obtained on natural questions long answer although marginal however reviewers raised numerous concerns first the novelty of the paper compared to prior work like reformer or routing transformer which also conceptually does clustering is not resolved second the claim that kmeans yields a more balancedstable clustering than lsh is not well established finally why clustering ie attention between similar vectors is better than dissimilar or randomly chosen vectors or does is it even as expressive is not clear thus unfortunately i cannot recommend an acceptance of the paper in its current form to iclr

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper proposes a method to dissect a policy trained for a simple task to extract motor primitives or set of neurons that provides different behaviors or attributes the authors then use these attributes to have humans control the agent for more complex tasks or to create different behaviors by combination the paper showcases the algorithm in 2 domains driving and quadruped locomotion in simulation the authors first train for driving or walking forward without any explicit goal conditioning then the authors dissect the policy for different behaviors by replaying trained policy and doing frequency matching next humans use the provided interface for obstacle avoidance in addition the authors show that they can obtain very different behaviors by combining these motor primitives in different domains such as 2d walker as an example they obtain front flipping behavior by activating jumping and roll rate which are discovered using policy dissection the method proposed by the authors is very interesting for interpretability and discovery of skills in reinforcement learning as the authors state instead of training for complex behaviors the authors can train for simple ones and discover different skills from replay i think the front flipping is a very good example it is very hard to design reward functions for all these different skills and train a network that can perform all of them but the authors can find these skills and combine them without reward engineering which is very interesting especially from locomotion perspective where the training are usually goal conditioned not skillrewarded another interesting sideeffect of the approach is from robotics perspective although the authors are working on simulation not real robots the proposed method allows discovery of skills with small amount of replay data this can be a very interesting approach to the well known simtoreal problem where trained policies are not behave the same way in the real world the proposed method can be used to discover skills on the hardware although it does not match the simulated behavior this would lead to motor skills that work on the real robot in my opionion the apprach has some weaknesses as well 1 the authors use the approach to have humans control the robot to solve slightly more complex task on the other hand the obstacle avoidance problem can already be solved with goal conditioned policies so the results does not fully exploit the method to its full capacity instead the authors could try to achieve a problem that cant be solved with simple training one example i can think of is to have a parkour setup that requires jumping walking low 2the authors could automate the higher level controls instead of collaborating with humans the proposed schema is perfect for a hierarchical reinforcement learning approach once skills are discovered the authors could retrain with the higher level controls provided to the humans with additional sensory information that did not exist in the initial training this could very well train in very short time and handle the complex scenarios 3the authors could autmoate the discovery of the skills the literature contains many examples of such approaches based on maximizing entropy during training instead of using human designed motor primitives learning could provide more diverse behaviors as well 4the discovery is based on replay of the very simple behavior if i understand correctly the matching is based on behaviors seen in very small timeframes ie it turns slightly left and then slightly right to go straight but if the initially trained policy was great the behavior could very well just go straight which would lead to failure to match again if i understand the matching process correctly adding disturbances at the neuron level to the replay would provide much more than the initial data set where the agent is just going straight of course this might require some more work and careful design of the disturbing process despite these weaknesses i think the method has a very good potential and very interesting to read and the provided experiments is considerably enough to show some of this potential as well the limitations of the method is explained well but does not contain the weaknesses or potential directions that i explained above i dont think that the paper contains any major societal impact docsepin this paper the authors propose a novel method to modify the behavior of pretrained neural network policies to obtain the desired control effects without changing the weights of the networks unchanged instead the authors analyze the activation patterns of all neuron units in the network and create associations between the neuron activation and desired kinematic properties such as angular velocity translational velocity etc in the frequency domain through fourier