launch/ampere huggingface dataset:
There may be some interesting ideas here,  but I think in many places the mathematical\ndescription is very confusing and/or flawed. To give some examples:\n\n* Just before section 2.1.1, P(T) = \\prod_{p \\in Path(T)} ... : it's not clear \nat all clear that this defines a valid distribution over trees. There is an\nimplicit order over the paths in Path(T) that is simply not defined (otherwise\nhow for x^p could we decide which symbols x^1 ... x^{p-1} to condition\nupon?)\n\n \"We can write S -> O | v | \\epsilon...\"  with S, O and v defined as sets.\n This is certainly non-standard notation, more explanation is needed.\n\n \"The observation is generated by the sequence of left most \nproduction rules\". This appears to be related to the idea of left-most\nderivations in context-free grammars.  But no discussion is given,  and\nthe writing is again vague/imprecise.\n\n \"Although the above grammar is not, in general, context free\" - I'm not\nsure what is being referred to here.  Are the authors referring to the underlying grammar,\nor the lack of independence assumptions in the model?  The grammar\nis clearly context-free;  the lack of independence assumptions is a separate\nissue.\n\n \"In a probabilistic context-free grammar (PCFG), all production rules are\nindependent\":  this is not an accurate statement,  it's not clear what is meant\nby production rules being independent.  More accurate would be to say that\nthe choice of rule is conditionally independent of all other information \nearlier in the derivation, once the non-terminal being expanded is\nconditioned upon.
This paper generates adversarial examples using the fast gradient sign (FGS) and iterated fast gradient sign (IFGS) methods, but replacing the gradient computation with finite differences or another gradient approximation method.  Since finite differences is expensive in high dimensions,  the authors propose using directional derivatives based on random feature groupings or PCA.  This paper would be much stronger if it surveyed a wider variety of gradient-free optimization methods.  Notably, there's two important black-box optimization baselines that were not included:  simultaneous perturbation stochastic approximation ( https://en.wikipedia.org/wiki/Simultaneous_perturbation_stochastic_approximation), which avoids computing the gradient explicitly, and evolutionary strategies ( https://blog.openai.com/evolution-strategies/ ), a similar method that uses several random directions to estimate a better descent direction. The gradient approximation methods proposed in this paper may or may not be better than SPSA or ES.  Without a direct comparison, it's hard to know.   Thus, the main contribution of this paper is in demonstrating that gradient approximation methods are sufficient for generating good adversarial attacks and applying those attacks to Clarifai models.  That's interesting and useful to know, but is still a relatively small contribution, making this paper borderline.  I lean towards rejection,  since the paper proposes new methods without comparing to or even mentioning well-known alternatives.
Many black-box optimization problems are "multi-fidelity", in which it is possible to acquire data with different levels of cost and associated uncertainty. The training of machine learning models is a common example, in which more data and/or more training may lead to more precise measurements of the quality of a hyperparameter configuration. This has previously been referred to as a special case of "multi-task" Bayesian optimization, in which the tasks can be constructed to reflect different fidelities. The present paper examines this construction with three twists: using the knowledge gradient acquisition function, using batched function evaluations, and incorporating derivative observations. Broadly speaking, the idea is to allow fidelity to be represented as a point in a hypercube and then include this hypercube as a covariate in the Gaussian process. The knowledge gradient acquisition function then becomes "knowledge gradient per unit cost" the KG equivalent to the "expected improvement per unit cost" discussed in Snoek et al (2012), although that paper did not consider treating fidelity separately. I don't understand the claim that this is "the first multi-fidelity algorithm that can leverage gradients". Can't any Gaussian process model use gradient observations trivially, as discussed in the Rasmussen and Williams book? Why can't any EI or entropy search method also use gradient observations? This doesn't usually come up in hyperparameter optimization, but it seems like a grandiose claim. Similarly, although I don't know of a paper that explicitly does "A + B" for multi-fidelity BO and parallel BO, it is an incremental contribution to combine them, not least because no other parallel BO methods get evaluated as baselines. Figure 1 does not make sense to me. How can the batched algorithm outperform the sequential algorithm on total cost? The sequential cfKG algorithm should always be able to make better decisions with its remaining budget than 8-cfKG. Is the answer that "cost" here means "wall-clock time when parallelism is available"? If that's the case, then it is necessary to include plots of parallelized EI, entropy search, and KG. The same is true for Figure 2; other parallel BO algorithms need to appear.
