The pros and cons of this paper can be summarized as follows:

Pros:
* It seems that the method has very good intuitions: consideration of partial rewards, estimation of rewards from modified sequences, etc.

Cons:
* The writing of the paper is scattered and not very well structured, which makes it difficult to follow exactly what the method is doing. If I were to give advice, I would flip the order of the sections to 4, 3, 2 (first describe the overall method, then describe the method for partial rewards, and finally describe the relationship with SeqGAN)
* It is strange that the proposed method does not consider subsequences that do not contain y_{t+1}. This seems to go contrary to the idea of using RL or similar methods to optimize the global coherence of the generated sequence.
* For some of the key elements of the paper, there are similar (widely used) methods that are not cited, and it is a bit difficult  to understand the relationship between them:
** Partial rewards: this is similar to "reward shaping" which is widely used in RL, for example in the actor-critic method of Bahdanau et al.
** Making modifications of the reference into a modified reference: this is done in, for example, the scheduled sampling method of Bengio et al.
** Weighting modifications by their reward: A similar idea is presented in "Reward Augmented Maximum Likelihood for Neural Structured Prediction" by Norouzi et al.

The approach in this paper is potentially promising, as it definitely contains a lot of promising insights, but the clarity issues and fact that many of the key insights already exist in other approaches to which no empirical analysis is provided makes the contribution of the paper at the current time feel a bit weak. I am not recommending for acceptance at this time, but would certainly encourage the authors to do clean up the exposition, perhaps add a comparison to other methods such as RL with reward shaping, scheduled sampling, and RAML, and re-submit to another venue.

(optional) Comment on this decision.


ICLR 2018 Conference Acceptance Decision

The idea studied here is fairly incremental and the empirical evaluation could be improved.

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Accept (Oral)
Accept (Poster)
Reject
Invite to Workshop Track


The authors present GraphVAE, a method for fitting a generative deep model, a variational autoencoder, to small graphs.  Fitting deep learning models to graphs remains challenging (although there is relevant literature as brought up by the reviewers and anonymous comments) and this paper is a strong start.

In weighing the various reviews, AnonReviewer3 is weighed more highly than AnonReviewer1 and AnonReviewer2 since that review is far more thorough and the reviewer is more expert on this subject.  Unfortunately, the review from AnonReviewer1 is extremely short and of very low confidence.  As such, this paper sits just below the borderline for acceptance.  In general, the main criticisms of the paper are that some claims are too strong (e.g. non-differentiability of discrete structures), treatment of related work (missing references, etc.) and weak experiments and baselines.  The consensus among the reviews (even AnonReviewer2) is that the paper is preliminary.  The paper is close, however, and addressing these concerns will make the paper much stronger.

Pros:
- Proposes a method to build a generative deep model of graphs
- Addresses a timely and interesting topic in deep learning
- Exposition is clear

Cons:
- Treatment of related literature should be improved
- Experiments and baselines are somewhat weak
- "Preliminary"
- Only works on rather small graphs (i.e. O(k^4) for graphs with k nodes)

(optional) Comment on this decision.


The paper studies the use of PixelCNN density models for the detection of adversarial images, which tend to lie in low-probability parts of image space. The work is novel, relevant to the ICLR community, and appears to be technically sound.

A downside of the paper is its limited empirical evaluation: there evidence suggesting that defenses against adversarial examples that work well on MNIST/CIFAR do not necessarily transfer well to much higher-dimensional datasets, for instance, ImageNet. The paper could, therefore, would benefit from empirical evaluations of the defense on a dataset like ImageNet.

(optional) Comment on this decision.


The chief contribution of this paper is to show that a single set of policy parameters can be optimized in an alternating fashion while the design parameters of the body are also optimized with policy gradients and sampled. The fact that this simple approach seems to work is interesting and worthy of note. However, the paper is otherwise quite limited - other methods are not considered or compared, incomplete experimental results are given, and important limitations of the method are not addressed. As it is an interesting but preliminary work, the workshop track would be appropriate.

(optional) Comment on this decision.


Given that the paper proposes a new evaluation scheme for generative models, I agree with the reviewers that it is essential that the paper compare with existing metrics (even if they are imperfect). The choice of datasets was very limited as well, given the nature of the paper. I acknowledge that the authors took care to respond in detail to each of the reviews.

(optional) Comment on this decision.


this submission proposes a novel extension of existing recurrent networks that focus on capturing long-term dependencies via tracking entities/their statesand tested it on a new task. there's a concern that the proposed approach is heavily engineered toward the proposed task and may not be applicable to other tasks, which i fully agree with. i however find the proposed approach and the authors' justification to be thorough enough, and for now, recommend it to be accepted.

(optional) Comment on this decision.


This paper is novel, but relatively incremental and relatively niche; the reviewers (despite discussion) are still unsure why this approach is needed.

(optional) Comment on this decision.


The reviewers think that the theoretical contribution is not significant on its own. The reviewers find the empirical aspect of the paper interesting, but more analysis of the empirical behavior is required, especially for large datasets. Even for small datasets with input augmentation (e.g. random crops in CIFAR-10) the pre-processing can become prohibitive. I recommend improving the manuscript for a re-submission to another venue and an ICLR workshop presentation.

(optional) Comment on this decision.


The paper presents yet another approach for modeling words based on their characters. Unfortunately the authors do not compare properly to previous approaches and the idea is very incremental.

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This submission explores recent theoretical work by Shwartz-Ziv and Tishby on explaining the generalization ability of deep networks. The paper gives counter-examples that suggest aspects of the theory might not be relevant for all neural networks.

There is some uncertainty surrounding the results where mutual information is estimated empirically. Even state-of-the-art estimation methods might lead to misleading empirical results. However, the submission appears to follow reasonable practice following previous work, making the reported results at least suggestive. They warrant reporting for further study and discussion.

The reviewers generally found the paper interesting enough for acceptance, however strong objections were posted by Tishby. A lengthy public exchange resulted between the groups of authors. Not every part of this exchange is resolved. It is not clear whether Tishby's group would be able to fix the full-connected ReLU demonstration in this paper, or whether the authors of this submission have anything to say about Tishby's ReLU+convnet demonstration. By accepting this work, we are not declaring where this debate will end. However, we felt the current submission is a constructive part of ongoing discussion in the literature on furthering our theoretical understanding of neural networks.

(optional) Comment on this decision.


Authors present a method for modeling neurodegenerative diseases using a multitask learning framework that considers "censored regression" problems (to model where the outputs have discrete values and ranges). Given the pros/cons, the committee feels this paper is not ready for acceptance in its current state.

Pro:
- This approach to modeling discrete regression problems is interesting and may hold potential, but the evaluation is not in a state where strong meaningful conclusions can be made.

Con:
- Reviewers raise multiple concerns regarding evaluation and comparison standards for tasks. While authors have added some model comparisons in response, in other areas comparisons don't appear complete. For example, when using MRI data, networks compared all use features derived from images, rather than systems that may learn from images themselves. Authors claim dataset is too small to learn directly from pixels in this data (in comments), but transfer learning and data augmentation have been successfully applied to learn from datasets of this size. In addition, new multitask techniques in the imaging domain have also been presented that dynamically learn the network structure, rather than relying on a hand-crafted neural network design. How this approach would compare is not addressed.

(optional) Comment on this decision.


The reviewers consider the paper to promising, but raise issues with the increase in the complexity of the MDP caused by the authors' parameterization of the action space, and comparisons with earlier work (Pazis and Lagoudakis).   While the authors cite this work, and say that they that they needed to make changes to PL to make it work in their setting (in addition to adding the deep networks), they do not explicitly show comparisons in the paper to any other discretization schemes.

(optional) Comment on this decision.


The reviewers point out that this is a well known result and is not novel.

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The paper based on cGAN developed a data augmentation GAN to deal with unseen classes of data. The paper developed new modifications to each component and designed network structure using ideas from state-of-the-art nets. As pointed out by reviewer 1 & 2, the technical contribution is not sufficient. We hence recommend it to workshop publication.

(optional) Comment on this decision.


The reviewers agree that the idea of utilizing covariance information in the few-shot setting is interesting. There are concerns with the novelty of the paper, as well as the correctness in terms of ensuring the covariance matrix is PSD in all cases. There are some concerns with the experimental evaluation as well. In this area, Omniglot is a good sanity check, but other baseline datasets like miniImagenet are necessary to determine if this approach is truly useful.

(optional) Comment on this decision.


This paper is well written, addresses and interesting problem, and provides an interesting solution.

(optional) Comment on this decision.


The reviewers agree that the results are promising and there are some interesting and novel aspect to the formulation. However, two of the reviews have raised concerns regarding the exposition and the discussion of previous work. The paper benefits from a detailed description of soft Q-learning, PCL, and off-policy actor-critic algorithms, and how SAC is different from those. Instead of differentiating against previous work by saying soft Q-learning and PCL are not actor-critic algorithms, discuss the similarities and differences and present empirical evaluation.

(optional) Comment on this decision.


