All the reviewers agree that the paper is studying an important problem and makes a good first step towards understanding learning in GANs. But the reviewers are concerned that the setup is too simplistic and not relevant in practical settings. I recommend the authors to carefully go through reviews and to present it at the workshop track. This will hopefully foster further discussions and lead to results in more practically relevant settings.

(optional) Comment on this decision.


ICLR 2018 Conference Acceptance Decision

Thank you for submitting you paper to ICLR. ICLR. The consensus from the reviewers is that this is not quite ready for publication. In particular, the experimental results are promising, but further work is required to fully demonstrate the efficacy of the approach.

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


All the reviewers are agree on the significance of the topic of understanding expressivity of deep networks. This paper makes good progress in analyzing the ability of deep networks to fit multivariate polynomials. They show exponential depth advantage for general sparse polynomials.

 I am very surprised that the paper misses the original contribution of Andrew Barron. He analyzes the size of the shallow neural networks needed to fit a wide class of functions including polynomials. The deep learning community likes to think that everything has been invented in the current decade.

@article{barron1994approximation,
  title={Approximation and estimation bounds for artificial neural networks},
  author={Barron, Andrew R},
  journal={Machine Learning},
  volume={14},
  number={1},
  pages={115--133},
  year={1994},
  publisher={Springer}
}

(optional) Comment on this decision.


There is a general consensus that the novelty is too limited, as it is a straightforward combination of two simple and well-known ideas (dense skip connections / multi-head attention).

Pros:
-- the modifications yield (moderate) improvements in performance
Cons:
-- the modifications are relatively trivial, basically a combination of 2 well-known ideas

The authors addressed some of the reviewers concerns (e.g., disentangling impact of the two modifications) but unfortunately I still do not see as appropriate for the conference.

(optional) Comment on this decision.


Reviewers unanimous on rejection.
Authors don't maintain anonymity.
No rebuttal from authors.
Poorly written

(optional) Comment on this decision.


All reviewers agreed that, despite the lack of novelty, the proposed method is sound and correctly linked to existing work. As the topic of automatically learning the stepsize is of great practical interest, I am glad to have this paper presented as a poster at ICLR.

(optional) Comment on this decision.


The paper proposes a VAE variant by embedding spatial information with multiple layers of latent variables. Although the paper reports state-of-the-art results on multiple datasets, some results may be due to a bug. This has been discussed, and the author acknowledges the bug. We hope the problem can be fixed, and the paper reconsidered at another venue.

(optional) Comment on this decision.


The paper performs a theoretical analysis of the representation power of convolutional networks with inter-layer connections. Whilst the results themselves are interesting, the current presentation of the paper stands in the way of the reading grasping and appreciating the main insights from the paper.

The authors acknowledge these issues in their rebuttal, but have not yet revised the paper to resolve them. I encourage the authors to revise the paper to address the reviewer comments, and re-submit it to another venue.

(optional) Comment on this decision.


All of the reviewers agree that the paper clearly presents promising ideas in developing a novel actor critic algorithm. The experiments do not show a significant gain against the baselines, but they support the presented ideas. I appreciated the ablation study on dual-AC.

Detailed comments:
My understanding is that the x-axis in Figures 1 & 2 shows the number of iterations each of which contains batch_size*1000 environment steps. It is more standard to show those plots in terms of the number of environment steps. Further, the optimal batch_size for different algorithms may be different, so using the same batch_size for all of the algorithms is not fair.

(optional) Comment on this decision.


This an interesting new contribution to construction of random features for approximating kernel functions. While the empirical results look promising, the reviewers have raised concerns about not having insights into why the approach is more effective;  the exposition of the quadrature method is difficult to follow; and the connection between the quadrature rules and the random feature map is never explicitly stated. Some comparisons are missing (e.g., QMC methods). As such the paper will benefit from a revision and is not ready for ICLR-2018 acceptance.

(optional) Comment on this decision.


The presented method essentially builds a model that remaps features into a new space that optimizes nearest-neighbor classification. The model is a neural network, and the optimization is carried out through a genetic algorithm.

Pros:
 - One major issue with neural network classification is that of a lack of explainability. Many networks are currently "black box" approaches. By moving to the optimization problem to that of building a feature space for nearest neighbor classification, one can, to a degree, alleviate the "black box" issue by providing the discovered nearest neighbor instances as "evidence" of the decision.
- Authors use established datasets.

Cons:
- Authors do not properly cite previous work, as brought up by reviewers. There is much literature on optimization of feature spaces (such as the entire field of metric learning), as well as prior approaches using genetic optimization. The originality and significance here is therefore not clear.

(optional) Comment on this decision.


The paper proposes a small modification to the loss on a network's softmax layer, which encourages the input to the softmax to be isotropic for each class.  This is motivated by applications where one may want to measure Euclidean distance between the feature vectors.  The main concern is that it's not clear that there is really a widespread need for this modified loss, i.e. that there are many applications (or a few very important ones) where one would want to train a network with a softmax layer but also need the Euclidean distances in the feature space to be sensible.

(optional) Comment on this decision.


The paper proposes an autoencoder for sets, an interesting and timely problem.  The encoder here is based on prior related work (Vinyals et al. 2016) while the decoder uses a loss based on finding a matching between the input and output set elements.  Experiments on multiple data sets are given, but none are realistic.   The reviewers have also pointed out a number of experimental comparisons that would improve the contribution of the paper, such as considering multiple matching algorithms and more baselines.  In the end the idea is reasonable and results are encouraging, but too preliminary at this point.

(optional) Comment on this decision.


the idea is interesting, but as pointed out the reviewers (and also agreed by the authors), the current manuscript lacks clear motivations, reasons underlying specific design choices and convincing empirical evaluation.

(optional) Comment on this decision.


The paper proposes iterative training strategies for learning teacher and student models. They show how iterative training can lead to interpretable strategies over joint training on multiple datasets. All the reviewers felt the idea was interesting, although, one of the reviewers had concerns about the experimentation.

However, there is a BIG problem with this submission. The author names appear in the manuscript thus disregarding anonymity.

(optional) Comment on this decision.


This paper initially received borderline reviews. The main concern raised by all reviewers was a limited experimental evaluation (synthetic only). In rebuttal, the authors provided new results on the CelebA dataset, which turned the first reviewer positive. The AC agrees there is merit to this approach, and generally appreciates the idea of compositional concept learning.

(optional) Comment on this decision.


This work takes dialogue acts into account to generate responses in a human-machine conversation. However, incorporating dialogue acts into open-domain dialogue was already the focus of Zhao et al's ACL 2017 paper, Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders, and using dialogue acts in a policy for human-machine conversation was also an idea that already appeared in Serban et al 2017, A Deep Reinforcement Learning Chatbot. Despite the authors' response that tries to adjust their claims and incorporate a more thorough overview, I encourage the authors to re-work their research with a much more careful and reliable examination of previous work and how their effort should be understood in that more comprehensive context.

(optional) Comment on this decision.


There is a concern from one of the reviewers that the paper needs deeper analysis. On the other hand, applying finite horizon techniques to deep RL is relatively unexplored, and the paper does provide some interesting results in that direction.

(optional) Comment on this decision.


This paper proposes a “warp operator” based on Taylor expansion that can replace a block of layers in a residual network, allowing for parallelization. Taking advantage of multi-GPU parallelization the paper shows increased speedup with similar performance on CIFAR-10 and CIFAR-100. R1 asked for clarification on rotational symmetry. The authors instead removed the discussion that was causing confusion (replacing with additional experimental results that had been requested). R2 had the most detailed review and thought that the idea and analysis were interesting. They also had difficulty following the discussion of symmetry (noted above). They also pointed out several other issues around clarity and had several suggestions for improving the experiments which seem to have been taken to heart by the authors, who detailed their changes in response to this review. There was also an anonymous public comment that pointed out a “fatal mathematical flaw and weak experiments”. There was a lengthy exchange between this reviewer and the authors, and the paper was actually corrected and clarified in the process. This anonymous poster was rather demanding of the authors, asking for latex-formatted equations, pseudo-code, and giving direction on how to respond to his/her rebuttal. I don't agree with the point that the paper is flawed by "only" presenting a speed-up over ResNet, and furthermore the comment of "not everyone has access to parallelization" isn’t a fair criticism of the paper.

(optional) Comment on this decision.


The paper proposes to use simple regression models for predicting the accuracy of a neural network based on its initial training curve, architecture, and hyper-parameters; this can be used for speeding up architecture search. While this is an interesting direction and the presented experiments look quite encouraging, the paper would benefit from more evaluation, as suggested by reviewers, especially within state-of-the-art architecture search frameworks and/or large datasets.

(optional) Comment on this decision.


i am a big fan of this idea, but i agree with the reviewers that evaluating this idea on bAbI (which was originally created from a small set of rules and primitives) discounts quite a bit of what is being claimed here. one of the future directions mentioned by the authors ("investigating whether the proposed n-gram representation is sufficient for natural languages") should have been included even with a negative result, which would've increased the significance significantly.

(optional) Comment on this decision.