In this paper, the authors trains a large number of MNIST classifier networks with differing attributes (batch-size, activation function, no. layers etc.)  and then utilises the inputs and outputs of these networks to predict said attributes successfully.  They then show that they are able to use the methods developed to predict the family of Imagenet-trained networks and use this information to improve adversarial attack. I enjoyed reading this paper.  It is a very interesting set up, and a novel idea. A few comments:The paper is easy to read, and largely written well.  The article is missing from the nouns quite often though  so this is something that should be amended.  There are a few spelling slip ups  ("to a certain extend" --> "to a certain extent",  "as will see" --> "as we will see") It appears that the output for kennen-o is a discrete probability vector for each attribute, where each entry corresponds to a possibility  (for example, for "batch-size" it is a length 3 vector where the first entry corresponds to 64, the second 128, and the third 256).  What happens if you instead treat it as a regression task, would it then be able to hint at intermediates (a batch size of 96) or extremes (say, 512). A flaw of this paper is that kennen-i and io appear to require gradients from the network being probed (you do mention this in passing), which realistically you would never have access to.  (Please do correct me if I have misunderstood this) It would be helpful if Section 4 had a paragraph as to your thoughts regarding why certain attributes are easier/harder to predict.  Also, the caption for Table 2 could contain more information regarding the network outputs. You have jumped from predicting 12 attributes on MNIST to 1 attribute on Imagenet.  It could be beneficial to do an intermediate experiment (a handful of attributes on a middling task). I think this paper should be accepted  as it is interesting and novel. Pros ------ - Interesting idea - Reads well - Fairly good experimental results Cons ------ - kennen-i seems like it couldn't be realistically deployed - lack of an intermediate difficulty task
This paper proposes a ranking-based similarity metric for distributional semantic models.  The main idea is to learn "baseline" word embeddings, retrofitting those and applying localized centering, to then calculate similarity using a measure called "Ranking-based Exponential Similarity Measure" (RESM), which is based on the recently proposed APSyn measure. I think the work has several important issues: 1. The work is very light on references.  There is a lot of previous work on evaluating similarity in word embeddings (e.g. Hill et al, a lot of the papers in RepEval workshops, etc.); specialization for similarity of word embeddings (e.g. Kiela et al., Mrksic et al., and many others); multi-sense embeddings (e.g. from Navigli's group); and the hubness problem (e.g. Dinu et al.).  For the localized centering approach, Hara et al.'s introduced that method.  None of this work is cited, which I find inexcusable. 2. The evaluation is limited, in that the standard evaluations (e.g. SimLex would be a good one to add, as well as many others, please refer to the literature) are not used and there is no comparison to previous work.  The results are also presented in a confusing way,  with the current state of the art results separate from the main results of the paper.  It is unclear what exactly helps, in which case, and why. 3. There are technical issues with what is presented, with some seemingly factual errors.  For example, "In this case we could apply the inversion, however it is much more convinient [sic] to take the negative of distance. Number 1 in the equation stands for the normalizing, hence the similarity is defined as follows"  - the 1 does not stand for normalizing, that is the way to invert the cosine distance  (put differently, cosine distance is 1-cosine similarity, which is a metric in Euclidean space due to the properties of the dot product).  Another example, "are obtained using the GloVe vector, not using PPMI"  - there are close relationships between what GloVe learns and PPMI,  which the authors seem unaware of (see e.g. the GloVe paper and Omer Levy's work). 4. Then there is the additional question, why should we care?  The paper does not really motivate why it is important to score well on these tests:  these kinds of tests are often used as ways to measure the quality of word embeddings,  but in this case the main contribution is the similarity metric used *on top* of the word embeddings.  In other words, what is supposed to be the take-away, and why should we care? As such, I do not recommend it for acceptance -  it needs significant work before it can be accepted at a conference. Minor points:- Typo in Eq 10 - Typo on page 6 (/cite instead of \cite)
# Summary This paper presents a new external-memory-based neural network (Neural Map) for handling partial observability in reinforcement learning.  The proposed memory architecture is spatially-structured so that the agent can read/write from/to specific positions in the memory.  The results on several memory-related tasks in 2D and 3D environments show that the proposed method outperforms existing baselines such as LSTM and MQN/FRMQN.  [Pros] - The overall direction toward more flexible/scalable memory is an important research direction in RL. - The proposed memory architecture is new.  - The paper is well-written. [Cons] - The proposed memory architecture is new but a bit limited to 2D/3D navigation tasks. - Lack of analysis of the learned memory behavior. # Novelty and Significance The proposed idea is novel in general.  Though [Gupta et al.] proposed an ego-centric neural memory in the RL context,  the proposed memory architecture is still new in that read/write operations are flexible enough for the agent to write any information to the memory,  whereas [Gupta et al.] designed the memory specifically for predicting free space.  On the other hand, the proposed method is also specific to navigation tasks in 2D or 3D environment,  which is hard to apply to more general memory-related tasks in non-spatial environments.  But, it is still interesting to see that the ego-centric neural memory works well on challenging tasks in a 3D environment. # Quality The experiment does not show any analysis of the learned memory read/write behavior especially for ego-centric neural map and the 3D environment.  It is hard to understand how the agent utilizes the external memory without such an analysis.  # Clarity The paper is overall clear and easy-to-follow except for the following.  In the introduction section, the paper claims that "the expert must set M to a value that is larger than the time horizon of the currently considered task" when mentioning the limitation of the previous work.  In some sense, however, Neural Map also requires an expert to specify the proper size of the memory based on prior knowledge about the task.
