This work extends upon recent ideas to build a complete summarization system using clever attention, copying, and RL training. Reviewers like the work but have some criticisms. Particularly in terms of its originality and potential significance  noting "It is a good incremental research, but the downside of this paper is lack of innovations since most of the methods proposed in this paper are not new to us.". Still reviewers note the experimental results are of high quality performing excellent on several datasets and building "a strong summarization model." Furthermore the model is extensively tested including in "human readability and relevance assessments ".  The work itself is well written and clear.

(optional) Comment on this decision.


ICLR 2018 Conference Acceptance Decision

This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection.

How your identity will be displayed with the above content.

The users who will be allowed to read the above content.

(optional) Comment on this decision.

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


this is an interesting approach that applies the idea of dynamically controlling the amount of information from the input fed into the classifier (some of the earlier approaches have used this idea for, e.g., parsing, real-time translation, online speech recognition, and so on...) this is also related to some of the recent work on hierarchical recurrent nets [Chung et al.]. unfortunately, two of the reviewers and other commenters found this manuscript needs more work to clarify motivation, implication and relationship to other existing works, with which i don't necessarily disagree.

(optional) Comment on this decision.


The paper proposes evaluation metrics for quantifying the quality of disentangled representations. There is consensus among reviewers that the paper makes a useful contribution towards this end. Authors have addressed most of reviewers' concerns in their response.

(optional) Comment on this decision.


The author's revisions addressed clarity issues and some experimental issues (e.g., including MAML results in the comparison). The work takes an original path to an important problem (transfer learning, essentially). There is a question of significance, and this is due to the fact that the empirical comparisons are still very limited. The task is an artificial one derived from MNIST. I would call this "toy" as well. On this toy task, the approach isn't that much different from MAML, which is not in of itself a problem, but it would be interested to have a less superficial discussion of the differences.

The authors mention that they didn't have time for a larger empirical study. I think one is necessary in this case because the work is purposing a new learning algorithm/framework, and the question of its potential impact/significance is an empirical one.

(optional) Comment on this decision.


The reviewers agree that the method is original and mostly well communicated, but have some doubts about the significance of the work.

(optional) Comment on this decision.


The author's propose to use swish and show that it performs significantly better than Relus on sota vision models. Reviewers and anonymous ones counter that PRelus should be doing quite well too. Unfortunately, the paper falls in the category where it is hard to prove the utility of the method through one paper alone, and broader consensus relies on reproduction by the community. As a results, I'm going to recommend publishing to a workshop for now.

(optional) Comment on this decision.


The idea studied here is interesting, if incremental. The empirical results are not particularly stellar, but it's clear that the authors have done their best to provide reproducible and defensible results. A few sticking points: a) The use of the term 'UCB', as mentioned in an anonymous comment, is somewhat misleading. "Approximate Confidence Interval" might be less controversial; b) there are a number of recent research results on exploration that are worth paying attention to (Plappert et al, O'Donoghue et al.) and worth comparing to, and c) the theoretical results are not always justified or useful (e.g. Equation 9: the bound is trivial, posterior >= 0 or 1).

(optional) Comment on this decision.


Overall, the committee finds this paper to be interesting, well written and proposes an end to end model for a very relevant task.  The comparisons are also interesting and well rounded.  Reviewer 2 is critical of the paper, but the committee finds the answers to the criticisms to be satisfactory.  The paper will bring value to the conference.

(optional) Comment on this decision.


The effectiveness of active learning techniques for training modern deep learning pipelines in a label efficient manner is certainly a very well motivated topic. The reviewers unanimously found the contributions of this paper to be of interest, particularly nice empirical gains over several natural baselines.

(optional) Comment on this decision.


This paper looks at  building new density estimation methods and new methods for tranformations and autoregressive models. The request from reviewers for comparison improves the paper. These models have seen a wide range of applications and have been highly successful, needing the added benefits shown and their potential impact to be expanded further.

(optional) Comment on this decision.


Pros:
The proposed regularization for GAN training is interesting and simple to implement.

Cons:
- Reviewers agree that the methodology is incremental over the WGAN with gradient penalty and the modification is not well motivated.
- Experimental results do not clearly demonstrate the benefits of the proposed algorithm and the paper also lacks comparisons with related works.
GIven the pros/cons, the committee feels the paper is not ready for acceptance in its current state.

(optional) Comment on this decision.


This paper introduces a GAN-based framework for inferring human category representations. The reviewers agree that the idea is interesting and well-motivated, and the results are promising. The technical contribution is not significant, but nevertheless the paper combines existing ideas in an interesting way. The reviewers would also like to see some more work towards the direction of investigation of the results and extraction of insights, without which the paper feels somehow incomplete.

(optional) Comment on this decision.


Two reviewers recommended rejection, and one was on the edge. There was no rebuttal to address the concerns and questions posed by the reviewers.

(optional) Comment on this decision.


 + The paper offers a well-written survey and taxonomy of deep-learning approaches to clustering.
 - The novel proposed combination is not very original, and unconvincingly supported by experiments.
 => As the primary contribution of this paper seems to be its well-done survey, ICLR is probably not the appropriate venue for it.

(optional) Comment on this decision.


Authors apply dense nets and LSTM to model dependencies among labels and demonstrate new state-of-art performance on an X-Ray dataset.

Pros:
- Well written.
- New improvement to state-of-art

Cons:
- Novelties are not strong. One combination of existing approaches are used to achieve state-of-art on what is still a relatively new dataset. (All Reviewers)

- Using LSTM to model dependencies would be affected by the selected order of the disease states. In this sense, LSTM seems like the wrong architecture to use to model dependencies among labels. This may be a drawback in comparison to other methods of modeling dependencies, but this is not thoroughly discussed or evaluated. (Reviewer 1 & 3)

- There is a large body of work on multi-task learning with shared information, which have not been evaluated for comparison. Because of this, the contribution of the LSTM to model dependencies between labels in comparison to other available approaches cannot be verified. (Reviewer 1 & 3)

- Top AUC performance on this dataset does not carry much significance on its own, as the dataset is new (CVPR 2017), and few approaches have been tested against it.

- Medical literature not cited to justify with evidence the discovered dependencies among disease states. (Reviewer 1)

(optional) Comment on this decision.


The paper proposes a new way of learning image representations from unlabeled data by predicting the image rotations. The problem formulation implicitly encourages the learned representation to be informative about the (foreground) object and its rotation. The idea is simple, but it turns out to be very effective. The authors demonstrate strong performance in multiple transfer learning scenarios, such as  ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification.

(optional) Comment on this decision.


The submission is motivated by an empirical observation of a phase transition when a sufficiently high L1 or L2 penalty on the weights is applied.  The proposed solution is to optimize for several epochs without the penalty followed by introduction of the penalty.  Although empirical results seem to moderately support this approach, there does not seem to be sufficient theoretical justification, and comparisons are missing.  Furthermore, the author response to reviewer concerns contain unclear statements e.g.
"The reason is that, to reach the level of L1 norm that is low enough, the model needs to go through the strong regularization for the first few epochs, and the neurons already lose its learning ability during this period like the baseline method."
It is not at all clear what "neurons already lose its learning ability" is supposed to mean.

(optional) Comment on this decision.


The authors propose a distillation-based approach that is applied to transfer knowledge from a classification network to non-classification tasks (face alignment and verification). The writing is very imprecise - for instance repeatedly referring to a 'simple trick' rather than actually defining the procedure - and the method is described in very task-specific ways that make it hard to understand how or whether it would generalize to other problems.

(optional) Comment on this decision.


The authors provide an alternative method to [1] for placement of ops in blocks. The results are shown to be an improvement over prior RL based placement in [1] and superior to *some* (maybe not the best) earlier methods for operations placements. The paper seems to have benefited strongly from reviewer feedback and seems like a reasonable contribution. We hope that the implementation may be made available to the community.

[1] Mirhoseini A, Pham H, Le Q V, et al. Device Placement Optimization with Reinforcement Learning[J]. arXiv preprint arXiv:1706.04972, 2017.

(optional) Comment on this decision.


this submission proposes a learning algorithm for resnets based on their interpreration of them as a discrete approximation to a continuous-time dynamical system.  all the reviewers have found the submission to be clearly written, well motivated and have proposed an interesting and effective learning algorithm for resnets.

(optional) Comment on this decision.


The problem of discovering ordering in an unordered dataset is quite interesting, and the authors have outlined a few potential applications. However, the reviewer consensus is that this draft is too preliminary for acceptance. The main issues were clarity, lack of quantitative results for the order discovery experiments, and missing references. The authors have not yet addressed these issues with a new draft, and therefore the reviewers have not changed their opinions.

(optional) Comment on this decision.


The paper proposes a method to embed graph nodes into a gaussian distribution rather than the standard latent vector embeddings. The reviewers concur that the method is interesting and the paper is well-written especially after the opportunity to update.

(optional) Comment on this decision.


The problem addressed here is an important one: What is a good evaluation metric for generative models?  A good selection of popular metrics are analyzed for their appropriateness for model selection of GANs.  Two popular approaches are recommended: the kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest-Neighbour (1-NN) two-sample test.  This seems reasonable, but the present work was not recommended for acceptance by 2 reviewers who raised valid concerns.