The paper proposes a simple modification to conditional GANs, where the discriminator involves an inner product term between the condition vector y and the feature vector of x. This formulation is reasonable and well motivated from popular models (e.g., log-linear, Gaussians). Experimentally, the proposed method is evaluated on conditional image generation and super-resolution tasks, demonstrating improved qualitative and qualitative performance over the existing state-of-the-art (AC-GAN).

(optional) Comment on this decision.


This paper presents a neural compositional model for visual question answering.  The overall idea may be exciting but the committee agrees with the evaluation of Reviewer 1:  the experimental section is a bit thin and it only evaluates against an artificial dataset for visual QA that does not really need a knowledge base.  It would have been better to evaluate on more traditional question answering settings where the answer can be retrieved from a knowledge base (WebQuestions, Free917, etc.), and then compare with state of the art on those.

(optional) Comment on this decision.


Pro:
 - Interesting approach to tie together reinforcement Q-learning with CNN for prediction and reward function learning in predicting downstream effects of chemical structures, while providing relevant areas for decision-making.

Con:
- Datasets are small, generalizability not clear.
- Performance is not high (although performance wasn't the goal necessarily)
- Sometimes test performance is higher than training performance, making results questionable.
- Should include comparison to other wrapper-based combinatorial approaches.
- Too targeted an appeal/audience (better for chemical journal)

(optional) Comment on this decision.


The reviewers were unanimous in their assessment that the paper was not ready for publication in ICLR.  Their concerns included:
 - lack of novelty over Niepert, Ahmed, Kutzkov, ICML 2016
 - The approach learns combinations of graph kernels and its expressive capacity is thus limited
 - The results are close to the state of the art and it is not clear whether any improvement is statistically significant.

The authors have not provided a response to these concerns.

(optional) Comment on this decision.


This one was really on the fence.  After some additional rounds of discussion post-rebuttal with the reviewers I think the general consensus is that it's a good paper and almost there but not quite ready for acceptance at this time.  A detailed list of issues and concerns below.

PROS:
1. good idea: an additional loss term that enforces semantic constraints on the network output (like exactly 1 output element must be 1).
2. well written generally
3. a nice variety of different experiments

CONS:
1. paper organization.  The authors start with the axioms they would like a semantic loss function to obey, then provide a general definition, then show it does obey the axioms.  The general definition is intractable in a naive implementation.  The authors use boolean circuits to tractably solve the problem but this isn't discussed enough and it's unreasonable to expect readers to just give a pass on it without some more background.  I personally would prefer an organization that presented the motivation (in english) for the loss definition; then the  definition with a description of its pieces and why they are there; then a short discussion of how to implement such a loss in practice using boolean circuits (or if this is too much put it in the appendix); and a pointer to the axiomatization in an appendix.

2. related to 1, I didn't see anything which discussed the training time of this approach.  Given that the semantic loss has to be computed in a more involved way than usual, it's not clear whether it is practical.

(optional) Comment on this decision.


This paper present a functional extension to NPI, allowing the learning of simpler, more expressive programs.

Although the conference does not put explicit bounds on the length of papers, the authors pushed their luck with their initial submission (a body of 14 pages). It is clear, from the discussion and the reviews, however, that the authors have sought to substantially reduce the length of their paper while improving its clarity.

Reviewers found the method and experiments interesting, and two out of three heartily recommend it for acceptance to ICLR. I am forced to discount the score of the third reviewer, which does not align with the content of their review. I had discussed the issue of length with them, and am disappointed that they chose not to adjust their score to reflect their assessment of the paper, but rather their displeasure at the length of the paper (which, as stated above, does push the boundary a little).

Overall, I recommend accepting this paper, but warn the authors that this is a generous decision, heavily motivated by my appreciation for the work, and that they should be careful not to try such stunts in future conference in order to preserve the fairness of the submission process.

(optional) Comment on this decision.


This paper studies trainable deep encoders/decoders in the context of coding theory, based on recurrent neural networks. It presents highly promising results showing that one may be able to use learnt encoders and decoders on channels where no predefined codes are known.

Besides these encouraging aspects, there are important concerns that the authors are encouraged to address; in particular, reviewers noted that the main contribution of this paper is mostly on the learnt encoding/decoding scheme rather than in the replacement of Viterbi/BCJR. Also, complexity should be taken into account when comparing different decoding schemes.

Overall, the AC leans towards acceptance, since this paper may trigger further research in this direction.

(optional) Comment on this decision.


The authors show evidence that an RL agent with a new neural architecture with an external memory is superior on a version of the concentration game to a baseline.   However, other works have proposed neural architectures with episodic memories, and the reviewers feel that the proposed model was not adequately compared to these.  Furthermore, there are concerns about the novelty of the proposed model.

(optional) Comment on this decision.


The paper sets out to analyze the problem of exploding gradients in deep nets which is of fundamental importance. Reviewers largely acknowledge the novelty of the main ideas in the paper towards this goal, however it is also strongly felt that the writing/presentation of the paper needs significant improvement to make it into a coherent and clean story before it can be published. There are also some concerns on networks used in the experiments not being close to practice.

I recommend invitation to the workshop track as it has novel ideas and will likely generate interesting discussion.

(optional) Comment on this decision.


There are some interesting ideas discussed in the paper, but the reviewers expressed difficulty understanding the motivation and the theoretical results. The experiments do not seem convincing in showing that SQDML achieves significant gains. Overall, the the paper needs either stronger and clearer theoretical results, or more convincing experiments for publication at ICLR.

(optional) Comment on this decision.


This paper presents a fairly straightforward algorithm for learning a set of sub-controllers that can be re-used between tasks.  The development of these concepts in a relatively clear way is a nice contribution.  However, the real problem is how niche the setup is.  However, it's over the bar in general.

(optional) Comment on this decision.


This paper is not aimed at introducing new methodologies (and does not claim to do so), but instead it aims at presenting a well-executed empirical study. The presentation and outcomes of this study are quite instructive, and with the ever-growing list of academic papers, this kind of studies are a useful regularizer.

(optional) Comment on this decision.


The reviewers agreed that while this is a well-written paper, it is low on novelty and does not make a substantial enough contribution. They also pointed out that although the reported MNIST results are highly competitive, possibly due to the use of a powerful ResNet decoder, the CIFAR10/ImageNet results are underwhelming.

(optional) Comment on this decision.


PROS:
1. Good results on CLEVER datasets
2. Writing is clear
3. The MAC unit is novel and interesting.
4. Ablation experiments are helpful

CONS:
The authors overstate the degree to which they are doing "sound" and "transparent" reasoning.  In particular statements such as "Most neural networks are essentially very large correlation engines that will hone in on any statistical, potentially spurious pattern that allows them to model the observed data more accurately. In contrast, we seek to create a model structure that requires combining sound inference steps to solve a problem instance." I think are not supported.  As far as I can tell, the authors' do not show that the steps of these solutions are really doing inference in any sound way

I also found the interpretability section to be a bit unconvincing.  The reviewers and I discussed this and there was some attempt to assess what the operations were actually doing but it is not clear how the language and the image attention are linked.

I wonder whether the learned control activations are abstract and re-used across problems the way that the accompanying functional solution's primitives are.  Have you looked at how similar the controls are across problems which are identical except for a different choice of attributes?  To me, one of the hallmarks of a truly "compositional" solution is one in which the pieces are re-used across problems, not just that there is some sequence of explicit control activations used to solve each individual problem.

(optional) Comment on this decision.


The paper studied defenses against adversarial examples by training a GAN and, at inference time, finding the GAN-generated sample that is nearest to the (adversarial) input example. Next, it classifies the generated example rather than the input example. This defense is interesting and novel. The CelebA experiments the authors added in their revision suggest that the defense can be effective on high-resolution RGB images.

(optional) Comment on this decision.


Dear authors,

I agree with the reviewers that the paper tries to do several things at once and the results are not that convincing. Overall, this work is mostly incremental, which is fine if there is no issue in the execution. Thus, I regret to inform you that this paper will not be accepted to ICLR.

(optional) Comment on this decision.


While the reviewers considered the basic idea of adding supervision intermediate to differentiable programming style architectures to be interesting and worthy of effort, they were unsure if
1: the proposed abstractions for discussing ntm and nram are well motivated/more generally applicable
2: the methods used in this work to give intermediate supervision are more generally applicable

(optional) Comment on this decision.


This paper seemingly joins a cohort of ICLR submissions which attempt to port mature concepts from physics to machine learning, make a complex and non-trivial theoretical contribution, and fall short on the empirical front. The one aspect that sets this apart from its peers is that the reviewers agree that the theoretical contribution of this work is clear, interesting, and highly non-trivial. While the experiment sections (MNIST!) is indubitably weak, when treating this as a primarily theoretical contribution, the reviewers (in particular 6 and 3) are happy to suggest that the paper is worth reading. Taking this into account, and discounting somewhat the short (and, by their own admission, uncertain) assessment of reviewer 5, I am leaning  towards pushing for the acceptance of this paper. At very least, it would be a shame not to accept it to the workshop track, as this is by far the strongest paper of this type submitted to this conference.

(optional) Comment on this decision.


