hexuandeng/SupraReviewBench · Datasets at Hugging Face

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HJNGGmZ0Z	{ "title": "What is image captioning made of?", "abstract": "We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Paper reviewed by three experts who have provided detailed feedback. All three recommend rejection, and this AC sees no reason to overrule their recommendation. " } } ] ]	[ "This paper analyzes the effect of image features on image captioning. The authors propose to use a model similar to that of Vinyals et al., 2015 and change the image features it is conditioned on. The MSCOCO captioning and Flickr30K datasets are used for evaluation.\nIntroduction", "- The introduction to the pap...	[ [ 10 ], [ 11 ], [ 1 ], [ 2 ], [ 24 ], [ 13 ], [ 26 ], [ 29 ], [ 37 ], [ 27 ], [ 4, 17 ], [ 7 ], [ 12 ], [ 14, 18 ], [ 19 ], [ 21 ], [ 31 ], [ 32 ], [ 33 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_HJNGGmZ0Z.pdf	openreview	benchmark/MD/ICLR2018_HJNGGmZ0Z.md	ICLR 2018
HJr4QJ26W	{ "title": "Improving image generative models with human interactions", "abstract": "GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The reviewers agree that the idea of incorporating humans in the training of generative adversarial networks is interesting and worthwhile exploring. However, they felt that the paper fell short in providing strong support for their approach. The AC ag...	[ "+ Quality:\nThe paper discusses an interesting direction of incorporating humans in the training of a generative adversarial networks in the hope of improving generated samples. I personally find this exciting/refreshing and will be useful in the future of machine learning.", "However, the paper shows only preli...	[ [ 32 ], [ 7 ], [ 8 ], [ 14 ], [ 19 ], [ 31, 35 ], [ 33 ], [ 34 ], [ 36 ], [ 9 ], [ 27 ], [ 16 ], [ 21 ], [ 2 ], [ 5 ], [ 6 ], [ 13, 30 ], [ 18 ], [ 22 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...	benchmark/PDF/ICLR2018_HJr4QJ26W.pdf	openreview	benchmark/MD/ICLR2018_HJr4QJ26W.md	ICLR 2018
B1NOXfWR-	{ "title": "Neural Task Graph Execution", "abstract": "In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and depend...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Paper presents and interesting new direction, but the evaluation leaves many questions open, and situation with respect to state of the art is lacking" } } ] ]	[ "In the context of multitask reinforcement learning, this paper considers the problem of learning behaviours when given specifications of subtasks and the relationship between them, in the form of a task graph. The paper presents a neural task graph solver (NTS), which encodes this as a recursive-reverse-recursive ...	[ [ 6 ], [ 27 ], [ 5 ], [ 7 ], [ 13 ], [ 15 ], [ 33 ], [ 22 ], [ 21 ], [ 1 ], [ 3 ], [ 16 ], [ 24 ], [ 30 ], [ 0 ], [ 2, 12 ], [ 10 ], [ 11, 29 ], [ 32 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_B1NOXfWR-.pdf	openreview	benchmark/MD/ICLR2018_B1NOXfWR-.md	ICLR 2018
Byd-EfWCb	{ "title": "Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks", "abstract": "Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. Introducing the concept of an optimal representation space, we provide a si...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "this submission has two results; (1) it defines what it means for the optimal representation is, although this is rather uninteresting that it simply says that if the representation from a model is going to be used based on some given metric, the cost ...	[ "The authors provide some theoretical justification for why simple log-linear decoders perform better than RNN decoders for various unsupervised sentence similarity tasks. They also provide a simple method for improving the performance of RNN based models. Please find below my comments/questions/suggestions:", "1...	[ [ 9 ], [ 1 ], [ 11, 16, 33 ], [ 25 ], [ 5, 6, 7 ], [ 8 ], [ 10 ], [ 24 ], [ 27 ], [ 34 ], [ 37 ], [ 38 ], [ 32 ], [ 3 ], [ 20 ], [ 21 ], [ 22 ], [ 26 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_Byd-EfWCb.pdf	openreview	benchmark/MD/ICLR2018_Byd-EfWCb.md	ICLR 2018
BJ4prNx0W	{ "title": "Learning what to learn in a neural program", "abstract": "Learning programs with neural networks is a challenging task, addressed by a long line of existing work. It is difficult to learn neural networks which will generalize to problem instances that are much larger than those used during training. Fur...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "This paper is novel, but relatively incremental and relatively niche; the reviewers (despite discussion) are still unsure why this approach is needed." } } ] ]	[ "Quality\nThe paper is well-written and clear, and includes relevant comparisons to previous work (NPI and recursive NPI).", "Clarity\nThe paper is clearly written.", "Originality\nTo my knowledge the method proposed in this work is novel. It is the first to study constructing minimal training sets for NPI give...	[ [ 0 ], [ 1 ], [ 11 ], [ 7 ], [ 2 ], [ 3 ], [ 15 ], [ 12 ], [ 8 ], [ 16, 17 ], [ 14 ], [ 4 ], [ 5, 9 ], [ 6 ], [ 10 ], [ 13 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT", "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_BJ4prNx0W.pdf	openreview	benchmark/MD/ICLR2018_BJ4prNx0W.md	ICLR 2018
r15kjpHa-	{ "title": "Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing", "abstract": "In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "All reviewers are unanimous that the paper is below threshold for acceptance. The authors have not provided rebuttals, but merely perfunctory generic responses.\n\nI think the most important criticism is that the approach is \"very ad-hoc.\" I would ...	[ "The authors suggest using a mixture of shared and individual rewards within a MARL environment to induce cooperation among independent agents. They show that on their specific application this can lead to a better overall global performance than purely sharing the global signal, or using just the independent rewar...	[ [ 2 ], [ 3 ], [ 6 ], [ 7 ], [ 8 ], [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 22 ], [ 23 ], [ 1, 5 ], [ 4 ], [ 9 ], [ 20 ], [ 17 ], [ 19 ], [ 21 ],...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_r15kjpHa-.pdf	openreview	benchmark/MD/ICLR2018_r15kjpHa-.md	ICLR 2018
BJj6qGbRW	{ "title": "Few-Shot Learning with Graph Neural Networks", "abstract": "We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-p...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 7, ...	[ [ { "role": "PC", "data": { "comment": "All reviewers agree that the proposed method is novel and experiments do a good job in establishing its value for few-shot learning. Most the concerns raised by the reviewers on experimental protocols have been addressed in the author response and revi...	[ "This paper studies the problem of one-shot and few-shot learning using the Graph Neural Network (GNN) architecture that has been proposed and simplified by several authors. The data points form the nodes of the graph with the edge weights being learned, using ideas similar to message passing algorithms similar to ...	[ [ 11, 29 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 1, 6, 10 ], [ 12 ], [ 14 ], [ 25 ], [ 28 ], [ 3 ], [ 4 ], [ 26 ], [ 8 ], [ 16 ], [ 17 ], [ 31 ], [ 7 ], [ 13 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_BJj6qGbRW.pdf	openreview	benchmark/MD/ICLR2018_BJj6qGbRW.md	ICLR 2018
BkPrDFgR-	{ "title": "Piecewise Linear Neural Networks verification: A comparative study", "abstract": "The success of Deep Learning and its potential use in many important safety-\ncritical applications has motivated research on formal verification of Neural Net-\nwork (NN) models. Despite the reputation of learned NN model...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "All three reviewers are in agreement that this paper is not ready for ICLR in its current state. Given the pros/cons, the committee feels the paper is not ready for acceptance in its current form." } } ] ]	[ "The paper studies methods for verifying neural nets through their piecewise", "linear structure. The authors survey different methods from the literature,", "propose a novel one, and evaluate them on a set of benchmarks.", "A major drawback of the evaluation of the different approaches is that", "everythin...	[ [ 2 ], [ 7 ], [ 8 ], [ 4 ], [ 15 ], [ 13 ], [ 17 ], [ 1, 10 ], [ 3, 11 ], [ 18 ], [ 19 ], [ 5 ], [ 6 ], [ 16 ], [ 0 ], [ 9 ], [ 12 ], [ 14 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5, 6, ...	benchmark/PDF/ICLR2018_BkPrDFgR-.pdf	openreview	benchmark/MD/ICLR2018_BkPrDFgR-.md	ICLR 2018
BJypUGZ0Z	{ "title": "Accelerating Neural Architecture Search using Performance Prediction", "abstract": "Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that standard frequentist reg...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes to use simple regression models for predicting the accuracy of a neural network based on its initial training curve, architecture, and hyper-parameters; this can be used for speeding up architecture search. While this is an interesti...	[ "This paper explores the use of simple models for predicting the final", "validation performance of a neural network, from intermediate values", "during training. It uses support vector regression to show that a", "relatively small number of samples of hyperparameters, architectures,", "and validation time...	[ [ 28 ], [ 48 ], [ 13 ], [ 14 ], [ 15, 29 ], [ 20 ], [ 27 ], [ 30 ], [ 42 ], [ 43, 44 ], [ 50 ], [ 0 ], [ 6 ], [ 7 ], [ 19 ], [ 21, 38 ], [ 25 ], [ 3 ], [ 4 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ] } ], "category": [ "QUAL-CMP" ] },...	benchmark/PDF/ICLR2018_BJypUGZ0Z.pdf	openreview	benchmark/MD/ICLR2018_BJypUGZ0Z.md	ICLR 2018
S1uxsye0Z	{ "title": "Adaptive Dropout with Rademacher Complexity Regularization", "abstract": "We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-ad...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "AC", "data": { "comment": "Are the authors aware of the work by David McAllister using PAC-Bayes bounds to analyze dropout? Last I saw, it was not mentioned in the paper. IT seems like important related work. Could the authors, very quickly (!), comment as to the relationship...	[ "This paper studies the adjustment of dropout rates which is a useful tool to prevent the overfitting of deep neural networks. The authors derive a generalization error bound in terms of dropout rates. Based on this, the authors propose a regularization framework to adaptively select dropout rates. Experimental res...	[ [ 30 ], [ 1 ], [ 2 ], [ 3 ], [ 5 ], [ 8 ], [ 14 ], [ 18 ], [ 32 ], [ 6 ], [ 7 ], [ 17 ], [ 23 ], [ 19 ], [ 20 ], [ 22 ], [ 28 ], [ 4 ], [ 9, 10, 11, 12 ],...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_S1uxsye0Z.pdf	openreview	benchmark/MD/ICLR2018_S1uxsye0Z.md	ICLR 2018