transforms for each kinematic attribute the proposed method identifies one evoking neuron so during the online policy roll out the users can directly stimulate a certain desired behavior of the robot by activating the corresponding neuron even though the policy is never trained for this new behaviortask the authors demonstrate in a set of experiments that with their method and humanintheloop they can improve the performance of many safety environments the strengths of the paper it introduces a new concept to do zeroshot generalization of existing policies without any retraining provides a new insight on the interpretability of neural network policies ie from the frequency domain to associate neuron activations with behavior demonstrates that even though a policy may not be trained with certain tasks some behaviors might have already been seen during the training phases and somehow are memorized through the network structure the weaknesses of the paper the motivation of the paper is to make neural controllers safer by incorporating humans in the loop thus i assume that control accuracy and robustness of the nn policy is very important however by hacking into a nn policy that is not trained with human inputs eg steering throttle cars i am not convinced if the behavior such as velocity tracking accuracy etc can be as good as goalhuman input conditioned policies the authors assume that for each kinematic attribute there is one neuron that is responsible for this assumption is not quite sound to me as the activation of a neuron will have chain effects for other neurons down the propagation path and the other neurons might be responsible for other kinematic behaviors according to the same assumption also i am not sure how this method will scale with truly deep neural networks with many hidden layers and neurons as there are many more activation paths for the network na docsep this paper proposes a framework where a human can intervene to steer the behaviour of an rl agent this is done by allowing the human to select from a stimulationevoked map i think the paper has a big problem with clarity the setting is very unclear experiments dont compare against a baseline requires full attention from the human and system isnt able to learn from the human interventions apologies for not discussing this more but this is not my area and i found the paper really unclear so i struggle to write a proper review agent isnt able to learn from interventions docsepthis paper introduces a method termed policy dissection which finds a mapping between neurons in a trained continuous control policy and highlevel kinematic motions of interest eg turn left right stop go forward this mapping is then used to allow for human intervention of modification of the trained policy the core contributions are a method for identifying this mapping using a frequencybased analysis technique and to show that this can be exploited to allow policy intervention by amplifying units in a direction that correlates with an increase in the desired kinematic motion and high level policy modification by humans strengths this is a creative and interesting idea which i have not come across before i enjoyed reading this paper i think this work is significant from the perspective of trying to understand the implicit representations or primitives captured by neural control policies and because it introduces a method for humans to controllably modify a learned policy to achieve a goal a thorough set of experiments are provided on a number of experimental settings weaknesses the paper could do with some smoothing and improvements to the writing there are numerous typos and grammatical errors but these did not get in the way of the idea being communicated although the proposed approach does allow for some level of human intervention and modification this appears to be very imprecise and it is unlikely that the proposed approach allows more than very coarse control limitations are discussed neurons that do not align with any primitives but i would value more discussion on the coarseness of the method the video does make human takeover appear quite crude i would also value a discussion of competing approaches eg policies that are explicitly trained to allow for kinematic intervention and motivation for the proposed approach over this ### Summary:

this paper proposes to dissect a trained policy which finds the correspondence between the neuron activations and the motion primitives this enables a human to control the agent to complete complex and diverse tasks even though only one simple task is trained all reviewers agree that the proposed method is a creative solution to an important problem and it may also have important applications in robotics in addition this is an important step towards understandinginterpreting neural network policies which may inspire followup works in the future there are a few concerns and suggestions in the original reviews most of them are sufficiently addressed in the rebuttal and discussions the new experimental results look impressive and help to resolve a major concern about the coarseness of the human control that this paper enables while the paper writing is still rough the creative solution and the potential important applications compensate for the shortcomings thus i recommend accepting this paper please revise the paper by incorporating reviewers comments

