launch/ampere huggingface dataset:
The paper presents a Depthwise Separable Graph Convolution network that aims at generalizing Depthwise convolutions, that exhibit a nice performance in image related tasks, to the graph domain.  In particular it targets Graph Convolutional Networks. In the abstract the authors mention that the Depthwise Separable Graph Convolution that they propose is the key to understand the connections between geometric convolution methods and traditional 2D ones.  I am afraid I have to disagree  as the proposed approach is not giving any better understanding of what needs to be done and why.  It is an efficient way to mimic what has worked so far for the planar domain  but I would not consider it as fundamental in "closing the gap". I feel that the text is often redundant and that it could be simplified a lot. For example the authors state in various parts that DSC does not work on non-Euclidean data.  Section 2 should be clearer and used to better explain related approaches to motivate the proposed one. In fact, the entire motivation, at least for me, never went beyond the simple fact that this happens to be a good way to improve performance.  The intuition given is not sufficient to substantiate some of the claims on generality and understanding of graph based DL. In 3.1, at point (2), the authors mention that DSC filters are learned from the data whereas GC uses a constant matrix.  This is not correct,  as also reported in equation 2.  The matrix U is learned from the data as well. Equation (4) shows that the proposed approach would weight Q different GC layers.  In practical terms this is a linear combination of these graph convolutional layers. What is not clear is the \Delta_{ij} definition.  It is first introduced in 2.3 and described as the relative position of pixel i and pixel j on the image, but then used in the context of a graph in (4).  What is the coordinate system used by the authors in this case?  This is a very important point that should be made clearer. Why is the Related Work section at the end?  I would put it at the front. The experiments compare with the recent relevant literature.  I think that having less number of parameters is a good thing in this setting  as the data is scarce, however I would like to see a more in-depth comparison with respect to the number of features produced by the model itself.  For example GCN has a representation space (latent) much smaller than DSCG. No statistics over multiple runs are reported,  and given the high variance of results on these datasets I would like them to be reported. I think the separability of the filters in this case brings the right level of simplification to the learning task,  however as it also holds for the planar case it is not clear whether this is necessarily the best way forward. What are the underlying mathematical insights that lead towards selecting separable convolutions? Overall I found the paper interesting but not ground-breaking.  A nice application of the separable principle to GCN.  Results are also interesting  but should be further verified by multiple runs.
This paper proposes a new way of sampling data for updates in deep-Q networks.  The basic principle is to update Q values starting from the end of the episode in order to facility quick propagation of rewards back along the episode. The paper is interesting,  but it lacks the proper comparisons to previously published techniques. The results presented by this paper shows improvement over the baseline.  But the Atari results is still significantly worse than the current SOTA. In the non-tabular case, the authors have actually moved away from Q learning and defined an objective that is both on and off-policy.  Some (theoretical) analysis would be nice.  It is hard to judge whether the objective defined in the non-tabular defines a contraction operator at all in the tabular case. There has been a number of highly relevant papers.  Prioritized replay, for example, could have a very similar effect to proposed approach in the tabular case. In the non-tabular case, the Retrace algorithm, tree backup, Watkin's Q learning all bear significant resemblance to the proposed method.  Although the proposed algorithm is different from all 3,  the authors should still have compared to at least one of them as a baseline.  The Retrace algorithm specifically has also been shown to help significantly in the Atari case,  and it defines a convergent update rule.
This paper addresses the important problem of understanding mathematically how GANs work.  The approach taken here is to look at GAN through the lense of the scattering transform. Unfortunately the manuscrit submitted is very poorly written. Introduction and flow of thoughts is really hard to follow. In method sections, the text jumps from one concept to the next without proper definitions. Sorry I stopped reading on page 3. I suggest to rewrite this work before sending it to review. Among many things: - For citations use citep and not citet to have () at the right places. - Why does it seems -> Why does it seem etc.