From a readability perspective, it would be nice to simply list the answer to question (1) directly in the introduction.  One must read more than a few pages to get to the answer of why the metrics that are advocated were picked.  It need not read like a mystery.

R4: "The evaluations rely on using a pre-trained imagenet model as a representation. The authors point out that different architectures yield similar results for their analysis, however it is not clear how the biases of the learned representations affect the results. The use of learned representations needs more rigorous justification"

R2: "First, it only considers a single task for which GANs are very popular. Second, it could benefit from a deeper (maybe theoretical analysis) of some of the questions." - the first point of which is also related to a concern of R4.

Given the overall high selectivity of ICLR, the present submission falls short.

(optional) Comment on this decision.


This paper presents a marginally interesting idea -- that of an interaction tensor that compares two sentence representations word by word, and feeds the interaction tensor into a higher level feature extraction mechanism.  It produces good results on multi-NLI and SNLI datasets.  There is some criticism about comparing with several baselines for multi-NLI where there was a restriction of not using inter-sentence comparison networks, but the authors do compare with a similar approach without that restriction and shows improvements.   However, there is no solid error analysis that shows what type of examples this interaction tensor idea captures better than other strong baselines such as ESIM. Overall, the committee feels this paper will add value to the conference.

(optional) Comment on this decision.


There was substantial disagreement between reviewers on how this paper contributes to the literature; it seems (having read the paper) that the problem tackled here is clearly quite interesting, but it is hard to tease out in the current version exactly what the contribution does to extend beyond current art.

(optional) Comment on this decision.


All reviewers gave "accept" ratings.
it seems that everyone thinks this is interesting work.

The paper generated a large number of anonymous comments and these were addressed by the authors.

(optional) Comment on this decision.


The method proposed in the paper for latent disentanglement and attribute-conditional image generation is novel to the best of my understanding but reviewers (Anon1 and Anon3) have expressed concerns on the quality of results (CelebA images) as well as on the technical presentation and claims in the paper.

Given the novelty of the proposed method, I would *not* like to recommend a "reject" for this paper but the concerns raised by the reviewers on the quality of results and lack of quantitative results seem valid. Authors rule out possibility of any quantitative results in their response but I am not fully convinced -- in particular, effectiveness of attribute-conditional image generation can be captured by first training an attribute classifier on the generated images and then measuring how often the predicted attributes are flipped when conditioning signal is changed. There are also other metrics in the literature for evaluating generative models.

I would recommend inviting it to the workshop track, given that the work is novel and interesting but has scope for improvements.

(optional) Comment on this decision.


This paper is borderline.  The reviewers agree that the method is novel and interesting, but have concerns about scalability and weakness to attacks with larger epsilon.  I will recommend accepting; but I think the paper would be well served by imagenet experiments, and hope the authors are able to include these for the final version

(optional) Comment on this decision.


Pros:
+ Builds in important ways on the work of Sagun et al., 2016.

Cons:
- The reviewers were very concerned that the assumption in the paper that the second term of Equation (6) is negligible was insufficiently supported, and this concern remained after the discussion and the revision.
- The paper needs to be more precise in its language about the Hessian, particularly in distinguishing between ill conditioning and degeneracy.
- The reviewers did not find the experiment very convincing because it relied on initializing the small-batch optimization from the end point of the large-batch optimization.  Again, this concern remained following the discussion and revision.

The area chair agrees with the authors' comments in their OpenReview post of 08 Jan. 2018 "A remark on relative evaluation," and has discounted the reviewers' comments about the relative novelty of the work.  It is important not to penalize authors for submitting their papers to conferences with an open review process, especially when that process is still being refined.

However, even discounting the remarks about novelty, there are key issues in the paper that need to be addressed to strengthen it (the 3 "cons" above), so this paper does not quite meet the threshold for ICLR Conference acceptance.

However, because it raises really interesting questions and is likely to provoke useful discussions in the community, it might be a good workshop track paper.

(optional) Comment on this decision.


The paper is looking at an interesting problem, but it seems too early. The approach requires training a new language model  from scratch for each new word, rendering it completely impractical for real use. The main evaluation therefore only considers four words - "bonuses", "explained", "marketers", "strategist" (expanded to 20 during the rebuttal). This is not sufficient for ICLR.

(optional) Comment on this decision.


This work proposes a hybrid system for large-scale distributed and federated training of commonly used deep networks. This problem is of broad interest and these methods have the potential to be significantly impactful, as is attested by the active and interesting discussion on this work. At first there were questions about the originality of this study, but it seems that the authors have now added extra references and comparisons.

Reviewers were split about the clarity of the paper itself. One notes that "on the whole clearly presented", but another finds it too dense, disorganized and needing of more clear explanation. Reviewers were also concerned that methods were a bit heuristic and could benefit from more details. There were also many questions about these details in the discussion forum, these should make it into the next version.  The main stellar aspect of the work were the experimental results, and reviewers call them "thorough" and note they are convincing.

(optional) Comment on this decision.


This paper proposes training binary-values LSTMs for NLP using the Gumbel-softmax reparameterization.  The motivation is that this will generalize better, and this is demonstrated in a couple of instances.

However, it's not clear how cherry-picked the examples are, since the training loss wasn't reported for most experiments.  And, if the motivation is better generalization, it's not clear why we would use this particular setup.

(optional) Comment on this decision.


This paper builds on top of Cycle GAN ideas where the main idea is to jointly optimize the domain-level translation function with an instance-level matching objective. Initially the paper received two negative reviews (4,5) and a positive (7). After the rebuttal and several back and forth between the first reviewer and the authors, the reviewer was finally swayed by the new experiments. While not officially changing the score, the reviewer recommended acceptance. The AC agrees that the paper is interesting and of value to the ICLR audience.

(optional) Comment on this decision.


This paper studies the problem of synthesizing adversarial examples that will succeed at fooling a classification system under unknown viewpoint, lighting, etc conditions. For that purpose, the authors propose a data-augmentation technique (called "EOT") that makes adversarial examples robust against a predetermined family of transformations.

Reviewers were mixed in their assessment of this work, on the one hand highlighting the potential practical applications, but on the other hand warning about weak comparisons with existing literature, as well as lack of discussion about how to improve the robustness of the deep neural net against that form of attacks.
The AC thus believes this paper will greatly benefit from a further round of iteration/review, and therefore recommends rejection at this time.

(optional) Comment on this decision.


This paper proposes several different ways of improving generator-discriminator balance in GAN training. The reviewers were consistent in their assessment of the paper: it was written in an ad-hoc way and difficult to understand the main conclusions without motivation or theory behind the proposed techniques. Moreover it did not justify the use of GAM and did not report on any other metrics. The reviewers have proposed several areas of improvement in their detailed assessment. The authors are encouraged to take these into consideration in further developing their work.

(optional) Comment on this decision.


This paper attempts a theoretical treatment of the influence of depth in RNNs on their ability to capture dependencies in the data. All reviewers found the theoretical contribution of the paper interesting, and while there were problems raised regarding formalisation, they appear to have been adequately addressed in the revisions to the paper. The main concern in all three reviews surrounds the evaluation, and weakness thereof. The overarching point of contention seems to be that the theory relates to a particular formulation of RNNs (RAC), causing doubts that the results lift to other architectural variants which are used to obtain state-of-the-art results on tasks such as language modelling. It seems that the paper could be significantly improved by the provision of stronger empirical results to support the theory, or a more convincing argument as to why the results should transfer from, say, RAC to LSTMs. The authors point to two papers on the matter in their response, but it is not clear this is a substitute for experimental validation. I find the paper a bit borderline because of this, and recommend redirection to the workshop.

(optional) Comment on this decision.


I've summarized the pros and cons of the reviews below:

Pros:
* The method for time representation in event sequences is novel and well founded
* It shows improvements on several but not all datasets that may have real-world applications

Cons:
* Gains are somewhat small
* The task is also not of huge interest to ICLR in particular, and thus the paper might be of limited interest

As a result, because the paper is well done, but drew little excitement from any of the reviewers, I suggest that this not be accepted to the main conference, but encouraged to present at the workshop track.

(optional) Comment on this decision.


I tend to agree with the most positive reviewer who characterizes the work with the following statements:

"Kronecker factorization was introduced for Convolutional networks (citation is in the paper). Soft unitary constraints also have been introduced in earlier work (citations are also in the paper). Nevertheless, showing that these two ideas work also for RNNs in combination (and seeing, e.g. the nice relationship between Kronecker factors and unitary) is a relevant contribution."

The most negative reviewer feels that the experimental work could have evaluated the different components explored here more clearly. For this reason the AC recommends an invitation to the workshop track.

(optional) Comment on this decision.


This method has a lot of strong points, but the reviewers had concerns about baselines, comparisons, and hand-engineered aspects of the method. The authors gave a strong rebuttal and made substantial updates to the paper to address the concerns. I think that this has saved the submission and tipped the balance towards acceptance.

(optional) Comment on this decision.


The paper considers Markov potential games (MPGs),  where the agents share some common resource. They consider MPGs with continuous state-action variables, coupled constraints and nonconvex rewards, which is novel. The reviews are all positive and point out the novel contributions in the paper

(optional) Comment on this decision.