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: in this paper the authors consider byzantinerobustness of a distributed learning system in the setting of noniid data distribution the bucketing technique is applied to reduce the heterogeneity across the workers to further reduce the variance within each worker the authors adopts the momentum technique these two techniques can be combined with various robust aggregation rules the authors prove the convergence of the combined methods and show that they reach the lower bound the authors also claim that for the overparametrized case the negative impact of data heterogeneity can be eliminated 0 the investigated problem byzantinerobust distributed learning over noniid data is important the algorithm development and the analysis both contain new results 1 in the previous version of this paper the authors propose resampling to reduce the heterogeneity across the workers why do the authors switch to bucketing in this version 2 in page 2 the authors claim that none of these noniid byzantinerobust methods are applicable to the standard federated learning please justify this claim 3 related to the above comment the authors do not compare the proposed methods with any other noniid byzantinerobust methods 4 also about the numerical experiments the mnist dataset is too simple testing on one or two additional large datasets could make the results more convincing in particular for the overparametrized case 5 in section 32 the mentioned mimic attack has appeared in other papers that investigate noniid byzantinerobustness please cite 6 in section 42 why place centered clipping here it should be located in section 3 7 the role of momentum is not well investigated the authors should indicate its contribution to the performance improvement 8 for the numerical experiments of the overparametrized case the authors use centered clipping as the basic aggregator but centered clipping has certain robustness in the noniid setting using other base aggregators such as geometric median could be better here 9 theorem iv requires that b2 is sufficiently small please check whether this condition can be satisfied in the numerical experiments overall this paper has publication merits but some issues need to be addressed docsepthis paper focuses on byzantinerobust federated learning a learning paradigm where a centralized server coordinates learning across a data set partitioned across multiple worker nodes some of which are adversarial typically this is done via robust aggregation schemes which ensure that adversarial nodes do not hinder learning in this setting the authors main focus is on studying the setting where honest worker nodes own heterogeneous data sets as the authors mention the existing literature on byzantinerobust federated learning focuses on the setting in which honest worker nodes draw iid samples from an underlying data distribution and although there is also existing literature on nonbyzantine federated learning over heterogenous data distributions amongst nodes this work brings these two strands of research together the main results are the following providing simple settings and empirical results wherein common aggregation methods fail in the presence of heterogeneous data even when there are no adversarial nodes providing a specific attack vector for the heterogeneous byzantine setting whereby adversarial nodes choose an honest node to replicate mimic attack this exposition comes with an efficient algorithm for computing the most hurtful choice of node to mimic and the authors empirically demonstrate the potential of this attack against common aggregation methods formally defining the design objective of agnostic robust aggregators which quantifies degradation of aggregator performance as a function of byzantine tolerance and dataset heterogeneity providing a simple randomized bucketing scheme messages from nodes are randomly averaged in buckets where the number of buckets is a free parameter increasing the number of buckets reduces the variance of bucket representatives but potentially increases the number of byzantine agents postbucketing if there is sufficient margin in existing aggregation protocols performing judicious bucketing before applying the aggregation rule permits making agnostic robust aggregators out of existing aggregation schemes with specifically quantifiable guarantees the authors also theoretically study optimization methods that make use of aggregation methods via stochastic gradient descent in this setting they provide upper bounds on convergence rates of optimization using robust aggregators in terms of the parameters that govern the robustness of aggregation method as per definition a and show that these upper bounds match existing upper bounds in the case where there are no byzantine agents delta 0 or when data is homogeneous rho 0 in the regime where these terms are nonzero convergence is not guaranteed but via information theoretic methods the authors also provide a matching lower bound if heterogeneity bounds are more refined whereby gradient variation is bounded by the order global gradient of the entire dataset then the authors demonstrate convergence of robust aggregation methods on sgd this setting applies when systems are overparameterized finally the authors provide multiple experimental results that validate the theoretical findings of the work strengths i think that this is an elegant way of combining two key areas of work in the fl community noniid data sets byzantine nodes empirical