This paper proposes a new generative model that has the stability of variational autoencoders (VAE) while producing better samples. The authors clearly compare their work to previous efforts that combine VAEs and Generative Adversarial Networks with similar goals.  Authors show that the proposed algorithm is a generalization of Adversarial Autoencoder (AAE) and minimizes Wasserstein distance between model and target distribution. The paper is well written with convincing results. Reviewers agree that the algorithm is novel and practical; and close connections of the algorithm to related approaches are clearly discussed with useful insights.  Overall, the paper is strong and I recommend acceptance.

(optional) Comment on this decision.


meta score: 8

This is a good paper which augments the data by mixing sound classes, and then learns the  mixing ratio.  Experiments performed on a number of sound classification results

Pros
 - novel approach, clearly explained
 - very good set of experimentation with excellent results
 - good approach to mixing using perceptual criteria

Cons
 - discussion doesn't really generalise beyond sound recognition

(optional) Comment on this decision.


An interesting paper, generally well-written. Though it would be nice to see that the methods and observations generalize to other datasets, it is probably too much to ask as datasets with required properties do not seem to exist.  There is a clear consensus to accept the paper.

+ an interesting extension of previous work on emergent communications (e.g., referential games)
+ well written paper

(optional) Comment on this decision.


This work develops a methodology for exploration in deep Q-learning through Thompson sampling to learn to play Atari games.  The major innovation is to perform a Bayesian linear regression on the last layer of the deep neural network mapping from frames to Q-values.  This Bayesian linear regression allows for efficiently drawing (approximate) samples from the network.  A careful methodology is presented that achieves impressive results on a subset of Atari games.

The initial reviews all indicated that the results were impressive but questioned the rigor of the empirical analysis and the implementation of the baselines.  The authors have since improved the baselines and demonstrated impressive results across more games but questions over the empirical analysis remain (by AnonReviewer3 for instance) and the results still span only a small subset of the Atari suite.  The reviewers took issue with the treatment of related work, placing the contributions of this paper in relation to previous literature.

In general, this paper shows tremendous promise, but is just below borderline.  It is very close to a strong and impressive paper, but requires more careful empirical work and a better treatment of related work.  Hopefully the reviews and the discussion process will help make the paper much stronger for a future submission.

Pros:
- Very impressive results on a subset of Atari games
- A simple and elegant solution to achieving approximate samples from the Q-network
- The paper is well written and the methodology is clearly explained

Cons:
- Questions remain about the rigor of the empirical analysis (comparison to baselines)
- Requires more thoughtful comparison in the manuscript to related literature
- The theoretical justification for the proposed methods is not strong

(optional) Comment on this decision.


The paper presents a reasonable idea, probably an improved version of method (combination of GAN and SSL for semantic segmentation) over the existing works. Novelty is not ground-breaking (e.g., discriminator network taking only pixel-labeling predictions, application of self-training for semantic segmentation---each of this component is not highly novel by itself). It looks like a well-engineered model that manages to get a small improvement with a semi-supervised learning setting. However, given that the focus of the paper is on semi-supervised learning, the improvement from the proposed loss (L_semi) is fairly small (0.4-0.8%).

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The authors analyze the IWAE bound as an estimator of the marginal log-likelihood and show how to reduce its bias by using the jackknife. They then evaluate the effect of using the resulting estimator (JVI) for training and evaluating VAEs on MNIST. This is an interesting and well written paper. It could be improved by including a convincing explanation of the relatively poor performance of the JVI-trained, JVI-evaluated models.

(optional) Comment on this decision.


The main contribution of the paper is a technique for training GANs which consists in progressively increasing the resolution of generated images by gradually enabling layers in the generator and the discriminator. The method is novel, and outperforms the state of the art in adversarial image generation both quantitatively and qualitatively. The evaluation is carried out on several datasets; it also contains an ablation study showing the effect of contributions (I recommend that the authors follow the suggestions of AnonReviewer2 and further improve it). Finally, the source code is released which should facilitate the reproducibility of the results and further progress in the field.

AnonReviewer1 has noted that the authors have revealed their names through GitHub, thus violating the double-blind submission requirement of ICLR; if not for this issue, the reviewer’s rating would have been 8. While these concerns should be taken very seriously, I believe that in this particular case the paper should still be accepted for the following reasons:
1) the double blind rule is new for ICLR this year, and posting the paper on arxiv is allowed;
2) the author list has been revealed through the supplementary material (Github page) rather than the paper itself;
3) all reviewers agree on the high impact of the paper, so having it presented and discussed at the conference would be very useful for the community.

(optional) Comment on this decision.


This is a (question answering) dataset paper with some baseline models.

The evaluation metric seems far from ideal and not quite ready for prime-time yet. They use F1 and Exact Match - these metrics make sense for extractive question answering systems, they don't make sense IMO for abstractive systems where the answer can be generated by the model (BLEU-type eval metrics seem more appropriate).

I therefore recommend this work for the workshop track.

(optional) Comment on this decision.


The paper is interesting, and the update to the paper and additional experiments has already improved it in many ways, but the paper still does still not have as much impact as it could, by further strengthening the comparisons and usefulness in many of situations of current practice.

(optional) Comment on this decision.


The authors present a toy stacking task where the goal is to stack blocks to match a given configuration, and a method that is a slightly modified DQN algorithm where the target configuration is observed by the network as well as the current state. There are a few problems with this paper. First, the method lacks novelty - it is very similar to DQN. Second, the claims of learning physical intuitions is not borne out by the method or experimental results. Third, the tasks are very simple and there is no held-out test set of target configurations.

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. The revision improved the paper e.g. moving Appendix A3 to the main text has improved clarity, but, like reviewer 3, I still found section 4 hard to follow. As the authors suggest, shifting the terminology to "posterior shifting” rather than “sharpening" would help at a high level, but the design choices should be more carefully explained. The experiments are interesting and promising. The title, although altered, still seems a misnomer given that the experimental evaluation focusses on RNNs.

Summary: There is the basis of a good paper here, but the rationale for the design choices should be more carefully explained.

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This paper applies a form of recurrent autoencoder for a specific type of industrial sensor signal analysis.  The application is very narrow and the data set is proprietary.  The approach is not clearly described, but seems very straightforward and is not placed in context of prior work.  It is therefore not clear how to evaluate the contribution of the method.  The authors have revised the paper to include more details and prior work, but it still needs a lot more work on all of the above dimensions before it can make a significant contribution to the ICLR community.

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Authors propose an approach to generation of adversarial examples that jointly examine the effects to classification within a local neighborhood, to yield a more robust example. This idea is taken a step further for defense, whereby the classification boundaries within a local neighborhood of a presented example are examined to determine if the data was adversarially generated or not.

Pro:
- The idea of examining local neighborhoods around data points appears new and interesting.
- Evaluation and investigation is thorough and insightful.
- Authors made reasonable attempts to address reviewer concerns.

Con
 - Generation of adversarial examples an incremental improvement over prior methods

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. This paper provides an informative analysis of the approximation contributions from the various assumptions made in variational auto-encoders. The revision has demonstrated the robustness of the paper’s conclusions, however these conclusions are arguably unsurprising. Although the work provides a thorough and interesting piece of detective work, the significance of the findings is not quite great enough to warrant publication.

Reviewer 1 was searching for a reference for work in similar vein to section 5.4: The second problem identified in the reference below shows examples where using an approximating distribution of a particular form biases the model parameter estimates to settings that mean the true posterior is closer to that form.

R. E. Turner and M. Sahani. (2011) Two problems with variational Expectation Maximisation for time-series models. Inference and Learning in Dynamic Models. Eds. D. Barber, T. Cemgil and S. Chiappa, Cambridge University Press, 104–123, 2011.

(optional) Comment on this decision.


This paper proposes a specific architecture for training an ensemble of separate policies on a family of easier tasks with the goal of obtaining a single policy that can perform well on a harder task. There are significant similarities to the recently published Distral algorithm, but I am convinced that this work offers a meaningful contribution beyond that work. Moreover, the authors performed a thorough comparison between their method and Distral and found that DnC performs better.

(optional) Comment on this decision.


This paper provides a simple technique for stabilizing GAN training, and works over a variety of GAN models.

One of the reviewers expressed concerns with the value of the theory. I think that it would be worth emphasizing that similar arguments could be made for alternating gradient descent, and simultaneous gradient descent. In this case, if possible, it would be good to highlight how the convergence of the prediction method approach differs from the alternating descent approach. Otherwise, highlight that this theory simply shows that the prediction method is not a completely crazy idea (in that it doesn't break existing theory).

Practically, I think the experiments are sufficiently interesting to show that this approach has promise. I don't see the updated results for Stacked GAN for a fixed set of epochs (20 and 40 at different learning rates). Perhaps put this below Table 1.

(optional) Comment on this decision.


The output kernel idea for lifelong learning is interesting, but insufficiently developed in the current draft.

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The paper studies the global convergence for policy gradient methods for linear control problems.  Multiple reviewers point out strong concerns about the novelty of the results.

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This paper proposes a new metric to evaluate the robustness of neural networks to adversarial attacks. This metric comes with theoretical guarantees and can be efficiently computed on large-scale neural networks.

Reviewers were generally positive about the strengths of the paper, especially after major revisions during the rebuttal process. The AC believes this paper will contribute to the growing body of literature in robust training of neural networks.