S1TgE7WR-	{ "title": "Covariant Compositional Networks For Learning Graphs", "abstract": "Most existing neural networks for learning graphs deal with the issue of permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that ...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "This is a good contribution, with the potential to become extremely good and significant if presentation is substantially improved.\nAll reviewers comment on the lack of clarity of the paper, especially concerning its central contributions (Section 4 a...	[ "Thank you for your contribution to ICLR. The paper covers a very interesting topic and presents some though-provoking ideas.", "The paper introduces \"covariant compositional networks\" with the purpose of learning graph representations. An example application also covered in the experimental section is graph cl...	[ [ 1, 10, 20, 22 ], [ 4 ], [ 5 ], [ 6 ], [ 7, 9 ], [ 17 ], [ 18 ], [ 26 ], [ 16, 31 ], [ 25 ], [ 19 ], [ 23 ], [ 27 ], [ 28 ], [ 29 ], [ 30 ], [ 32 ], [ 33 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "incorrect", "correct", "correct", "incorrect", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct"...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4, 5 ] }, { ...	benchmark/PDF/ICLR2018_S1TgE7WR-.pdf	openreview	benchmark/MD/ICLR2018_S1TgE7WR-.md	ICLR 2018
rJk51gJRb	{ "title": "Adversarial Policy Gradient for Alternating Markov Games", "abstract": "Policy gradient reinforcement learning has been applied to two-player alternate-turn zero-sum games, e.g., in AlphaGo, self-play REINFORCE was used to improve the neural net model after supervised learning. In this paper, we emphasi...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...	[ [ { "role": "PC", "data": { "comment": "The reviewers agree that the paper is below threshold for acceptance in the main track (one with very low confidence), but they favor submitting the paper to the workshop track.\n\nThe paper considers policy gradient methods for two-player zero-sum Alt...	[ "This paper is outside of my area of expertise, so I'll just provide a light review:", "- the idea of assuming that the opponent will take the worst possible action is reasonable in widely used in classic search, so making value functions follow this intuition seems sensible,", "- but somehow I wonder if this i...	[ [ 6 ], [ 13 ], [ 14 ], [ 10 ], [ 12 ], [ 2 ], [ 11 ], [ 17 ], [ 3 ], [ 5 ], [ 9 ], [ 7 ], [ 8 ], [ 16 ], [ 19 ], [ 1 ], [ 15 ], [ 0 ], [ 4 ], [ 18 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-MET" ] }, ...	benchmark/PDF/ICLR2018_rJk51gJRb.pdf	openreview	benchmark/MD/ICLR2018_rJk51gJRb.md	ICLR 2018
ByzvHagA-	{ "title": "Disentangled activations in deep networks", "abstract": "Deep neural networks have been tremendously successful in a number of tasks.\nOne of the main reasons for this is their capability to automatically\nlearn representations of data in levels of abstraction,\nincreasingly disentangling the data as th...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "The novelty of the paper is limited and it lacks on comparisons with relevant baselines, as pointed out by the reviewers. " } } ] ]	[ "The authors propose a penalization term that enforces decorrelation between the dimensions of the representation", "They show that it can be included as additional term in cost functions to train generic models.", "The idea is simple and it seems to work for the presented examples.", "However, they talk abou...	[ [ 5 ], [ 2 ], [ 9 ], [ 7 ], [ 8, 18 ], [ 4 ], [ 15 ], [ 19 ], [ 20 ], [ 1 ], [ 6 ], [ 11 ], [ 12 ], [ 13 ], [ 16, 21 ], [ 3 ], [ 10 ], [ 14 ], [ 0 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL-EXP"...	benchmark/PDF/ICLR2018_ByzvHagA-.pdf	openreview	benchmark/MD/ICLR2018_ByzvHagA-.md	ICLR 2018
r1NYjfbR-	{ "title": "Generative networks as inverse problems with Scattering transforms", "abstract": "Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4 ] }, "scores": { "Solid": null, "Presentation": null, "No...	[ [ { "role": "PC", "data": { "comment": "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, wa...	[ "After a first manuscript that needed majors edits, the revised version", "offers an interesting GAN approach based the scattering transform.", "Approach is well motivated with proper references to the recent literature.", "Experiments are not state of the art but clearly demonstrate that the", "proposed ap...	[ [ 18 ], [ 8 ], [ 4, 10 ], [ 5 ], [ 15 ], [ 21 ], [ 22, 23 ], [ 13 ], [ 0, 3 ], [ 1 ], [ 6 ], [ 7 ], [ 11, 14 ], [ 2 ], [ 16 ], [ 12 ], [ 19 ], [ 20 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QU...	benchmark/PDF/ICLR2018_r1NYjfbR-.pdf	openreview	benchmark/MD/ICLR2018_r1NYjfbR-.md	ICLR 2018
BJk7Gf-CZ	{ "title": "Global Optimality Conditions for Deep Neural Networks", "abstract": "We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, and despite recent progress, our understanding of this l...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "Understanding global optimality conditions for deep nets even in the restricted case of linear layers is a valuable contribution. Please add clarifications to ways in which the paper goes beyond the results of Kawaguchi'16, which was the main concern e...	[ "Summary:\nThe paper gives theoretical results regarding the existence of local minima in the objective function of deep neural networks.\nIn particular:", "- in the case of deep linear networks, they characterize whether a critical point is a global optimum or a saddle point by a simple criterion. This improves ...	[ [ 4 ], [ 7 ], [ 9 ], [ 8 ], [ 10 ], [ 1 ], [ 5 ], [ 12 ], [ 2 ], [ 3 ], [ 6 ], [ 13 ], [ 0 ], [ 11 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, {...	benchmark/PDF/ICLR2018_BJk7Gf-CZ.pdf	openreview	benchmark/MD/ICLR2018_BJk7Gf-CZ.md	ICLR 2018
SyL9u-WA-	{ "title": "Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization", "abstract": "Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs). In this paper, we presen...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...	[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clearly written paper.\n+ Good theoretical analysis of the expressivity of the proposed model.\n+ Efficient model update is appealing.\n+ Reviewers appreciated the addition of results on the copy and adding tasks in Appendix C.\n\nCons:\n- Eva...	[ "This paper proposed a new parametrization scheme for weight matrices in neural network based on the Householder reflectors to solve the gradient vanishing and exploding problems in training. The proposed method improved two previous papers:", "1) stronger expressive power than Mahammedi et al. (2017),", "2) f...	[ [ 10 ], [ 6 ], [ 3 ], [ 4 ], [ 5 ], [ 11 ], [ 1 ], [ 2 ], [ 8 ], [ 12 ], [ 14 ], [ 13 ], [ 9 ], [ 15 ], [ 0 ], [ 7 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-CMP" ] }, ...	benchmark/PDF/ICLR2018_SyL9u-WA-.pdf	openreview	benchmark/MD/ICLR2018_SyL9u-WA-.md	ICLR 2018
HJcSzz-CZ	{ "title": "Meta-Learning for Semi-Supervised Few-Shot Classification", "abstract": "In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized m...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "The paper extends the earlier work on Prototypical networks to semi-supervised setting. Reviewers largely agree that the paper is well-written. There are some concerns on the incremental nature of the paper wrt to the novelty aspect but in the light of...	[ "This paper is an extension of the “prototypical network” which will be published in NIPS 2017. The classical few-shot learning has been limited to using the unlabeled data, while this paper considers employing the unlabeled examples available to help train each episode. The paper solves a new semi-supervised situa...	[ [ 8 ], [ 12 ], [ 2 ], [ 6 ], [ 9 ], [ 0 ], [ 1, 5 ], [ 3 ], [ 13 ], [ 4 ], [ 10 ], [ 11 ], [ 7 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-PROB", "QUAL-REP", "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { ...	benchmark/PDF/ICLR2018_HJcSzz-CZ.pdf	openreview	benchmark/MD/ICLR2018_HJcSzz-CZ.md	ICLR 2018
BkUHlMZ0b	{ "title": "Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach", "abstract": "The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, li...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper proposes a new metric to evaluate the robustness of neural networks to adversarial attacks. This metric comes with theoretical guarantees and can be efficiently computed on large-scale neural networks.\n\nReviewers were generally positive ab...	[ "Summary\nThe authors present CLEVER, an algorithm which consists in evaluating the (local) Lipschitz constant of a trained network around a data point. This is used to compute a lower-bound on the minimal perturbation of the data point needed to fool the network.", "The method proposed in the paper already exist...	[ [ 6 ], [ 8 ], [ 15 ], [ 19 ], [ 3 ], [ 9 ], [ 2, 5 ], [ 14 ], [ 21 ], [ 1 ], [ 4 ], [ 13 ], [ 17, 22 ], [ 10, 18 ], [ 11 ], [ 7 ], [ 0 ], [ 12 ], [ 16 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "incorrect", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_BkUHlMZ0b.pdf	openreview	benchmark/MD/ICLR2018_BkUHlMZ0b.md	ICLR 2018
Byk4My-RZ	{ "title": "Flexible Prior Distributions for Deep Generative Models", "abstract": "We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper presents a method for learning more flexible prior distributions for GANs by learning another distribution on top of the latent codes for training examples. It's reminiscent of layerwise training of deep generative models. This seems like a ...	[ "Summary:\nThe paper proposes to learn new priors for latent codes z for GAN training. for this the paper shows that there is a mismatch between the gaussian prior and an estimated of the latent codes of real data by reversal of the generator . To fix this the paper proposes to learn a second GAN to learn the pri...	[ [ 17 ], [ 22 ], [ 1, 14 ], [ 23 ], [ 2 ], [ 3 ], [ 6 ], [ 21 ], [ 5 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 15 ], [ 16 ], [ 4 ], [ 12 ], [ 20 ], [ 24 ], [ 25 ],...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "i...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...	benchmark/PDF/ICLR2018_Byk4My-RZ.pdf	openreview	benchmark/MD/ICLR2018_Byk4My-RZ.md	ICLR 2018
rkONG0xAW	{ "title": "Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement", "abstract": "This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Byt...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...	[ [ { "role": "PC", "data": { "comment": "This is an interesting paper and addresses an important problem of neural networks with memory constrains. New experiments have been added that add to the paper, but the full impact of the paper is not yet realised, needing further exploration of model...	[ "The idea of this work is fairly simple. Two main problems exist in end devices for deep learning: power and memory. There have been a series of works showing how to discretisize neural networks. This work, discretisize a NN incrementally. It does so in the following way: First, we train the network with the memory...	[ [ 11 ], [ 5, 13 ], [ 7 ], [ 12 ], [ 9 ], [ 3 ], [ 4 ], [ 6 ], [ 10 ], [ 15 ], [ 16 ], [ 2 ], [ 1 ], [ 8 ], [ 0 ], [ 14 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4 ] }, { "role": "Au...	benchmark/PDF/ICLR2018_rkONG0xAW.pdf	openreview	benchmark/MD/ICLR2018_rkONG0xAW.md	ICLR 2018