The paper proposes a BNP topic model that uses a stick-breaking prior over document topics and performs VAE-style inference over them. Unfortunately, the novelty of this work is limited, as VAE-like inference for LDA-like models, inference with stick-breaking priors for VAEs, and placing a prior on the concentration parameter in a non-parametric topic model have all been done before (see e.g. Srivastava & Sutton (2017), Nalisnick & Smyth (2017), and Teh, Kurihara & Welling (2007) respectively). There are also concerns about the correctness of treating topics as parameters (as opposed to random variables) in the proposed model. The authors' clarification regarding this point was helpful but not sufficient to show the validity of the approach.

(optional) Comment on this decision.


The paper proposes adversarial flow-based neural network architecture with adversarial training for video prediction. Although the reported experimental results are promising, the paper seems below ICLR threshold due to limited novelty and issues in evaluation (e.g., mechanical turk experiment). No rebuttal was submitted.

(optional) Comment on this decision.


The reviewers feel that the novelties in the model are not significant.   Furthermore, they suggest that empirical results could be improved by
1:  analyses showing how the significance network functions and directly measuring its impact
2: More reproducible experiments.  In particular, this is really an applications paper, and the experiments on the main application are not reproducible because the data is proprietary.
3: baselines that make assumptions more in line with the authors' problem setup

(optional) Comment on this decision.


This paper combines ideas from student-teacher training and multi-view learning in a simple but clever way.  There is not much novelty in the methods, but promising results are given across several tasks, including realistic NLP tasks.  The improvements are not huge but are consistent.  Considering the limited novelty, the paper should include some more convincing analysis and insight on why/when the approach works. Given the intersting results, the committee recommends this for workshop track.

(optional) Comment on this decision.


An interesting application of self-ensembling/temporal ensembling for visual domain adaptation that achieves state of the art on the visual domain adaptation challenge. Reviewers noted that the approach is quite engineering-heavy, but I am not sure it's really much worse than making a pixel-to-pixel approach work well for domain adaptation.

I hope the authors follow through with their promise to add experiments to the final version (notably the minimal augmentation experiments to show just how much this domain adaptation technique is tailored towards imagenet-like things).

As it stands, this paper would be a good contribution to ICLR as it shows an efficient and interesting way to solve a particular visual domain adaptation problem.

(optional) Comment on this decision.


Very solid paper exploring an interpretation of LSTMs.
good reviewss

(optional) Comment on this decision.


meta score: 4

This paper proposes an activation function, called displaced ReLU (DReLU), to improve the performance of CNNs that use batch normalization.

Pros
 - good set of experiments using CIFAR, with good results
 - attempt to explain the approach using expectations
Cons
 - theoretical explanations are not so convincing
 - limited novelty
 - CIFAR is relatively limited set of experiments
 - does not compare with using bn after relu, which is now well-studied and seems to address the motivation of this paper (and thus questions the conclusions)

(optional) Comment on this decision.


The reviewers are unanimous in their opinion that the theoretical results in this paper are of limited novelty and significance. Several parts of the paper are not presented clearly enough. As such the paper is not ready for ICLR-2018 acceptance.

(optional) Comment on this decision.


The paper proposes to add noise to the weights of a policy network during learning in Deep-RL settings and finds that this results in better performance on DQN, A3C and other algorithms that use other exploration strategies. Unfortunately, the paper does not do a thorough job of exploring the reasons and doesn't offer a comparison to other methods that have been out on arxiv for several months before the submission, in spite of reviewers and anonymous requests. Otherwise I might have supported recommending the paper for a talk.

(optional) Comment on this decision.


This paper presents an analysis of using multiple generators in a GAN setup, to address the mode-collapse problem. R1 was generally positive about the paper, raising the concern on how to choose the number of generators, and also whether parameter sharing was essential. The authors reported back on parameter sharing, showing its benefits yet did not have any principled method of selecting the number of generators. R2 was less positive about the paper, pointing out that mixture GANs and multiple generators have been tried before. They also raised concern with the (flawed) Inception score as the basis for comparison. R2 also pointed out that fixing the mixing proportions to uniform was an unrealistic assumption. The authors responded to these claims, clarifying the differences between this paper and the previous mixture GAN/multiple generator papers, and reporting FID scores. R3 was generally positive, also citing some novelty concerns similar to that of R2. I acknowledge the authors detailed responses to the reviews (in particular in response to R2) and I believe that the majority of concerns expressed have now been addressed. I also encourage the authors to include the FID scores in the final version of the paper.

(optional) Comment on this decision.


The authors propose an architecture that uses a curriculum and multi-task distillation to gain higher performance without forgetting. The paper is largely a smart composition of known methods, and it requires keeping data from all tasks to do the distillation, so it is not truly a scalable continual learning approach. There were a lot of concerns about clarity in the manuscript, but many of these have been assuaged by an update to the paper. This is a borderline paper, but the author's rebuttal and update probably tip it towards acceptance.

(optional) Comment on this decision.


The reviewers were uniformly unimpressed with the contributions of this paper. The method is somewhat derivative and the paper is quite long and lacks clarity. Moreover, the tactic of storing autoencoder variables rather than full samples is clearly an improvement, but it still does not allow the method to scale to a truly lifelong learning setting.

(optional) Comment on this decision.


The submission proposes a method for quantization.  The approach is reasonably straightforward, and is summarized in Algorithm 1.  It is the analysis which is more interesting, showing the relationship between quantization and adding Gaussian noise (Appendix B) - motivating quantization as regularization.

The submission has a reasonable mix of empirical and theoretical results, motivating a simple-to-implement algorithm.  All three reviewers recommended acceptance.

(optional) Comment on this decision.


The reviewers agree that the problem being addressed is interesting, however there are concerns with novelty and with the experimental results. An experiment beyond dealing with class imbalance would help strengthen this paper, as would experiments with other kinds of GANs.

(optional) Comment on this decision.


This paper analyzes mathematically why weights of trained networks can be replaced with ternary weights without much loss in accuracy. Understanding this is an important problem, as binary or ternary weights can be much more efficient on limited hardware, and we've seen much empirical success of binarization schemes. This paper shows that the continuous angles and dot products are well approximated in the discretized network. The paper concludes with an input rotation trick to fix discretization failures in the first layer.

Overall, the contribution seems substantial, and the reviewers haven't found any significant issues. One reviewer wasn't convinced of the problem's importance, but I disagree here. I think the paper will plausibly be helpful for guiding architectural and algorithmic decisions. I recommend acceptance.

(optional) Comment on this decision.


The paper studies the robustness of deep learning against label noise on MNIST, CIFAR-10 and ImageNet. But the generalization of the claim "deep learning is robust to massive label noise" is still questionable due to the limited noise types investigated.
The paper presents some tricks to improve learning with high label noise (batch size and learning rate), which is not novel enough.

(optional) Comment on this decision.


This paper attempts to connect the expressivity of neural networks with a measure of topological complexity. The authors present some empirical results on simplified datasets.
All reviewers agreed that this is an intriguing line of research, but that the current manuscript is still presenting preliminary results, and that further work is needed before it can be published.

(optional) Comment on this decision.


In this paper the authors show how to allow deep neural network training on logged contextual bandit feedback. The newly introduced framework comprises a new kind of output layer and an associated training procedure. This is a solid piece of work and a significant contribution to the literature, opening up the way for applications of deep neural networks when losses based on manual feedback and labels is not possible.

(optional) Comment on this decision.


Meta score: 6

The paper approaches the problem of identifying out-of-distribution data by modifying the objective function to include a generative term.  Experiments on a number of image datasets.

Pros:
 - clearly expressed idea, well-supported by experimentation
 - good experimental results
 - well-written

Cons:
 - slightly limited novelty
 - could be improved by linking to work on semi-supervised learning approaches using GANs

The authors note that ICLR submission 267 (https://openreview.net/forum?id=H1VGkIxRZ) covers similar ground to theirs.

(optional) Comment on this decision.


The reviewers present strong concerns regarding presentation of the paper. The approach appears overly complex, some design choices are not clear and the experiments are not conducted properly. I recommend the authors to carefully go through the reviews.

(optional) Comment on this decision.


This paper proposes a non-parametric method for metric learning and classification. One of the reviewers points out that it can be viewed as an extension of NCA. There is in fact a non-linear version of NCA that was subsequently published, see [1]. In this sense, the approach here appears to be a version of nonlinear NCA with learnable per-example weights, approximate nearest neighbour search, and the allowance of stale exemplars. In this view, there is concern from the reviewers that there may not be sufficient novelty for acceptance.

The reviewers have concerns with scalability. It would be helpful to include clarification or even some empirical results on how this scales compared to softmax. It is particularly relevant for larger datasets like Imagenet, where it may be impossible to store all exemplars in memory.

It is also recommended to relate this approach to metric-learning approaches in few-shot learning. Particularly to address the claim that this is the first approach to combine metric learning and classification.

[1]: Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. Ruslan Salakhutdinov and Geoffrey Hinton.  AISTATS 2007

(optional) Comment on this decision.


this work adapts cycle GAN to the problem of decipherment with some success. it's still an early result, but all the reviewers have found it to be interesting and worthwhile for publication.