The authors present RDA, the Recurrent Discounted Attention unit, that improves upon RWA, the earlier introduced Recurrent Weighted Average unit, by adding a discount factor.  While the RWA was an interesting idea with bad results (far worse than the standard GRU or LSTM with standard attention except for hand-picked tasks),  the RDA brings it more on-par with the standard methods. On the positive side, the paper is clearly written and adding discount to RWA, while a small change, is original.  On the negative side, in almost all tasks the RDA is on par or worse than the standard GRU -  except for MultiCopy where it trains faster, but not to better results  and it looks like the difference is between few and very-few training steps anyway.  The most interesting result is language modeling on Hutter Prize Wikipedia,  where RDA very significantly improves upon RWA -  but again, only matches a standard GRU or LSTM.  So the results are not strongly convincing,  and the paper lacks any mention of newer work on attention.  This year strong improvements over state-of-the-art have been achieved using attention for translation ("Attention is All You Need") and image classification (e.g., Non-local Neural Networks, but also others in ImageNet competition).  To make the evaluation convincing enough for acceptance, RDA should be combined with those models and evaluated more competitively on multiple widely-studied tasks.
This manuscript is fairly well-written,  and discusses how the batch normalization step helps to stabilize the scale of the gradients.   Intriguingly, the analysis suggests that using a shallower but wider resnet should provide competitive performance, which is supported by empirical evidence.   This work should help elucidate the structure in the learning, and help to support efforts to improve both learning algorithms and the architecture. Pros: Clean, simple analysis Empirical support suggests that theory captures reasonable effects behind learning Cons: The reasonableness of the assumptions used in the analysis needs a more careful analysis.   In particular, the assumption that all weights are independent is valid only at the first random iteration.   Therefore, the utility of this theory during initialization seems reasonable,  but during learning the theory seems quite tenuous.   I would encourage the authors to discuss their assumptions, and talk about how the math would change as a result of relaxing the assumptions. The empirical support does provide evidence that the theory is reasonable.   However, it is limited to a single dataset.   It would be nice to see that the effect happens more generally.  Second, it is clear that shallow+wide networks may be better than deep+narrow networks,  but it's not clear about how the width is evaluated and supported.  I would encourage the authors to do more extensive experiments and evaluate the architecture further.
Summary:The paper proposes a new dialog model combining both retrieval-based and generation-based modules.  Answers are produced in three phases: a retrieval-based model extracts candidate answers; a generator model, conditioned on retrieved answers, produces an additional candidate; a reranker outputs the best among all candidates. The approach is interesting:  the proposed ensemble can improve on both the retrieval module and the generation module,  since it does not restrict modeling power (e.g. the generator is not forced to be consistent with the candidates).  I am not aware of similar approaches for this task.  One work that comes to mind regarding the blend of retrieval and generation is Memory Networks  (e.g. https://arxiv.org/pdf/1606.03126.pdf and references):  given a query, a set of relevant memories is extracted from a KB using an inverted index and the memories are fed into the generator.  However, the extracted items in the current work are candidate answers which are used both to feed the generator and to participate in reranking. The experimental section focuses on the task of building conversational systems.  The performance measures used are 1) a human evaluation score with three volunteers and 2) BLUE scores.  While these methods are not very satisfying,  effective evaluation of such systems is a known difficulty.  The results show that the ensemble outperforms the individual modules, indicating that:  the multi-seq2seq models have learned to use the new inputs as needed and that the ranker is correlated with the evaluation metrics. However, the results themselves do not look impressive to me:  the subjective evaluation is close to the "borderline" score;  in the examples provided, one is good, the other is borderline/bad, and the baseline always provides something very short.  Does the LSTM work particularly poor on this dataset?  Given that this is a novel dataset, I don't know what the state-of-the-art should be.  Could you provide more insight?  Have you considered adding a benchmark dataset (e.g. a QA dataset)? Specific questions:1. The paper motivates conditioning on the candidates in two ways.  First, that the candidates bring additional information which the decoder can use (e.g. read from the candidates locations, actions, etc.).  Second, that the probability of universal replies must decrease due to the additional condition.  I think the second argument depends on how the conditioning is performed:  if the candidates are simply appended to the input, the model can learn to ignore them. 2. The copy mechanism is a nice touch, encouraging the decoder to use the provided queries.  Why not copy from the query too, e.g. with some answers reusing part of the query <"Where are you going?", "I'm going to the park">? 3. How often does the model select the generated answer vs. the extracted answers?  In both examples provided the selected answer is the one merging the candidate answers. Minor issues:- Section 3.2: using and the state - Section 3.2: more than one replies - last sentence on page 3: what are the "following principles"?