results seem robust the bucketing model is simple believable and it is nice that it composes well with existing methods the theoretical analysis of the model is extensive it matches existing bounds when delta or rho0 and the lower bound matches the nontrivial term that occurs when both deltarho neq 0 weaknesses are there more complicated examples of noniid models under nonadversarial attacks that perform poorly the rademacher distribution is a good starting point to justify the analysis but seeing the performance of existing aggregators on more complicated data sets when delta0 would be interesting still not convinced about the upper bound on gradient norm in the overparameterized setting where the term is bounded by b times grad fx is this realistic perhaps i am not seeing how this falls out of overparameterization a note on this might be useful in the writeup of the paper the lower bound analysis theorem 3 could have some more intuition as well in terms of what the argument is like and what the functions that are used consist of in particular it would be interesting to note whether the author thinks the adversarial functions for the bound are efficiently computable and if not whether hard functions can occur in practice over the byzantine nodes could this happen without some amount of coordination amongst the byzantine nodes for example what about other types of attack objectives ie backdoor attacks this seems highly relevant in the noniid setting especially if the adversary has knowledge of which clients have which data i recommend this paper for acceptance this seems like a fitting extension of existing work and the authors have provided a corresponding framework for quantifying performance loss in the presence of byzantine agents and heterogeneous data for federated learning their results match existing work in regimes when there are no byzantine agents and when there is a lack of heterogenous data furthermore the nontrivial loss of performance in the regime where both byzantine agents and heterogenous data is present is accounted for in a matching lower bound these results will be of interest to the growing body of work in federated learning at iclr docsepthis paper proposes a bucketing scheme for robust distributed learning with heterogeneous data gradients are more homogeneous after bucketing which will increase the robustness of existing algorithms the author provides theoretical analysis as well as empirical results strengths the bucketing scheme proposed in this paper is interesting it is easy to adopt and does not bring much extra computational cost but can improve the performance of existing byzantineresilient algorithms as shown in the empirical results also i appreciate that the authors provide convergence results for general aggregation rules together with precise analysis for three common aggregation rules in theorem i weaknesses when decreasing the heterogeneity the bucketing scheme also reduces the number of candidate vectors gradients however in the worst case the number of corrupted vectors will remain the same thus the number of byzantine workers that can be tolerated will decrease to 1s when adopting bucketing although the empirical results show that s2 is sufficient to overcome heterogeneity the number of byzantine workers that can be tolerated already drops by half in this case i am not aiming to criticize the bucketing scheme but to point out the limitation i think that this problem has truly restricted the application prospects of bucketing in general cases although there are weaknesses in this work considering the challenges from heterogeneity i think this work is slightly above the threshold postrebuttal my major concerns have been properly addressed by the authors and the quality of this paper has been improved after revision thus i have raised my rating docsepthe paper provides a systematic and deep theoretical study of the problem of byzantinerobustness in the heterogeneous setup ie when workers have nonidentical datasets and as s result their local loss functions are nonidentical the authors prove that even under bounded heterogeneity it is impossible to provably achieve any predefined accuracy of the solution by any method next the authors propose and analyze an algorithmic tool called bucketing and show that it makes some known aggregators such as krum coordinatewise median and geometric median to be agnostic robust aggregators under bounded heterogeneity assumption the authors derive that robust client momentum method from karimireddy et al 2021 converges for nonconvex problems to a neighborhood of a stationary point next it is shown that the size of the neighborhood can be made arbitrarily small for overparameterized problems numerical experiments corroborate theoretical findings and in particular show the benefits of the proposed bucketing procedure overall the paper is wellmotivated clearly written and contains solid contributions there are also some minor inaccuracies in the proofs small typos and other minor weaknesses i list them below i encourage the authors to address all of them strengths 1 lower bounds for byzantinerobust optimization under bounded heterogeneity in theorem iii the authors prove that even in the case of bounded heterogeneity in the classical sense for papers on fl one cannot achieve any predefined accuracy even for the strongly convex problems in particular they prove that functional suboptimality cannot be made smaller than omegafracdelta