(optional) Comment on this decision.


The paper proposes a new deep architecture based on polar transformation for improving rotational invariance. The proposed method is interesting and the experimental results strong classification performance on small/medium-scale datasets (e.g., rotated MNIST and its variants with added translations and clutters, ModelNet40, etc.). It will be more impressive and impactful if the proposed method can bring performance improvement on large-scale, real datasets with potentially cluttered scenes (e.g., Imagenet, Pascal VOC, MS-COCO, etc.).

(optional) Comment on this decision.


This paper introduces a student-teacher method for learning from labels of varying quality (i.e. varying fidelity data). This is an interesting idea which shows promising results.

Some further connections to various kinds of semi-supervised and multi-fidelity learning would strengthen the paper, although understandably it is not easy to cover the vast literature, which also spans different scientific domains. One reviewer had a concern about some design decisions that seemed ad-hoc, but at least the authors have intuitively and experimentally justified them.

(optional) Comment on this decision.


This paper is nowhere near standards for publication anywhere.

(optional) Comment on this decision.


The paper proposes a new approach for scalable training of deep topic models based on amortized inference for the local parameters and stochastic-gradient MCMC for the global ones.  The key aspect of the method involves using Weibull  distributions (instead of Gammas) to model the variational posteriors over the local parameters, enabling the use of the reparameterization trick. The resulting methods perform slightly worse that the Gibbs-sampling-based approaches but are much faster at test time. Amortized inference has already been applied to topic models, but the use of Weibull posteriors proposed here appears novel. However, there seems to be no clear advantage to using stochastic-gradient MCMC instead of vanilla SGD to infer the global parameters, so the value of this aspect of WHAI unclear.

(optional) Comment on this decision.


Meta score: 7

The paper combined low precision computation with different approaches to teacher-student knowledge distillation.  The experimentation is good, with good experimental analysis.  Very clearly written.  The main contribution is in the different forms of teacher-student training combined with low precision.

Pros:
 - good practical contribution
 - good experiments
 - good analysis
 - well written
Cons:
 - limited originality

(optional) Comment on this decision.


Pros
-- Interesting approach to induce sparsity, trains faster than alternative approaches
Cons
-- Fairly complex set of heuristics for pruning weights
-- Han et al. works well, although the authors claim it takes more time to train, which may not not hold for all training sets and doesn’t seem like a strong enough reason to choose an alternative appraoch

Given these comments, the AC recommends that the paper be rejected.

(optional) Comment on this decision.


This paper proposes a regularizer for recurrent neural networks, based on injecting random noise into the hidden unit activations.  In general the reviewers thought that the paper was well written and easy to understand.  However, the major concern among the reviewers was a lack of empirical evidence that the method works consistently.  Essentially, the reviewers were not compelled by the presented experiments and demanded more rigorous empirical validation of the approach.

Pros:
- Well written and easy to follow
- An interesting idea
- Regularizing RNNs is an interesting and active area of research in the community

Cons:
- The experiments are not compelling and are questioned by all the reviewers
- The writing does not cite relevant related work
- The work seems underexplored (empirically and methodologically)

(optional) Comment on this decision.


The paper presents a method for learning from expert state trajectories using a similarity metric in a learned feature space. The approach uses only the states, not the actions of the expert. The reviewers were variously dissatisfied with the novelty, the theoretical presentation, and the robustness of the approach. Though it empirically works better than the baselines (without expert demos) this is not surprising, especially since thousands of expert demonstrations were used. This would have been more impressive with fewer demonstrations, or more novelty in the method, or more evidence of robustness when the agent's state is far from the demonstrations.

(optional) Comment on this decision.


Theoretical analysis and understanding of DNNs is a crucial area for ML community. This paper studies characteristics of the relu DNNs and makes several important contributions.

(optional) Comment on this decision.


this submission presents a novel way in which a neural machine reader could be improved. that is, by learning to reformulate a question specifically for the downstream machine reader. all the reviewers found it positive, and so do i.

(optional) Comment on this decision.


Pros:
+ Interesting perspective on training deep networks

Cons:
- Not a lot of practical significance: why would one want to use this algorithm over standard methods like ResNets or highway networks given that the proposed algorithm is more complex than established methods?

(optional) Comment on this decision.


This paper implements Group convolutions on inputs defined over hexagonal lattices instead of square lattices, using the roto-translation group. The internal symmetries of the hexagonal grid allow for a larger discrete rotation group than when using square pixels, leading to improved performance on CIFAR and aerial datasets.

The paper is well-written and the reviewers were positive about its results. That said, the AC wonders what is the main contribution of this work relative to existing related works (such as Group Equivarant CNNS, Cohen & Welling'16, or steerable CNNs, Cohen & Welling'17). While it is true that extending GCNNs to hexagonal lattices is a non-trivial implementation task, the contribution lacks significance in the mathematical/learning fronts, which are perhaps the ones ICLR audience will care more about. Besides, the numerical results, while improved versus their square lattice counterparts, are not a major improvement over the state-of-the-art.

In summary, the AC believes this is a borderline paper. The unanimous favorable reviews tilt the decision towards acceptance.

(optional) Comment on this decision.


This paper adapts (Nachum et al 2017) to continuous control via TRPO.   The work is incremental (not in the dirty sense of the word popular amongst researchers, but rather in the sense of "building atop a closely related work"), nontrivial,  and shows empirical promise.    The reviewers would like more exploration of the sensitivity of the hyper-parameters.

(optional) Comment on this decision.


Each of the reviewers had a slightly different set of issues with this paper but here is an attempt at a summary:

PROS:
1. Paper is mostly clear and well structured.

CONS:
1. Lack of novelty
2. Unsupported claims
3. Questionable methodology (using dropout confounds the goal of the experiment)

The authors did not submit a rebuttal.

(optional) Comment on this decision.


The paper got mixed scores of 4 (R1), 6 (R3), 8 (R2). R1 initially gave up after a few pages of reading, due to clarity problems. But looking over the revised version was much happier, so raised their score to 7. R2, who is knowledge about the area, was very positive about the paper, feeling it is a very interesting idea. R3 was also cautiously positive. The authors have absorbed the comments by the reviewers to make significant changes to the paper. The AC feels the idea is interesting, even if the experimental results aren't that compelling, so feels the paper can be accepted.

(optional) Comment on this decision.


This paper constructs a variant of deep CNNs which is provably invertible, by replacing spatial pooling with multiple shifted spatial downsampling, and capitalizing on residual layers to define a simple, invertible representation. The authors show that the resulting representation is equally effective at large-scale object classification, opening up a number of interesting questions.

Reviewers agreed this is an strong contribution, despite some comments about the significance of the result; ie, why is invertibility a "surprising" property for learnability, in the sense that F(x) = {x,  phi(x)}, where phi is a standard CNN satisfies both properties: invertible and linear measurements of F producing good classification. All in all, this will be a great contribution to the conference.

(optional) Comment on this decision.


The reviewers thought the paper provides an interesting line of research.

(optional) Comment on this decision.


I (and some of the reviewers) find the general motivation quite interesting (operationalizing the Gricean maxims in order to improve language generation). However, we are not  convinced that the actual model encodes these maxims in a natural and proper way.  Without this motivation, the approach can be regarded as a set of heuristics which happen to be relatively effective on a couple of datasets.  In other words, the work seems too preliminary to be accepted at the conference.

Pros:
-- Interesting motivation (and potential impact on follow-up work)
-- Good results on a number of datasets
Cons:
-- The actual approach can be regarded as a set of heuristics, not necessarily following from the maxims
-- More serious evaluation needed (e.g., image captioning or MT) and potential better ways of encoding the maxims

It is suitable for the workshop track, as it is likely to stimulate an interesting discussion and more convincing follow-up work.

(optional) Comment on this decision.


Pros:
+ Nice demonstration of the equivalence between scaling the learning rate and increasing the batch size in SGD optimization.

Cons:
- While reporting convergence as a function of number of parameter updates is consistent, the paper would be more compelling if wall-clock times were given in some cases, as that will help to illustrate the utility of the approach.
- The paper would be stronger if additional experimental results, which the authors appear to have at hand (based on their comments in the discussion) were included as supplemental material.
- The results are not all that surprising in light of other recent papers on the subject.

(optional) Comment on this decision.


Pros
-- Shows alternative strategies to train low-rank factored weight matrices for recurrent nets.

Cons
-- Minor modifications (and gains) over other forms of regularization like L2.
-- Results are only on an ASR task, so it’s not entirely clear how they’ll work on other tasks.

As pointed out by the reviewers, unless the authors show that the techniques generalize well to other tasks, and larger datasets it hard to accept it to the main conference. The AC, therefore, recommends that the paper be rejected.

(optional) Comment on this decision.


The paper proposes a new metric to measure GAN performance by training a classifier on the true labeled dataset and then comparing the distribution of the labels of the generated samples to the true label distribution. Reviewers find that the paper is well written but lacks novelty and is quite experimental does not present any new insights. The paper investigates well-known model collapse and diversity issues. Reviewers are not convinced that this is a good metric to measure sample quality or diversity as the generator can drop examples far away from the boundary and still achieve a good score on this metric.