Pros and cons of the paper can be summarized as follows:

Pros:
* The underlying idea may be interesting
* Results are reasonably strong on the test set used

Cons:
* Testing on the single dataset indicates that the model may be of limited applicability
* As noted by reviewer 2, core parts of the paper are extremely difficult to understand, and the author response did little to assuage these concerns
* There is little mathematical notation, which compounds the problems of clarity

After reading the method section of the paper, I agree with reviewer 2: there are serious clarity issues here. As a result, I do cannot recommend that this paper be accepted to ICLR in its current form. I would suggest the authors define their method precisely in mathematical notation in a future submission.

(optional) Comment on this decision.


First off, this was a difficult paper to decide on. There was some vigorous discussion on the paper centering around the choices that were available to the conv-nets.  The author's strongly emphasized the improvements on the PTB task.

For my part, I think the method is very compelling -- sharing weights for all the models we are optimizing on seems like a great idea -- and that we can make it work is even more interesting. So from this point of view, I think its a novel contribution and worth accepting.

On the other hand, I'm likely to agree with some of the motivations behind the questions raised by R3. Are all the choices really necessary ? perhaps the gains came from just a couple of things like number of skip connections and channels, etc. That exploration is useful. On the flip side, I think it may be an irrelevant question -- the model is able to make the correct decisions from a big set.

The authors emphasize the language modelling part, but for me, this was actually less compelling. The authors use some of the tricks from Merity in their model training (perplexity 52.8), and as a result are already using some techniques that produces better results. Further, PTB is a regularization game -- and that's not really the claim of this paper. Although, one could argue that weight sharing between different models can produce an ensembling / regularization effect and those gains may show up on PTB. A much more compelling story would have been to show that this method works on a large dataset where the impact of the architecture cannot be conflated with controlling overfitting better.

As a result, this puts the paper on the fence for me; even though I very much like the idea. Polishing the paper and making a more convincing case for both the CNNs and RNNs will make this paper a solid contribution in the future.

(optional) Comment on this decision.


The reviewers are unanimous that this is an interesting paper, but that ultimately the empirical results are not sufficiently promising to warrant the added complexity.

(optional) Comment on this decision.


This paper presents a method for using byte level convolutional networks for building text-based autoencoders.  They show that these models do well compared to RNN-based methods which model text in a sequence.  Evaluation is solely based on byte level prediction error.   The committee feels that the paper would have been stronger if evaluation was on some actual task (say summarization, Miao and Blunsom, for example) and show that it works as well as RNNs, the paper would have been stronger.

(optional) Comment on this decision.


This paper was reviewed by 3 expert reviewers and all three recommend rejection. This AC agrees with their assessment.

(optional) Comment on this decision.


Dear authors,

Despite the desirable goal, that is to move away from regularization in parameter space toward regularization in function space, the reviewers all thought that the paper was not convincing enough, both in the choice of the particular regularization and in the experimental section.

While I appreciate that you have done a major rework of the paper, the rebuttal period should not be used for that and we can not expect the reviewers to do a complete re-review of a new version.

This paper thus cannot be accepted to ICLR.

(optional) Comment on this decision.


The paper proposes a method for accented speech generation using GANs.
The reviewers have pointed out the problems in the justification of the method (e.g. the need for using policy gradients with a differentiable objective) as well as its evaluation.

(optional) Comment on this decision.


Reviewers recognize that the method proposed is somewhat novel but have strong reservations on the experimental evaluation. Discussion of some relevant papers is also missing (eg, Li et al, 2017 : ALICE). The authors have not responded to the many concerns expressed by the reviewers.

(optional) Comment on this decision.


As stated by reviewer 3 "This paper introduces a new model to perform image classification with limited computational resources at test time. The model is based on a multi-scale convolutional neural network similar to the neural fabric (Saxena and Verbeek 2016), but with dense connections (Huang et al., 2017) and with a classifier at each layer."
As stated by reviewer 2 "My only major concern is the degree of technical novelty with respect to the original DenseNet paper of Huang et al. (2017). ".  The authors assert novelty in the sense that they provide a solution to improve computational efficiency and focus on this aspect of the problem. Overall, the technical innovation is not huge, but I think this could be a very useful idea in practice.

(optional) Comment on this decision.


The paper addresses the important question of determining the intrinsic dimensionality, but there remain several issue, which make the paper not ready at this point: unclear exposition, lack of contextualisation of existing work and seemingly limited insights. The reviewers have provided many suggestions to improve the paper which we hope will be useful to improve the paper.

(optional) Comment on this decision.


This paper addresses the very important problem of ensuring that sensitive training data remain private. It proposes an attack whereby the attacker can reconstruct information about the training data given only the trained classifier and an auxiliary dataset. If done well, such an attack would be a useful contribution that helps make discussion of differential privacy more complete. But as the reviewers pointed out, it's not clear from the paper whether the attack has succeeded. It works only when the auxiliary data is very similar to the training data, and it's not clear if it leaks information about the training set itself, or is just summarizing the auxiliary data. This work doesn't seem quite ready for publication, but could be a strong paper if it's convincingly demonstrated that information about the training set has been leaked.

(optional) Comment on this decision.


The results in the paper are interesting, and the modifications improve the paper further. Reviewers found teh paper interesting and potentailly applicable to many models.

(optional) Comment on this decision.


This paper proposes a general regularization algorithm which builds on the dropout idea. This is a very significant topic. The overall motivation is good, but the specific design choices are less well motivated over, for example, ad-hoc choices. Some concerns remain after the post-rebuttal discussion with the reviewers: the improvement is incremental in terms of concepts and methodology, the clarity needs to be improved and the experiments are somehow weak.
In summary, the main idea and research direction is interesting, but the attempted generality of the algorithm and the significance of the area call for a more clear and convincing presentation.

(optional) Comment on this decision.


This paper presents a learned inference architecture which generalizes HMC. It defines a parameterized family of MCMC transition operators which share the volume preserving structure of HMC updates, which allows the acceptance ratio to be computed efficiently. Experiments show that the learned operators are able to mix significantly faster on some simple toy examples, and evidence is presented that it can improve posterior inference for a deep latent variable model. This paper has not quite demonstrated usefulness of the method, but it is still a good proof of concept for adaptive extensions of HMC.

(optional) Comment on this decision.


Dear authors,

While the reviewers appreciated the idea, the significant loss of accuracy was a concern. Even though you made significant changes to the submission, it is unfortunately unrealistic to ask the reviewers to do another review of a heavily modified version in such a short amount of time.

Thus, I cannot accept this paper for publication but I encourage you to address the reviewers' concerns and resubmit at a later conference.

(optional) Comment on this decision.


This paper address the increasingly studied problem of predictions over long-term horizons. Despite this, and the important updates from the authors, the paper is not yeat ready and improvements identified include more control over the fair comparisons, improved clarity in exposition.

(optional) Comment on this decision.


The paper presents an approach for learning continuous-valued vector representations combining multiple input feature sets of different types, in both unsupervised and supervised settings.  The revised paper is a merger of the original submission and another ICLR submission.  This meta-review takes into account all of the comments on both submissions and revisions.

The merged paper is an improvement over the two separate ones.  However, the contribution over previous work is still a bit unclear.  It still does not sufficiently compare to/discuss in the context of other recent work on combining multiple feature groups.

The experiments are also quite limited.  The idea is introduced as extremely general, but the experiments focus on a small number of specific tasks, some of them non-standard.

(optional) Comment on this decision.


Dear authors,

While I appreciate the result that a convolutional layer can have full rank output, this allowing a dataset to be classified perfectly under mild conditions, the fact that all reviewers expressed concern about the statement is an indication that the presentation sill needs quite a bit of work.

Thus, I recommend it as an ICLR workshop paper.

(optional) Comment on this decision.


The paper makes overly strong claims, too weakly supported by a hard to follow and insufficiently rigorous mathematical argument. Connections with a large body of relevant prior literature are missing.

(optional) Comment on this decision.


R3 summarizes the reasons for the decision on this paper: "The universal learning algorithm approximator result is a nice result, although I do not agree with the other reviewer that it is a  "significant contribution to the theoretical understanding of meta-learning," which the authors have reinforced (although it can probably be considered a significant contribution to the theoretical understanding of MAML in particular). Expressivity of the model or algorithm is far from the main or most significant consideration in a machine learning problem, even in the standard supervised learning scenario. Questions pertaining to issues such as optimization and model selection are just as, if not more, important. These sorts of ideas are explored in the empirical part of the paper, but I did not find the actual experiments in this section to be very compelling. Still, I think the universal learning algorithm approximator result is sufficient on its own for the paper to be accepted."

(optional) Comment on this decision.


The reviewers agree that the proposed method is theoretically interesting, but disagree on whether it has been properly experimentally validated.   My view is that the the theoretical contribution is interesting enough to warrant inclusion in the conference, and so I will err on the side of accepting.

(optional) Comment on this decision.


This paper shows that batch normalization can be cast as approximate inference in deep neural networks.  This is an appealing result as batch normalization is used in practice in a wide variety of models.   The reviewers found the paper well written and easy to understand and were motivated by underlying idea.  However, they found the empirical analysis lacking and found that there was not enough detail in the main text to verify whether the claims were true.

The authors empirically compared to a recent method showing that dropout can be cast as approximate inference with the claim that by transitivity they were comparing to a variety of recent methods.  AnonReviewer1 casts significant doubt on the results of that work.  This is very unfortunate and not the fault of the authors of this paper.  The authors have since gone to great length to compare to Louizos and Welling, 2017.  Unfortunately, that comparison doesn't appear to be complete in the manuscript.