(optional) Comment on this decision.


This paper analyzes a problem with the convergence of Adam, and presents a solution. It identifies an error in the convergence proof of Adam (which also applies to related methods such as RMSProp) and gives a simple example where it fails to converge. The paper then repairs the algorithm in a way that guarantees convergence without introducing much computational or memory overhead. There ought to be a lot of interest in this paper: Adam is a widely used algorithm, but sometimes underperforms SGD on certain problems, and this could be part of the explanation. The fix is both principled and practical. Overall, this is a strong paper, and I recommend acceptance.

(optional) Comment on this decision.


The paper received generally positive reviews, but the reviewers also had some concerns about the evaluations.

Pros:
-- An improvement over HashNet, the model ties weights more systematically, and gets better accuracy.
Cons:
-- Tying weights to compress models already tried before.
-- Tasks are all small and/or audio related.
-- Unclear how well the results will generalize for 2D convolutions.
-- HashNet results are preliminary; comparisons with HashNet missing for audio tasks.

Given the expert reviews, I am recommending the paper to the workshop track.

(optional) Comment on this decision.


The idea of using the determinant of the covariance matrix over inputs to select experiments to run is a foundational concept of experimental design.  Thus it is natural to think about extending such a strategy to sequential model based optimization for the hyperparameters of machine learning models, using recent advances in determinantal point processes.  The idea of sampling from k-DPPs to do parallel hyperparameter search, balancing quality and diversity of expected outcomes, seems neat.  While the reviewers found the idea interesting, they saw weaknesses in the approach and most importantly were not convinced by the empirical results.  All reviewers thought that the baselines were inappropriate given recent work in hyperparameter optimization (and classic work in statistics).

Pros:
- Useful to a large portion of the community (if it works)
- An interesting idea that seems timely

Cons:
- Only slightly outperforms baselines that are too weak
- Not empirically compared to recent literature
- Some of the design and methodology require more justification
- Experiments are limited to small scale problems

(optional) Comment on this decision.


This paper introduces a possibly useful new RL idea (though it's a incremental on Liang et al), but the evaluations don't say much about why it works (when it does), and we didn't find the target application convincing.

(optional) Comment on this decision.


New effective kernel learning methods are very well aligned with ICLR's focus on Representation Learning. As a reviewer pointed out, not all aspects of the paper are algorithmically "clean". However, the proposed approach is natural and appears to give consistent improvements over a couple of expected baselines. The paper could be strengthened with more comparisons against other kernel learning methods, but acceptance at ICLR-2018 will increase the diversity of the conversation around advances in Representation Learning.

(optional) Comment on this decision.


This paper extends Shen et al 2017 with a style discrepancy loss (that makes the style discrepancies consistent with a pre-trained discriminator) and a cycle consistency loss (to avoid content loss by ensuring that the input can be reconstructed from the content vector and the original style). Relying on a pre-trained discriminator makes the style metric quite hard to interpret, and the cycle content loss is not checking that the content is actually preserved, only that it could be recovered. The additional experiments in the appendix with Human annotation to judge for similarity of content are a step in the right direction for checking that content is indeed preserved, but they aren't conclusive and indeed seem to suggest that the proposed approach with the cycle content loss isn't working so well when it comes to preserving content. The experiments proposed are rather toy-ish (making yelp reviews positive vs. negative, making chats "romantic"), so that it's hard to know whether this method would work at all.

(optional) Comment on this decision.


I am going to recommend acceptance of this paper despite being worried about the issues raised by reviewer 1.  In particular,

1:  the best possible inception score would be obtained by copying the training dataset
2:  the highest visual quality samples would be obtained by copying the training dataset
3:  perturbations (in the hidden space of a convnet) of training data might not be perturbations in l2, and so one might not find a close nearest neighbor with an l2 search
4:  it has been demonstrated in other works that perturbations of convnet features of training data (e.g. trained as auto-encoders) can make convincing "new samples"; or more generally, paths between nearby samples in the hidden space of a convnet can be convincing new samples.

These together suggest the possibility that the method presented is not necessarily doing a great job as a generative model or as a density model (it may be, we just can't tell...), but it is doing a good job at hacking the metrics (inception score, visual quality).      This is not an issue with only this paper, and I do not want to punish the authors of this papers for the failings of the field; but this work, especially because of its explicit use of training examples in the memory,  nicely exposes the deficiencies in our community's methodology for evaluating GANs and other generative models.

(optional) Comment on this decision.


Pros:
+ Clearly written paper.
+ Good theoretical analysis of the expressivity of the proposed model.
+ Efficient model update is appealing.
+ Reviewers appreciated the addition of results on the copy and adding tasks in Appendix C.

Cons:
- Evaluation was on less-standard RNN tasks.  A language modeling task should have been included in the empirical evaluation because language modeling is such an important application of RNNs.

This paper is close to the decision boundary, but the reviewers strongly felt that demonstration of the method on a language modeling task was necessary for acceptance.

(optional) Comment on this decision.


The scores were not favorable: 5,5,2. R2 felt the motivation of the paper was inadequate. R3 raised numerous technical points, some of which were addressed in the rebuttal, but not all. R3 continues to have issue with some of the results. The AC agrees with R3's concerns and feels that the paper cannot be accepted in its current form.

(optional) Comment on this decision.


This paper presents an ensemble method for conversation systems, where a retrieval-based system is ensembled with a generation-based system.  The combination is done via a reranker.  Evaluation is done on one dataset containing query reply pairs with both BLEU and human evalutations.  The experimental results are good using the ensemble model.  Although this presents some novel ideas and may be useful for chatbots (not for goal oriented systems), the committee feels that the approach and the presented material does not have enough substance for publication at ICLR:  it will be interesting to evaluate this system in a goal oriented setting; many prior papers have built generation based conversation systems (1-step) -- this paper does not present any comparison with those papers.  Addressing these issues may strengthen the paper for a future venue.

(optional) Comment on this decision.


The paper proposes the use of GANs to match the joint distribution of features to the product of their marginals for ICA. The approach is totally plausible but reviewers have complaints about lack of rigor and analysis in terms of (i) mixing conditions under which the proposed GAN based approach will work, given that ICA is ill-posed for general nonlinear mixing  (ii) comparison with prior work on linear and PNL ICA.

Further, in most scenarios where GANs are used, one of the distributions is fixed (say, the real distribution) and the other is dynamic (fake distribution) trying to come close to the fixed distribution during optimization. In the proposed method, the discriminator encodes the distance b/w joint and product of marginals which are both dynamic during the learning. It might be useful to comment whether or not it has any implications wrt increased instability of training, etc.

(optional) Comment on this decision.


this submission presents the positive impact of using orthogonal random features instead of unstructured random features for predictive state recurrent neural nets. there's been some sentiment by the reviewers that the contribution is rather limited, but after further discussion with another AC and PC's, we have concluded that it may be limited but a solid follow-up on the previous work on predictive state RNN.

(optional) Comment on this decision.


The reviewers generally thought the proposed algorithm was a straightforward extension of Yin et al., 2017, and not enough for a new paper.  They also objected to a lack of test results (to show generalization), but the authors did provide these in their revision.

Pros:
+ Adaptive batch sizing is useful, especially if the larger batches license parallelization.

Cons:
- Small, incremental change to the algorithm from Yin et al., 2017
- Test performance did not improve over well-tuned momentum optimization, which limits the appeal of the method.

(optional) Comment on this decision.


All of the reviewers have found some aspects of the formulation interesting, but they raised concerns regarding the practical use of the experimental setup.

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. ICLR. The consensus from the reviewers is that this is not quite ready for publication.

(optional) Comment on this decision.


meta score: 4

This paper is primarily an application paper applying known RL techniques to dialogue.    Very little reference to the extensive literature in this area.

Pros:
 - interesting application (digital search)
 - revised version contains subjective evaluation of experiments

Cons:
 - limited technical novelty
 - very weak links to the state-of-the-art, missing many key aspects of the research domain

(optional) Comment on this decision.


The paper addresses and interesting problem, but the reviewers found that the paper is not as strong as it could be: improving the range of evaluated data (significantly improve the convincingness of the experiments, and clearly adressing any alternatives, their limitations and as baselines).

(optional) Comment on this decision.


The reviewers are unanimous in accepting the paper.  They generally view it as introducing an original approach to online RL using bandit-style selection from a fixed portfolio of off-policy algorithms.  Furthermore, rigorous theoretical analysis shows that the algorithm achieves near-optimal performance.

The only real knock on the paper is that they use a weak notion of regret i.e. short-sighted pseudo regret.  This is considered inevitable, given the setting.

(optional) Comment on this decision.


General consensus among reviewers that paper does not meet criteria for publication.

Pro:
- Improvement over the original IDP proposal.
- Some promising preliminary results.

Con:
- Insufficient comparison to other methods of network compression,
- Insufficient comparison to other datasets (such as ImageNet)
- Insufficient evaluation on variety of other models
- Writing could be more clear

(optional) Comment on this decision.


This is a nicely written paper proposing a reasonably interesting extension to existing work (e.g.
VPN). While the Atari results are not particular convincing, they do show promise. I encourage
the authors to carefully take the reviewers' comment into consideration and incorporate them
to the final version.