zeta2mu mu str convexity parameter delta fraction of byzantines zeta2 dissimilarity measure of the gradients of local loss functions typically mu is quite small and zeta2 can be large for some fl problems when the clients naturally have highly heterogeneous data in such situations the lower bound is really pessimistic although this fact theorem iii is expected for the experts in optimization it is very important for the field of byzantinerobust optimization it creates a clear picture of the limits of robustness 2 new upper bounds for byzantinerobust optimization under bounded heterogeneity the authors prove new complexity bounds for byzantinerobust optimization under bounded heterogeneity in the nonconvex case the derived results match the established lower bound moreover it is shown that one can achieve any predefined accuracy theorem iv when the heterogeneity level at the point x is proportional to nabla fx2 can be seen as a strong growth condition from vaswani et al 2019 3 bucketing as a tool to make krum geometric median and coordinatewise median robust karimireddy et al 2021 show that even in the homogeneous case krum geometric median and coordinatewise median are provably nonrobust to byzantine attacks this work fixes this drawback of the mentioned aggregation rules theorem i via a simple tool called bucketing this result together with theorem ii is very important for byzantinetolerant optimization in the homogeneous case since krum geometric median and coordinatewise median were not analyzed previously without restrictive assumptions 4 clarity the paper is wellmotivated clearly written and has a good structure weaknesses 1 inaccuracies in the proofs i have checked all the proofs and noticed several inaccuracies and unexplained derivations although it is possible to fix all the issues in the current shape it is hard to follow some parts of the proofs i list all my questions and comments in section questions and comments about the proofs of my review 2 comparison with related work unfortunately it is not trivial to compare the results from this paper with other related works given the information provided in the related work section the current version of the related work section summarizes the known works without going into the details however it is important for the paper to provide an explicit comparison with the related works with the discussion of the derived rates and assumptions i strongly encourage the authors to provide such a comparison at least in the appendix moreover the authors should pay a lot of attention to the comparison with yang li 2021 since they also use bucketing general questions and comments 1 abstract sentence our work is the first this sentence is not correct since li et al 2019 also derive convergence results under realistic assumptions for strongly convex problems perhaps the authors wanted to emphasize that their work provides the first guarantees under the not too strong assumption in the nonconvex case 2 page 2 however none of these methods are applicable to the standard federated learning this claim requires additional clarifications for example it is not clear why the method from li et al 2019 is not applicable 3 definition a i suggest the authors additionally emphasize that this definition is useful for both homogeneous and heterogeneous cases 4 remark 3 this is 1 iteration of cclip which is not necessarily the output of the aggregator 5 second paragraph after remark 4 which matches the optimal iid byzantine robust rates of karimireddy et al 2021 it is not clear why the mentioned rate is optimal and in what setting karimireddy et al 2021 do not prove the lower bound moreover there is a recent work where the better rate is achieved in terms of the number of iterations gorbunov et al secure distributed training at scale arxiv preprint arxiv210611257 2021 6 page 7 the sentence above theorem 7 typically holds in most realistic settings vaswani et al 2019 vaswani et al 2019 do not provide evidence that this assumption holds in most realistic settings in fact they show it strong growth condition for an example of squared hingeloss in the case of linearly separable data they also prove that strong growth condition follows from the interpolation condition when the summands in the loss function are smooth and the loss satisfies plcondition however in this case the known bound upper bound for b is proportional to fraclmu where l is the maximal smoothness constant of fi and mu is the plparameter of f therefore the current theoretical estimates for b are quite large even for simple special cases 7 theorem iv condition b2 frac13cdelta in view of my previous comment this requirement may imply that delta is tiny i think the discussion of this requirement should be added to the paper 8 missing reference this work also addresses the heterogeneous case for byzantinerobust optimization zhaoxian wu qing ling tianyi chen and georgios b giannakis federated variancereduced stochastic gradient descent with robustness to byzantine attacks ieee transactions on signal processing 6845834596 2020 moreover wu et al 2020 also prove convergence guarantees under similar assumptions therefore a detailed comparison of the derived results should be added 9 conclusion the last sentence our results represent a major breakthrough although the work makes a strong contribution to the field in my opinion it cannot be called a breakthrough taking into account that many of the building blocks