The main text was also lacking specific detail relating to fundamental parts of the proposed method (noted by all reviewers).

Overall, this paper seems to be tremendously promising and the underlying idea potentially very impactful.  However, given the reviews, it doesn't seem that this paper would achieve its potential impact.  The response from the authors is appreciated and goes a long way to improving the paper.  Taking the reviews into account, adding specific detail about the methodology and model (e.g. the prior) and completing careful empirical analysis will make this a strong paper that should be much more impactful.

(optional) Comment on this decision.


The reviewer reactions to the initial manuscript were generally positive.  They considered the paper to be well written and clear, providing an original contribution to learning to cooperate in multi-agent deep RL in imperfect domains.  The reviewers raised a number of specific issues to address, including improved definitions and descriptions, and proper citations of related work.  The authors have substantially  revised the manuscript to address most or all of these issues.  At this point, the only knock on this paper is that the findings seemed unsurprising from a game-theoretic or deep learning point of view.

Pros: algorithmic contribution, technical quality, clarity
Cons: no real surprises

(optional) Comment on this decision.


The reviewers were quite unanimous in their assessment of this paper.

PROS:
1. The paper is relatively clear and the approach makes sense
2. The paper presents and evaluates a collection of approaches to speed learning of policies for manipulation tasks.
3. Improving the data efficiency of learning algorithms and enabling learning across multiple robots is important for practical use in robot manipulation.
4. The multi-stage structure of manipulation is nicely exploited in reward shaping and distribution of starting states for training.

CONS
1. Lack of novelty e.g. wrt to Finn et al. in "Deep Spatial Autoencoders for Visuomotor Learning"
2. The techniques of asynchronous update and multiple replay steps may have limited novelty, building closely on previous work and applying it to this new problem.
3. The contribution on reward shaping would benefit from a more detailed description and investigation.
4. There is concern that results may be specific to the chosen task.
5. Experiments using real robots are needed for practical evaluation.

(optional) Comment on this decision.


Pros:
+ Clearly written paper.

Cons:
- Limited empirical evaluation: paper should compare to first-order methods with well-tuned hyperparameters, since the block Hessian-free hyperparameters likely were well tuned, and plots of convergence as a function of time need to be included.
- Somewhat limited novelty in that block-diagonal curvature approximations have been used before, though the application to Hessian-free optimization is new.

The reviewers liked the clear description of the proposed algorithm and well-structured paper, but after discussion were not prepared to accept it primarily because (1) they wanted to see algorithmic comparisons in terms of convergence vs. time in addition to the convergence vs. updates that were provided; (2) they wanted more assurance that the baseline first-order optimizers had been carefully tuned; and (3) they wanted results on larger scale tasks.

(optional) Comment on this decision.


The paper proposes learning the prior for AAEs by training a code-generator that is seeded by the standard Gaussian distribution and whose output is taken as the prior. The code generator is trained by minimizing the GAN loss b/w the distribution coming out of the decoder and the real image distribution. The paper also modifies the AAE by replacing the L2 loss in pixel domain with "learned similarity metric" loss inspired by the earlier work (Larsen et al., 2015).

The contribution of the paper is specific to AAE which makes the scope narrow. Even there, the benefits of learning the prior using the proposed method are not clear. Experiments make two claims: (i) improved image generation over AAE, (ii) improved "disentanglement".

Towards (i), the paper compares images generated by AAE with those generated by their model. However, it is not clear if the improved generation quality is due to the use of decoder loss on the learned similarity metric (Larsen at al, 2015), or due to the use of GAN loss in the image space (ie, just having GAN loss over decoder's output w/o having a code generator), or due to learning the prior which is the main contribution of the paper. This has also been hinted at by AnonReviewer1. Hence, it's not clear if the sharper generated images are really due to the learned prior.

Towards (ii), the paper uses InfoGAN inspired objective to generate class conditional images. It shows the class-conditional generated images for AAE and the proposed method. Here AAE is also trained on "learned similarity metric" and augmented with similar InfoGAN type objective so the only difference is in the prior. Authors say the performance of both models is similar on MNIST and SVHN but on CIFAR their model with "learned prior" generates images that match the conditioned-upon labels better. However this claim is also subjective/qualitative and even if true, it is not clear if this is due to learned prior or due to the extra GAN discriminator loss in the image-space -- in other words, how do the results look for AAE + a discriminator in the image space, just like in the proposed model but without a code generator?

The t-SNE plots for the learned prior are also shown but they are only shown when InfoGAN loss is added. The same plots are not shown for AAE with added InfoGAN loss so it is difficult to know the benefits of learning the code-generator as proposed.

Overall, I feel the scope of the paper is narrow and the benefits of learning the prior using the method proposed in the paper are not clearly established by the reported experiments. I am hesitant to recommend acceptance to the main conference in its current form.

(optional) Comment on this decision.


The paper introduces an interesting family of two-player zero-sum games with tunable complexity, called Erdos-Selfridge-Spencer games, as a new domain for RL.  The authors report on extensive empirical results using a wide variety of training methods, including supervised learning and several flavors of RL (PPO, A2C, DQN) as well as single-agent vs. multi-agent training.  The reviewers also appear to agree that the method appears to be technically correct, clearly written, and easy to read.

A drawback of the paper is that it does not make a *significant* contribution to the field.  In combing through the reviewer comments, none of them identify a significant contribution.  Even in the text of the paper, the authors do not anywhere claim to have made a significant contribution. As the paper is still interesting, the committee would like to recommend this for the workshop track.

Pros:
        Interesting domain with tunable complexity
        High-quality extensive empirical results
        Writing is clear

Cons:
        Lacks a significant contribution
        Appears to overlook self-play, the dominant RL training paradigm for decades (multiagent training appears to be related but different)
        Per Reviewer3, "I remain unconvinced that these games are good general tests for Deep RL"

(optional) Comment on this decision.


This paper explores what might be characterized as an adaptive form of ZoneOut.
With the improvements and clarifications added to the paper during the rebuttal the paper could be accepted.

(optional) Comment on this decision.


The pros and cons of the paper cited by the reviewers can be summarized as follows:

Pros:
- good problem, NL2SQL is an important task given how dominant SQL is
- incorporating a grammar ("sketch") is a sensible improvement.

Cons:
- The dataset used makes very strong simplification assumptions (that every token is an SQL keyword or appears in the NL)
- The use of a grammar in the context of semantic parsing is not novel, and no empirical comparison is made against other reasonable recent baselines that do so (e.g. Rabinovich et al. 2017).

Overall, the paper seems to do some engineering for the task of generating SQL, but without an empirical comparison to other general-purpose architectures that incorporate grammars in a similar way, the results seem incomplete, and thus I cannot recommend that the paper be accepted at this time.

(optional) Comment on this decision.


This method makes a connection between evolutionary and variational methods in a particular model.  This is a good contribution, but there has been little effort to position it in comparison to standard methods that do the same thing, showing relative strengths and weaknesses.

Also, please shorten the abstract.

(optional) Comment on this decision.


The reviewers agree this is an interesting paper with interesting ideas, but is not ready for publication in its current shape. In particular, there is a need for strong empirical results.

(optional) Comment on this decision.


The experiments are not sufficient to support the claim. The authors plan to improve it for future publication.

(optional) Comment on this decision.


This paper presents a new multi-document summarization task of trying to write a wikipedia article based on its sources. Reviewers found the paper and the task clear to understand and well-explained. The modeling aspects are clear as well, although lacking justification. Reviewers are split on the originality of the task, saying that it is certainly big, but wondering if that makes it difficult to compare with. The main split was the feeling that "the paper presents strong quantitative results and qualitative examples. " versus a frustration that the experimental results did not take into account extractive baselines or analysis. However the authors provide a significantly updated version of the work targeting many of these concerns, which does alleviate some of the main issues. For these reasons, despite one low review, my recommendation is that this work be accepted as a very interesting contribution.

(optional) Comment on this decision.


A version of GCNs of Kipf and Welling is introduced with (1) no non-linearity; (2) a basic form of (softmax) attention over neighbors where the attention scores are computed as the cosine of endpoints' representations (scaled with a single learned scalar). There is a moderate improvement on Citeseer, Cora, Pubmed.

Since the use of gates with GCNs / Graph neural networks is becoming increasingly common (starting perhaps with GGSNNs of Li et al, ICLR 2016)) and using attention in graph neural networks is also not new  (see reviews and comments for references), the novelty is very limited.  In order to make the submission more convincing the authors could: (1) present results on harder datasets; (2)  carefully evaluate against other forms of attention (i.e. previous work).

As it stands, though it is interesting to see that such simple model performs well on the three datasets, I do not see it as an ICLR paper.

Pros:
-- a simple model, achieves results close / on par with state of the art

Cons:
-- limited originality
-- either results on harder datasets or / and evaluation agains other forms of attention (i.e. previous work) are needed

(optional) Comment on this decision.