The authors propose a model for learning physical interaction skills through trial and error. They use end-to-end deep reinforcement learning - the DQN model - including the task goal as an input in order to to improve generalization over several tasks, and shaping the reward depending on the visual differences between the goal state and the current state. They show that the task performance of their model is better than the DQN on two simulated tasks. The paper is well-written, clarity is good, it could be slightly improved by updating the title "Toy example with Goal integration" to make it consistent with the naming "navigation task" used elsewhere. If the proposed model is new given the reviewer's knowledge, the contribution is small. The biggest change compared to the DQN model is the addition of information in the input. The authors initially claim that "In this paper, [they] study how an artificial agent can autonomously acquire this intuition through interaction with the environment", however the proposed tasks present little to no realistic physical interaction: the navigation task is a toy problem where no physics is simulated. In the stacking task, only part of the simulation actually use the physical simulation result. Given that machine learning methods are in general good at finding optimal policies that exploit simulation limitations, this problem seems a threat to the significance of this work. The proposed GDQN model shows better performance than the DQN model. However, as the authors do not provide in-depth analysis of what the network learns (e.g. by testing policies in the absence of an explicit goal), it is difficult to judge if the network learnt a meaningful representation of the world's physics. This limitation along with potential other are not discussed in the paper. Finally, more than a third (10/26) references point to Arxiv papers. Despite Arxiv definitely being an important tool for paper availability, it is not peer-reviewed and there are also work that are non-finished or erroneous. It is thus a necessary condition that all Arxiv references are replaced by the peer-reviewed material when it exist (e.g. Lerer 2016 in ICML or Denil 2016 in ICLR 2017), once again to strengthen the author's claim.
* sec.2.2 is about label-preserving translation  and many notations are introduced.  However, it is not clear what label here refers to,  and it does not shown in the notation so far at all.  Only until the end of sec.2.2, the function F(.) is introduced and its revelation - Google Search as label function is discussed only at Fig.4 and sec.2.3. * pp.5 first paragraph: when assuming D_X and D_Y being perfect, why L_GAN_forward = L_GAN_backward = 0?  To trace back, in fact it is helpful to have at least a simple intro/def. to the functions D(.) and G(.) of Eq.(1).  * Somehow there is a feeling that the notations in sec.2.1 and sec.2.2 are not well aligned.  It is helpful to start providing the math notations as early as sec.2.1,  so labels, pseudo labels, the algorithm illustrated in Fig.2 etc. can be consistently integrated with the rest notations.  * F() is firstly shown in Fig.2 the beginning of pp.3, and is mentioned in the main text as late as of pp.5. * Table 2: The CNN baseline gives an error rate of 7.80  while the proposed variants are 7.73 and 7.60 respectively.  The difference of 0.07/0.20 are not so significant.  Any explanation for that? Minor issues: * The uppercase X in the sentence before Eq.(2) should be calligraphic X
This paper combines the tensor contraction method and the tensor regression method and applies them to CNN. This paper is well written and easy to read. However, I cannot find a strong or unique contribution from this paper. Both of the methods (tensor contraction and tensor decomposition) are well developed in the existing studies, and combining these ideas does not seem non-trivial. --Main question Why authors focus on the combination of the methods? Both of the two methods can perform independently. Is there a special synergy effect? --Minor question The performance of the tensor contraction method depends on a size of tensors. Is there any effective way to determine the size of tensors?