H1eJxngCW	{ "title": "DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension", "abstract": "We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC c...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overa...	[ [ { "role": "PC", "data": { "comment": "This is a (question answering) dataset paper with some baseline models.\n\nThe evaluation metric seems far from ideal and not quite ready for prime-time yet. They use F1 and Exact Match - these metrics make sense for extractive question answering syste...	[ "This paper presents a useful dataset for testing reading comprehension while avoiding significant lexical overlap between question and document. The paper rightly mentions that existing reading comprehension datasets (e.g. SQuAD) where the current methods are already performing at the human level largely due to la...	[ [ 6 ], [ 8 ], [ 13 ], [ 18 ], [ 20 ], [ 0, 1, 4 ], [ 2 ], [ 3 ], [ 9 ], [ 12 ], [ 16 ], [ 21 ], [ 15 ], [ 14 ], [ 17 ], [ 7 ], [ 10 ], [ 19 ], [ 5 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-EXP" ]...	benchmark/PDF/ICLR2018_H1eJxngCW.pdf	openreview	benchmark/MD/ICLR2018_H1eJxngCW.md	ICLR 2018
BkfEzz-0-	{ "title": "Neuron as an Agent", "abstract": "Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\\em Neuron as an A...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...	[ [ { "role": "PC", "data": { "comment": "\nThe reviewers have significantly different views, with one strongly negative,\none strongly positive, and one borderline negative. However, all three\nreviews seem to regard the NaaA framework as a very interesting and novel approach to training neu...	[ "In this paper, the authors present a novel way to look at a neural network such that each neuron (node) in the network is an agent working to optimize its reward. The paper shows that by appropriately defining the neuron level reward function, the model can learn a better policy in different tasks.", "For exampl...	[ [ 38 ], [ 1 ], [ 5 ], [ 30 ], [ 32 ], [ 34 ], [ 35 ], [ 4, 14 ], [ 24 ], [ 2 ], [ 7 ], [ 8 ], [ 9, 21 ], [ 10 ], [ 11 ], [ 12 ], [ 15 ], [ 16 ], [ 18 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 5 ] }, { ...	benchmark/PDF/ICLR2018_BkfEzz-0-.pdf	openreview	benchmark/MD/ICLR2018_BkfEzz-0-.md	ICLR 2018
HyyP33gAZ	{ "title": "Activation Maximization Generative Adversarial Nets", "abstract": "Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label inf...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "The authors investigate various class aware GANs and provide extensive analysis of their ability to address mode collapse and sample quality issues. Based on this analysis they propose an extension called Activation Maximization-GAN which tries to push...	[ "I thank the authors for the thoughtful responses and updated manuscript.", "Although the manuscript is improved, I still feel it is unfocused and may be substantially improved, thus my review score remains unchanged.", "The authors describe a new version of a generative adversarial network (GAN) for generating...	[ [ 17 ], [ 1 ], [ 4 ], [ 16 ], [ 18 ], [ 0 ], [ 12 ], [ 14 ], [ 15 ], [ 23 ], [ 25 ], [ 13 ], [ 19 ], [ 22 ], [ 7 ], [ 9 ], [ 2 ], [ 5 ], [ 8 ], [ 11 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] }, { "role"...	benchmark/PDF/ICLR2018_HyyP33gAZ.pdf	openreview	benchmark/MD/ICLR2018_HyyP33gAZ.md	ICLR 2018
ryCM8zWRb	{ "title": "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations", "abstract": "RNNs have been shown to be excellent models for sequential data and in particular for session-based user behavior. The use of RNNs provides impressive performance benefits over classical methods in session-based ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "While the use of RNNs for building session-based recommender systems is certainly an important class of applications, the main strength of the paper is to propose and benchmark practical modifications to prior RNN-based systems that lead to performance...	[ "This is an interesting paper that analyzes existing loss functions for session-based recommendations. Based on the result of these analysis the authors propose two novel losses functions which add a weighting to existing ranking-based loss functions. These novelties are meant to improve issues related to vanishing...	[ [ 7, 9 ], [ 16 ], [ 1 ], [ 6 ], [ 14 ], [ 15 ], [ 4 ], [ 0, 10 ], [ 11 ], [ 12 ], [ 19 ], [ 5 ], [ 13 ], [ 2 ], [ 18 ], [ 17 ], [ 3 ], [ 8 ] ]	[ "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-MTH", "ORIG-ANL", "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], ...	benchmark/PDF/ICLR2018_ryCM8zWRb.pdf	openreview	benchmark/MD/ICLR2018_ryCM8zWRb.md	ICLR 2018
S1Euwz-Rb	{ "title": "Compositional Attention Networks for Machine Reasoning", "abstract": "We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural networks are effective at learning and general...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "PROS:\n1. Good results on CLEVER datasets\n2. Writing is clear\n3. The MAC unit is novel and interesting.\n4. Ablation experiments are helpful\n\nCONS:\nThe authors overstate the degree to which they are doing \"sound\" and \"transparent\" reasoning. ...	[ "This paper describes a new model architecture for machine reasoning.", "In contrast\nto previous approaches that explicitly predict a question-specific module", "network layout, the current paper introduces a monolithic feedforward network", "with iterated rounds of attention and memory. On a few variants of...	[ [ 16 ], [ 37 ], [ 12 ], [ 13 ], [ 14 ], [ 15 ], [ 21 ], [ 28 ], [ 38 ], [ 4 ], [ 1 ], [ 2 ], [ 3, 6 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 11 ], [ 17 ], [ 22 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6, ...	benchmark/PDF/ICLR2018_S1Euwz-Rb.pdf	openreview	benchmark/MD/ICLR2018_S1Euwz-Rb.md	ICLR 2018
S1WRibb0Z	{ "title": "Expressive power of recurrent neural networks", "abstract": "Deep neural networks are surprisingly efficient at solving practical tasks,\nbut the theory behind this phenomenon is only starting to catch up with\nthe practice. Numerous works show that depth is the key to this efficiency.\nA certain class ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...	[ [ { "role": "PC", "data": { "comment": "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 ...	[ "In this paper, the expressive power of neural networks characterized by tensor train (TT) decomposition, a chain-type tensor decomposition, is investigated. Here, the expressive power refers to the rank of tensor decomposition, i.e., the number of latent components. The authors compare the complexity of TT-type ne...	[ [ 18 ], [ 1 ], [ 5 ], [ 10 ], [ 13 ], [ 14 ], [ 8 ], [ 2 ], [ 4 ], [ 15 ], [ 7 ], [ 11 ], [ 17 ], [ 6 ], [ 16 ], [ 0 ], [ 3 ], [ 9 ], [ 12 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...	benchmark/PDF/ICLR2018_S1WRibb0Z.pdf	openreview	benchmark/MD/ICLR2018_S1WRibb0Z.md	ICLR 2018
rk49Mg-CW	{ "title": "Stochastic Variational Video Prediction", "abstract": "Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural image...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "Not quite enough for an oral but a very solid poster." } } ] ]	[ "Quality: above threshold\nClarity: above threshold, but experiment details are missing.\nOriginality: slightly above threshold.", "Significance: above threshold", "Pros:\nThis paper proposes a stochastic variational video prediction model. It can be used for prediction in optionally available external action c...	[ [ 20 ], [ 21 ], [ 4 ], [ 8 ], [ 6 ], [ 22 ], [ 0 ], [ 1 ], [ 2 ], [ 7 ], [ 10 ], [ 12, 14, 19 ], [ 13 ], [ 18 ], [ 15 ], [ 3 ], [ 11 ], [ 17 ], [ 9 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4 ] }, { ...	benchmark/PDF/ICLR2018_rk49Mg-CW.pdf	openreview	benchmark/MD/ICLR2018_rk49Mg-CW.md	ICLR 2018
S1DWPP1A-	{ "title": "Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration", "abstract": "Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been sho...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "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...	[ "This paper introduces a representation learning step in the Intrinsically Motivated Exploration Process (IMGEP) framework.", "Though this work is far from my expertise fields I find it quite easy to read and a good introduction to IMGEP.", "Nevertheless I have some major concerns that prevent me from giving a...	[ [ 22 ], [ 29 ], [ 42 ], [ 37 ], [ 38 ], [ 7 ], [ 25 ], [ 27 ], [ 31 ], [ 32 ], [ 36 ], [ 39, 43 ], [ 40 ], [ 44 ], [ 6 ], [ 9 ], [ 41 ], [ 24 ], [ 11 ], [ 1...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5 ] }, ...	benchmark/PDF/ICLR2018_S1DWPP1A-.pdf	openreview	benchmark/MD/ICLR2018_S1DWPP1A-.md	ICLR 2018
BJ8c3f-0b	{ "title": "Auto-Encoding Sequential Monte Carlo", "abstract": "We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the effic...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "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...	[ "Overall:", "I had a really hard time reading this paper because I found the writing to be quite confusing.", "For this reason I cannot recommend publication as I am not sure how to evaluate the paper’s contribution.", "Summary\nThe authors study state space models in the unsupervised learning case. We have a...	[ [ 12 ], [ 35 ], [ 13 ], [ 38 ], [ 0 ], [ 2 ], [ 3, 6 ], [ 4 ], [ 10 ], [ 15 ], [ 16 ], [ 17 ], [ 28 ], [ 33 ], [ 36 ], [ 37 ], [ 29 ], [ 31, 32 ], [ 7, 9 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 3, 4, 5 ] ...	benchmark/PDF/ICLR2018_BJ8c3f-0b.pdf	openreview	benchmark/MD/ICLR2018_BJ8c3f-0b.md	ICLR 2018
B1ZZTfZAW	{ "title": "Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs", "abstract": "Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Overall I agree with the assessment of R1 that the paper touches on many interesting issues (deep learning for time series, privacy-respecting ML, simulated-to-real-world adaptation) but does not make a strong contribution to any of these. Especially w...	[ "The authors propose to use synthetic data generated by GANs as a replacement for personally identifiable data in training ML models for privacy-sensitive applications such as medicine.", "In particular it demonstrates adversarial training of a recurrent generator for an ICU monitoring multidimensional time serie...	[ [ 20 ], [ 10 ], [ 1, 15 ], [ 5 ], [ 7 ], [ 2 ], [ 8 ], [ 12 ], [ 13 ], [ 18 ], [ 19 ], [ 17 ], [ 11 ], [ 16 ], [ 0 ], [ 3 ], [ 4 ], [ 6 ], [ 9 ], [ 14 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 4 ] }, { ...	benchmark/PDF/ICLR2018_B1ZZTfZAW.pdf	openreview	benchmark/MD/ICLR2018_B1ZZTfZAW.md	ICLR 2018