(optional) Comment on this decision.


The reviewers agree this is a really interesting paper, with an interesting idea (in particular
the use of regret clipping might provide a benefit over typical policy gradient methods). However,
there are two major concerns: 1) clarity / exposition and more importantly 2) lack of a strong
empirical motivation for the new approach (why do standard methods work just as well on these
partially observable domains?).

(optional) Comment on this decision.


This paper proposes an approach for jointly learning a label embedding and prediction network, as a way of taking advantage of relationships between labels.  This general idea is well-motivated, but the specifics of the proposed approach are not motivated or described well.  More discussion of relationship with prior work (e.g. other ways of "softening" the softmax) is needed.  The authors claim to have state-of-the-art results, but reviewers point out that much better results exist.

(optional) Comment on this decision.


This work proposes an improved visualisation techniques for ReLU networks that compensates for filter scale symmetries/invariances, thus allowing a more meaningful comparison of low-dimensional projected optimization landscapes between different network architectures.

- the visualisation techniques are a small variation over previous works
+ extensive experiments provide nice visualisations and yield a clearer visual picture of some properties of the optimization landscape of various architectural variants

A promising research direction, which could be further improved by providing more extensive and convincing support for the significance of its contribution in comparison to prior techniques, and to its empirically derived observations, findings and claims.

(optional) Comment on this decision.


The proposed relational reasoning algorithm is basically a fairly standard graph neural network, with a few modifications (e.g., the prediction loss at each layer - also not a new idea per se).

 The claim that previously reasoning has not been considered in previous applications of graph neural networks (see discussion) is questionable.  It is not even clear what is meant here by 'reasoning' as many applications of graph neural networks may be regarded as performing some kind of inference on graphs (e.g., matrix completion tasks by Berg, Kipf and Welling; statistical relational learning by  Schlichtkrull et al).

So the contribution seems a bit over-stated.  Rather than introduces a new model, the work basically proposes an application of largely known model to two (not-so-hard) tasks which have not been studied in the context of GNNs. The claim that the approach is a general framework for dealing with complex reasoning problems is not well supported as both problems are (arguably) not complex reasoning problems (see R2).

There is a general consensus between reviewers that the paper, in its current form, does not quite meet acceptance criteria.

Pros:
-- an interesting direction
-- clarity
Cons:
-- the claim of generality is not well supported
-- the approach is not so novel
-- the approach should be better grounded in previous work

(optional) Comment on this decision.


This paper presents a bias/variance decomposition for Boltzmann machines using the generalized Pythagorean Theorem from information geometry. The main conclusion is that counterintuitively, the variance may decrease as the model is made larger. There are probably some interesting ideas here, but there isn't a clear take-away message, and it's not clear how far this goes beyond previous work on estimation of exponential families (which is a well-studied topic).

Some of the reviewers caught mathematical errors in the original draft; the revised version fixed these, but did so partly by removing a substantial part of the paper about hidden variables. The analysis, then, is limited to fully observed Boltzmann machines, which have less practical interest to the field of deep learning.

(optional) Comment on this decision.


This paper had some quality and clarity issues and the lack of motivation for the approach was pointed out by multiple reviewers.  Just too far away from the acceptance threshold.

(optional) Comment on this decision.


This paper studies the control of symmetric linear dynamical systems with unknown dynamics. While the reviewers agree that this is an interesting topic, there are concerns that the assumptions are not realistic. Lack of experiments also stands out. I recommend the paper to workshop track with the hope that it will foster more discussions and lead to more realistic assumptions.

(optional) Comment on this decision.


All three reviewers recommend acceptance. The authors did a good job at the rebuttal which swayed the first reviewer to increase the final rating. This is a clear accept.

(optional) Comment on this decision.


The reviewers agree this paper is not yet ready for publication.

(optional) Comment on this decision.


The reviewers agree that the manuscript is below the acceptance threshold at ICLR.  Many points of criticism were evident in the reviewer comments, including small artificial test domain, no new methods introduced, poor writing in some places, and dubious need for DeepRL in this domain.  The reviews mentioned a number of constructive comments to improve the paper, and we hope this will provide useful guidance for the authors to rewrite and resubmit to a future venue.

(optional) Comment on this decision.


The reviewers thought that idea of trying to exploit low-rank structure in the loss gradients of a feedforward network to improve training was interesting; however they expressed many concerns about the clarity of the presentation, quality of the empirical evaluation, and significance of the result (since the tests were not done on an architecture anywhere near state-of-the-art). Because the authors did not participate in the discussion period, none of these concerns were addressed.

Pros:
+ Promising idea for new approaches to optimization.

Cons:
- Unclear notation for the intended machine learning audience
- Algorithm should be illustrated using pseudocode
- Limited significance if the method is only usable with purely feedforward networks.
- Limited empirical evaluation: positive results only if weights are poorly initialized.

(optional) Comment on this decision.


An interesting new approach for doing meta-learning incorporating temporal convolution blocks and soft attention. Achieves impressive SOTA results on few shot learning tasks and a number of RL tasks. I appreciate the authors doing the ablation studies in the appendix as that raises my confidence in the novelty aspect of this work. I thus recommend acceptance, but do encourage the authors to perform the ablation experiments promised to Reviewer 1 (especially the one to "show how much SNAILs performance degrades when TCs are replaced with this method [of Vaswani et al.].")

(optional) Comment on this decision.


(optional) Comment on this decision.


While using self-play for training a goal-oriented dialogue system makes sense, the contribution of this paper compared to previous work (that the paper itself cites) seems too minor, and the limitations of using toy synthetic data further weaken the work.

(optional) Comment on this decision.


The paper appears unfinished in many ways: the experiments are preliminary, the paper completely ignored a large body of prior work on the subject, and the presentation needs substantial improvements. The authors did not provide a rebuttal.

I encourage the authors to refrain from submitting unfinished papers such as this one in the future, as it unnecessarily increases the load on a review system that is already strained.

(optional) Comment on this decision.


The reviewers have unanimously expressed strong concerns about the technical correctness of the theoretical results in the paper. The paper should be carefully revised and checked for technical errors. In its current form, the paper is not suitable for acceptance at ICLR 2018.

(optional) Comment on this decision.


The paper studies transferability of adversarial examples between model architectures, and proposes a method to improve this transferability. Whilst it covers an interesting and relevant line of research, the paper does not provide strong evidence for its main underling hypothesis: namely, that adversarial perturbations can be split in a model-specified and a data-specific part. The paper's presentation also warrants improvements. The authors have not provided a rebuttal.

(optional) Comment on this decision.


The paper got generally positive scores of 6,7,7. The reviewers found the paper to be novel but hard to understand. The AC feels the paper should be accepted but the authors should revise their paper to take into account the comments from the reviewers to improve clarity.

(optional) Comment on this decision.


Paper was well written and rebuttal was well thought out and convincing.

The reviewers agree that the paper showed BNNs were good (relatively speaking) at resisting adversarial examples. Some question was raised about whether the methods would work on larger datasets and models. The authors offered some experiments in this regard in the rebuttal to this end. Also, a public comment appeared to follow up on CIFAR and report correlated results.

(optional) Comment on this decision.


The reviewers find the gradient compression approach novel and interesting, but they find the empirical evaluation not fully satisfactory. Some aspects of the paper have improved with the feedback from the reviewers, but because of the domain of the paper, experimental evaluation is very important. I recommend improving the experiments by incorporating the reviewers' comments.

(optional) Comment on this decision.


The authors have proposed a 'soft' version of VIN which is differentiable, where the cost function is trained by behavior cloning / imitation learning from expert/computer trajectories. The method is applied to a toy problem and to real historical data from mars rovers. The paper does not acknowledge nor compare against other methods, and the contribution is unclear, as is the justification for some of the aspects of the method.  Additionally it is difficult to interpret the relevance or significance of the results (45% correct).

(optional) Comment on this decision.


PROS:
1. All the reviewers thought that the work was interesting and showed promise
2. The paper is relatively well written

CONS:
1. Limited experimental evaluation (just MNIST)

The reviewers were all really on the fence about this but in the end felt that while the idea was a good one and the authors were responsive in their rebuttal, the experimental evaluation needed more work.

(optional) Comment on this decision.


The reviewers feel there are two issues that make this paper fall short of acceptance: first, the
lack of a clear emphasis and focus (evidenced by the significant revisions) and second, a lack of
comparison to similar, existing methods for multi-agent reinforcement learning.

(optional) Comment on this decision.


Split opinions on paper: 6 (R1), 3 (R2), 6 (R3). Much of the debate centered on the novelty of the algorithm. R2 felt that the paper was a straight-forward combination of CycleGAN with S+U, while R3 felt it made a significant contribution. The AC has looked at the paper and the reviews and discussion. The topic is very interesting and topical. The experiments are ok, but would be helped a lot by including the real/synth car data currently in appendix B: seeing the method work on natural images is much more compelling. The approach still seems a bit incremental: yes, it's not a straight combination but the extra stuff isn't so profound. The AC is inclined to accept, just because this is an interesting problem.

(optional) Comment on this decision.