The manuscript proposes a new framework for inference in RNN based upon the Bayes by Backprop (BBB) algorithm.   In particular, the authors propose a new framework to "sharpen" the posterior. In particular, the hierarchical prior in (6) and (7) frame an interesting modification to directly learning a multivariate normal variational approximation.   In the experimental results, it seems clear that this approach is beneficial, but it's not clear as to why.   In particular, how does the variational posterior change as a result of the hierarchical prior?   It seems that (7) would push the center of the variational structure back towards the MAP point and reduces the variance of the output of the hierarchical prior;  however, with the two layers in the prior it's unclear what actually is happening.   Carefully explaining *what* the authors believe is happening and exploring how it changes the variational approximation in a classic modeling framework would be beneficial to understanding the proposed change and evaluating it.   As a final point, the authors state, "as long as the improvement along the gradient is great than the KL loss incurred...this method is guaranteed to make progress towards optimizing L."   Do the authors mean that the negative log-likelihood will be improved in this case?   Or the actual optimization?   Improving the negative log-likelihood seems straightforward,  but I am confused by what the authors mean by optimization. The new evaluation metric proposed in Section 6.1.1 is confusing,  and I do not understand what the metric is trying to capture.   This needs significantly more detail and explanation.   Also, it is unclear to me what would happen when you input data examples that are opposite to the original input sequence;  in particular, for many neural networks the predictions are unstable outside of the input domain and inputting infeasible data leads to unusable outputs.   It's completely feasible that these outputs would just be highly uncertain,  and I'm not sure how you can ascribe meaning to them.   The authors should not compare to the uniform prior as a baseline for entropy.   It's much more revealing to compare it to the empirical likelihoods of the words.
The paper "IMPROVING SEARCH THROUGH A3C REINFORCEMENT LEARNING BASED CONVERSATIONAL AGENT" proposes to define an agent to guide users in information retrieval tasks. By proposing refinements of the query, categorizations of the results or some other bookmarking actions, the agent is supposed to help the user in achieving his search. The proposed agent is learned via reinforcement learning. My concern with this paper is about the experiments that are only based on simulated agents, as it is the case for learning. While it can be questionable for learning (but we understand why it is difficult to overcome), it is very problematic for the experiments to not have anything that demonstrates the usability of the approach in a real-world scenario. I have serious doubts about the performances of such an artificially learned approach for achieving real-world search tasks. Also, for me the experimental section is not sufficiently detailed, which lead to not reproducible results. Moreover, authors should have considered baselines (only the two proposed agents are compared which is clearly not sufficient). Also, both models have some issues from my point of view. First, the Q-learning methods looks very complex: how could we expect to get an accurate model with 10^7 states ? No generalization about the situations is done here, examples of trajectories have to be collected for each individual considered state, which looks very huge (especially if we think about the number of possible trajectories in such an MDP). The second model is able to generalize from similar situations thanks to the neural architecture that is proposed. However, I have some concerns about it: why keeping the history of actions in the inputs since it is captured by the LSTM cell ? It is a redondant information that might disturb the process. Secondly, the proposed loss looks very heuristic for me, it is difficult to understand what is really optimized here. Particularly, the loss entropy function looks strange to me. Is it classical ? Are there some references of such a method to maintain some exploration ability. I understand the need of exploration, but including it in the loss function reduces the interpretability of the objective (wouldn't it be preferable to use a more classical loss but with an epsilon greedy policy?). Other remarks:    - In the begining of "varying memory capacity" section, what is "100, 150 and 250" ? Time steps ? What is the unit ? Seconds ? - I did not understand the "Capturing seach context at local and global level" at all - In the loss entropy formula, the two negation signs could be removed - Wouldn't it be possible to use REINFORCE or other policy gradient method rather than roll-outs used in the paper (which lead to biased gradient updates) ?
This paper proposed to combine three kinds of data sources: real, simulated and unlabeled, to help solve "small" data issue occurring in packet stream. A directed information flow graph was constructed, a multi-headed network was trained by using Keras and GAN library. Its use on the packet sequence classification can archive comparable accuracy while relieve operation engineers from heavy background learning. The presentation of this paper can be improved. * With the missing citations as "(?)" and not clearly defined concepts, including property of function H (any function? convex?) in (3), full name of TCP/abbr of GAN when first appear, etc. reader might need to make guesses to follow. * P2: You can draw your audience by expanding the "related work" like a story: more background of GAN etc. and one or two highlight formula to help clear the idea * P3: What's the purpose of inserting "dummy packets to denote the timeout between two time stamps"? * P3: Help sell to "non-engineer" by maybe having image example or even plainer language to describe the meaning (deep difference/purpose) of "3 levels of feature engineering"; and when addressing features, mentioned as 1,2,3, while in Table 1, shown as Feature=0,1,2; * P6: section 4.2 mentioned "only metrics cared by operators", is this what you mean by "relieve operation engineers ...", and which is or how to decide the cutoff accuracy the engineers should make a Go or No Go decision?