SJTB5GZCb	{ "title": "Extending the Framework of Equilibrium Propagation to General Dynamics", "abstract": "The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward pha...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": " + interesting novel extension of equilibrium propagation, as a biologically more plausible alternative to backpropagation, with encouraging initial experimental validation.\n - currently lacks theoretical guarantees regarding convergence of the algor...	[ "The manuscript discusses a learning algorithm that is based on the equilibrium propagation method, which can be applied to networks with asymmetric connections. This extension is interesting, but the results seem to be incomplete and missing necessary additional analyses.", "Therefore, I do not recommend accepta...	[ [ 4 ], [ 11 ], [ 0 ], [ 2 ], [ 5 ], [ 8 ], [ 10, 16 ], [ 12, 17 ], [ 13 ], [ 14 ], [ 15 ], [ 3 ], [ 6 ], [ 7 ], [ 9 ], [ 18 ], [ 1 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 25, 26, 27, 28 ] } ], "category": [ "QUAL-EXP", "QUAL-MET", "QUAL-STA" ]...	benchmark/PDF/ICLR2018_SJTB5GZCb.pdf	openreview	benchmark/MD/ICLR2018_SJTB5GZCb.md	ICLR 2018
rybDdHe0Z	{ "title": "Sequence Transfer Learning for Neural Decoding", "abstract": "A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. In typical applications, decoders are constructed for individual subjects and with limited data leading to restr...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper tries to establish that LSTMs are suitable for modeling neural signals from the brain. However, the committee and most reviewers find that results are inconclusive. Results are mixed across subjects. We think it would have been far more i...	[ "The paper describes an approach to use LSTM’s for finger classification based on ECOG. and a transfer learning extension of which two variations exists. From the presented results, the LSTM model is not an improvement over a basic linear model. The transfer learning models performs better than subject specific mod...	[ [ 41 ], [ 1 ], [ 35 ], [ 2 ], [ 7 ], [ 12, 17 ], [ 19 ], [ 20 ], [ 3 ], [ 4, 22 ], [ 8 ], [ 9 ], [ 11 ], [ 13 ], [ 25 ], [ 32 ], [ 34 ], [ 36 ], [ 37 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...	benchmark/PDF/ICLR2018_rybDdHe0Z.pdf	openreview	benchmark/MD/ICLR2018_rybDdHe0Z.md	ICLR 2018
Sy5OAyZC-	{ "title": "On the Use of Word Embeddings Alone to Represent Natural Language Sequences", "abstract": "To construct representations for natural language sequences, information from two main sources needs to be captured: (i) semantic meaning of individual words, and (ii) their compositionality. These two types of in...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This work presents a strong baseline model for several NLP-ish tasks such as document classification, sentence classification, representation learning based NLI, and text matching. In terms of originality, reviewers found that \"there is not much contr...	[ "This paper empirically investigates the differences realized by using compositional functions over word embeddings as compared to directly operating the word embeddings.", "That is, the authors seek to explore the advantages afforded by RNN/CNN based models that induce intermediate semantic representations of te...	[ [ 22 ], [ 10 ], [ 11 ], [ 16 ], [ 25 ], [ 2 ], [ 26 ], [ 24 ], [ 7, 14 ], [ 3 ], [ 4 ], [ 5 ], [ 6, 13 ], [ 9 ], [ 15 ], [ 17 ], [ 18 ], [ 19 ], [ 21 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_Sy5OAyZC-.pdf	openreview	benchmark/MD/ICLR2018_Sy5OAyZC-.md	ICLR 2018
B1EPYJ-C-	{ "title": "Federated Learning: Strategies for Improving Communication Efficiency", "abstract": "Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slo...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...	[ [ { "role": "PC", "data": { "comment": "The authors study the problem of reducing uplink communication costs in training a ML model where the training data is distributed over many clients. The reviewers consider the problem interesting, but have concerns about the extent of the novelty of...	[ "This paper proposes several client-server neural network gradient update strategies aimed at reducing uplink usage while maintaining prediction performance. The main approaches fall into two categories: structured, where low-rank/sparse updates are learned, and sketched, where full updates are either sub-sampled ...	[ [ 15 ], [ 1, 8 ], [ 19 ], [ 11 ], [ 22 ], [ 3 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 2, 5 ], [ 7 ], [ 10 ], [ 23 ], [ 4 ], [ 21 ], [ 6 ], [ 18 ], [ 0 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_B1EPYJ-C-.pdf	openreview	benchmark/MD/ICLR2018_B1EPYJ-C-.md	ICLR 2018
BJuWrGW0Z	{ "title": "Dynamic Neural Program Embeddings for Program Repair", "abstract": "Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization. However, most existing program embeddings are base...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...	[ [ { "role": "PC", "data": { "comment": "PROS:\n\n1. Interesting and clearly useful idea\n2. The paper is clearly written.\n3. This work doesn't seem that original from an algorithmic point of view since Reed & De Freitas (2015) and Cai et. al (2017) among others have considered using executi...	[ "The authors present 3 architectures for learning representations of programs from execution traces. In the variable trace embedding, the input to the model is given by a sequence of variable values. The state trace embedding combines embeddings for variable traces using a second recurrent encoder. The dependency e...	[ [ 3 ], [ 4 ], [ 1 ], [ 0, 7 ], [ 2, 14 ], [ 15 ], [ 8 ], [ 6 ], [ 13 ], [ 16 ], [ 11 ], [ 17 ], [ 9 ], [ 10 ], [ 5 ], [ 12 ], [ 18 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_BJuWrGW0Z.pdf	openreview	benchmark/MD/ICLR2018_BJuWrGW0Z.md	ICLR 2018
ry-TW-WAb	{ "title": "Variational Network Quantization", "abstract": "In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced...	Accept (Poster)	[ [ { "role": "Author", "data": { "value": { "comment": [ 0, 1, 2, 3, 4, 5, 6 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper...	[ [ { "role": "PC", "data": { "comment": "The paper presents a variational Bayesian approach for quantising neural network weights and makes interesting and useful steps in this increasingly popular area of deep learning." } } ] ]	[ "The authors would like to correct four typos in the current version of the manuscript:", "-) Table 1: Percentage of non-zero weights for Soft Weight-Sharing (P&Q) is 0.5 (not 3 as reported in the table) and bits for Deep Compression is 5 - 8 (not 10 - 13 as reported in the table)", "-) Last paragraph before 4....	[ [ 13 ], [ 14 ], [ 20 ], [ 1 ], [ 11 ], [ 12 ], [ 16 ], [ 18 ], [ 21 ], [ 22 ], [ 2 ], [ 4 ], [ 5 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 19 ], [ 3 ], [ 0 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 7 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 8 ] } ], "category": [ "CLAR-WRT", "ORIG...	benchmark/PDF/ICLR2018_ry-TW-WAb.pdf	openreview	benchmark/MD/ICLR2018_ry-TW-WAb.md	ICLR 2018
ByeqORgAW	{ "title": "Proximal Backpropagation", "abstract": "We propose proximal backpropagation (ProxProp) as a novel algorithm that takes implicit instead of explicit gradient steps to update the network parameters during neural network training. Our algorithm is motivated by the step size limitation of explicit gradient ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clear, well-written paper that tackles an interesting problem.\n+ Interesting potential connections to other approaches in the literature such as Carreira-Perpiñán and Wang, 2014 and Taylor et al., 2016.\n+ Paper shows good understanding of th...	[ "This work proposes to replace the gradient step for updating the network parameters to a proximal step (implicit gradient) so that a large stepsize can be taken.", "Then to make it fast, the implicit step is approximated using conjugate gradient method because the step is solving a quadratic problem.", "The th...	[ [ 3 ], [ 42 ], [ 18 ], [ 20 ], [ 22 ], [ 37 ], [ 39 ], [ 6, 28 ], [ 13 ], [ 14 ], [ 15 ], [ 21 ], [ 24 ], [ 31 ], [ 38 ], [ 10 ], [ 10 ], [ 10 ], [ 11 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] }, { "role": "Au...	benchmark/PDF/ICLR2018_ByeqORgAW.pdf	openreview	benchmark/MD/ICLR2018_ByeqORgAW.md	ICLR 2018
BJInEZsTb	{ "title": "Learning Representations and Generative Models for 3D Point Clouds", "abstract": "Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "This paper compares autoencoder and GAN-based methods for 3D point cloud representation and generation, as well as new (and welcome) metrics for quantitatively evaluating generative models. The experiments form a good but still a bit too incomplete ex...	[ "This paper introduces a generative approach for 3D point clouds. More specifically, two Generative Adversarial approaches are introduced: Raw point cloud GAN, and Latent-space GAN (r-GAN and l-GAN as referred to in the paper).", "In addition, a GMM sampling + GAN decoder approach to generation is also among the ...	[ [ 8 ], [ 18 ], [ 11 ], [ 6 ], [ 16 ], [ 2, 15 ], [ 4 ], [ 1 ], [ 13 ], [ 14 ], [ 17 ], [ 7 ], [ 9 ], [ 3 ], [ 19 ], [ 5 ], [ 12 ], [ 0 ], [ 10 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL-EXP"...	benchmark/PDF/ICLR2018_BJInEZsTb.pdf	openreview	benchmark/MD/ICLR2018_BJInEZsTb.md	ICLR 2018
HknbyQbC-	{ "title": "Generating Adversarial Examples with Adversarial Networks", "abstract": "Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results.\n...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper presents AdvGAN: a GAN that is trained to generate adversarial examples against a convolutional network. The motivation for this method is unclear: the proposed attack does not outperform simpler attack methods such as Carlini-Wagner attack. ...	[ "I thank the authors for the thoughtful response and rebuttal. The authors have substantially updated their manuscript and improved the presentation.", "Re: Speed. I brought up this point because this was a bulleted item in the Introduction in the earlier version of the manuscript. In the revised manuscript, this...	[ [ 9 ], [ 12 ], [ 16 ], [ 1, 4, 15 ], [ 2, 5 ], [ 10 ], [ 6 ], [ 7 ], [ 0 ], [ 3 ], [ 8 ], [ 11 ], [ 13 ], [ 14 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]	[ "correct", "correct", "incorrect", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_HknbyQbC-.pdf	openreview	benchmark/MD/ICLR2018_HknbyQbC-.md	ICLR 2018
By9iRkWA-	{ "title": "Phase Conductor on Multi-layered Attentions for Machine Comprehension", "abstract": "Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and self-matching attention model. Our research proposes phase conductor ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Generally solid engineering work but a bit lacking in terms of novelty and some issues with clarity. At the end of the day the empirical gains are not sufficient for acceptance - the results are state-of-the-art relative to published work, but not in t...	[ "Summary: The paper introduces \"Phase Conductor\", which consists of two phases, context-question attention phase and context-context (self) attention phase. Each phase has multiple layers of attention, for which the paper uses a novel way to fuse the layers, and context-question attention uses different question ...	[ [ 11 ], [ 19 ], [ 20 ], [ 21 ], [ 4 ], [ 9 ], [ 10 ], [ 12 ], [ 23 ], [ 3 ], [ 5, 13 ], [ 18 ], [ 8 ], [ 15 ], [ 16, 17 ], [ 24 ], [ 1 ], [ 2 ], [ 6 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-SOT" ] }, ...	benchmark/PDF/ICLR2018_By9iRkWA-.pdf	openreview	benchmark/MD/ICLR2018_By9iRkWA-.md	ICLR 2018