This paper presents a theoretical justification for the Adam optimizer in terms of decoupling the signs and magnitudes of the gradients. The overall analysis seems reasonable, though there's been much back-and-forth with the reviewers about particular claims and assumptions. Overall, the contributions don't feel quite substantial enough for an ICLR publication. The interpretation in terms of signs is interesting, but it's very similar to the motivation for RMSprop, of which Adam is an extension. The performance result on diagonally dominant noisy quadratics is interesting, but it feels unsurprising that a diagonal curvature approximation would work well in this setting. I don't recommend acceptance at this point, though these ideas could potentially be developed further into a strong submission.

(optional) Comment on this decision.


While there are some interesting and novel aspects in this paper, none of the reviewers recommends acceptance.

(optional) Comment on this decision.


 + interesting novel extension of equilibrium propagation, as a biologically more plausible alternative to  backpropagation, with encouraging initial experimental validation.
 - currently lacks theoretical guarantees regarding convergence of the algorithm to a meaningful result
 - experimental study should be more extensive to support the claims

(optional) Comment on this decision.


This paper describes a new library for forward propagation of binary CNNs. R1 for clarification on the contributions and novelty, which the authors provided. They subsequently updated their score. I think that optimized code with permissive licensing (as R2 points out) benefits the community. The paper will benefit those who decide to work with the library.

(optional) Comment on this decision.


This paper offers a theoretical and empirical analysis of the expressivity of RNNs, in particular in comparison to TT decomposition. The reviewers argued the results was interesting and important, although there were issues with clarity of some of the explanations. More critical reviewers argued the comparison basis with CP networks was not "fair" in that their shallowness restricted their expressivity w.r.t. TT. The experiments could be strengthened by making the explanations surrounding the set up clearer. This paper is borderline acceptable, and would have benefited from a more active discussion between the reviewers and the author. From reading the reviews and the author responses, I am leaning towards recommending acceptance to the main conference rather than the workshop track, as it is important to have theoretical work of this nature discussed at ICLR.

(optional) Comment on this decision.


This paper studies the interplay between adversarial examples and generalization in the uniform setting (not specific assumptions on the architecture) in a toy high-dimensional setting. In particular, the authors show a fundamental tradeoff between generalization error and the average distance of adversarial examples.

Reviewers were skeptical about the possible significance of this work, but the paper underwent a major revision that greatly improved the quality of presentation. That said, the results are still preliminary since they only consider a toy dataset (concentric spheres). The AC recommends re-submitting this work to the workshop track.

(optional) Comment on this decision.


The authors have proposed an architecture that incorporates a VIN with a DNC to combine low level planning with high level memory-based optimization, resulting in a single policy for navigation and other similar problems that is trained end-to-end with sparse rewards. The reviews are mixed, but the authors did allay the concerns of the most negative reviewer by adding a comparison to traditional motion planning (A*) algorithms.

(optional) Comment on this decision.


The paper presents a simple but surprisingly effective data augmentation technique which is thoroughly evaluated on a variety of classification tasks, leading to improvement over state-of-the-art baselines. The paper is somewhat lacking a theoretical justification beyond intuitions, but extensive evaluation makes up for that.

(optional) Comment on this decision.


The proposed conditional variance regularizer looks interesting and the results show some promise. However, as the reviewers pointed out, the connection between the information-theoretic argument provided and the final form of the regularizer is too tenuous in its current form. Since this argument is central to the paper, the authors are urged to either provide a more rigorous derivation or motivate the regularizer more directly and place more emphasis on its empirical evaluation.

(optional) Comment on this decision.


This paper seeks to integrate tensor-based models from physics into machine learning architectures. The two main objections to this paper are first that, despite honest (I assume) efforts from the authors, it remains somewhat hard to understand without substantial background knowledge of physics. Second, that the experiments focus on MNIST and CIFAR image classification tasks, two datasets where linear models perform with high accuracy, and as such are unsuitable for properly evaluating the claims made about the models in this paper. Unfortunately, it does not seem there is sufficient enthusiasm for this paper amongst the reviewers to justify its inclusion in the conference.

(optional) Comment on this decision.


The submission provides an interesting way to tackle the so-called distributional shift problem in machine learning. One familiar example is unsupervised domain adaptation. The main contribution of this work is deriving a bound on the generalization error/risk for a target domain as a combo of re-weighted empirical risk on the source domain and some discrepancy between the re-weighted source domain and the target domain. The authors then use this to formulate an objective function.

The reviewers generally liked the paper for its theoretical results, but found the empirical evaluation somewhat lacking, as do I. Especially the unsupervised domain adaptation results are very toy-ish in nature (synthetic data), whereas the literature in this field, cited by the authors, does significantly larger scale experiments. I am unsure as to how much I value I can place in the IHDP results since I am not familiar with the benchmark (and hence my lower confidence in the recommendation).

Finally, I am not very convinced that this is the appropriate venue for this work, despite containing some interesting results.

(optional) Comment on this decision.


The reviewers agreed that this paper is not quite ready for publication at ICLR.  One of the reviewers thought the paper was well written and easy to follow while the two others said the opposite.  One of the main criticisms was issues with the composition.  The paper seems to lack a clear formal explanation of the problem and the proposed methodology.  The reviewers in general weren't convinced by the experiments, complaining about the lack of a required baseline and that the proposed method doesn't seem to significantly help in the experiment presented.

Pros:
- The proposed idea is interesting
- The problem is timely and of interest to the community
- Addresses multiple important problems at the intersection of ML and RL in sequence generation

Cons:
- Novel but somewhat incremental
- The experiments are not compelling (i.e. the results are not strong)
- A necessary baseline is missing
- Significant issues with the writing - both in terms of clarity and correctness.

(optional) Comment on this decision.


Important problem (modular continual RL) and novel contributions. The initial submission was judged to be a little dense and hard to read, but the authors have been responsive in responding and updating the paper. I support accepting this paper.

(optional) Comment on this decision.


Overall the reviewers appear to like the ideas in this paper, though this is some disagreement about novelty (I agree with the reviewer who believes that the top-level search can very easily be interpreted as an MDP, making this very similar to SMDPs). The reviewers generally felt that the experimental results need to more closely compare with some existing techniques, even if they're not exactly for the same setting.

(optional) Comment on this decision.


Pros
-- Competitive results on LibriSpeech.
Cons
-- Limited novelty, and lacks enough comparisons.
-- Comparison with other end-to-end approaches, and on other commonly used datasets, like WSJ, missing.
-- Gated convnets have already been proposed.
-- Letter based systems have been shown to be competitive to phone based systems.
-- Optimization criterion is quite similar to lattice-free MMI proposed by Povey et al., but with a letter based LM and a slightly different HMM topology.

Given the cons pointed out by reviews, the AC is recommending that the paper be rejected.

(optional) Comment on this decision.


The reviewers highlight a lack of technical content and poor writing.
They all agree on rejection.
There was no author rebuttal or pointer to a new version.

(optional) Comment on this decision.


This work introduces a new type of structured attention network that learn latent structured alignments between sentences in a fully differentiable manner, which allows the network to learn not only the target task, but also the latent relationships. Reviewers seem partial to the idea of the work, and it's originality, but have issues with the contributions. In particular:

- The reviewers note that the gains in performance from using this approach are quite small and do not outperform previous structured approaches.

(optional) Comment on this decision.


The reviewers find the work interesting and well made, but are concerned that ICLR is not the right venue for the work.  I will recommend that the paper be accepted, but ask the authors to add the NMT results to the main paper (any other non-synthetic applications they could add would be helpful).

(optional) Comment on this decision.


This paper presents a different method for learning autoencoders with discrete hidden states (compared to recent discrete-like VAE type models). The reviewers in general like the method being proposed and are convinced that there is worth to the underlying proposal. However there are several shared complaints about the setup and writing of the paper.

- Several reviewers complained about the use of qualitative evaluation, particularly in the "Deciphering the latent code" section of the paper.
- One reviewer in particular had significant issues with the experimental setup of the paper and felt that there was insignificant quantitative evaluation, particularly using standard metrics for the task (compared to the metric introduced in the paper).
- There were further critiques about the "procedural" nature of the writing and the lack of formal justifications for the ideas introduced.

(optional) Comment on this decision.


Pros:
 - The paper proposes interesting new ideas on evaluating generative models.
 - Paper provides hints at interesting links between structural prediction and adversarial learning.
 - Authors propose a new dataset called Thin-8 to demonstrate the new ideas and argue that it is useful in general to study generative models.
 - The paper is well written and the authors have made a good attempt to update the paper after reviewer comments.

Cons:
- The proposed ideas are high level and the paper lack deeper analysis.
- Apart from demonstrating that the parametric divergences perform better than non-parametric divergences are interesting, but the reviewers think that practical importance of the results are weak in comparison to previous works.
With this analysis, the committee recommends this paper for workshop.

(optional) Comment on this decision.


The authors appear to have largely addressed the concerns of the reviewers and commenters regarding related work and experiments. The results are strong, and this will likely be a useful contribution for the graph neural network literature.

(optional) Comment on this decision.