(optional) Comment on this decision.


This paper proposes a novel application of generative adversarial networks to model neural spiking activity.  Their technical contribution, SpikeGAN, generates neural spikes that accurately match the statistics of real recorded spiking behavior from a small number of neurons.

The paper is controversial among the reviewers with a 4, a 6 and an 8.  The 6 is short and finds the idea exciting but questions the utility of the proposed approach in terms of actually studying neural spiking.  The 4 and 8 are both quite thorough reviews.  4 seems to mostly question the motivation of using a GAN over a MaxEnt model and demands empirical comparison to other approaches.  8 applauds the paper as a well-executed pure application paper, applying recent innovations in machine learning to an important application with some technical innovation.  Overall the reviewers found the paper clear and easy to follow and agree that the application of GANs to neural spiking activity is novel.  In general, I find that such high variance in scores (with thorough reviews) indicate that the paper is exciting, innovative and might stir up some interesting discussion.  As such, and under the belief that ICLR is made stronger with interesting application papers, I feel inclined to accept as a poster.

Pros:
- A novel application of GANs to neural spiking data
- Addresses an important and highly studied application area (computational neuroscience)
- Clearly written and well presented
- The approach appears to model well real neural spiking activity from salamander retina

Cons:
- Known pitfalls of GANs aren't really addressed in the paper (mode collapse, etc.)
- The authors don't compare to state of the art models of neural spiking activity (although they compare to an accepted standard approach - MaxEnt)
- Limited technical innovation over existing methods for GANs

(optional) Comment on this decision.


The paper tries to show that many of the state-of-the-art interpretability methods are brittle and do not provide consistent stable explanations. The authors show this by perturbing (even randomly) the inputs so that the differences are imperceptible to a human observer but the interpretability methods provide completely different explanations. Although the output class is maintained before and after the perturbation it is not clear to me or the reviewers why one shouldn't have different explanations. The difference in explanations can be attributed to the fragility of the learned models (highly non-smooth decision boundaries) rather than the explanation methods. This is a critical point and has to come out more clearly in the paper.

(optional) Comment on this decision.


Three reviewers recommended rejection and there was no rebuttal.

(optional) Comment on this decision.


This paper was perceived as being well written, but the technical contribution was seen as being incremental and somewhat heuristic in nature. Some important prior work was not discussed and more extensive experimentation was recommended.
However, the proposed approach of partitioning the graph into sub graphs and a schedule alternating between intra and inter graph partitions operations has some merit.

The AC recommends inviting this paper to the Workshop Track.

(optional) Comment on this decision.


This paper asks when SGD+M can beat adaptive methods such as Adam, and then suggests a variant of SGD+M with an adaptive controller for a single learning rate and momentum parameter.  There is are comparisons with some popular alternatives.  However, the bulk of the paper is concerned with a motivation that didn't convince any of the reviewers.

(optional) Comment on this decision.


The paper received mostly positive comments from experts. To summarize:

Pros:
-- The paper provides complex counterparts for typical architectures / optimization strategies used by real valued networks.
Cons:
-- Although the authors include plots explaining how nonlinearities transform phase, intuition about how phase gets processed can be improved.
-- Improving evaluations: Wisdom et al. computes log magnitude; real valued networks may not be suited for computing real / complex numbers which have a large dynamic range, like the complex spectra. So please compare performance by estimating magnitude as in Wisdom et al.
-- Please add computational cost, in terms of the number of multiplies and adds, to the final version of the paper.

I am recommending that the paper be accepted based on these reviews.

(optional) Comment on this decision.


The authors seem to miss important related literature for their comparison.
They also tuned hyperparameters and tested on the same validation set.
They should split between train/validation/test.

Reviews are just too low across the board to accept.

(optional) Comment on this decision.


This paper proposes an offline neural method using concrete/gumbel for learning a sparse codebook for use in NLP tasks such as sentiment analysis and MT. The method outperforms other methods using pruning and other sparse coding methods, and also produces somewhat interpretable codes. Reviewers found the paper to be simple, clear, and effective. There was particular praise for the strength of the results and the practicality of application. There were some issues, such as only being applicable to input layers, and not being able to be applied end-to-end. The author also did a very admirable job of responding to questions about analysis with clear and comprehensive additional experiments.

(optional) Comment on this decision.


This paper exposes a simple recipe to manipulate the latent space of generative models in such a way to minimize the mismatch between the prior distribution and that of the manipulated latent space. Manipulations such as linear interpolation are commonplace in the literature, and this work will be helpful to improve assessment on that front.

Reviewers found this paper interesting, yet unpolished and incomplete. In subsequent iterations, the paper has significantly improved on those fronts, however the AC believes an extra iteration will make this work even more solid. Thus, unfortunately this paper cannot be accepted at this time.

(optional) Comment on this decision.


This paper presents a new pipeline for nn compression that extends that of Han et. al, but show that it reduces parameters further, maintains higher accuracy and can be applied to methods behind classification (semantic segmentation). While the authors found the paper clearly written, excepting for some typos, and potentially useful, there were questions about originality, and significance.

- Reviewers were not completely convinced the method was different enough from deep compression: "The overall pipeline including the last two stage looks quite similar to Han[1].", or that enough focus was paid to the differences inherent with classification focused work: "The paper in the title and abstract refers to segmentation as the main area of focus. However, there does not seem to be much related to it except an experiment on the CityScapes dataset."

- In terms of impact, the additional benefits from pruning seem to require a significant amount of computation, and the reviewers were not convinced these were worth a small gain in compression. Furthermore, authors felt that this approach was not being applied to the most state-of-the-art approaches to demonstrate their use.

(optional) Comment on this decision.


This paper proposes a method for quantitatively evaluating GANs. Better quantitative metrics for GANs are badly needed, as the field is being held back by excessive focus on generated samples. This paper proposes to estimate the Wasserstein distance to the data distribution. A paper which does this well would be a significant contribution, but unfortunately (as the reviewers point out) the experimental validation in this paper seems insufficient.

To be convincing, a paper would first need to demonstrate the ability to accurately estimate Wasserstein distance -- not an easy task, but one which receives little mention in this paper. Then it would need to validate that the method can either quantitatively confirm known results about GANs or uncover previously unknown phenomena. As it stands, I don't think this submission is ready for publication in ICLR, but I'd encourage resubmission after more careful experimental validation along the lines suggested by the reviewers.

(optional) Comment on this decision.


Well motivated and well written, with extensive results. The paper also received positive comments from all reviewers. The AC recommends that the paper be accepted.

(optional) Comment on this decision.


High quality paper, appreciated by reviewers, likely to be of substantial interest to the community. It's worth an oral to facilitate a group discussion.

(optional) Comment on this decision.


The pros and cons of this paper cited by the reviewers can be summarized below:

Pros:
* The method proposed here is highly technically sophisticated and appropriate for the problem of program synthesis from examples
* The results are convincing, demonstrating that the proposed method is able to greatly speed up search in an existing synthesis system

Cons:
* The contribution in terms of machine learning or representation learning is minimal (mainly adding an LSTM to an existing system)
* The overall system itself is quite complicated, which might raise the barrier of entry to other researchers who might want to follow the work, limiting impact

In our decision, the fact that the paper significantly moves forward the state of the art in this area outweighs the concerns about lack of machine learning contribution or barrier of entry.

(optional) Comment on this decision.


The authors study the problem of reducing uplink communication costs in training a ML model where the training data is distributed over many clients.   The reviewers consider the problem interesting, but have concerns about the extent of the novelty of the approach.  As the reviewers and authors agree that the paper is an empirical study, and the authors agree that the novelty is in the problem studied and the combination of approaches used, a more thorough experimental analysis would
benefit the paper.

(optional) Comment on this decision.


This work presents new results on unsupervised machine translation using a clever combination of techniques. In terms of originality, the reviewers find that the paper over-claims, and promises a breakthrough, which they do not feel is justified.
However there is "more than enough new content" and "preliminary" results on a new task. The experimental quality also has some issues, there is a lack of good qualitative analysis, and reviewers felt the claims about semi-supervised work had issues. Still the main number is a good start, and the authors are correct to note that there is another work with similarly promising results. Of the two works, the reviewers found the other more clearly written, and with better experimental analysis, noting that they both over claim in terms of novelty. The most promising aspect of the work, will likely be the significance of this task going forward, as there is now more interest in the use of multi-lingual embeddings and nmt as a benchmark task.

(optional) Comment on this decision.


The proposed method combines supervised pretraining given some expert data and further uses the supervision to regularize the Q-updates to prevent the agent from exploring 'nonsense' directions. There a significant problems with the paper: the approach is not novel, the assumption of large amounts of expert data is problematic, and the claim of vastly accelerated learning is not supported empirically, either in the main paper or in the additional mujoco experiments added in the appendix.

(optional) Comment on this decision.


The paper addresses the problem of continual learning and solutions based on variational inference. Updates to the paper have improved it and addresses many of the concerns raised by the reviewers during the discussion period.

(optional) Comment on this decision.