ByOnmlWC-	{ "title": "Policy Optimization by Genetic Distillation ", "abstract": "Genetic algorithms have been widely used in many practical optimization problems.\nInspired by natural selection, operators, including mutation, crossover\nand selection, provide effective heuristics for search and black-box optimization.\nHowe...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "At least two of the reviewers found the proposed approach novel and interesting and worthy of publication at ICLR. The reviewers raised concerns regarding the paper's terminology, which may lead to some misunderstanding. I agree that upon a quick skim,...	[ "This is a highly interesting paper that proposes a set of methods that combine ideas from imitation learning, evolutionary computation and reinforcement learning in a novel way. It combines the following ingredients:", "a) a population-based setup for RL", "b) a pair-selection and crossover operator", "c) a ...	[ [ 11 ], [ 12 ], [ 23, 25, 28 ], [ 29 ], [ 34 ], [ 0 ], [ 6, 15, 27 ], [ 7 ], [ 13 ], [ 14 ], [ 17 ], [ 21 ], [ 22 ], [ 30 ], [ 31 ], [ 32 ], [ 35 ], [ 9 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "ORIG-COM" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, {...	benchmark/PDF/ICLR2018_ByOnmlWC-.pdf	openreview	benchmark/MD/ICLR2018_ByOnmlWC-.md	ICLR 2018
SJa9iHgAZ	{ "title": "Residual Connections Encourage Iterative Inference", "abstract": "Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of f...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...	[ [ { "role": "PC", "data": { "comment": "The paper presents an interesting view of ResNets and the findings should be of broad interest. R1 did not update their score/review, but I am satisfied with the author response, and recommend this paper for acceptance. " } } ] ]	[ "This paper investigates residual networks (ResNets) in an empirical way. The authors argue that shallow layers are responsible for learning important feature representations, while deeper layers focus on refining the features. They validate this point by performing a series of lesion study on ResNet.", "Overall,...	[ [ 16 ], [ 12 ], [ 2 ], [ 11 ], [ 14 ], [ 17 ], [ 3 ], [ 9, 10 ], [ 1 ], [ 15 ], [ 0 ], [ 4 ], [ 7 ], [ 8 ], [ 5 ], [ 6 ], [ 13 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_SJa9iHgAZ.pdf	openreview	benchmark/MD/ICLR2018_SJa9iHgAZ.md	ICLR 2018
ByJHuTgA-	{ "title": "On the State of the Art of Evaluation in Neural Language Models", "abstract": "Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 7, ...	[ [ { "role": "PC", "data": { "comment": "this submission demonstrates an existing loop-hole (?) in rushing out new neural language models by carefully (and expensively) running hyperparameter tuning of baseline approaches. i feel this is an important contribution, but as pointed out by some r...	[ "The submitted manuscript describes an exercise in performance comparison for neural language models under standardization of the hyperparameter tuning and model selection strategies and costs. This type of study is important to give perspective to non-standardized performance scores reported across separate publi...	[ [ 9 ], [ 10 ], [ 12 ], [ 15 ], [ 2, 3 ], [ 4 ], [ 5, 6 ], [ 11 ], [ 14 ], [ 16 ], [ 7 ], [ 8 ], [ 13 ], [ 0 ], [ 1 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, ...	benchmark/PDF/ICLR2018_ByJHuTgA-.pdf	openreview	benchmark/MD/ICLR2018_ByJHuTgA-.md	ICLR 2018
B13EC5u6W	{ "title": "Thinking like a machine — generating visual rationales through latent space optimization", "abstract": "Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes a semi-supervised method to make deep learning more interpretable and at the same time be accurate on small datasets. The main idea is to learn dense representations from unlabelled data and then use those for building classifiers on...	[ "* This paper models images with a latent code representation, and then tries to modify the latent code to minimize changes in image space, while changing the classification label. As the authors indicate, it lies in the space of algorithms looking to modify the image while changing the label (e.g. LIME etc).", "...	[ [ 8, 31 ], [ 16 ], [ 20, 34, 45 ], [ 30 ], [ 32 ], [ 3 ], [ 4 ], [ 11 ], [ 12, 21 ], [ 13 ], [ 15 ], [ 19 ], [ 40 ], [ 36 ], [ 28 ], [ 29 ], [ 6 ], [ 7 ], [...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-SOT" ] }, ...	benchmark/PDF/ICLR2018_B13EC5u6W.pdf	openreview	benchmark/MD/ICLR2018_B13EC5u6W.md	ICLR 2018
HyIFzx-0b	{ "title": "BinaryFlex: On-the-Fly Kernel Generation in Binary Convolutional Networks", "abstract": "In this work we present BinaryFlex, a neural network architecture that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes using a set of orthogonal bases that combine to form convolution kernels for CNNs leading to a significant reduction of memory usage. The main concerns raised by the reviewers were 1) clarity; 2) issues with writing and presentation ...	[ "The paper presents a binary neural network architecture that operated on predefined orthogonal binary basis. The binary filters that are used as basis are generated using Orthogonal Variable Spreading Factor.", "Because the filters are weighted combinations of predefined basis, only the weights need to be traine...	[ [ 2 ], [ 18 ], [ 23 ], [ 1 ], [ 19 ], [ 3 ], [ 10 ], [ 14 ], [ 15 ], [ 17 ], [ 20 ], [ 21 ], [ 22 ], [ 24 ], [ 25 ], [ 28 ], [ 7 ], [ 4 ], [ 5 ], [ 9 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_HyIFzx-0b.pdf	openreview	benchmark/MD/ICLR2018_HyIFzx-0b.md	ICLR 2018
BkA7gfZAb	{ "title": "Stable Distribution Alignment Using the Dual of the Adversarial Distance", "abstract": "Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often ...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...	[ [ { "role": "PC", "data": { "comment": "All the reviewers noted that the dual formulation, as presented, only applies to the logistic family of classifiers. The kernelization is of course something that can be done, as argued by the authors, but is not in fact approached in the submission,...	[ "The paper deals with “fixing GANs at the computational level”, in a similar sprit to f-GANs and WGANs. The fix is very specific and restricted. It relies on the logistic regression model as the discriminator, and the dual formulation of logistic regression by Jaakkola and Haussler.\nComments:", "1) Experiments a...	[ [ 3 ], [ 17 ], [ 11, 16 ], [ 18 ], [ 19, 22 ], [ 20 ], [ 5 ], [ 6 ], [ 7 ], [ 10, 15 ], [ 21, 23 ], [ 28 ], [ 1, 14 ], [ 9, 13 ], [ 0 ], [ 8 ], [ 12, 29 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 5 ] } ], "category": [ "QUAL-MET" ] }, { "sentences": [ { "role": "Reviewer 1", "dat...	benchmark/PDF/ICLR2018_BkA7gfZAb.pdf	openreview	benchmark/MD/ICLR2018_BkA7gfZAb.md	ICLR 2018
rkmu5b0a-	{ "title": "MGAN: Training Generative Adversarial Nets with Multiple Generators", "abstract": "We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "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 shar...	[ "The present manuscript attempts to address the problem of mode collapse in GANs using a constrained mixture distribution for the generator, and an auxiliary classifier which predicts the source mixture component, plus a loss term which encourages diversity amongst components.", "All told the proposed method is q...	[ [ 17 ], [ 25 ], [ 10 ], [ 7 ], [ 9 ], [ 11 ], [ 16 ], [ 20, 29 ], [ 21 ], [ 23, 27 ], [ 1 ], [ 22 ], [ 5 ], [ 6 ], [ 18 ], [ 3 ], [ 19 ], [ 24 ], [ 26, 28, ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_rkmu5b0a-.pdf	openreview	benchmark/MD/ICLR2018_rkmu5b0a-.md	ICLR 2018
SJA7xfb0b	{ "title": "Sobolev GAN", "abstract": "We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) restricted to a Sobolev ball defined with respect to a dominant measure mu. We show that the Sob...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper provides a useful analysis of the role of gradient penalties and the performance of the proposed approach in semi-supervised cases." } } ] ]	[ "The paper deals with the increasingly popular GAN approach to constructing generative models. Following the first formulation of GANs in 2014, it was soon realized that the training dynamics was highly unstable, leading to significant difficulties in achieving stable results. The paper by Arjovsky et al (2017) pro...	[ [ 7 ], [ 9 ], [ 1, 30 ], [ 3 ], [ 10 ], [ 21 ], [ 24 ], [ 0, 11 ], [ 6 ], [ 18 ], [ 26 ], [ 2 ], [ 4 ], [ 5 ], [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 20 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "incorrect", "incorrect", "incorrect", "incorrect", "correct", "correct", "corr...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6 ...	benchmark/PDF/ICLR2018_SJA7xfb0b.pdf	openreview	benchmark/MD/ICLR2018_SJA7xfb0b.md	ICLR 2018
HyRnez-RW	{ "title": "Multi-Mention Learning for Reading Comprehension with Neural Cascades", "abstract": "Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to t...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...	[ [ { "role": "PC", "data": { "comment": "The authors did a good job addressing reviewer concerns and analyzing and testing their model on interesting datasets with convincing results." } } ] ]	[ "The authors present a scalable model for questioning answering that is able to train on long documents. On the TriviaQA dataset, the proposed model achieves state of the art results on both domains (wikipedia and web). The formulation of the model is straight-forward, however I am skeptical about whether the resul...	[ [ 8 ], [ 5 ], [ 7 ], [ 18 ], [ 19 ], [ 21 ], [ 24 ], [ 6 ], [ 13 ], [ 0, 3, 23 ], [ 2, 9 ], [ 4 ], [ 11 ], [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 25 ], [ 10 ],...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "incorrect", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correc...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] }, { "role": "Author", "data": [ 13, 14, 15, 16, 17, 65, 74 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_HyRnez-RW.pdf	openreview	benchmark/MD/ICLR2018_HyRnez-RW.md	ICLR 2018
B1EA-M-0Z	{ "title": "Deep Neural Networks as Gaussian Processes", "abstract": "It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian infer...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper presents several theoretical results linking deep, wide neural networks to GPs. It even includes illuminating experiments.\n\nMany of the results were already developed in earlier works. However, many at ICLR may be unaware of these links, ...	[ "Neal (1994) showed that a one hidden layer Bayesian neural network, under certain conditions, converges to a Gaussian process as the number of hidden units approaches infinity. Neal (1994) and Williams (1997) derive the resulting kernel functions for such Gaussian processes when the neural networks have certain tr...	[ [ 30 ], [ 31 ], [ 40 ], [ 13 ], [ 36 ], [ 2 ], [ 23 ], [ 37 ], [ 3 ], [ 8, 34 ], [ 10 ], [ 27 ], [ 29 ], [ 0 ], [ 4 ], [ 5 ], [ 6, 9 ], [ 7 ], [ 18 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, ...	benchmark/PDF/ICLR2018_B1EA-M-0Z.pdf	openreview	benchmark/MD/ICLR2018_B1EA-M-0Z.md	ICLR 2018