were known and analyzed to some extent bucketing client momentum i think only a couple of papers can be called a breakthrough objectively eg nesterovs acceleration and discoveries of the same caliber so i suggest the authors rewrite the sentence let the readers decide for themselves whether this paper is a breakthrough or not questions and comments about the proofs 1 proof of lemma 1 formula for mathbbepiyi i in widetildemathcalg this is true but the detailed derivation should be added 2 lemma 7 first of all the lower and upper bounds should be multiplied by n moreover i have the following question about the proof of the lower bound does the described distribution of yi correspond to any distribution of the initial vectors xi before the bucketing this is crucial for the correctness of the lower bound 3 page 20 robustness of krum the first formula is not proven moreover it is not clear what is sast next in the formula above taking expectation now on both sides yields the minimization should be taken over the sets s such that s frac3m4 in the next formula it seems that the numerator should contain 4ntilderho2 after that the sentence then the number of byzantine workers can be bounded as tildemathcalb leq m14 delta should be rewritten as then the number of byzantine buckets can be bounded as tildemathcalb leq m14 nu finally in the upper bound for mathbbeykast barx2 the denominator of the first fraction should have nu m instead of nu n 4 page 21 robustness of geometric median in the third sentence the word worker should be replaced by setbucket there is also a typo in the formula after the words squaring both sides expanding the sum in the first row should not have mathbbe inside 5 appendix d the proof of theorem iii the first formula should contain delta instead of hatdelta next the formula after the words note that the gradient heterogeneity should be supported by the full derivation or explained it is true but requires few extra steps 6 proof of lemma 8 contains several inaccuracies and unexplained derivations first of all what is mathbbecdot i if it is an expectation conditioned on i then mathbbegixt1 i neq nabla fixt1 since xt1 depends on the stochasticity not related to the choice of i but the proof uses mathbbegixt1 i nabla fixt1 this should be fixed here and below next it is not clear what is mathbbeicdot the first formula on page 24 is incorrect due to the same reason as the first formula in the proof this issue should be fixed as well the second formula on page 24 is also inaccurate the rhs should have zeta21 1alphat next there is a typo in the sentence this is because the randomness in the sampling finally the last sentence in the proof the authors claim that it is enough to apply definition a but it is not correct this definition requires mathbbemit mjt2 leq rhot2 while the authors prove a weaker result that mathbbeimit barmt2 leq rhot2 overall the proof of lemma 8 requires a major revision and probably definition a should also be changed to fit the proof 7 lemma 10 numerical constants are incorrect instead of frac2alpha5 the formula should have frac5alpha16 and instead of fracalpha10 one should have frac3alpha32 8 the proof of theorem v contains several places that should be better explained and most likely corrected because of the inaccuracies first of all it seems that the authors forgot to upper bound mathbbemt barmt2 it is contained in the rhs of the second formula in the proof but it is omitted in the derivations on page 26 next the last formula on page 25 is inaccurate one should have nabla fxt1 in the rhs moreover the next step in the proof is unclear it seems that a lot of derivations are omitted for me it is not clear how the term mathbbemt barmt2 is handled therefore this derivation should be significantly rewritten and checked minor comments 1 mathcalb f the symbol f is already used to denote the objective function i suggest using a different notation for the number of byzantines 2 lemma 1 widetildemathcalg is defined only in the proof the authors should add the definition in the statement of the lemma 3 figure 9 contains lowresolution images the authors should replace them with the ones with higher resolution 4 page 18 the last formula full stop is missing in the end of the of the formula please check other formulas that end sentences as well 5 page 19 the second sentence each can belong to only 1 bucket each one each should be removed 6 lemma 9 in this lemma the authors use mt instead of mt the notation should be unified 7 lemma 10 the authors use xt2 and xt1 the position of the indices should be unified to sum up i am sure that the paper should be accepted to the conference after minor improvements if the authors apply all necessary corrections and address my comments properly i will increase my score the paper deserves acceptance as a spotlight or even oral talk ### Summary:

this manuscript proposes and analyses a bucketing method for byzantinerobustness in noniid federated learning the manuscript shows how existing byzantinerobust methods suffer vulnerabilities when the devices are noniid and describe a simple coordinated attack that defeats many existing defenses in response the primary algorithmic contribution is a bucketing approach that aggregates subgroups of devices before robust aggregation this approach is also easily composed with existing byzantinerobust methods the manuscript includes an analysis of the performance of the proposed approach including an informationtheoretic lower bound for certain settings during the review the main concerns are related to the clarity of the technical contributions and unclear technical statements the authors respond to these concerns and have satisfied the reviewers after discussion reviewers are generally strongly positive about the strength of the manuscript contributions the authors are reminded to make the final changes agreed in the public discussion eg discussion of the reduction to sgd when delta0