This is a borderline paper.  The reviewers are happy with the simplicity of the proposed method and the fact that it can be applied after training; but are concerned by the lack of theory explaining the results.  I will recommend accepting, but I would ask the authors add the additional experiments they have promised, and would also suggest experiments on imagenet.

(optional) Comment on this decision.


This paper was reviewed by 3 expert reviews. While they all see value in the new task and dataset, they raise concerns (templated language, unclear what exactly are the new challenges posed by this task and dataset, etc) that this AC agrees with. To be clear, the lack of a fundamentally new model is not a problem (or a requirement for every paper introducing a new task/dataset), but make a clear compelling case for why people should work on the task is a reasonable bar. We encourage the authors to incorporate reviewer feedback and invite to the workshop track.

(optional) Comment on this decision.


This paper provides a game-based interface to have Turkers compete to analyze data for a learning task over multiple rounds. Reviewers found the work interesting and clear written, saying "the paper is easy to follow and the evaluation is meaningful." They also note that there is clear empirical benefit "the results seem to suggest that MTD provides an improvement over non-HITL methods." They also like the task compared to synthetic grounding experiments. There was some concern about the methodology of the experiments but the authors provide reasonable explanations and clarification.

One final concern that I hope the readers take into account. While the reviewers were convinced by the work and did not require it, I feel like the work does not engage enough with the literature of crowd-sourcing in other disciplines. While there are likely some unique aspects to ML use of crowdsourcing, there are many papers about encouraging crowd-workers to produce more useful data.

(optional) Comment on this decision.


All reviewers agree that the contribution of this paper, a new way of training neural nets to execute Monte-Carlo Tree Search, is an appealing idea.  For the most part, the reviewers found the exposition to be fairly clear, and the proposed architecture of good technical quality.  Two of the reviewers point out flaws in implementing in a single domain, 10x10 Sokoban with four boxes and four targets.  Since their training methodology uses supervised training on approximate ground-truth trajectories derived from extensive plain MCTS trials, it seems unlikely that the trained DNN will be able to generalize to other geometries (beyond 10x10x4) that were not seen during training.  Sokoban also has a low branching ratio, so that these experiments do not provide any insight into how the methodology will scale at much higher branching ratios.

Pros: Good technical quality, interesting novel idea, exposition is mostly clear.  Good empirical results in one very limited domain.
Cons: Single 10x10x4 Sokoban domain is too limited to derive any general conclusions.

Point for improvement: The paper compares performance of MCTSnet trials vs. plain MCTS trials based on the number of trials performed.  This is not an appropriate comparison, because the NN trials will be much more heavyweight in terms of CPU time, and there is usually a time limit to cut off MCTS trials and execute an action.  It will be much better to plot performance of MCTSnet and plain MCTS vs. CPU time used.

(optional) Comment on this decision.


Dear authors,

The reviewers agreed that the theoretical part lacked novelty and that the paper should focus on its experimental part which at the moment is not strong enough to warrant publication.

Regarding the theoretical part, here are the main concerns:
- Even though it is used in previous works, the continuous time approximation of stochastic gradient overlooks its practical behaviour, especially since a good rule of thumb is to use as large as stepsize as possible (without reaching divergence), as for instance mentioned in The Marginal Value of Adaptive Gradient Methods in Machine Learning by Wilson et al.
- The isotropic approximation is very strong and I don't know settings where this would hold. Since it seems central to your statements, I wonder what can be deduced from the obtained results.
- I do not think the Gaussian assumption is unreasonable and I am fine with it. Though there are clearly cases where this will not be true, it will probably be OK most of the time.

I encourage the authors to focus on the experimental part in a resubmission.

(optional) Comment on this decision.


The paper explores an increasingly important questions, especially showing the attack on existing APIs. The update to the paper has also improved it, but the paper is still not yet as impactful as it could be and needs much more comprehensive analysis to correctly appreciate its benefits and role.

(optional) Comment on this decision.


 + The paper proposes an interesting empirical measure of ""learnability"" of a trained network: how well the predictive function it represents can be learned by another network. And shows it empirically seems to correlate with better generalization.
 - The work is purely empirical: it features no theory relating this learnability to generalization
 - Learnability measure is somewhat ad-hoc with moving parts left to be specified (learning network, data splits, ...)
 - as pointed out by a reviewer, learnability doesn't really provide any answers for now.
 - the work would be much stronger if it went beyond a mere correlation study, and if learnability considerations allowed to derive a new approach/regularization scheme that was convincingly shown to improve generalization.

(optional) Comment on this decision.


This paper proposes a simple idea for using expert data to improve a deep RL agent's performance. Its main flaw is the lack of justification for the specific techniques used. The empirical evaluation is also fairly limited.

(optional) Comment on this decision.


The paper provides an interesting take on GAN training based on Coulomb dynamics. The proposed formulation is theoretically well motivated and authors provide guarantees for convergence. Reviewers agree that the theoretical analysis is interesting but are not completely impressed by the results. The method addresses mode collapse issue but still lacks in sample quality. Nevertheless, reviewers agree that this is a good step towards the understanding of GAN training.

(optional) Comment on this decision.


The paper addresses an interesting problem, is novel and works.
While the paper improved through reviews + rebuttal, the reviewers still find the presentation lacking.

(optional) Comment on this decision.


The authors addressed the reviewers concerns but the scores remain somewhat low.
The method is not super novel, but it is an incremental improvement over existing approaches.

(optional) Comment on this decision.


 + Paper proposes simple joint deep autoencoder + classifier training where the hidden representation is split between (observed) class and (unobserved) style nodes.
 - Empirical evaluation is very limited, focusing on only qualitative evaluation of reconstructions and interpolations (on MNIST and EMNIST).
 - Unclear goal: if it is improving classifier robustness, then quantitative classifier robustness improvements should be experimentally demonstrated. If it is as a (conditional) generative model, then it should be compared to strong generative baselines (in the GVAE or GAN families). The paper currently has neither.

(optional) Comment on this decision.


The reviewers agree that the problem of learning learning credit assignment from terminal rewards is interesting, and that the presented approach is promising. There are some concerns regarding the rigor and correctness of the theoretical results, and I ask the authors to improve those aspects of the paper. I also ask the authors to the result figures easier to read. The chosen colors are not ideal and the use of log-scale x-axis is not standard. Finally, including DAgger in the same plot is confusing assuming that DAgger user more information.

(optional) Comment on this decision.


The reviewers were generally positive about this paper with a few caveats:

PROS:
1. Important and challenging topic to analyze and any progress on unsupervised learning is interesting.
2. the paper is clear, although more formalization would help sometimes
3. The paper presents an analysis for unsupervised learning of mapping between 2 domains that is totally new as far as I know.
4. A large set of experiments

CONS:
1. Some concerns about whether the claims are sufficiently justified in the experiments
2. The paper is very long and quite dense

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. The consensus from the reviewers is that this is not quite ready for publication. The work is related to (although different from) Gu et al Neural Sequential Monte Carlo NIPS2015 and it would be useful to point this out in the related work section.

(optional) Comment on this decision.


While the paper addresses interesting ideas, it provides little novelty
The packaging of a dataset is useful, but not significant, and the specification of a Turing Test for face discrimination is interesting but not sufficiently technically interesting.

(optional) Comment on this decision.


Training GANs to generate discrete data is a hard problem. This paper introduces a principled approach to it that uses importance sampling to estimate the gradient of the generator. The quantitative results, though minimal, appear promising and the generated samples look fine. The writing is clear, if unnecessarily heavy on mathematical notation.

(optional) Comment on this decision.


Simple idea (which is a positive) to regularize RNNs, broad applicability, well-written paper. Initially, there were concerns about  comparisons, but he authors have provided additional experiments that have made the paper stronger.

(optional) Comment on this decision.


This paper showcases how saliency methods are brittle and cannot be trusted to obtain robust explanations. They define a property called input invariance that they claim all reliable explanation methods must possess. The reviewers have concerns regarding the motivation of this property in terms of why is it needed. This is not clear from the exposition. Moreover, even after having the opportunity to update the manuscript they seem to have not touched upon this issue other than providing a generic response.

(optional) Comment on this decision.


I am somewhat of two minds from the paper. The authors show empirically that adversarial perturbation error follows power law and looks for a possible explanation. The tie in with generalization is not clear to me and makes me wonder how to evaluate the significance of the finding of the power law distribution..  On the other hand, the authors present an interesting analysis, show that the finding holds in all the cases they explored and also found that architecture search can be used to find neural networks that are more resilient to adversarial search (the last shouldn't be surprising if that was indeed the training criterion).

All in all, I think that while the paper needs a further iteration prior to publication, it already contains interesting bits that could spur very interesting discussion at the Workshop.

(Side note: There's a reference missing on page 4, first paragraph)

(optional) Comment on this decision.


This paper presents a sensible, but somewhat incremental, generalization of neural architecture search.  However, the experiments are only done in a single artificial setting (albeit composed of real, large-scale subtasks).  It's also not clear that such an expensive meta-learning based approach is even necessary, compared to more traditional approaches.

If this paper was less about proposing a single new extension, and more about putting that extension in a larger context, (either conceptually or experimentally), it would be above the bar.

(optional) Comment on this decision.