rkLyJl-0-	{ "title": "Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks", "abstract": "Progress in deep learning is slowed by the days or weeks it takes to train large models. The natural solution of using more hardware is limited by diminishing returns, and leads to inefficient use of additional...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clearly written paper.\n+ Easily implemented algorithm that appears to have excellent scaling properties and can even improve on validation error in some cases.\n+ Thorough evaluation against the state of the art.\n\nCons:\n- No theoretical gu...	[ "This paper presents a new 2nd-order algorithm that implicitly uses curvature information, and it shows the intuition behind the approximation schemes in the algorithms and also validates the heuristics in various experiments. The method involves using Neumann Series and Richardson iteration to avoid Hessian-vecto...	[ [ 10 ], [ 3 ], [ 13 ], [ 11 ], [ 9, 16 ], [ 1 ], [ 2 ], [ 4 ], [ 5 ], [ 14 ], [ 17 ], [ 6 ], [ 8, 12 ], [ 0 ], [ 7 ], [ 15 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_rkLyJl-0-.pdf	openreview	benchmark/MD/ICLR2018_rkLyJl-0-.md	ICLR 2018
ry018WZAZ	{ "title": "Deep Active Learning for Named Entity Recognition", "abstract": "Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that th...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "The reviewers liked this paper quite a bit. The novelty seems modest and the results are limited to a fairly simple NER task, but there is nothing wrong with the paper, hence recommending acceptance." } } ] ]	[ "Summary:\nThis paper applies active learning to a deep neural model (CNN-CNN-LSTM) for named-entity recognition, which allows the model to match state-of-the-art performance with about 25% of the full training data.", "Strength:\nThe paper is relatively easy to follow. Experiments show significant reduction of ...	[ [ 1 ], [ 2 ], [ 6 ], [ 12 ], [ 15 ], [ 16 ], [ 4 ], [ 18 ], [ 11 ], [ 3, 13 ], [ 5 ], [ 8 ], [ 19 ], [ 10 ], [ 0 ], [ 7 ], [ 9 ], [ 14 ], [ 17 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "SIGN...	benchmark/PDF/ICLR2018_ry018WZAZ.pdf	openreview	benchmark/MD/ICLR2018_ry018WZAZ.md	ICLR 2018
HJWLfGWRb	{ "title": "Matrix capsules with EM routing", "abstract": "A capsule is a group of neurons whose outputs represent different properties of the same entity. Each layer in a capsule network contains many capsules. We describe a version of capsules in which each capsule has a logistic unit to represent the presence of...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "Authors present a new multi-layered capsule network architecture, implemented an EM routing procedure, and introduced \"Coordinate Addition\". Capsule architectures are gaining interest because of their ability to achieve equivariance of parts, and em...	[ "The paper proposes a novel architecture for capsule networks. Each capsule has a logistic unit representing the presence of an entity plus a 4x4 pose matrix representing the entity/viewer relationship. This new representation comes with a novel iterative routing scheme, based on the EM algorithm.", "Evaluated on...	[ [ 20 ], [ 3, 14, 19 ], [ 10 ], [ 5 ], [ 13 ], [ 18 ], [ 21 ], [ 7 ], [ 15 ], [ 2 ], [ 1 ], [ 12, 16 ], [ 8 ], [ 9 ], [ 4 ], [ 6, 17 ], [ 0 ], [ 11 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-EXP", "QUAL...	benchmark/PDF/ICLR2018_HJWLfGWRb.pdf	openreview	benchmark/MD/ICLR2018_HJWLfGWRb.md	ICLR 2018
S1p31z-Ab	{ "title": "Deep contextualized word representations", "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word ve...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 6, ...	[ [ { "role": "PC", "data": { "comment": "This is a good paper that presents state-of-the-art results on a number of challenging NLP tasks. The idea is fairly simple and clean, I therefore expect it to get adopted in the community. It also seems to work across several tasks, which is nice. At ...	[ "The authors learn token embeddings that use surrounding context by concatenating representations obtained by training a bidirectional language model, very similar to Peters et al. 2017. They learn a distribution of weights for each layer of embeddings of the pre-trained bi-lm language model. These embeddings impro...	[ [ 3 ], [ 6 ], [ 1 ], [ 2 ], [ 10 ], [ 4 ], [ 5 ], [ 7 ], [ 9 ], [ 11 ], [ 12 ], [ 13 ], [ 0 ], [ 8 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "incorrect", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "incorrect" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_S1p31z-Ab.pdf	openreview	benchmark/MD/ICLR2018_S1p31z-Ab.md	ICLR 2018
HyZoi-WRb	{ "title": "Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference", "abstract": "The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The authors analyze the IWAE bound as an estimator of the marginal log-likelihood and show how to reduce its bias by using the jackknife. They then evaluate the effect of using the resulting estimator (JVI) for training and evaluating VAEs on MNIST. Th...	[ "[After author feedback]", "I think this is an interesting paper and recommend acceptance. My remaining main comments are described in the response to author feedback below.\n[Original review]", "The authors introduce jackknife variational inference (JVI), a method for debiasing Monte Carlo objectives such as t...	[ [ 19 ], [ 7 ], [ 4, 17 ], [ 8 ], [ 9 ], [ 14 ], [ 1 ], [ 2 ], [ 3 ], [ 6 ], [ 10 ], [ 11 ], [ 12, 12 ], [ 15 ], [ 16 ], [ 18 ], [ 20 ], [ 5 ], [ 21 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4, 5, 6 ...	benchmark/PDF/ICLR2018_HyZoi-WRb.pdf	openreview	benchmark/MD/ICLR2018_HyZoi-WRb.md	ICLR 2018
SJlhPMWAW	{ "title": "GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders", "abstract": "Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative mode...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overa...	[ [ { "role": "PC", "data": { "comment": "The authors present GraphVAE, a method for fitting a generative deep model, a variational autoencoder, to small graphs. Fitting deep learning models to graphs remains challenging (although there is relevant literature as brought up by the reviewers an...	[ "This work proposed an interesting graph generator using a variational autoencoder. The work should be interesting to researchers in the various areas.", "However, the work can only work on small graphs. The search space of small graph generation is usually very small, is there any other traditional methods can w...	[ [ 2 ], [ 21 ], [ 6 ], [ 4 ], [ 1 ], [ 19, 25, 28 ], [ 26 ], [ 11 ], [ 13 ], [ 14 ], [ 5 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 12 ], [ 16, 23 ], [ 17, 24 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...	benchmark/PDF/ICLR2018_SJlhPMWAW.pdf	openreview	benchmark/MD/ICLR2018_SJlhPMWAW.md	ICLR 2018
HymuJz-A-	{ "title": "Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks", "abstract": "The robust and efficient recognition of visual relations in images is a hallmark of biological vision. Here, we argue that, despite recent progress in visual recognition, modern machine vision algorithms are severely limite...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...	[ [ { "role": "PC", "data": { "comment": "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 thi...	[ "Quality\nThis paper demonstrates that convolutional and relational neural networks fail to solve visual relation problems by training networks on artificially generated visual relation data. This points at important limitations of current neural network architectures where architectures depend mainly on rote memor...	[ [ 1 ], [ 7 ], [ 21 ], [ 16 ], [ 15 ], [ 17 ], [ 2 ], [ 4 ], [ 5 ], [ 0 ], [ 3 ], [ 18 ], [ 8 ], [ 9 ], [ 10 ], [ 11 ], [ 12 ], [ 19 ], [ 20 ], [ 22 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct"...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-NEG" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...	benchmark/PDF/ICLR2018_HymuJz-A-.pdf	openreview	benchmark/MD/ICLR2018_HymuJz-A-.md	ICLR 2018
B1n8LexRZ	{ "title": "Generalizing Hamiltonian Monte Carlo with Neural Networks", "abstract": "We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "This paper presents a learned inference architecture which generalizes HMC. It defines a parameterized family of MCMC transition operators which share the volume preserving structure of HMC updates, which allows the acceptance ratio to be computed effi...	[ "In this work, the authors propose a procedure for tuning the parameters of an HMC algorithm (I guess, if I have understood correctly).", "I think this paper has a good and strong point: this work points out the difficulties in choosing properly the parameters in a HMC method (such as the step and the number of i...	[ [ 11 ], [ 4 ], [ 10 ], [ 0 ], [ 2 ], [ 13 ], [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 18 ], [ 19 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 24 ], [ 25 ], [ 1 ], [ 5 ], [...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 12, 13 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1...	benchmark/PDF/ICLR2018_B1n8LexRZ.pdf	openreview	benchmark/MD/ICLR2018_B1n8LexRZ.md	ICLR 2018
Syhr6pxCW	{ "title": "PixelNN: Example-based Image Synthesis", "abstract": "We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models des...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes a novel method for conditional image generation which is based on nearest neighbor matching for transferring high-frequency statistics. The evaluation is carried out on several image synthesis tasks, where the technique is shown to p...	[ "Overall I like the paper and the results look nice in a diverse set of datasets and tasks such as edge-to-image, super-resolution, etc.", "Unlike the generative distribution sampling of GANs, the method provides an interesting compositional scheme, where the low frequencies are regressed and the high frequencies...	[ [ 15 ], [ 14 ], [ 1 ], [ 3 ], [ 7 ], [ 13 ], [ 5 ], [ 6 ], [ 8 ], [ 2 ], [ 9 ], [ 10 ], [ 11 ], [ 12 ], [ 16 ], [ 18 ], [ 20 ], [ 21 ], [ 0 ], [ 19 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5 ] ...	benchmark/PDF/ICLR2018_Syhr6pxCW.pdf	openreview	benchmark/MD/ICLR2018_Syhr6pxCW.md	ICLR 2018
H1meywxRW	{ "title": "DCN+: Mixed Objective And Deep Residual Coattention for Question Answering", "abstract": "Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a ...	Accept (Poster)	[ [ { "role": "Author", "data": { "value": { "comment": [ 0 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 1, 2, ...	[ [ { "role": "PC", "data": { "comment": "This is an interesting paper that provides modeling improvements over several strong baselines and presents SOTA on Squad. One criticism of the paper is that it evaluates only on Squad, which is somewhat of an artificial task, but we think for publica...	[ "In Equation 17 (page 5), we made a typo in that we did not include the regularization terms $$\\log \\sigma_{ce}^2 + \\log \\sigma_{rl}^2$$.", "Summary:\nThis paper proposed an extension of the dynamic coattention network (DCN) with deeper residual layers and self-attention. It also introduced a mixed objective ...	[ [ 4 ], [ 2 ], [ 1 ], [ 6 ], [ 5 ], [ 0 ], [ 3 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 2, 3 ] } ], "category": [ "QUAL-MET"...	benchmark/PDF/ICLR2018_H1meywxRW.pdf	openreview	benchmark/MD/ICLR2018_H1meywxRW.md	ICLR 2018