This paper presents an analysis of the communication systems that arose when neural network based agents played simple referential games.  The set up is that a speaker and a listener engage in a game where both can see a set of possible referents (either represented symbolically in terms of features, or represented as simple images) and the speaker produces a message consisting of a sequence of numbers while the listener has to make the choice of which referent the speaker intends.  This is a set up that has been used in a large amount of previous work,  and the authors summarize some of this work.  The main novelty in this paper is the choice of models to be used by speaker and listener,  which are based on LSTMs and convolutional neural networks.  The results show that the agents generate effective communication systems,  and some analysis is given of the extent to which these communications systems develop compositional properties  – a question that is currently being explored in the literature on language creation. This is an interesting question,  and it is nice to see worker playing modern neural network models to his question and exploring the properties of the solutions of the phone.  However, there are also a number of issues with the work. 1. One of the key question is the extent to which the constructed communication systems demonstrate compositionality.  The authors note that there is not a good quantitative measure of this.  However, this is been the topic of much research of the literature and language evolution.  This work has resulted in some measures that could be applied here,  see for example Carr et al. (2016): http://www.research.ed.ac.uk/portal/files/25091325/Carr_et_al_2016_Cognitive_Science.pdf 2. In general the results occurred be more quantitative.  In section 3.3.2 it would be nice to see statistical tests used to evaluate the claims.  Minimally I think it is necessary to calculate a null distribution for the statistics that are reported. 3. As noted above the main novelty of this work is the use of contemporary network models.  One of the advantages of this is that it makes it possible to work with more complex data stimuli, such as images.  However, unfortunately the image example that is used is still very artificial being based on a small set of synthetically generated images. Overall, I see this as an interesting piece of work that may be of interest to researchers exploring questions around language creation and language evolution,  but I think the results require more careful analysis and the novelty is relatively limited, at least in the way that the results are presented here.
This paper examines the very popular and useful ADAM optimization algorithm, and locates a mistake in its proof of convergence (for convex problems). Not only that, the authors also show a specific toy convex problem on which ADAM fails to converge. Once the problem was identified to be the decrease in v_t (and increase in learning rate), they modified the algorithm to solve that problem. They then show the modified algorithm does indeed converge and show some experimental results comparing it to ADAM. The paper is well written, interesting  and very important given the popularity of ADAM. Remarks: - The fact that your algorithm cannot increase the learning rate seems like a possible problem in practice. A large gradient at the first steps due to bad initialization can slow the rest of training. The experimental part is limited, as you state "preliminary", which is a unfortunate for a work with possibly an important practical implication. Considering how easy it is to run experiments with standard networks using open-source software, this can easily improve the paper. That being said, I understand that the focus of this work is theoretical and well deserves to be accepted based on the theoretical work. - On page 14 the fourth inequality not is clear to me. - On page 6 you talk about an alternative algorithm using smoothed gradients which you do not mention anywhere else and this isn't that clear (more then one way to smooth). A simple pseudo-code in the appendix would be welcome. Minor remarks:- After the proof of theorem 1 you jump to the proof of theorem 6 (which isn't in the paper) and then continue with theorem 2. It is a bit confusing. - Page 16 at the bottom v_t= ... sum beta^{t-1-i}g_i should be g_i^2 - Page 19 second line, you switch between j&t and it is confusing. Better notation would help. - The cifarnet uses LRN layer that isn't used anymore.