The paper proposes a modification to the Transformer network, which mostly consists in changing how the attention heads are combined. The contribution is incremental, and its novelty is limited. The results demonstrate an improvement over the baseline at the cost of a more complicated training procedure with more hyper-parameters, and it is possible that with similar tuning the baseline performance could be improved in a similar way.

(optional) Comment on this decision.


This paper deals with the important topic of learning better graph representations and shows promise in helping to detect critical substructures of graph that would help with the interpretability of representations. Unfortunately, this work fails to accurately portray how it relates to previous work (in particular, Niepert et al, Kipf et al, Duvenaud et al) and falls short of providing clear and convincing explanations of what it can do that these models can't, without including all of them in experimental comparisons.

(optional) Comment on this decision.


An empirical study of weight sharing for neural networks is interesting, but all of the reviewers found the experiments insufficient without enough baseline comparisons.

(optional) Comment on this decision.


The paper studies a dropout variant, called fraternal dropout. The paper is somewhat incremental in that the proposed approach is closely related to expectation linear dropout. Having said that, fraternal dropout does improve a state-of-the-art language model on PTB and WikiText2 by ~0.5-1.7 perplexity points. The paper is well-written and appears technically sound.

Some reviewers complain that the authors could have performed a more careful hyperparameter search on the fraternal dropout model. The authors appear to have partly addressed those concerns, which frankly, I don't really agree with either. By doing only a limited hyperparameter optimization, the authors are putting their "own" method at a disadvantage. If anything, the fact that their method gets strong performance despite this disadvantage (compared to very strong baseline models) is an argument in favor of fraternal dropout.

(optional) Comment on this decision.


R1 and R3’s  main concern was that the work was not actually outperforming existing work and therefore its advantages were unclear. R2 brought up several questions on the experiments and asked for clarification with respect to previous work. R3 had several other detailed questions for the authors. The authors did not provide a response.

(optional) Comment on this decision.


This paper describes active vision for object recognition learned in an RL framework.
Reviewers think the paper is not of sufficient quality: Insufficient detail, and insufficient evaluation.
While the authors have provided a lengthy rebuttal, the shortcomings have not yet been addressed in the paper.

(optional) Comment on this decision.


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

Pros:
* The method of combining together multiple information sources is effective
* Experimental evaluation is thorough

Cons:
* The method is a relatively minor contribution, combining together multiple existing methods to improve word embeddings. This also necessitates the model being at least as complicated as all the constituent models, which might be a barrier to practical applicability

As an auxiliary comment, the title and emphasis on computing embeddings "on the fly" is a bit puzzling. This is certainly not the first paper that is able to calculate word embeddings for unknown words (e.g. all the cited work on character-based or dictionary-based methods can do so as well). If the emphasis is calculating word embeddings just-in-time instead of ahead-of-time, then I would also expect an evaluation of the speed or memory requirements benefits of doing so. Perhaps a better title for the paper would be "integrating multiple information sources in training of word embeddings", or perhaps a more sexy paraphrase of the same.

Overall, the method seems to be solid, but the paper was pushed out by other submissions.

(optional) Comment on this decision.


Well written paper on a novel application of the local reprarametrisation trick to learn networks with discrete weights. The approach achieves state-of-the-art results.

Note: I apreciate that the authors added a comparison to the Gumbel-softmax continuous relaxation approach during the review period, following the suggestion of a reviewer. This additional comparison strengthens the paper.

(optional) Comment on this decision.


Although paper has been improved with new quantitative results and additional clarity, the reviewers agree though that larger-scale experiments would better highlight the utility of the method. There are some concerns with computational cost, despite the fact that the two networks are trained asynchronously. A baseline against a single, asynchronously trained network (multiple GPUs) would help strengthen this point. Some reviewers expressed concerns with novelty.

(optional) Comment on this decision.


While the reviewers all seem to think this is interesting and basically good work, the Reviewers are consistent and unanimous in rejecting the paper.
While the authors did provide a thorough rebuttal, the original paper did not meet the criteria and the reviewers have not changed their scores.

(optional) Comment on this decision.


R1 thought the proposed method was novel and the idea interesting. However, he/she raised concerns with consistency in the experimental validation, the trade-off between accuracy and running time, and the positioning/motivation, specifically the claim about interpretability. The authors responded to these concerns, and R1 upgraded their score. R2 didn’t raise major concerns or strengths. R3 questioned the novelty of the work and the experimental validations. All reviewers raised concerns with the writing. Though I think the work is interesting, issues raised about experiments and writing make me hesitant to go against the overall recommendation of the reviewers, which is just below the bar. I think this is a paper that could make a good workshop contribution.

(optional) Comment on this decision.


This paper incorporates attention in the PixelCNN model and shows how to use MAML to enable few-shot density estimation. The paper received mixed reviews (7,6,4). After rebuttal the first reviewer updated the score to accept. The AC shares the concern of novelty with the first reviewer. However, it is also not trivial to incorporate attention and MAML in PixelCNN, thus the AC decided to accept the paper.

(optional) Comment on this decision.


This paper presents a novel model for generating images and natural language descriptions simultaneously. The aim is to distangle representations learned for image generation by connecting them to the paired text. The reviews praise the problem setup and the mathematical formulation. However they point out significant issues with the clarity of the presentation in particular the diagrams, citations, and optimization procedure in general. They also point out issues with the experimental setup in terms of datasets used and lack of natural images for the tasks in question.  Reviews are impressively thorough and should be of use for a future submission.

(optional) Comment on this decision.


The reviewer scores are fairly close, and the comments in their reviews are likewise similar.  All reviewers indicate that they find this to be an interesting learning domain.  However, they also agree in assessing the proposed method as having limited novelty and significance.  They also critiqued the empirical evaluation as being too specific to Starcraft and not comprehensive, without providing evidence that the defogger contributes to winning at StarCraft.  The authors wrote a substantial rebuttal to the reviews, but it did not convince anyone to increase their scores.

(optional) Comment on this decision.


Proposed network compression method offers limited technical novelty over existing approaches, and empirical evaluations do not clearly demonstrate an advantage over current state-of-the-art.
Paper presentation quality also needs to be improved.

(optional) Comment on this decision.


This paper received divergent reviews (7, 3, 9). The main contributions of the paper -- that multi-agent competition serves as a natural curriculum, opponent sampling strategies, and the characterization of emergent complex strategies -- are certainly of broad interest (although the first is essentially the same observation as AlphaZero, but the different environment makes this of broader interest).

In the discussion between R2 and the authors, I am sympathetic to (a subset of) both viewpoints.

To be fair to the authors, discovery (in this case, characterization of emergent behavior) can be often difficult to quantify. R2's initial review was unnecessary harsh and combative. The points presented by R2 as evidence of poor evaluation have clear answers by the authors. It would have been better to provide suggestions for what the authors could try, rather than raise philosophical objections that the authors cannot experimentally rebut.

On the other hand, I am disappointed that the authors were asked a reasonable, specific, quantifiable request by R2 --
"By the end of Section 5.2, you allude to transfer learning phenomena. It would be nice to study these transfer effects in your results with a quantitative methodology.”
-- and they chose to respond with informal and qualitative assessments. It doesn't matter if the results are obvious visually, why not provide quantitative evaluation when it is specifically asked?

Overall, we recommend this paper for acceptance, and ask the authors to incorporate feedback from R2.

(optional) Comment on this decision.


This paper provides a comparison of different types of a memory augmented models and extends some of them to beyond their simple form. Reviewers found the paper to be clearly written, saying it "nice introduction to the topic" and noting that they "enjoyed reading this paper". In general though there was a feeling that the "substance of the work is limited". One reviewer complained that experiments were limited to small English datasets PTB and Wikitext-2 and asked why they didn't try "machine translation or speech recognition". (The author's note that they did try the Linzen dataset, and while the reviewers found the experiments impressive, the task itself felt artificial) . Another felt that the "multipop model" alone was not too large a contribution. The actual experiments in the work are well done, although given the fact that the models are known there was expectation of "more "in-depth" analysis of the different models". Overall this is a good empirical study, which shows the limited gains achieved by these models, a nevertheless useful piece of information for those working in this area.

(optional) Comment on this decision.


All three reviewers recommended rejection and there was no rebuttal.

(optional) Comment on this decision.


The paper has some potentially interesting ideas but it feels very preliminary. The experimental section in particular needs a lot more work.

(optional) Comment on this decision.


The general consensus is that this method provides a practical and interesting approach to unsupervised domain adaptation. One reviewer had concerns with comparing to state of the art baselines, but those have been addressed in the revision.

There were also issues concerning correctness due to a typo. Based on the responses, and on the pseudocode, it seems like there wasn't an issue with the results, just in the way the entropy objective was reported.

You may want to consider reporting the example given by reviewer 2 as a negative example where you expect the method to fail. This will be helpful for researchers using and building on your paper.

(optional) Comment on this decision.


Biological memory systems are grounded in spatial representation and spatial memory, so neural methods for spatial memory are highly interesting. The proposed method is novel, well-designed and the empirical results are good on unseen environments, although the noise model may be too weak. Moreover, it would have been great to evaluate this method on real data rather than in simulation.

(optional) Comment on this decision.