ByS1VpgRZ	{ "title": "cGANs with Projection Discriminator", "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. \nThis approach is in contrast with most fra...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes a simple modification to conditional GANs, where the discriminator involves an inner product term between the condition vector y and the feature vector of x. This formulation is reasonable and well motivated from popular models (e.g....	[ "I thank the authors for the thoughtful response and updated manuscript. After reading through both, my review score remains unchanged.", "The authors describe a new variant of a generative adversarial network (GAN) for generating images. This model employs a 'projection discriminator' in order to incorporate ima...	[ [ 6 ], [ 13 ], [ 14 ], [ 19 ], [ 15 ], [ 5 ], [ 7 ], [ 11 ], [ 1 ], [ 3 ], [ 4 ], [ 10 ], [ 12, 17 ], [ 16 ], [ 18 ], [ 2 ], [ 9 ], [ 0 ], [ 8 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_ByS1VpgRZ.pdf	openreview	benchmark/MD/ICLR2018_ByS1VpgRZ.md	ICLR 2018
SkhQHMW0W	{ "title": "Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training", "abstract": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network in...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "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...	[ "I think this is a good work that I am sure will have some influence in the near future. I think it should be accepted and my comments are mostly suggestions for improvement or requests for additional information that would be interesting to have.", "Generally, my feeling is that this work is a little bit too den...	[ [ 2 ], [ 29 ], [ 1, 32 ], [ 3, 27 ], [ 4 ], [ 5 ], [ 23 ], [ 28 ], [ 33 ], [ 35 ], [ 36 ], [ 9 ], [ 10 ], [ 11 ], [ 14 ], [ 18 ], [ 34 ], [ 6 ], [ 8 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "inc...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...	benchmark/PDF/ICLR2018_SkhQHMW0W.pdf	openreview	benchmark/MD/ICLR2018_SkhQHMW0W.md	ICLR 2018
HyiAuyb0b	{ "title": "TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning", "abstract": "Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators. Th...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This is an interesting piece of work that provides solid evidence on the topic of bootstrapping in deep reinforcement learning." } } ] ]	[ "This paper includes several controlled empirical studies comparing MC and TD methods in predicting of value function with complex DNN function approximators. Such comparison has been carried out both in theory and practice for simple low dimensional environments with linear (and RKHS) value function approximation ...	[ [ 8 ], [ 9 ], [ 16 ], [ 22 ], [ 7 ], [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 15 ], [ 17 ], [ 19 ], [ 20 ], [ 21 ], [ 0 ], [ 1 ], [ 2 ], [ 3 ], [ 4 ], [ 5 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, { ...	benchmark/PDF/ICLR2018_HyiAuyb0b.pdf	openreview	benchmark/MD/ICLR2018_HyiAuyb0b.md	ICLR 2018
BydjJte0-	{ "title": "Towards Reverse-Engineering Black-Box Neural Networks", "abstract": "Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain propri...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Novel way of analyzing neural networks to predict NN attributes such as architecture, training method, batch size etc. And the method works surprisingly good on the MNIST and ImageNet." } } ] ]	[ "-----UPDATE------\nHaving read the responses from the authors, and the other reviews, I am happy with my rating and maintain that this paper should be accepted.", "In this paper, the authors trains a large number of MNIST classifier networks with differing attributes (batch-size, activation function, no. layers ...	[ [ 6 ], [ 0, 8 ], [ 2 ], [ 9 ], [ 13 ], [ 15 ], [ 5 ], [ 17, 22 ], [ 21 ], [ 3 ], [ 7, 11 ], [ 14 ], [ 16 ], [ 18 ], [ 19 ], [ 20 ], [ 4 ], [ 10 ], [ 1 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 2 ] }, { "role": "Author", "data": [ 49 ] } ], "category": [ "CLAR-WRT", "QUAL-EXP" ] }, { "sentences": [ { "r...	benchmark/PDF/ICLR2018_BydjJte0-.pdf	openreview	benchmark/MD/ICLR2018_BydjJte0-.md	ICLR 2018
SJiHXGWAZ	{ "title": "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting", "abstract": "Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...	[ [ { "role": "PC", "data": { "comment": "The paper received highly diverging scores: 5 (R1) ,9 (R2), 4(R3). Both R1 and R3 complained about the comparisons to related methods. R3 suggested some kNN and GP baselines, while R1 mentioned concurrent work using deepnets for trafffic prediction.\n\...	[ "The paper proposes to build a graph where the edge weight is defined using the road network distance which is shown to be more realistic than the Euclidean distance. The defined diffusion convolution operation is essentially conducting random walks over the road segment graph. To avoid the expensive matrix operati...	[ [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 9 ], [ 7 ], [ 8 ], [ 1 ], [ 2 ], [ 3 ], [ 5 ], [ 14 ], [ 4 ], [ 0 ], [ 6 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "incorrect", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_SJiHXGWAZ.pdf	openreview	benchmark/MD/ICLR2018_SJiHXGWAZ.md	ICLR 2018
B14TlG-RW	{ "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension", "abstract": " Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "AC", "data": { "comment": "Authors, \n\nPlease post a rebuttal for this work. Discussion period ends Jan 5th. " } }, { "role": "Author", "data": { "value": { "comment": "Dear Area Chair,\n\nWe have submitted the rebuttal and revision. ...	[ "This paper proposes two contributions: first, applying CNNs+self-attention modules instead of LSTMs, which could result in significant speedup and good RC performance;", "second, enhancing the RC model training with passage paraphrases generated by a neural paraphrasing model, which could improve the RC performa...	[ [ 1 ], [ 5 ], [ 6 ], [ 11 ], [ 13, 16 ], [ 23 ], [ 24 ], [ 3 ], [ 2 ], [ 4 ], [ 7, 21 ], [ 9 ], [ 10, 20 ], [ 14 ], [ 19 ], [ 18 ], [ 15 ], [ 22 ], [ 8, ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 4 ] }, { ...	benchmark/PDF/ICLR2018_B14TlG-RW.pdf	openreview	benchmark/MD/ICLR2018_B14TlG-RW.md	ICLR 2018
SkqV-XZRZ	{ "title": "Variational Bi-LSTMs", "abstract": "Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs), which model sequences along both for...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "This paper proposes a method for performing stochastic variational inference for bidirectional LSTMs through introducing an additional latent variable that induces a dependence between the forward and backward directions. The authors demonstrate that ...	[ "Quality\nThe paper is easy to parse, with clear diagrams and derivations at the start. The problem context is clearly stated, as is the proposed model.", "The improvements in terms of average log-likelihood are clear. The model does improve over state-of-the-art in some cases, but not all.", "Based on the pr...	[ [ 8 ], [ 26 ], [ 0 ], [ 3 ], [ 4 ], [ 12 ], [ 16, 17 ], [ 22 ], [ 23 ], [ 25 ], [ 13 ], [ 1 ], [ 5 ], [ 6 ], [ 7 ], [ 15 ], [ 18 ], [ 27 ], [ 10 ], [ 11 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2, 3, 5 ] }, {...	benchmark/PDF/ICLR2018_SkqV-XZRZ.pdf	openreview	benchmark/MD/ICLR2018_SkqV-XZRZ.md	ICLR 2018
r1VVsebAZ	{ "title": "Synthesizing realistic neural population activity patterns using Generative Adversarial Networks", "abstract": "The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simu...	Accept (Poster)	[ [ { "role": "Author", "data": { "value": { "comment": [ 0, 1, 2, 3, 4, 5, 6 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper...	[ [ { "role": "PC", "data": { "comment": "This paper proposes a novel application of generative adversarial networks to model neural spiking activity. Their technical contribution, SpikeGAN, generates neural spikes that accurately match the statistics of real recorded spiking behavior from a ...	[ "We provide below a few comments aiming at correcting/clarifying some sentences:", "* First sentence in Section 2.1. should read: We adapted the Generative Adversarial Networks described by Goodfellow et al. (2014) to produce samples that simulate the spiking activity of a population of N neurons as binary vector...	[ [ 16 ], [ 20 ], [ 21 ], [ 33 ], [ 7 ], [ 11 ], [ 13 ], [ 22 ], [ 25 ], [ 1, 23 ], [ 32 ], [ 9, 30 ], [ 10 ], [ 27 ], [ 15 ], [ 18, 19 ], [ 28 ], [ 29 ], [ 3...	[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 7, 8 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 9 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_r1VVsebAZ.pdf	openreview	benchmark/MD/ICLR2018_r1VVsebAZ.md	ICLR 2018
BkM27IxR-	{ "title": "Learning to Optimize Neural Nets", "abstract": "Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimiz...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The presented work is a good attempt to expand the work of Li and Malik to the high-dimensional, stochastic setting. Given the reviewer comments, I think the paper would benefit from highlighting the comparatively novel aspects, and in particular doing...	[ "[Main comments]", "* I would advice the authors to explain in more details in the intro", "what's new compared to Li & Malik (2016) and Andrychowicz et al. (2016).", "It took me until section 3.5 to figure it out.", "* If I understand correctly, the only new part compared to Li & Malik (2016) is", "secti...	[ [ 3 ], [ 21 ], [ 22 ], [ 0 ], [ 1 ], [ 2 ], [ 7 ], [ 8 ], [ 9, 10 ], [ 11 ], [ 19 ], [ 23 ], [ 27 ], [ 28 ], [ 24 ], [ 4 ], [ 15 ], [ 17 ], [ 18 ], [ 26 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2, 3 ] }, { "role": "Author", "data": [ 31, 32, 33, 34, 35, 36, 37 ] } ...	benchmark/PDF/ICLR2018_BkM27IxR-.pdf	openreview	benchmark/MD/ICLR2018_BkM27IxR-.md	ICLR 2018
BJ_UL-k0b	{ "title": "Recasting Gradient-Based Meta-Learning as Hierarchical Bayes", "abstract": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-lea...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Pros:\n+ The paper introduces a non-trivial interpretation of MAML as hierarchical Bayesian learning and uses this perspective to develop a new variation of MAML that accounts for curvature information.\n\nCons:\n- Relatively small gains over MAML on m...	[ "Summary\nThe paper presents an interesting view on the recently proposed MAML formulation of meta-learning (Finn et al). The main contribution is a) insight into the connection between the MAML procedure and MAP estimation in an equivalent linear hierarchical Bayes model with explicit priors, b) insight into the c...	[ [ 4 ], [ 22 ], [ 9 ], [ 18 ], [ 26 ], [ 27 ], [ 33 ], [ 35 ], [ 40 ], [ 3 ], [ 10 ], [ 11 ], [ 13 ], [ 21 ], [ 28 ], [ 29 ], [ 30 ], [ 31 ], [ 32 ], [ 34 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-BRD" ] }, ...	benchmark/PDF/ICLR2018_BJ_UL-k0b.pdf	openreview	benchmark/MD/ICLR2018_BJ_UL-k0b.md	ICLR 2018