The reviewers have unanimously expressed concerns about clarity, novelty, sound theoretical justification and intuitive motivation of the proposed approach.

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The reviewers really liked this paper. This paper presents a tweak to the LSTM cell that introduces sparsity, thus reducing the number of parameters in the model.

The authors show that their sparse models match the performance of the non-sparse baselines. While the results are not state-of-the-art but vanilla implementations of standard models, this is still of interest to the community.

(optional) Comment on this decision.


This is an interesting piece of work that provides solid evidence on the topic of bootstrapping in deep reinforcement learning.

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The paper proposes various improvements to Wasserstein distance based GAN training. Reviewers agree that the method produces good quality samples and are impressed by the state of the art results in several semi-supervised learning benchmarks. The paper is well written and the authors have further improved the empirical analysis in the paper based on reviewer comments.

(optional) Comment on this decision.


The paper received borderline negative scores: 5,6,4.

The authors response to R1 question about the motivations was "...thus can achieve similar classification results with much smaller network sizes. This translates into smaller memory requirements, faster computational speeds and higher expressivity." If this is really the case, then some experimental comparison to compression methods (e.g. Song Han's PhD work at Stanford) is needed to back up this.

R4 raises issues with the experimental evaluation and the AC agrees with them that they are disappointing. In general R4 makes some good suggestions for improving the paper.

The author's rebuttal also makes the general point that the paper should be accepted as it contains ideas, that these are sufficient alone: "We strongly believe that with some fine-tuning it could achieve considerably better results, however we also believe that this is not the point in a first submission...". The AC disagrees with this. Ideas are cheap. *Good ideas*, i.e. those that work, as in get good performance on standard benchmarks are valuable however. The reason for having benchmarks is to give some of objective way of seeing if an idea has any merit to it. So while the reviewers and the AC accept that the paper has some interesting ideas, this is not enough for warrant acceptance.

(optional) Comment on this decision.


There is a very nice discussion with one of the reviewers on the experiments, that I think would need to be battened down in an ideal setting. I'm also a bit surprised at the lack of discussion or comparison to two seemingly highly related papers:

1. T. G. Dietterich and G. Bakiri (1995) Solving Multiclass via Error Correcting Output Codes.
2. Hsu, Kakade, Langford and Zhang (2009) Multi-Label Prediction via Compressed Sensing.

(optional) Comment on this decision.


meta score: 8

The paper present a distributed architecture using prioritized experience replay for deep reinforcement learning.  It is well-written and the experimentation is extremely strong.  The main issue is the originality - technically, it extends previous work in a limited way;  the main contribution is practical, and this is validated by the experiments.  The experimental support is such that the paper has meaningful conclusions and will surely be of interest to people working in the field.  Thus I would say it is comfortably over the acceptance threshold.

Pros:
 - good motivation and literature review
 - strong experimentation
 - well-written and clearly presented
 - details in the appendix are very helpful
Cons:
 - possibly limited originality in terms of modelling advances

(optional) Comment on this decision.


This paper combines existing models to detect topics and generate responses, and the resulting model is shown to be slightly preferred by human evaluators over baselines. This is quite incremental and the results are not impressive enough to stand on their own merit.

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The proposed Bi-BloSAN is a two-levels' block SAN, which has both parallelization efficiency and memory efficiency. The study is thoroughly conducted and well presented.

(optional) Comment on this decision.


In general, this seems like a sensible idea, but in my opinion the empirical results do not show a very compelling margin between using *entropy* as an active learning selection criterion vs the proposed methods. The difference is small enough that in practice it is very hard for me to believe that many researchers would choose to use the meta-learning via deep RL method (given that they'd need to train on multiple datasets and tune REINFORCE which is not going to be obviously easy). For that reason I am inclined to reject the paper.

In a follow-up version, I would heed the advice of Reviewer 1 and do more ablation analyses to understand the value of myopic vs non-myopic, cross-dataset vs. not, bandits vs RL, on the fly vs not (these are all intermingled issues). The relative lack of such analyses in the paper does not help in terms of it passing the bar.

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. ICLR. Although there revision has improved the paper, the consensus from the reviewers is that this is not quite ready for publication.

(optional) Comment on this decision.


I recommend acceptance based on the positive reviews. The paper analyzes critical points for linear neural networks and shallow ReLU networks. Getting characterization of critical points for shallow ReLU networks is a great first step.

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The paper proposes a new way to understand why neural networks generalize well. They introduce the concept of ensemble robustness and try to explain DNN generalization based on this concept. The reviewers feel the paper is a bit premature for publication in a top conference although this new way of explaining generalization is quite interesting.

(optional) Comment on this decision.


The paper presents a good analysis on the use of different linear maps instead of identity shortcuts for resnet.
It is interesting to the community but the experimental justification is insufficient.
1) As pointed out by the reviewer that this work shows "that on small size networks Tandem Block outperforms Residual Blocks, since He at. al. (2016) in Tab 1 showed a contrary effect, does it mean that the observations do not scale to higher capacity networks?", the paper would be much stronger if with experiments justify this claim.
2) "extremely deep networks take much longer to train" is not a valid reason to not conduct such exps.

(optional) Comment on this decision.


The paper studies factorizations of convolutional kernels. The proposed kernels lead to theoretical and practical efficiency improvements, but these improvements are very, very limited (for instance, Figure 5). It remains unclear how they compare to popular alternative approaches such as group convolutions (used in ResNeXt) or depth-separable convolutions (used in MobileNet). The reviewers identify a variety of smaller issues with the manuscript.

(optional) Comment on this decision.


Since this seems interesting, I suggest to accept this paper at the conference. However, there are still some serious issues with the paper, including missing references.

(optional) Comment on this decision.


The reviewers tend to agree that the empirical results in this paper are good compared to the baselines. However, the paper in its current form is considered a bit too incremental. Some reviewers also suggested additional theory could help strengthen the paper.

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. The reviewers are all in agreement that the paper is suitable for publication, each revising their score upwards in response to the revision that has made the paper stronger.

The authors may want to consider adding a discussion about whether the simple standard Gaussian prior, which is invariant under transformation by an orthogonal matrix, is a sensible one if the objective is to find disentangled representations. Alternatives, such as sparse priors, might be more sensible if a model-based solution to this problem is sought.

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The authors propose to use identity + some weights in the recurrent connections to prevent vanishing gradients. The reviewers found the experiments to have weak baselines, weakening the claims of the paper.

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The authors give evidence that is certain cases, the ordering of sample inclusion in a curriculum is not important.  However, the reviewers believe the experiments are inconclusive, both in the sense that as reported, they do not demonstrate the authors' hypothesis, and that they may leave out many relevant factors of variation (such as hyper-parameter tuning).

(optional) Comment on this decision.


All the reviewers noted that the dual formulation, as presented, only applies to the logistic family of classifiers. The kernelization is of course something that *can* be done, as argued by the authors, but is not in fact approached in the submission, only in the rebuttal. The toy-ish nature of the problems tackled in the submission limits the value of the presentation.

If the authors incorporate their domain adaptation results (SVHN-->MNIST and others) using the kernelized approach and do the stability analysis for those cases, and obtain reasonable results on domain adaptation benchmarks (70% on SVHN-->MNIST is for instance on the low side compared to the pixel-transfer-based GAN approaches out there!) then I think it'd be a great paper.

As such, I can only recommend it as an invitation to the workshop track, as the dual formulation is interesting and potentially useful.

(optional) Comment on this decision.


The reviewers present strong concerns about the lack of novelty in the paper. Further there are strong concerns about how the experiments are conducted. I recommend the authors to carefully go through the reviews.

(optional) Comment on this decision.


the reviewers all found the problem to be important, the proposed approach to be interesting, but the manuscript to be preliminary. i agree with them.

(optional) Comment on this decision.


The paper proposes a form of autoencoder that learns to predict the neighbors of a given input vector rather than the input itself.  The idea is nice but there are some reviewer concerns about insufficient evaluation and the effect of the curse of dimensionality.  The revised paper does address some questions and includes additional helpful experiments with different types of autoencoders.  However, the work is still a bit preliminary.  The area of auto-encoder variants, and corresponding experiments on CIFAR-10 and the like, is crowded.  In order to convince the reader that a new approach makes a real contribution, it should have very thorough experiments.  Suggestions:  try to improve the CIFAR-10 numbers (they need not be state-of-the-art but should be more credible), adding more data sets (especially high-dimensional ones), and analyzing the effects of factors that are likely to be important (e.g. dimensionality, choice of distance function for neighbor search).

(optional) Comment on this decision.


Pros:
- Addresses an important medical imaging application
- Uses an open dataset

Con:
- Authors do not cite original article describing challenge from which they use their data: https://arxiv.org/pdf/1612.08012.pdf , or the website for the corresponding challenge: https://luna16.grand-challenge.org/results/
- Authors either 1) do not follow the evaluation protocol set forth by the challenge, making it impossible to compare to other methods published on this dataset, or 2) incorrectly describe their use of that public dataset.
- Compares only to AlexNet architecture, and not to any of the other multiple methods published on this dataset (see: https://arxiv.org/pdf/1612.08012.pdf).
- Too much space is spent explaining well-understood evaluation functions.
- As reviewers point out, no motivation for new architecture is given.