This paper proposes a method for performing stochastic variational inference for bidirectional LSTMs through introducing an additional latent variable that induces a dependence between the forward and backward directions.  The authors demonstrate that their method achieves very strong empirical performance (log-likelihood on test data) on the benchmark TIMIT and BLIZZARD datasets.

The paper is borderline in terms of scores with a 7, 6 and 4.  Unfortunately the highest rating also corresponds to the least thorough review and that review seems to indicate that the reviewer found the technical exposition confusing.  AnonReviewer2 also found the writing confusing and discovered mistakes in the technical aspects of the paper (e.g. in Eq 1).  Unfortunately, the reviewer who seemed to find the paper most easy to understand also gave the lowest score.  A trend among the reviewers and anonymous comments was that the paper didn't do a good enough job of placing itself in the context of related work (Goyal et. al, "Z-forcing") in particular.  The authors seem to have addressed this (curiously in an anonymous link and not in an updated manuscript) but the manuscript itself has not been updated.

In general, this paper presents an interesting idea with strong empirical results.   The paper itself is not well composed, however, and can be improved upon significantly.  Taking the reviews into account and including a better treatment of related work in writing and empirically will make this a much stronger paper.

Pros:
- Strong empirical performance (log-likelihood on test data)
- A neat idea
- Deep generative models are of great interest to the community

Cons:
- Incremental in relation to Goyal et al., 2017
- Needs better treatment of related work
- The writing is confusing and the technical exposition is not clear enough

(optional) Comment on this decision.


This paper generated quite a bit of controversy among reviewers. The main claim of the paper is that Adam and related optimizers are broken because their "weight decay" regularization is not actually weight decay. It proposes to modify Adam to decay all weights the same regardless of the gradient variances.

Calling Adam's weight decay mechanism a mistake seems very far-fetched to me. Neural net optimization researchers are well aware of the connection between weight decay and L2 regularization and the fact that they don't correspond in preconditioned methods. L2 regularization is basically the only justification I have heard for weight decay, and despite rejecting this interpretation, the paper does not provide an alternative justification.

Decoupling the optimization from the cost function is a well-established principle. This abstraction barrier is not completely clean (e.g. gradient noise has well-known regularization effects), and the experiments of this paper perhaps provide evidence that the choices may be coupled in this case. This is an interesting finding, and probably worth following up on. However, the paper seems to sweep the "decoupling optimization and cost" issue under the carpet and take for granted that the decay rate is what should be held fixed. All three reviewers found the presentation to be misleading, and I would agree with them. While there may be an interesting contribution here, I cannot endorse the paper as-is.

(optional) Comment on this decision.


the paper validates the benefit of multi-task learning on MNIST datasets, which is not sufficient for ICLR publication

(optional) Comment on this decision.


This paper makes progress on the open problem of text generation with GANs, by a sensible combination of novel approaches.   The method was described clearly, and is somewhat original.   The only problem is the hand-engineering of the masking setup.

(optional) Comment on this decision.


This paper introduces a framework for specifying the model search space for exploring over the space of architectures and hyperparameters in deep learning models (often referred to as architecture search).  Optimizing over complex architectures is a challenging problem that has received significant attention as deep learning models become more exotic and complex.  This work helps to develop a methodology for describing and exploring the complex space of architectures, which is a challenging problem.  The authors demonstrate that their method helps to structure the search over hyperparameters using sequential model based optimization and Monte Carlo tree search.

The paper is well written and easy to follow.  However, the level of technical innovation is low and the experiments don't really demonstrate the merits of the method over existing strategies.  One reviewer took issue with the treatment of related work.  The underlying idea is compelling and addresses an open question that is of great interest currently.  However, without experiments demonstrating that this works better than, e.g., the specification in the hyperopt package, it is difficult to assess the contribution.  The authors must do a better job of placing this contributing in the context of existing literature and empirically demonstrate its advantages.  The presented experiments show that the method works in a limited setting and don't explore optimization over complex spaces (i.e. over architectures - e.g. number of layers, regularization for each layer, type of each layer, etc.).  There's nothing presented empirically that hasn't been possible with standard Bayesian optimization techniques.

This is a great start, but it needs more justification empirically (or theoretically).

Pros:
- Addresses an important and pertinent problem - architecture search for deep learning
- Provides an intuitive and interesting solution to specifying the architecture search problem
- Well written and clear

Cons:
- The empirical analysis does not demonstrate the advantages of this approach over existing literature
- Needs to place itself better in the context of existing literature

(optional) Comment on this decision.


While the reviewers agree that this paper does provide a contribution, it is small and does overlap with several concurrent works. it is a bit hand-engineered.
The authors have provided a lengthy rebuttal, but the final reviews are not strong enough.

(optional) Comment on this decision.


The paper proposes a new model called differential decision tree which captures the benefits of decision trees and VAEs. They evaluate the method only on the MNIST dataset. The reviewers thus rightly complain that the evaluation is thus insufficient and one also questions its technical novelty.

(optional) Comment on this decision.


All the reviewers agree that this is an interesting paper but have concerns about readability and presentation. There is also concern that many results are speculative and not concretely tested. I recommend the authors to carefully investigate their claims with stronger experiments and submit it to another venue. I recommend presenting at ICLR workshop to obtain further feedback.

(optional) Comment on this decision.


I recommend acceptance. The two positive reviews point out the theoretical contributions. The authors have responded extensively to the negative review and I see no serious flaw as claimed by the negative review.

(optional) Comment on this decision.


This manuscript was reviewed by 3 expert reviewers and their evaluation is generally positive. The authors have responded to the questions asked and the reviewers are satisfied with the responses. Although the 2D environments are underwhelming (compared to 3D environments such as SUNCG, Doom, Thor, etc), one thing that distinguishes this paper from other concurrent submissions on the similar topics is the demonstration that "words learned only from a VQA-style supervision condition can be successfully interpreted in an instruction-following setting."

(optional) Comment on this decision.


Good contribution. There was a (heated) debate over this paper but the authors stayed calm and patiently addressed all comments and supplied additional evaluations, etc.

(optional) Comment on this decision.


This paper is lacking in terms of clarity and experimentation, and would require a lot of additional work to bring it to the standards of any high quality venue.

(optional) Comment on this decision.


This paper proposes a method for training an neural network to operate stack-based mechanism in order to act as a CFG parser in order to, eventually, improve program synthesis and program induction systems. The reviewers agreed that the paper was compelling and well supported empirically, although one reviewer suggested that analysis of empirical results could stand some improvement. The reviewers were not able to achieve a clear consensus on the paper, but given that the most negative reviewer has also declared themselves the least confident in their assessment, I am happy to recommend acceptance on the basis of the median rather than mean score.

(optional) Comment on this decision.


All of the reviewers agree that the paper presents strong experimental results on continuous control benchmarks. The reviewers raised concerns regarding the analysis of the behavior of the algorithm, the possible impact of the technique, and requested more references and comparison with related work. The paper has significantly improved since the initial submission, but still not able fully satisfactory to the reviewers, partly due to the large extent of the changes needed.

(optional) Comment on this decision.


The authors propose to use attention over past time steps to try and solve the gradient flow problem in learning recurrent neural networks. Attention is performed over a subset of past states by a hueristic that boils to selecting best time-steps.

I agree with the authors that they offer a lot of comparisons, but like the reviewers, I am inclined to find the experiments not very convincing of the arguments they are attempting to make.  The model that they propose has similarities to seq2seq in that they use attention to pass more information in the forward pass; in a sense this is a seq2seq model with the same encoder and decoder, and there are parallels to self-attention. The model also has similarities to clockwork RNNs and other skip connection methods.. However, the experiments offered to not tease out these effects. It is unsurprising that a fixed size neural network is unable to do a long copy task perfectly, but an attention model can. What would have been more interesting would have been to explore if other RNN models could have done so. The experiments on pMNIST aren't really compelling as the baselines are far from SOTA (example: https://arxiv.org/pdf/1606.01305.pdf report 0.041 error rate (95.9% test acc) with LSTMs and regularization).  Text8 also shows worse results in full BPTT on LSTM.  If BPTT is consistently better than this method, it defeats the argument that gradient explosion and forgetting over long sequences is a problem for RNNs (one of the motivations offered for this attention model).

(optional) Comment on this decision.


While novelty is not the main strength of this paper, there is consensus that presentation is clear and the experimental results are convincing. Given the practical importance of designing and benchmarking methods to compactify deep nets, the paper deserves to be presented at ICLR-2018.

(optional) Comment on this decision.


The paper proposes a new convolutional network architecture, called CrescendoNet. Whilst achieving competitive performance on CIFAR-10 and SVHN, the accuracy of the proposed model on CIFAR-100 is substantially lower than that of state-of-the-art models with fewer parameters; the paper presents no experimental results on ImageNet. The proposed architecture does not provide clear new insights or successful new design principles. This makes it unlikely the current manuscript will have a lot of impact.

(optional) Comment on this decision.


The main ideas of the paper are promising, but there remain several important concerns.  The initial submission contained significant typos and missing proofs.  In the revision, these have been added, but the main article before references and appendices now stands at 11 pages.  It is therefore lacking focus and readability required for presentation in this venue.

From AnonReviewer2: "The work is quite straightforward, but improves over similar work in the GAN space by Nowozin et al. by using Ruderman's tighter variational representation instead of Nguyen's one."  This comment raises concerns about the overall novelty of the proposed approach.