My problem with this paper that all the theoretical contributions / the new approach refer to 2 arXiv papers,  what's then left is an application of that approach to learning form imperfect demonstrations. Quality ====== The approach seems sound  but the paper does not provide many details on the underlying approach.  The application to learning from (partially adversarial) demonstrations is a cool idea  but effectively is a very straightforward application based on the insight that the approach can handle truly off-policy samples.  The experiments are OK  but I would have liked a more thorough analysis. Clarity ===== The paper reads well,  but it is not really clear what the claimed contribution is. Originality ========= The application seems original. Significance ========== Having an RL approach that can benefit from truly off-policy samples is highly relevant. Pros and Cons ============ + good results + interesting idea of using the algorithm for RLfD - weak experiments for an application paper - not clear what's new
Training GAN in a hierarchical optimization schedule shows promising performance recently (e.g. Zhao et al., 2016).  However, these works utilize the prior knowledge of the data (e.g. image)  and it's hard to generalize it to other data types (e.g. text).  The paper aims to learn these hierarchies directly instead of designing by human.  However, several parts are missing and not well-explained.  Also, many claims in paper are not proved properly by theory results or empirical results.  (1) It is not clear to me how to train the proposed algorithm.  My understanding is train a simple ALI, then using the learned latent as the input and train the new layer.  Do the authors use a separate training ? or a joint training algorithms.  The authors should provide a more clear and rigorous objective function.  It would be even better to have a pseudo code.  (2) In abstract, the authors claim the theoretical results are provided.  I am not sure whether it is sec 3.2  The claims is not clear and limited.  For example, what's the theory statement of [Johnsone 200; Baik 2005].  What is the error measure used in the paper?  For different error, the matrix concentration bound might be different.  Also, the union bound discussed in sec 3.2 is also problematic.  Lats, for using simple standard GAN to learn mixture of Gaussian, the rigorous theory result doesn't seem easy (e.g. [1])   The author should strive for this results if they want to claim any theory guarantee. (3) The experiments part is not complete.  The experiment settings are not described clearly.  Therefore, it is hard to justify whether the proposed algorithm is really useful based on Fig 3.  Also, the authors claims it is applicable to text data in Section 1, this part is missing in the experiment.  Also, the idea of "local" disentangled LV is not well justified to be useful. [1] On the limitations of first order approximation in GAN dynamics, ICLR 2018 under review
The paper proposes the use of a GAN to learn the distribution of image classes from an existing classifier,  that is a nice and straightforward idea.  From the point of view of forensic analysis of a classifier, it supposes a more principled strategy than a brute force attack based on the classification of a database and some conditional density estimation of some intermediate image features.  Unfortunately, the experiments are inconclusive.   Quality: The key question of the proposed scheme is the role of the auxiliary dataset.  In the EMNIST experiment, the results for the “exact same” and “partly same” situations are good,  but it seems that for the “mutually exclusive” situation the generated samples look like letters, not numbers,  and raises the question on the interpolation ability of the generator.  In the FaceScrub experiment is even more difficult to interpret the results,  basically because we do not even know the full list of person identities.  It seems that generated images contain only parts of the auxiliary images related to the most discriminative features of the given classifier.  Does this imply that the GAN models a biased probability distribution of the image class?  What is the result when the auxiliary dataset comes from a different kind of images?  Due to the difficulty of evaluating GAN results, more experiments are needed to determine the quality and significance of this work. Clarity: The paper is well structured and written,  but Sections 1-4 could be significantly shorter to leave more space to additional and more conclusive experiments.  Some typos on Appendix A should be corrected. Originality: the paper is based on a very smart and interesting idea and a straightforward use of GANs.  Significance: If additional simulations confirm the author’s claims, this work can represent a significant contribution to the forensic analysis of discriminative classifiers.
In the centre loss, the centre is learned.  Now it's calculated as the average of the last layer's features To enable training with SGD, the authors calculate the centre within a mini batch
This paper creates a layered representation in order to better learn segmentation from unlabeled images.  It is well motivated,  as Fig. 1 clearly shows the idea that if the segmentation was removed properly, the result would still be a natural image.  However, the method itself as described in the paper leaves many questions about whether they can achieve the proposed goal. I cannot see from the formulation why would this model work as it is advertised.  The formulation (3-4) looks like a standard GAN, with some twist about measuring the GAN loss in the z space (this has been used in e.g. PPGN and CVAE-GAN).  I don't see any term that would guarantee:1) Each layer is a natural image.  This was advertised in the paper,  but the loss function is only on the final product G_K.  The way it is written in the paper, the result of each layer does not need to go through a discriminator.  Nothing seems to have been done to ensure that each layer outputs a natural image. 2) None of the layers is degenerate.  There does not seem to be any constraint either regularizing the content in each layer, or preventing any layer to be non-degenerate. 3) The mask being contiguous.  I don't see any term ensuring the mask being contiguous,  I imagine normally without such terms doing such kinds of optimization would lead to a lot of fragmented small areas being considered as the mask. The claim that this paper is for unsupervised semantic segmentation is overblown.  A major problem is that when conducting experiments, all the images seem to be taken from a single category, this implicitly uses the label information of the category.  In that regard, this cannot be viewed as an unsupervised algorithm. Even with that, the results definitely looked too good to be true.  I have a really difficult time believing why such a standard GAN optimization would not generate any of the aforementioned artifacts and would perform exactly as the authors advertised.  Even if it does work as advertised, the utilization of implicit labels would make it subject to comparisons with a lot of weakly-supervised learning papers with far better results than shown in this paper.  Hence I am pretty sure that this is not up to the standards of ICLR.