The paper analyzes neural network with hidden layer of piecewise linear units, a single output, and a quadratic loss. The reviewers find the results incremental and not "surprising", and also complained about comparison with previous work. I think the topic is very pertinent, and definitely more relevant compared to studying multi-layer linear networks. Hence, I recommend the paper be presented in the workshop track.

(optional) Comment on this decision.


This is a meta-learning approach to model compression which trains 2 policies using RL to reduce the capacity (computational cost) of a trained network while maintaining performance, such that it can be effectively transferred to a smaller student network. The approach has similarities to recently proposed methods for architecture search, but is significantly different. The paper is well written and the experiments are clear and convincing. One of the reviews was unacceptable; I am not considering it (R1).

(optional) Comment on this decision.


This paper studies an important problem (visual relationship detection and generalization capabilities existing networks for this task). Unfortunately, all reviewers raise concerns (e.g. limited relations studied) and are largely on the fence about this paper. While this paper does not propose solutions, it does present interesting "negative results" that should get some visibility in the workshop track.

(optional) Comment on this decision.


Pros:
+ Clearly written paper.
+ Easily implemented algorithm that appears to have excellent scaling properties and can even improve on validation error in some cases.
+ Thorough evaluation against the state of the art.

Cons:
- No theoretical guarantees for the algorithm.

This paper belongs in ICLR if there is enough space.

(optional) Comment on this decision.


This paper proposes adding noise to the parameters of a deep network when taking actions in deep reinforcement learning to encourage exploration.  The method is simple but the authors demonstrate its effectiveness through thorough empirical analysis across a variety of reinforcement learning tasks (i.e. DQN, DDPG, and TRPO).  Overall the paper is clear, well written and the reviewers enjoyed it.  However, a common trend among the reviews was that the authors overstated their claims and contributions.  The reviewers called out some statements in particular (e.g. the discussion of ES and RL) which the authors appear to have addressed when comparing their revisions (thank you).  Overall, a clear, well written paper conveying a simple but effective idea for exploration that often works across a variety of RL tasks.  The authors also released open-source code along with their paper for reproducibility (as evidenced by the reproducibility study below), which is appreciated.

Pros:
- Clear and well written
- Thorough experiments across deep RL domains
- A simple strategy for exploration that is effective empirically

Cons:
- Not a panacea for exploration (although nothing really is)
- Claims are somewhat overstated
- Lacks a strong justification for the method other than that it is empirically effective and intuitive

(optional) Comment on this decision.


pros:
* novel explanation: skip connections <--> singualrities
* thorough analysis
* significant topic in understanding deep nets

cons:
* more rigorous theoretical analysis would be better

overall, the committee feels this paper would be interesting to have at ICLR.

(optional) Comment on this decision.


The paper proposes an approach to jointly learning a data clustering and latent representation.  The main selling point is that the number of clusters need not be pre-specified.  However, there are other hyperparameters and it is not clear why trading # clusters for other hyperparameters is a win.  The empirical results are not strong enough to overcome these concerns.

(optional) Comment on this decision.


This paper is intersting but has a few flaws that still need to be addressed. As one reviewer noted, "the authors seems to have simply applied the method of Andrychowicz et al. If they added some discussion and experiments clearly showing why this is a better way to improve the existing inference methods, the paper might have more impact.".
Overall, this work builds on existing work, but does not really dig deep enough for answers to these questions raised by the reviewers. The committee still feels this paper will be of great value at ICLR and recommends it for a workshop paper.

(optional) Comment on this decision.


This is a nice but very narrow study of domain invariance in a microscopic imaging application.  Since the problem is very general, the paper should include much more substantial context, e.g. discussion of various alternative methods (e.g. the ones cited in Sun et al. 2017).  In order to contribute to the broader ICLR community, ideally the paper would also include application to more than just the one task.

(optional) Comment on this decision.


Dear authors,

The reviewers all appreciated your work and agree that this a very good first step in an interesting direction.

(optional) Comment on this decision.


The paper proposes a data augmentation technique for image classification which consists in averaging two input images and using the label of one of them. The method is shown to outperform the baseline on the image classification task, the but evaluation doesn’t extend beyond that (to other tasks or alternative augmentation mechanisms); theoretical justification is also lacking.

(optional) Comment on this decision.


Thank you for submitting your paper to ICLR. The reviewers agree that the idea of sharing the approximating distribution across sets of variables is an interesting one and that the Omniglot experiments are thorough. However, although the authors make the nice addition of some simple examples during the revision period and a new table of quantitative results on Omniglot, the consensus is that the experimental results are not quite persuasive enough for publication. Adding a second dataset, such as mini-imagenet or the youtube faces dataset, would make the paper very strong.

(optional) Comment on this decision.


This work develops importance weighted autoencoder-like training but with sequential Monte Carlo.  The paper is interesting, well written and the methods are very timely (there are two highly related concurrent papers -  Naesseth et al. and Maddison et al.).  Initially, the reviewers shared concerns about the technical details of the paper, but the authors appear to addressed those and two reviews have been raised accordingly.   There is one outlier review (two 7s and one 3).  The 3 is the least thorough and has the lowest confidence (2) so that review is being weighted accordingly.

This appears to be a timely and interesting paper that is interesting to the community and warrants publication at ICLR.

Pros:
- Well written and clear
- An interesting approach
- Neat technical innovations
- Generative deep models are of great interest to the community (e.g. Variational Autoencoders)

Cons:
- Could include a better treatment of recent related literature
- Leaves a variety of open questions about specific details (i.e. from the reviews)

(optional) Comment on this decision.


Reviewers are unanimous that this is a reject.
A "class project" level presentation.
Errors in methodology and presentation.
No author rebuttal or revision

(optional) Comment on this decision.


R1 was neutral on the paper: they liked the problem, simplicity of the approach, and thought the custom pooling layer was novel, but raised issues with the motivation and design of experiments. R1 makes a reasonable point that training a CNN to classify time series, then throw away the output layer and use the internal representation in 1-NN classification is hard to justify in practice.
Results of the reproducibility report were good, though pointed out some issues around robustness to initialization and hyper-parameters. R2 gave a very strong score, though the review didn’t really expound on the paper’s merits. R3 thought the paper was well written but also sided with R1 on novelty. Overall, I side with R1 and R3. Particularly with respect to the practicality of the approach (as pointed out by both these reviewers). I would feel differently if the metric was used in another application beyond classification.

(optional) Comment on this decision.


The paper provides a useful analysis of the role of gradient penalties and the performance of the proposed approach in semi-supervised cases.

(optional) Comment on this decision.


The reviewers point out that most of the results are already known and are not novel. There are also issues with the presentation. Studying only depth 2 and depth 3 networks is very limiting.

(optional) Comment on this decision.


 + Empirically convincing and clearly explained application: a novel deep learning architecture and approach is shown to significantly outperform state-of-the-art in unsupervised anomaly detection.
 - No clear theoretical foundation and justification is provided for the approach
 - Connexion and differentiation from prior work on simulataneous learning representation and fitting a Gaussian mixture to it would deserve a much more thorough discussion / treatment.

(optional) Comment on this decision.


This paper presents a way to generate adversarial examples for text classification.   The method is simple -- finding semantically similar words and replacing them in sentences with high language model score.  The committee identifies weaknesses in this paper that resonate with the reviews below -- reviewer 1 suggests that the authors should closely compare with the work of Papernot et al, and the response to that suggestion is not satisfactory.  Addressing such concerns would make the paper stronger for a future venue.

(optional) Comment on this decision.


The paper proposes two regularizers for encouraging "clustered feature embeddings" (use of "disentangled" in the title is misleading). Reviewers have raised points about the lack of proper motivation and justification of the regularizers. There are also concerns on the experiments conducted to evaluate the method, including for hierarchical classification setting. Missing comparison with relevant baselines has also been pointed out as a weakness. I feel the work is not yet mature.

(optional) Comment on this decision.


The paper received weak scores: 4,4,5. R2 complained about clarity. R3's point about the lack of fully connected layers in current SOA deepnets is very valid and the authors response far from convincing. Unfortunately the major revision provided by the authors was not commented on by the reviewers, but many of the major shortcomings of the work still remain.
Generally, the paper is below the acceptance threshold, so cannot be accepted.

(optional) Comment on this decision.


All the reviews like the theoretical result presented in the paper which relates the gating mechanism of LSTMS (and GRUs) to time invariance / warping. The theoretical result is great and is used to propose a heuristic for setting biases when time invariance scales are known. The experiments are not mind-boggling, but none of the reviewers seem to think that's a show stopper.

(optional) Comment on this decision.


The subject of model evaluation will always be a contentious one, and the reviewers were not yet fully-convinced by the discussion. The points you bring up at the end of your rresponse already point to directions for improvement as well as a greater degree of precision and control.

(optional) Comment on this decision.


The authors present an environment for semantic navigation that is based on an existing dataset, SUNCG. Datasets/environments are important for deep RL research, and the contribution of this paper is welcome. However, this paper does not offer enough novelty in terms of approach/method and its claims are somewhat misleading, so it would probably be a better fit to publish it at a workshop.

(optional) Comment on this decision.


The paper builds on earlier work by Wang et al (2015) on Visual Concepts (VCs) and explores the use of VCs for few-shot learning setting for novel classes.