AnonReviewer1 raised concerns about the suitability of the chosen experimental setup, namely two Gaussians.  It is not clear that the current experiments provide a sufficiently detailed evaluation in the context of a highly competitive venue like ICLR.  Given that the theoretical contribution was incomplete in the initial submission and has not been fully verified, empirical evaluation has a higher importance in the assessment of this paper.

(optional) Comment on this decision.


The reviewers agreed that the paper was too long (more than twice the recommended page limit not counting the appendix) and difficult to follow. They also pointed out that its central idea of learning the noise distribution in a VAE was not novel. While the shortened version uploaded by the authors looks like a step in the right direction, it was not sufficient to convince the reviewers.

(optional) Comment on this decision.


The paper presents a modification of the Winograd convolution algorithm that reduces the number of multiplications in a forward pass of a CNN with minimal loss of accuracy. The reviewers brought up the strong results, the readability of the paper, and the thoroughness of the experiments. One concern brought up was the applicability to deeper network structures. This was acknowledged by the authors to be a subject of future work. Another issue raised was the question of theoretical vs. actual speedup. Again, this was acknowledged by the authors to be an eventual goal but subject to further systems work and architecture optimizations. The reviewers were consistent in their support of the paper. I follow their recommendation: Accept.

(optional) Comment on this decision.


This paper does an excellent job at helping to clarify the relationship between various, recently proposed GAN models. The empirical contribution is small, but the KID metric will hopefully be a useful one for researchers. It would be really useful to show that it maintains its advantage when the dimensionality of the images increases (e.g., on Imagenet 128x128).

(optional) Comment on this decision.


There was certainly some interest in this paper which investigates learning latent models of the environment for model-based planning, particularly articulated by Reviewer3.  However, the bulk of reviewer remarks focused on negatives, such as:

--The model-based approach is disappointing compared to the model-free approach.
--The idea of learning a model based on the features from a model-free agent seems novel but lacks significance in that the results are not very compelling.
--I feel the paper overstates the results in saying that the learned forward model is usable in MCTS.
-- the paper in it’s current form is not written well and does not contain strong enough empirical results

(optional) Comment on this decision.


Pros
-- A novel way to incorporate LM into an end-to-end model, with good adaptation results.

Cons
-- Lacks results on public corpora or results are not close to SOTA (e.g., for Librispeech).

Given the reviews, it is clear that the experimental evaluations can be improved. But the presented approach is novel and interesting. Therefore I am recommending the paper to the workshop track.

(optional) Comment on this decision.


Reviewers always find problems in papers like this.

AnonReviewer1 would have preferred to have seen a study of traditional architectures, rather than fully connected ones, which are now less frequently used. They thought the paper was too long, the figures too cluttered, and were not convinced by the discussion around linear v. elliptical trajectories.

I appreciate the need for a parametrizable architecture, although it may not be justified to translate these insights to other architectures, and then the fact that fully connected architectures are less common undermines the impact of the work. I don't find the length a problem, and I don't find the figures a problem.

After the back and forth, AnonReviewer3 believes that there are data compatibility issues associated with the studied transformations and that non-linear transformations would have been more informative. I find the reviewers response to be convincing.

AnonReviewer2 is strongly in favor of acceptance, finding the work exhaustive, interesting, and of high quality. I'm inclined to agree.

(optional) Comment on this decision.


This paper addresses the question of how to solve image super-resolution, building on a connection between sparse regularization and neural networks.
Reviewers agreed that this paper needs to be rewritten, taking into account recent work in the area and significantly improving the grammar. The AC thus recommends rejection at this time.

(optional) Comment on this decision.


This work replaces the RNN layer of square with a self-attention and convolution, achieving a big speed up and performance gains, particularly with data augmentation. The work is mostly clear presented, one reviewer found it "well-written" although there was a complaint the work did not clear separate out the novel aspects. In terms of results the work is clearly of high quality, producing top numbers on the shared task. There were some initial complaints of only using the SQuAD dataset, but the authors have now included additional results that diversify the experiments. Perhaps the largest concern is novelty. The idea of non-RNN self-attention is now widely known, and there are several systems that are applying it. Reviewers felt that while this system does it well, it is maybe less novel or significant than other possible work.

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This paper presents a novel and interesting sketch-based approach to conditional program generation. I will say upfront that it is worth of acceptance, based on its contribution and the positivity of the reviews. I am annoyed to see that the review process has not called out the authors' lack of references to the decently body of existing work on generating structure on neural sketch programming and on generating under grammatical constraint. The authors' will need look no further than the proceedings of the *ACL conferences of the last few years to find papers such as:
* Dyer, Chris, et al. "Recurrent Neural Network Grammars." Proceedings of NAACL-HLT (2016).
* Kuncoro, Adhiguna, et al. "What Do Recurrent Neural Network Grammars Learn About Syntax?." Proceedings of EACL (2016).
* Yin, Pengcheng, and Graham Neubig. "A Syntactic Neural Model for General-Purpose Code Generation." Proceedings of ACL (2017).
* Rabinovich, Maxim, Mitchell Stern, and Dan Klein. "Abstract Syntax Networks for Code Generation and Semantic Parsing." Proceedings of ACL (2017).

Or other work on neural program synthesis, with sketch based methods:
* Gaunt, Alexander L., et al. "Terpret: A probabilistic programming language for program induction." arXiv preprint arXiv:1608.04428 (2016).
* Riedel, Sebastian, Matko Bosnjak, and Tim Rocktäschel. "Programming with a differentiable forth interpreter." CoRR, abs/1605.06640 (2016).

Likewise the references to the non-neural program synthesis and induction literature are thin, and the work is poorly situated as a result.

It is a disappointing but mild failure of the scientific process underlying peer review for this conference that such comments were not made. The authors are encouraged to take heed of these comments in preparing their final revision, but I will not object to the acceptance of the paper on these grounds, as the methods proposed therein are truly interesting and exciting.

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This paper introduces a method for making synchronous SGD more resistant to failed or slow workers. The idea seems plausible, but as the reviewers point out, the novelty and the experimental validation are somewhat limited. For a contribution such as this, it would be good to see some experiments on a wider range of tasks, and experiments with real rather than simulated workloads. I don't think this work is ready for publication at ICLR.

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Pros:
+ Interesting alternative algorithm for training autoencoders

Cons:
- Not a lot of practical value because DANTE does not outperform SGD in terms of time or classification performance using autoencoder features.

This is an interesting and well-written paper that doesn't quite meet the threshold for ICLR acceptance. If the authors can find use cases where DANTE has demonstrable advantages over competing training algorithms, I expect the paper would be accepted.

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The paper proposes to use multiple discriminators to stabilize the GAN training process. Additionally, the discriminators only see randomly projected real and generated samples.

Some valid concerns raised by the reviewers which makes the paper weak:
 - Multiple discriminators have been tried before and the authors do not clearly show experimentally / theoretically if the random projection is adding any value.
- Authors compare only with DCGAN and the results are mostly subjective. How much improvement the proposed approach provides when compared to other GAN models that are developed with stability as the main goal is hence not clear.

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The paper proposes a method to detect and correct adversarial examples at the input stage (using a sparse coding based model) and/or at a hidden layer (using a GMM). These detector/corrector models are trained using only the natural examples. While the proposed method is interesting and has some novelty wrt to the specific models used for detection/correction (ie sparse coding and GMMs), there are crucial gaps in the empirical studies:

- It does not compare with a highly relevant prior work MagNet (Meng and Chen, 2017) which also detects and corrects adversarial examples by modeling the distribution of the natural examples

- The attacks used in the evaluations do not consider the setting where the existence (and architecture) of the defender models is known to the attacker

- It does not evaluate the method on a stronger PGD attack (also known as iterative FGSM)

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As identified by most reviewers, this paper does a very thorough empirical evaluation of a relatively straightforward combination of known techniques for distributed RL. The work also builds on "Distributed prioritized experience replay", which could be noted more prominently in the introduction.

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This paper on automatic option discovery connects recent research on successor representations with eigenoptions. This is a solidly presented, conceptual paper with results in tabular and atari environments.

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This paper presents a memory architecture for RL based on reservoir sampling, and is meant to be an alternative to RNNs. The reviewers consider the idea to be potentially interesting and useful, but have concerns about the mathematical justification. They also point out limitations in the experiments: in particular, use of artificial toy problems, and a lack of strong baselines. I don't think the paper is ready for ICLR publication in its current form.

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The paper is tackling an important open problem.

AnonReviewer3 identified some technical issues that led them to rate the manuscript 5 (i.e., just below the acceptance threshold). Many of these issues are resolved by the reviewer in their review, and the author response makes it clear that these fixes are indeed correct.  However, other issues that the reviewer raises are not provided with solutions.  The authors address these points, but in one case at least (regarding w_infinity), I find the new text somewhat hand-waivy. Regardless, I'm inclined to accept the paper because the issues seem to be straightforward. Ultimately, the authors are responsible for the correctness of the results.

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The paper presents a hybrid architecture which combines WaveNet and LSTM for speeding-up raw audio generation. The novelty of the method is limited, as it’s a simple combination of existing techniques. The practical impact of the approach is rather questionable since the generated audio has significantly lower MOS scores than the state-of-the-art WaveNet model.