(optional) Comment on this decision.


The reviewers agree that the idea of incorporating humans in the training of generative adversarial networks is interesting and worthwhile exploring. However, they felt that the paper fell short in providing strong support for their approach. The AC agrees. The authors are encouraged to strengthen their work and resubmit to a future venue.

(optional) Comment on this decision.


The reviewers all outlined concerns regarding novelty and the maturity of this work. It would be helpful to clarify the relation to doubly stochastic kernel machines as opposed to random kitchen sinks, and to provide more insight into how this stochasticity helps. Finally, the approach should be tried on more difficult image datasets.

(optional) Comment on this decision.


The reviewers found a number of short-comings in this work that would prevent it from being accepted at ICLR in its current form, both in terms of writing (not specifying the loss function),  experiments that are too limited, and inconclusive comparisons with existing regularization techniques. I recommend the authors take into account the feedback from reviewers in any follow-up submissions.

(optional) Comment on this decision.


Viewing the problem of determining the validity of high-dimensional discrete sequences as a sequential decision problem, the authors propose learning a Q function that indicates whether the current sequence prefix can lead to a valid sequence. The paper is fairly well written and contains several interesting ideas. The experimental results appear promising but would be considerably more informative if more baselines were included. In particular, it would be good to compare the proposed approach (both conceptually and empirically) to learning a generative model of sequences. Also, given that your method is based on learning a Q function, you need to explain its exact relationship to classic Q-learning, which would also make for a good baseline.

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Three reviewers recommend rejection and there is no rebuttal.

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The submission proposes to use GANs to learn a generative model of fMRI scans that can then be used for downstream classification tasks.  Although there was some appreciation from the reviewers of the approach, there were several important remaining concerns:

1) From Reviewer 1: "Generating high resolution images with GANs even on faces for which there is almost infinite data is still a challenge. Here a few thousand data points are used. So it raises too concerns: First is it enough?"

and

2) R1 and R2 both raised concerns about the significance of the improvements.  Looking through the tables, there are many reported differences that are reasonably small, and no error bars or significance are given.  This should be a requirement for an empirical paper about fMRI.

(optional) Comment on this decision.


The paper nicely unifies previous results and develops the property of local openness. While interesting, I find the application to multi-layer linear networks extremely limiting. There appears to be a sub-field in theory now focusing on solely multi-layer linear networks which is meaningless in practice. I can appreciate that this could give rise to useful proof techniques and hence, I am recommending it to the workshop track with the hope that it can foster more discussions and help researchers move away from studying multi-layer linear networks.

(optional) Comment on this decision.


This paper presents a framework where GANs are used to improve detection of outliers (in this context, instances of the “background class”). This is a very interesting and, as demonstrated, promising idea. However, the general feeling of the reviewers is that more work is needed to make the technical and evaluations parts convincing. Suggestions for further work towards this direction include: theoretical analysis, better presentation of the manuscript and, most importantly, stronger experimental section.

(optional) Comment on this decision.


This paper proposes to combine Depthwise separable convolutions developed for 2d grids with recent graph convolutional architectures. The resulting architecture can be seen as learning both node and edge features, the latter encoding node similarities with learnt weights.
Reviewers agreed that this is an interesting line of work, but that further work is needed in both the presentation and the experimental front before publication. In particular, the paper should also compare against recent models (such as the MPNN from Gilmer et al) that also propose edge feature learning. THerefore, the AC recommends rejection at this time.

(optional) Comment on this decision.


There is significant discussion on this paper and high variance between reviewers:  one reviewer gave the paper a low score.  However the committee feels that this paper should be accepted at the conference since it provides a better framework for reproducibility, performs more large scale experiments than prior work.  One small issue the lack of comparison in terms of empirical results between this work and Zhang et al's work, but the responses provided to both the reviewers and anonymous commenters seem to be satisfactory.

(optional) Comment on this decision.


The submission formulates self paced learning as a specific iterative mini-max optimization, which incorporates both a risk minimization step and a submodular maximization for selecting the next training examples.

The strengths of the paper lie primarily in the theoretical analysis, while the experiments are somewhat limited to simple datasets: News20, MNIST, & CIFAR10.  Additionally, the main paper is probably too long in its current form, and could benefit from some of the proof details being moved to the appendix.

(optional) Comment on this decision.


The reviewers agree that the method is simple, the results are quite good, and the paper is well written. The issues the reviewers brought up have been adequately addressed. There is a slight concern about novelty, however the approach will likely be quite useful in practice.

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The paper studies subsampling techniques necessary to handle large graphs with graph convolutional networks.  The paper introduces two ideas: (1) preprocessing for GCNs (basically replacing dropout followed by linear transformation with linear transformation followed by drop out); (2) adding control variates based on historical activations.  Both ideas seem useful (but (1) is more empirically useful than (2), Figure 4*). The paper contains a fair bit of math (analysis / justification of the method).

Overall, the ideas are interesting and can be useful in practice. However, not all reviewers are convinced that the methods constitute a significant contribution.  There is also a question whether the math has much value (strong assumptions - also, from interpretation, may be too specific to the formulation of Kipf & Welling making it a bit narrow?).  Though I share these feelings and recommend rejection, I think that the reviewers 2 and 3 were a bit too harsh, and the scores do not reflect the quality of the paper.

*Potential typo: Figure 4 -- should it be CV +PP rather than CV?

+ an important problem
+ can be useful in practical applications
+ generally solid and sufficiently well written
- significance not sufficient
- math seems not terribly useful

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This paper marries the idea of Gaussian word embeddings and order embeddings, by imposing order among probabilistic word embeddings. Two reviewers vote for acceptance, and one finds the novelty of the paper incremental. The reviewer stuck to this view even after rebuttal, however, acknowledges the improvement in results. The AC read the paper, and agrees that the novelty is somewhat limited, however, the idea is still quite interesting, and the results are promising. The AC was missing more experiments on other tasks originally presented by Vendrov et al. Overall, this paper is slightly over the bar.

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The paper received scores of 8 (R1), 6 (R2), 6 (R3). R1's review is brief, and also is optimistic that these results demonstrated on ConvACs generalize to real convnets.  R2 and R3 feel this might be a potential problem. R2 advocates weak accept and given that R1 is keen on the paper, the AC feels it can be accepted.

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Thank you for submitting you paper to ICLR. Two of the reviewers are concerned that the paper’s contributions are not significant enough —either in terms of the theoretical or experimental contribution -- to warrant publication. The authors have improved the experimental aspect to include a more comprehensive comparison, but this has not moved the reviewers.

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Dear authors,

Thank you for your submission to ICLR. Sadly, the reviewers were not convinced by the novelty of your approach nor by its experimental results. Thus, your paper cannot be accepted to ICLR.

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The main idea of the paper is to transform graph classification into image representation (via adjacency matrices). Two reviewers are positive, while one is negative. The concerns are novelty (as mentioned by R2), while the last reviewer thinks the method is too simple and unprincipled (here the AC agrees with authors that simple is not necessarily bad). Overall, none of the reviewers champions this paper. Due to many excellent submissions, unfortunately this paper cannot be accepted in present form.

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Most of the concerns that Reviewer 1 expresses below echoes with the evaluation that the committee makes.  The evaluation of the paper is based on cross lingual document classification, which is not a very realistic large scale dataset.  The idea of the paper would be far more convincing if it focused on some real cross-lingual tasks, such as the ones that Reviewer 3 mentions.  Addressing these concerns would make the paper stronger for future venues.

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The reviewers found numerous issues in the paper, including unclear problem definitions, lack of motivation, no support for desiderata, clarity issues, points in discussion appearing to be technically incorrect, restrictive setting, sloppy definitions, and uninteresting experiments.  Unfortunately, little note of positive aspects was mentioned.  The authors wrote substantial rebuttals, including an extended exchange with Reviewer2, but this had no effect in terms of score changes. Given the current state of the paper, the committee feels the paper falls short of acceptance in its current form.

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despite not amazing scores, this is a solid paper.
it created a lot of discussion and was found to be reproducible.
we should accept it to let the iclr community partake in the discussion and learn about this method of n-gram embeddings

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The authors make an argument for constructing an MDP from the formal structures of temporal logic and associated finite state automata and then applying RL to learn a policy for the MDP. This does not provide a solution for low-level skill composition, because there are discontinuities between states, but does provide a means for high level skill composition.

The reviewers agreed that the paper suffered from sloppy writing and unclear methods. They had concerns about correctness, and were not impressed by the novelty (combining TL and RL has been done previously). These concerns tip this paper to rejection.

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This paper was reviewed by 3 expert reviewers. All three recommend rejection citing significant concerns (e.g. missing baselines).

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The paper is hard to follow at times. The heuristic reward has little justification -- not clear how
this would extend to other domains. Lack of empirical comparisons (see e.g. Hester et al., Deep Q-Learning from Demonstrations, 2017).

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Dear authors,

The authors all agreed that this was an interesting topic but that the novelty, either theoretical or empirical, was lacking. This, the paper cannot be accepted to ICLR in its current state but I encourage the authors to make the recommended updates and to push their idea further.