The work, as pointed out by two reviewers is somewhat incremental in nature, with main novelty being the demonstration of utilities of VCs for few shot learning. This would not have been a big limitation if the paper had a carefully conducted empirical evaluation providing insights on the effect of various configuration settings/hyperparameters on the performance in few shot learning, which two of the reviewers (Anon3, Anon2) state are missing. The paper falls short of the acceptance threshold in its current form.

PS: The authors posted a github link to the code on Jan 12 which may potentially compromise the anonymity of the submission (though it was after all the reviews were already in) https://openreview.net/forum?id=BJ_QxP1AZ&noteId=BJaIDpBEM

(optional) Comment on this decision.


The paper extends an existing work with three different frequency representations of audios and necessary network structure modifications for music style transfer.
It is an interesting study but does not provide "sufficiently novel or justified contributions compared to the baseline approach of Ulyanov and Lebedev". Also the revisions can not fully address reviewer 2's concerns.

(optional) Comment on this decision.


Graph neural networks (incl. GCNs) have been shown effective on a large range of tasks. However, it has been so far hard (i.e. computationally expensive or requiring the use of heuristics) to apply them to large graphs. This paper aims to address this problem and the solution is clean and elegant. The reviewers generally find it well written and interesting. There were some concerns about the comparison to GraphSAGE (an alternative approach), but these have been addressed in a subsequent revision.

+ an important problem
+ a simple approach
+ convincing results
+ clear and well written

(optional) Comment on this decision.


The reviewers are satisfied that this paper makes a good contribution to policy gradient methods.

(optional) Comment on this decision.


This is a well-written paper that aims to address an important problem. However, all the reviewers agreed that the experimental section is currently too weak for publication. They also made several good suggestions about improving the paper and the authors are encouraged to incorporate them before resubmitting.

(optional) Comment on this decision.


This is a strong paper presenting a very clean proof of a result that is similar, though now incomparable to one due to Bartlett et al. These bounds (and Bartlett's) are among the most promising norm-based bounds for NNs.

I would simply add that the citation of Dziugaite and Roy (2017) could be improved. There work also connects sharpness (or flatness) with generalization via the PAC-Bayes framework, and moreover, there bounds are nonvacuous.  Are the bounds in this paper nonvacuous, say, on MNIST for 60,000 training data, for the network learned by SGD?  If not, how close do they get to 1.0?

(optional) Comment on this decision.


Authors present an evaluation of end-to-end training connecting reconstruction network with detection network for lung nodules.

Pros:
- Optimizing a mapping jointly with the task may preserve more information that is relevant to the task.

Cons:
- Reconstruction network is not "needed" to generate an image -- other algorithms exist for reconstructing images from raw data. Therefore, adding the reconstruction network serves to essentially add more parameters to the neural network. As a baseline, authors should compare to a detection-only framework with a comparable number of parameters to the end-to-end system. Since this is not provided, the true benefit of end-to-end training cannot be assessed.

- Performance improvement presented is negligible

- Novelty is not clear / significant

(optional) Comment on this decision.


All reviewers agree the paper proposes an interesting setup and the main finding that "prosocial agents are able to learn to ground symbols using RL, but self-interested agents are not" progresses work in this area. R3 asked a number of detail-oriented questions and while they did not update their review based on the author response, I am satisfied by the answers.

(optional) Comment on this decision.


* presents a novel way analyzing GANs using the birthday paradox and provides a theoretical construction that shows bidirectional GANs cannot escape specific cases of mode collapse
* significant contribution to the discussion of whether GANs learn the target disctibution
* thorough justifications

(optional) Comment on this decision.


In principle, the idea behind the submission is sound: use a generative model (GANs in this case) to learn to generate desirable "goals" (subsets of the state space) and use that instead of uniform sampling for goals. Overall I tend to agree with Reviewer 3 in that the current set of results is not convincing in terms of it being able to generate goals in a high-dimensional state space, which seems to be be whole raison d'etre of GANs in this proposed method. The coverage experiment in Figure 5 seems like a good *illustration* of the method, but for this work to be convincing, I think we would need a more diverse set of experiments  (a la Figure 2) showing how this method performs on complicated tasks.

I encourage the authors to sharpen the definitions, as suggested by reviewers, and, if possible, provide experiments where the Assumptions being made in Section 3.3 are *violated* somehow (to actually test how the method fails in those cases).

(optional) Comment on this decision.


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

Summary: The approach is very promising, but more experimental work is still required to demonstrate significance.

(optional) Comment on this decision.


This paper proposes an approach for learning a sparsifying transform via a set of nonlinear transforms at learning time.  The presentation needs a lot of work.  The original paper was 17 pages long and very difficult to understand.  The revised paper is 12 pages long, which is still too long for the content.  The paper needs to better distinguish between the major and minor points.  It is still too difficult to judge the contribution.

(optional) Comment on this decision.


This paper proposes a real-time method for synthesizing human motion of highly complex styles. The key concern raised by R2 was that the method did not depart greatly from a standard LSTM: parts of the generated sequences are conditioned on generated data as opposed to ground truth data. However, the reviewer thought the idea was sensible and the results were very good in practice. R1 also agreed that the results were very good and asked for a more detailed analysis of conditioning length and some clarification. R3 brought up similarities to Professor Forcing (Goyal et al. 2016) -- also noted by R2 -- and Learning Human Motion Models for Long-term Predictions (Ghosh et al. 2017) -- noting not peer-reviewed. R3 also raised the open issue of how to best evaluate sequence prediction models like these. They brought up an interesting point, which was that the synthesized motions were low quality compared to recent works by Holden et al., however, they acknowledged that by rendering the characters this exposed the motion flaws. The authors responded to all of the reviews, committing to a comparison to Scheduled Sampling, though a comparison to Professor Forcing was proving difficult in the review timeline. While this paper may not receive the highest novelty score, I agree with the reviewers that it has merit. It is well written, has clear and reasonably thorough experiments, and the results are indeed good.

(optional) Comment on this decision.


All reviewers have acknowledged that the proposed regularization is novel and also results in some empirical improvements on the reported language modeling and image classification tasks. However there are serious concerns on writing and rigor (reviewers Anon1 and Anon3) of the paper. The authors have not uploaded any revision of the paper to address these concerns.

(optional) Comment on this decision.


This paper proposes a tree-structured tensor factorisation method for parameter reduction. The reviewers felt the paper was somewhat interesting, but agreed that more detail was needed in the method description, and that the experiments were on the whole uninformative. This seems like a promising research direction which needs more empirical work, but is not ready for publication as is.

(optional) Comment on this decision.


The reviewers agree that the formulation is novel and interesting, but they raised concerns regarding the motivation and the complexity of the approach. I find the authors' response mostly satisfying, and I ask them to improve the paper by incorporating the comments.

Detailed comments:
The maximum-entropy objective used in Eq. (13) reminds me of maximum-entropy RL objective in previous work including [Ziebart, 2010], [Azar, 12], [Nachum, 2017], and [Haarnoja, 2017].

(optional) Comment on this decision.


Thank you for submitting you paper to ICLR. This paper was enhanced noticeably in the rebuttal period and two of the reviewers improved their score as a result. There is a good range of experimental work on a number of different tasks. The addition of the comparison with Liu & Feng, 2016 to the appendix was sensible. Please make sure that the general conclusions drawn from this are explained in the main text and also the differences to Tran et al., 2017 (i.e. that the original model can also be implicit in this case).

(optional) Comment on this decision.


The reviewers agree that the paper presents nice results on model based RL with an ensemble of models. The limited novelty of the methods is questioned by one reviewer and briefly by the others, but they all agree that this paper's results justify its acceptance.

(optional) Comment on this decision.


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

Pros:
* The paper is a first attempt to investigate an under-studied area in neural MT (and potentially other applications of sequence-to-sequence models as well)
* This area might have a large impact; existing models such as Google Translate fail badly on the inputs described here
* Experiments are very carefully designed and thorough
* Experiments on not only synthetic but also natural noise add significant reliability to the results
* Paper is well-written and easy to follow

Cons:
* There may be better architectures for this problem than the ones proposed here
* Even the natural noise is not entirely natural, e.g. artificially constrained to exist within words
* Paper is not a perfect fit to ICLR (although ICLR is attempting to cast a wide net, so this alone is not a critical criticism of the paper)

This paper had uniformly positive reviews and has potential for large real-world impact.

(optional) Comment on this decision.


The paper proposes a method to robustify neural networks which is an important problem. They uses ideas from causality and create a model that would only depend on "stable" features ignoring the easy to manipulate ones. The paper has some interesting ideas, however, the main concern is regarding insufficient comparison to existing literature. One of the reviewers also has concerns regarding novelty of the approach.

(optional) Comment on this decision.


This paper aims to improve on the intrinsically motivated goal exploration framework by additionally incorporating representation learning for the space of goals. The paper is well motivated and follows a significant direction of research, as agreed by all reviewers. In particular, it provides a means for learning in complex environments, where manually designed goal spaces would not be available in practice. There had been significant concerns over the presentation of the paper, but the authors put great effort in improving the manuscript according to the reviewers’ suggestions, raising the average rating by 2 points after the rebuttal.

(optional) Comment on this decision.