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The paper proposes a method to disentangle style from content (two factor disentanglement) using weak labels (information about the common factor for a pair of images). It is similar to an earlier work by Mathieu et al (2016) with main novelty being in the use of the discriminator which operates with pairs of images in the proposed method. Authors also have some theoretical statements about two challenges in disentangling the factors but reviewers have complained about missing connection b/w theory and experiments, and about exposition in general.

The idea has novelty, although somewhat limited in the light of earlier work by Mathieu et al (2016)), and theoretical statements are also of interest but reviewers still feel the paper needs improvement in writing and presentation of results. I would recommend an invitation to the workshop track.

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This paper was reviewed by 3 expert reviews and received largely negative reviews, with concerns about the toy-ish nature of the 2D environments and limited novelty.

Since ICLR18 received multiple papers on similar topics, we took additional measures to ensure that papers were similar papers were judged under the same criteria. Specifically, we asked reviewers of (a) this paper and (b) of a concurrent submission that also studies language grounding in 2D environments to provide opinions on (b) and (a) respectively. Unfortunately, while they may be on similar topic and both working on 2D environments, we received unanimous feedback that (b) was much higher quality ("comparison with multiple baselines, better literature review, no bold claims about visual attention, etc). We realize this may be disappointing but we encourage the authors to incorporate reviewer feedback to make their manuscript stronger.

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The paper presents a really interesting take on the mode collapse problem and argue that the issue arises because of the current GAN models try to model distributions with disconnected support using continuous noise and generators. The authors try to fix this issue by training multiple generators with shared parameters except for the last layer.

The paper is well written and authors did a good job in addressing some of the reviewer concerns and improving the paper.

Even though arguments presented are novel and interesting, reviewers agree that the paper lacks sufficient theoretical or experimental analysis to substantiate the claims/arguments made in the paper. Limited quantitative and subjective results are not always in favor of the proposed algorithm. More controlled toy experiments and results on larger datasets are needed. The central argument about "tunneling" is interesting and needs deeper investigation. Overall the committee recommends this paper for workshop.

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A well written paper proposing some reasonable approaches to counter adversarial images. Proposed approaches include non-differentiable and randomized methods. Anonymous commentators pushed upon and cleared up some important issues regarding white, black and gray "box" settings. The approach appears to be a plausible defence strategy. One reviewers is a hold out on acceptance, but is open to the idea. The authors responded to the points of this reviewer sufficiently. The AC recommends accept.

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I appreciate the experimental results, which includes a comparison against several baselines, however, I echo some of the concerns raised by the reviewers that the formulation is unclear and hard to follow. Moreover, the novelty over [Nachum, 2017] and [Haarnoja, 2017] seems small. Especially because [Nachum, 2017] also used expert trajectories to improve the performance in their experiments.

Detailed comment:
The use of log-sum-exp state values is only valid for the optimal policy, so it is not clear how an on-policy state value is replaced with the log-sum-exp state value. Also, because the equations that you derive characterize the optimal policy, I am not sure if you need importance correction at all.

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although the authors argue that their experiments were selected from the earlier work from which major comparing approaches were taken, the reviewers found the empirical result to be weak. why not some real tasks (i do not believe bAbI nor PTB could be considered real) that could clearly reveal the superiority of the proposed unit against existing ones?

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The authors propose a system for asynchronous, model-parallel training, suitable for dynamic neural networks.  To summarize the reviewers:

PROS:
1. Paper contrasts well with existing work.
2. Positive results on dynamic neural network problems.
3. Well written and clear

CONS:
1. Some concern about extrapolations/estimates to hardware other than that on CPU.
2. Comparisons with Dynet seem to suggest auto-batching results in a dynamic mode aren't very positive.

For 1) the AC notes the author's objections to reviewer 1's views on the value of estimation/extrapolation to non-CPU hardware.  However, reviewer 3 voiced  a similar concern and  both still feel that there is more to be done to be convincing in the experiments.

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Pros
-- A novel formulation for cross-task and cross-domain transfer learning.
-- Extensive evaluations.

Cons
-- Presentation a bit confusing, please improve.

The paper received positive reviews from reviewers. But the reviewers pointed out some issues with presentation and flow of the paper. Even though the revised version has improved, the AC feels that it can be improved further. For example, as pointed out by reviewers, different parts of the model are trained using different losses and / or are pre-trained. It would be worth clarifying that. It might help if the authors include a pseudocode / algorithm block to the final version of the paper.

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There are two parts to this paper (1) an efficient procedure for solving trust-region subproblems in second-order optimization of neural nets, and (2) evidence that the proposed trust region method leads to better generalization performance than SGD in the large-batch setting. In both cases, there are some promising leads here. But it feels like two separate papers here, and I'm not sure either individual contribution is well enough supported to merit publication in ICLR.

For (1), the contribution is novel and potentially useful, to the best of my knowledge. But as there's been a lot of work on trust region solvers and second-order optimization of neural nets more generally, claims about computational efficiency would require comparisons against existing methods. The focus on efficiency also doesn't seem to fit with the experiments section, where the proposed method optimizes less efficiently than SGD and is instead meant to provide a regularization benefit.

For (2), it's an interesting empirical finding that the method improves generalization, but the explanation for this is very hand-wavy. If second-order optimization in general turned out to help with sharp minima, this would be an interesting finding indeed, but it doesn't seem to be supported by other work in the area. The training curves in Table 1 are interesting, but don't really distinguish the claims of Section 4.5 from other possible hypotheses.

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The authors present a thorough exploration of large-sparse models that are pruned down to a target size and show that these models can perform better than small dense models. Results are shown on a variety of datasets with as conv models and seq2seq. The authors even go so far as to release the code. I think the authors are to be thanked for their experimental contributions.
However, in terms of accepting the paper for a premier machine learning conference the method holds little surprise or non-obviousness. I think the paper is a good experimental contribution, and would make a good workshop paper instead but it offers little contribution by way of machine learning methods.

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The paper presents a differentiable upper bound on the performance of classifier on an adversarially perturbed example (with small perturbation in the L-infinity sense). The paper presents novel ideas, is well-written, and appears technically sound. It will likely be of interest to the ICLR community.

The only downside of the paper is its limited empirical evaluation: there is 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 defenses on a dataset like ImageNet.

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This paper tries to establish that LSTMs are suitable for modeling neural signals from the brain.  However, the committee and most reviewers find that results are inconclusive.  Results are mixed across subjects.  We think it would have been far more interesting to compare other types of sequence models for this task other than the few simple baselines implemented here.  It is also unclear what is the LSTM learning extra in contrast with the other models presented in the paper.

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Thank you for submitting you paper to ICLR. The reviewers and authors have engaged well and the revision has improved the paper. The reviewers are all in agreement that the paper substantially expands the prior work in this area,  e.g. by Balle et al. (2016, 2017), and is therefore suitable for publication. Although I understand that the authors have not optimised their compression method for runtime yet, a comment about this prospect in the main text would be a sensible addition.

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While the reviewers feel there might be some merit to this work,  they find enough ambiguities and inaccuracies that I think this paper would be better served by a resubmission.

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This paper considers graph neural representations that use Cayley polynomials of the graph Laplacian as generators. These polynomials offer better frequency localization than Chebyshev polynomials. The authors illustrate the advantages of Cayleynets on several benchmarks, producing modest improvements.

Reviewers were mixed in the assessment of this work, highlighting on the one hand the good quality of the presentation and the theoretical background, but on the other hand skeptical about the experimental section significance. In particular, some concerns were centered about the analysis of complexity of Cayley versus the existing alternatives.

Overall, the AC believes this paper is perhaps more suited to an audience more savvy in signal processing than ICLR, which may fail to appreciate the contributions.

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PROS:
1. Overall, the paper is well-written, clear in its exposition and technically sound.
2. With some caveats, an independent team concluded that the results were "largely reproducible"
3. The key idea is a smart evolution scheme. It circumvents the traditional tradeoff between search space size and complexity of the found models.
4. The implementation seems technically sound.

CONS:
1. The results were a bit over-stated (the authors promise to correct)
2. Could benefit from more comparison with other approaches (e.g. RL)

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All three reviewers are in agreement that this paper is not ready for ICLR in its current state. Given the pros/cons, the committee feels the paper is not ready for acceptance in its current form.

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We encourage the authors to improve the mentioned aspects of their work in the reviews.

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Looks like a great contribution to ICLR. Continuous adaptation in nonstationary (and competitive) environments is something that an intelligent agent acting in the real world would need to solve and this paper suggests that a meta-learning approach may be quite appropriate for this task.

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The paper presents a way of training 1bit wide resnet to reduce the model footprint while maintaining good performance. The revisions added more comparisons and discussions, which make it much better. Overall, the committee feels this work will bring value to the conference.

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This paper presents an interesting model which at the time of submission was still quite confusingly described to the reviewers.
A lot of improvements have been made for which I applaud the authors.
However, at this point, the original 20 babi tasks are not quite that exciting and several other models are able to fully solve them as well.
I would encourage the authors to tackle harder datasets that require reasoning or multitask settings that expand beyond babi.

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