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The paper presents an interesting view of ResNets and the findings should be of broad interest. R1 did not update their score/review, but I am satisfied with the author response, and recommend this paper for acceptance.

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Novel way of analyzing neural networks to predict NN attributes such as architecture, training method, batch size etc. And the method works surprisingly good on the MNIST and ImageNet.

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The paper proposes a regularisation technique based on Shake-Shake which leads to the state of the art performance on the CIFAR-10 and CIFAR-100 dataset. Despite good results on CIFAR, the novelty of the method is low, justification for the method is not provided, and the impact of the method on tasks beyond CIFAR classification is unclear.

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This paper proposes an interesting new idea which creates an interesting discussion.

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The paper contains an interesting way to do online multi task learning, by borrowing ideas from active learning and comparing and contrasting a number of ways on the arcade learning environment.  Like the reviewers, I have some concerns about using the target scores and I think more analysis would be needed to see just how robust this method is to the choice/distribution of target scores (the authors mention that things don't break down as long as the scores are "reasonable", but that's not a particularly empirical nor precise statement).

My inclination is to accept the paper, because of the earnest efforts made by the authors in understanding how DUA4C works. However, I do agree that the paper should have a larger focus on that: basically Section 6 should be expanded, and the experiments should be rerun in such a way that the setup for DUA4C is more "favorable" (in terms of hyper-parameter optimization).  If there's any gap between any of the proposed methods and DUA4C, then this would warrant further analysis of course (since it would mean that there's an advantage to using target scores).

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The authors make an experimental study of the relative merits of RNN-type approaches and graph-neural-network approaches to solving node-labeling problems on graphs.   They discuss various improvements in gnn constructions, such as residual connections.

This is a borderline paper.  On one hand, the reviewers feel that there is a place for this kind of empirical study, but on the other, there is agreement amongst the reviewers that the paper is not as well written as it could be.  Furthermore, some reviewers are worried about the degree of novelty (of adding residual connections to X).

I will recommend  rejection, but urge the authors to clarify the writing and expand on the empirical  study and resubmit.

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All three reviewers felt that the paper was just below the acceptance threshold, with scores of 5,4,5. R1 felt there were problems in the proofs, but the authors rebuttal satisfactorily addressed this. R3 and the authors had an extended discussion with the authors, but did not revise their score from its initial value (5). R4 had concerns about the experimental evaluation, that wasn't fully addressed in the rebuttal. With no reviewers advocating acceptance, the paper will have to rejected unfortunately.

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The paper attempts to develop a method for learning latent representations using deep predictive coding and deconvolutional networks. However, the theoretical motivation for the proposed model in relation to existing methods (such as original predictive coding, deconvolutional networks, ladder networks, etc.), as well as the empirical comparison against them is unclear. The experimental results on the CIFAR10 dataset do not provide much insight on what kind of meaningful/improved representations can be learned in comparison to existing methods, both qualitatively and quantitatively. No rebuttal was provided.

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The authors investigate various class aware GANs and provide extensive analysis of their ability to address mode collapse and sample quality issues. Based on this analysis they propose an extension called Activation Maximization-GAN which tries to push each generated sample to a specific class indicated by the Discriminator. As experiments show, this leads to better sample quality & helps with mode collapse issue. The authors also analyze inception score to measure sample quality and propose a new metric better suited for this task.

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The authors have proposed a method for imitating a given control trajectory even if it is sparsely sampled. The method relies on a parametrized skill function and uses a triplet loss for learning a stopping metric and for a dynamics consistency loss. The method is demonstrated with real robots on a navigation task and a knot-tying task. The reviewers agree that it is a novel and interesting alternative to pure RL which should inspire good discussion at the conference.

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This paper has some interesting ideas that have been implemented in a rather ad hoc way; the presentation focuses perhaps too much on engineering aspects.

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Pros:
+ The idea of end-to-end training that simultaneously learns the weights and appropriate precision for those weights is very appealing.

Cons:
- Experimental results are far from the state-of-the-art, which makes the empirical evaluation unconvincing.
- More justification is needed for the update of the number of bits using the sign of the gradient.

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There was some debate between the authors and an anonymous commentator on this paper.  The feeling of the commentator was that existing work (mostly from the PL community) was not compared to appropriately and, in fact, performs better than this approach.  The authors point out that their evaluation is hard to compare directly but that they disagreed with the assessment.  They modified their texts to accommodate some of the commentator's concerns; agreed to disagree on others; and promised a fuller comparison to other work in the future.

I largely agree with the authors here and think this is a good and worthwhile paper for its approach.

PROS:
1. well written
2. good ablation study
3. good evaluation including real bugs identified in real software projects
4. practical for real world usage

CONS:
1. perhaps not well compared to existing PL literature or on existing datasets from that community
2. the architecture (GGNN) is not a novel contribution

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This paper presents a more complex version of the grammar-VAE, which can be used to generate structured discrete objects for which a grammar is known, by adding a second 'attribute grammar', inspired by Knuth.

Overall, the idea is a bit incremental, but the space is wide open and I think that structured encoder/decoders is an important direction.  The experiments seem to have been done carefully (with some help from the reviewers) and the results are convincing.

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Pros:
The paper aims to unify classification and novelty detection which is interesting and challenging.

Cons:
- The reviewers find that the work is incremental and contains heuristics. Reviewers find the repurposing of the fake logit in semi-supervised GAN discriminator for assigning novelty strange.
- The experiments presented are weak and authors do not compare with traditional/stronger approaches for novelty detection such as "learning with abstention" models and density models.
GIven the pros and cons, the committe finds the paper to fall short of acceptance in its current form.

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AnonReviewers 2 and AnonReviewer 3 rated the paper highly, with AR3 even upgrading their score.  AnonReviewer1 was less generous:

" Overall, it is a good empirical study, raising a healthy set of questions. In this regard, the paper is worth accepting. However, I am still uncomfortable with the lack of answers and given that the revision does not include the additional discussion and experiments promised in the rebuttal, I will stay with my evaluation."

The authors have promised to produce the discussion and new experiments. Given the nature of both (1: the discussion is already outline in the response and 2: the experiments are straightforward to run), I'm inclined to accept the paper because it represents a solid body of empirical work.

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Based on the positive reviews, I recommend acceptance. The paper analyzes when empirical risk is close to the population version, when empirical saddle points are close to the population version and empirical gradients are close to the population version.

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This paper proposes an interesting analysis of the limitations of WGANs as well as a solution to these limitations. I am not too convinced by the experimental part as, as some of the reviewers have mentioned, it relies on hyperparameters which can be hard to tune.

The more theoretical part, even if it could be written with more care as pointed out by reviewer 2, is nonetheless interesting and could stir discussion. I think it would be a good addition to ICLR as a poster.

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The reviewers agree that the authors have made an interesting contribution studying the effect of data augmentation, but they also agree that the claims made by the paper require a broader empirical study beyond the limited number of tasks surveyed in the current revision.  I urge the authors to follow this advice and see what they find.

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The main idea of policy-as-inference is not new, but it seems to be the first application of this idea to deep RL, and is somewhat well motivated.  The computational details get a bit hairy, but the good experimental results and the inclusion of ablation studies pushes this above the bar.

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This paper gives a coding theory interpretation of VAEs and uses it to motivate an additional knob for tuning and evaluating VAEs: namely, the tradeoff between the rate and the distortion. This is a useful set of dimensions to investigate, and past work on variational models has often found it advantageous to penalize the latent variable and observation coding terms differently, for broadly similar motivations. This paper includes some careful experiments analyzing this tradeoff for various VAE formulations, and provides some interesting visualizations. However, as the reviewers point out, it's difficult to point to a single clear contribution here, as the coding theory view of variational inference is well established, and the VAE case has been discussed in various other works. Therefore, I recommend rejection.

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This paper presents new results on adversarial training, using the framework of robust optimization. Its minimax nature allows for principled methods of both training and attacking neural networks.

The reviewers were generally positive about its contributions, despite some concerns about 'overclaiming'. The AC recommends acceptance, and encourages the authors to also relate this work with the concurrent ICLR submission (https://openreview.net/forum?id=Hk6kPgZA-) which addresses the problem using a similar approach.

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The paper addresses the problem of learning a teacher model which selects the training samples for the next mini-batch used by the student model. The proposed solution is to learn the teacher model using policy gradient. It is an interesting training setting, and the evaluation demonstrates that the method outperforms the baseline. However, it remains unclear how the method would scale to larger datasets, e.g. ImageNet. I would strongly encourage the authors to extend their evaluation to larger datasets and state-of-the-art models, as well as include better baselines, e.g. from Graves et al.

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The reviewers have pointed out that there is a substantial amount of related work that this paper should be acknowledging and building on.

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The paper empirically evaluates the effectiveness of ensembles of deep networks against adversarial examples. The paper adds little to the existing literature in this area: an detailed study on "ensemble adversarial training" already exists, and the experimental evaluation in this paper is limited to MNIST and CIFAR (results on those datasets do not necessarily transfer very well to much higher-dimensional datasets such as ImageNet). Moreover, the reviewers identify several shortcomings in the experimental setup of the paper.

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