Below is given review of a research paper from cnoference journal. Please write a summary the review. ### Review: the paper presents a method that jointly optimizes shape appearance and foreground segmentation from a monocular video based on neural fields the author then fits the physical parameters of the scene the experiments demonstrate that the method works better than dynamic nerf and nonrigid nerf in nonrigid neural rendering further the authors demonstrate interesting video editing experiments based on the learned physics overall the quality of dynamic reconstruction and neural rendering from the authorprovided data shows superior performance compared to prior sota that said i have a few concerns about the physical component as well as the full pipeline i am currently on the borderline and open to going on either side based on whether the rebuttal addresses my major concerns on physics well pros the paper tackles a very interesting yet challenging problem the solution is technically sound experiments show superior results to strong competing algorithms code is provided and the video shows impressive editing results cons the method section particularly the physical component lacks many details making it hard to evaluate the technical contribution there the experiment section lacks a thorough study of the quality of the physical component the twostage framework is inconsistent with what is claimed in the intro see above docsepthis paper presented a method for learning the geometry and physics parameters of a dynamic scene the input source is a monocular rgb video the paper decoupled the learning of dynamic scenes into a static reference neural field and a deformation field the main structure of the learning part drew similarity to 57 including the offset field the divergence field with rigidity masks on top of these the paper performed mesh reconstruction for the video sequence and send those meshes to a differentiable physics simulator to optimize between the extracted and reconstructed meshes the method also allows scene editing strength 1 the results presented are pretty impressive both quantitative and qualitative results were shown with comparison to previous approaches weakness 1 the paper is hard to follow and not well organized especially the motivation of the differentiable physics simulator in fact most of the demonstration should be used for the simulator but the current version went over this part hastily therefore it left much confusion to me for example 11 the physical parameters to learn were unclear the lphysics is only l2 distance between two meshes with bending operation 12 the mesh extraction method line 177 is unclear how were the meshes extracted and how were they used as supervision signal for the physics simulator 2 it seems that the simulator can be viewed as a postprocessing step after dnerf optimization line 242 243 the integration of all the components was not well demonstrated this paper does not have limitations listed docsepthe paper proposes a method to recover geometry motion and physics parameters from a single video it recovers geometry and motion with differentiable volume rendering similar to dynamic nerf to recover physics parameters it uses diffpd it shows results on synthetic and real data with relatively small motion it also demonstrated applications of user editing originality and significance this work is the first to combine prior works of dynamic nerf and differentiable physics in a joint optimization framework however it is not wellmotivated why we need this joint optimization why not running differentiable rendering and simulation in a stepbystep manner where does joint optimization help in geometry and motion recovery as well as physics parameter recovery to give readers a better idea experiments and a thorough analysis is needed the challenges in combining differentiable rendering and differentiable physics is not clearly presented as a result the technical contribution appears weak l183l196 sparsely discussed an alternative solution but lacks indepth analysis how much does the proposed solution outperforms the alternative solution in compute and accuracy clarity the paper is difficult to follow in general some sentences are not logically connected see questions experiments the quantitative results have nice details benefiting from neus representation the material parameter estimation experiments is interesting the baseline comparison only shows the proposed method is better but does not provide insight and analysis what is the difference between the proposed method and the baselines what does the results suggest there is no ablation study for example id like understand how important to jointly solve physics parameters together with differentiable rendering how important are the loss terms yes ### Summary:

after rebuttal the new version of the paper reads much better and all reviewers were positive despite some remaining criticisms hence the paper should be accepted