rylSzl-R-	{ "title": "On Unifying Deep Generative Models", "abstract": "Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct p...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This is a thought-provoking paper that places GANs and VAEs in a single framework and, motivated by this perspective, proposes several novel extensions to them. The reviewers made several good suggestions for improving the paper and the authors are exp...	[ "Update 1/11/18:\nI'm happy with the comments from the authors. I think the explanation of non-saturating vs saturating objective is nice, and I've increased the score.", "Note though: I absolutely expect a revision at camera-ready if the paper gets accepted (we did not get one).", "Original review:\nThe paper ...	[ [ 18 ], [ 0 ], [ 4 ], [ 5 ], [ 9, 14 ], [ 11 ], [ 12 ], [ 13 ], [ 2 ], [ 3 ], [ 6 ], [ 10 ], [ 16 ], [ 17 ], [ 7 ], [ 8 ], [ 1 ], [ 15 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1, 2 ] } ], "category": [ "N/A"...	benchmark/PDF/ICLR2018_rylSzl-R-.pdf	openreview	benchmark/MD/ICLR2018_rylSzl-R-.md	ICLR 2018
rye7IMbAZ	{ "title": " Explicit Induction Bias for Transfer Learning with Convolutional Networks", "abstract": "In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.\nWhen using fine-tuning, the underlying assumption is that the pre-trained model extra...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper addresses the question of how to regularize when starting from a pre-trained convolutional network in the context of transfer learning. The authors propose to regularize toward the parameters of the pre-trained model and study multiple regu...	[ "The paper proposes an analysis on different adaptive regularization techniques for deep transfer learning.", "Specifically it focuses on the use of an L2-SP condition that constraints the new parameters to be close to the", "ones previously learned when solving a source task.", "+ The paper is easy to read a...	[ [ 11 ], [ 1 ], [ 2 ], [ 8 ], [ 13 ], [ 18 ], [ 3 ], [ 4 ], [ 6, 15 ], [ 9 ], [ 10 ], [ 14 ], [ 16 ], [ 12 ], [ 7 ], [ 0 ], [ 5 ], [ 17 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_rye7IMbAZ.pdf	openreview	benchmark/MD/ICLR2018_rye7IMbAZ.md	ICLR 2018
SyzKd1bCW	{ "title": "Backpropagation through the Void: Optimizing control variates for black-box gradient estimation", "abstract": "Gradient-based optimization is the foundation of deep learning and reinforcement learning.\nEven when the mechanism being optimized is unknown or not differentiable, optimization using high-var...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This is an interesting and well-written paper introducing two unbiased gradient estimators for optimizing expectations of black box functions. LAX can handle functions of both continuous and discrete random variables, while RELAX is specialized to func...	[ "This paper introduces LAX/RELAX, a method to reduce the variance of the REINFORCE gradient estimator. The method builds on and is directly inspired by REBAR.", "Similarly to REBAR, RELAX is an unbiased estimator, and the idea is to introduce a control variate that leverages the reparameterization gradient.", "...	[ [ 12 ], [ 22 ], [ 25 ], [ 34 ], [ 35 ], [ 2 ], [ 4 ], [ 9 ], [ 24 ], [ 27 ], [ 3 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 10 ], [ 11 ], [ 13 ], [ 14, 44 ], [ 17 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4 ] }, { ...	benchmark/PDF/ICLR2018_SyzKd1bCW.pdf	openreview	benchmark/MD/ICLR2018_SyzKd1bCW.md	ICLR 2018
HkGcX--0-	{ "title": "Auxiliary Guided Autoregressive Variational Autoencoders", "abstract": "Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "AC", "data": { "comment": "Hi, I'm a Master's student in Computer Science taking part in the ICLR reproducibility challenge, and I have a few questions regarding implementation. \n\nFirst, you mention that \"We allow each layer of the pixel-CNN to take additional input using non...	[ "Summary:\nThis paper attempts to solve the problem of meaningfully combining variational autoencoders (VAEs) and PixelCNNs. It proposes to do this by simultaneously optimizing a VAE with PixelCNN++ decoder, and a VAE with factorial decoder. The model is evaluated in terms of log-likelihood (with no improvement ove...	[ [ 2 ], [ 3 ], [ 8 ], [ 9 ], [ 18 ], [ 17 ], [ 4 ], [ 7 ], [ 12 ], [ 13 ], [ 6 ], [ 11 ], [ 5 ], [ 1, 15 ], [ 14 ], [ 0 ], [ 10 ], [ 16 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]	[ "correct", "incorrect", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...	benchmark/PDF/ICLR2018_HkGcX--0-.pdf	openreview	benchmark/MD/ICLR2018_HkGcX--0-.md	ICLR 2018
HkwVAXyCW	{ "title": "Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks", "abstract": "Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficul...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper explores what might be characterized as an adaptive form of ZoneOut.\nWith the improvements and clarifications added to the paper during the rebuttal the paper could be accepted.\n" } } ] ]	[ "UPDATE: Following the author's response I've increased my score from 5 to 6. The revised paper includes many of the additional references that I suggested, and the author response clarified my confusion over the Charades experiments; their results are indeed close to state-of-the-art on Charades activity localizat...	[ [ 3 ], [ 5 ], [ 9 ], [ 16 ], [ 0 ], [ 6 ], [ 7 ], [ 8, 12 ], [ 10 ], [ 11 ], [ 18 ], [ 13 ], [ 14 ], [ 4 ], [ 17, 22 ], [ 21, 23 ], [ 2 ], [ 20 ], [ 1 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "incorrect", "correct"...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 26, 27, 28, 29, 30, 31, 32 ] } ], "category": [ "QUAL-CMP"...	benchmark/PDF/ICLR2018_HkwVAXyCW.pdf	openreview	benchmark/MD/ICLR2018_HkwVAXyCW.md	ICLR 2018
SylJ1D1C-	{ "title": "PDE-Net: Learning PDEs from Data", "abstract": "Partial differential equations (PDEs) play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc. PDEs are commonly derived based on physical laws or empirical observations. However, ...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...	[ [ { "role": "PC", "data": { "comment": "This paper studies the approximation and integration of partial differential equations using convolutional neural networks. By constraining CNN filters to have prescribed vanishing moments, the authors interpret CNN-based temporal prediction in terms o...	[ "The paper explores the use of deep learning machinery for the purpose of identifying dynamical systems specified by PDEs.", "The paper advocates the following approach:", "One assumes a dynamic PDE system involving differential operators up to a given order. Each differential operator term is approximated by a...	[ [ 11 ], [ 16 ], [ 1 ], [ 4 ], [ 15 ], [ 21 ], [ 25 ], [ 5 ], [ 6 ], [ 18 ], [ 9 ], [ 23 ], [ 24 ], [ 3 ], [ 7 ], [ 13 ], [ 10 ], [ 14 ], [ 22 ], [ 2 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_SylJ1D1C-.pdf	openreview	benchmark/MD/ICLR2018_SylJ1D1C-.md	ICLR 2018
By4HsfWAZ	{ "title": "Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge", "abstract": "We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive para...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "This paper proposes to use data-driven deep convolutional architectures for modeling advection diffusion. It is well motivated and comes with convincing numerical experiments.\nReviewers agreed that this is a worthy contribution to ICLR with the potent...	[ "The paper ‘Deep learning for Physical Process: incorporating prior physical knowledge’ proposes", "to question the use of data-intensive strategies such as deep learning in solving physical", "inverse problems that are traditionally solved through assimilation strategies. They notably show", "how physical pr...	[ [ 19 ], [ 7 ], [ 12 ], [ 13 ], [ 14 ], [ 15 ], [ 17 ], [ 8 ], [ 2 ], [ 18 ], [ 3 ], [ 5 ], [ 9 ], [ 11 ], [ 4 ], [ 10 ], [ 1 ], [ 0 ], [ 6 ], [ 16 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6 ...	benchmark/PDF/ICLR2018_By4HsfWAZ.pdf	openreview	benchmark/MD/ICLR2018_By4HsfWAZ.md	ICLR 2018
HJIoJWZCZ	{ "title": "Adversarial Dropout Regularization", "abstract": "We present a domain adaptation method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by ``fooling'' a special do...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "The general consensus is that this method provides a practical and interesting approach to unsupervised domain adaptation. One reviewer had concerns with comparing to state of the art baselines, but those have been addressed in the revision.\n\nThere w...	[ "(Summary)\nThis paper is about learning discriminative features for the target domain in unsupervised DA problem. The key idea is to use a critic which randomly drops the activations in the logit and maximizes the sensitivity between two versions of discriminators.\n(Pros)", "The approach proposed in section 3.2...	[ [ 16 ], [ 3 ], [ 15 ], [ 11 ], [ 13 ], [ 14 ], [ 1 ], [ 2 ], [ 12 ], [ 9 ], [ 17 ], [ 7 ], [ 8 ], [ 10 ], [ 19 ], [ 4 ], [ 5 ], [ 18 ], [ 0 ], [ 6 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, ...	benchmark/PDF/ICLR2018_HJIoJWZCZ.pdf	openreview	benchmark/MD/ICLR2018_HJIoJWZCZ.md	ICLR 2018
BywyFQlAW	{ "title": "Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity", "abstract": "We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "The submission formulates self paced learning as a specific iterative mini-max optimization, which incorporates both a risk minimization step and a submodular maximization for selecting the next training examples.\n\nThe strengths of the paper lie prim...	[ "Overview:\nThis paper proposes an approach to curriculum learning, where subsets of examples to train on are chosen during the training process. The proposed method is based on a submodular set function over the examples, which is intended to capture diversity of the included examples and is added to the training ...	[ [ 7 ], [ 4 ], [ 6 ], [ 15 ], [ 9 ], [ 10 ], [ 17 ], [ 5 ], [ 3 ], [ 12 ], [ 16 ], [ 1 ], [ 11 ], [ 13 ], [ 14 ], [ 18 ], [ 19 ], [ 20 ], [ 0 ], [ 8 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-MET" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_BywyFQlAW.pdf	openreview	benchmark/MD/ICLR2018_BywyFQlAW.md	ICLR 2018
r1RQdCg0W	{ "title": "MACH: Embarrassingly parallel $K$-class classification in $O(d\\log{K})$ memory and $O(K\\log{K} + d\\log{K})$ time, instead of $O(Kd)$", "abstract": "We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that requ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "There is a very nice discussion with one of the reviewers on the experiments, that I think would need to be battened down in an ideal setting. I'm also a bit surprised at the lack of discussion or comparison to two seemingly highly related papers:\n\n1...	[ "The manuscript proposes an efficient hashing method, namely MACH, for softmax approximation in the context of large output space, which saves both memory and computation.", "In particular, the proposed MACH uses 2-universal hashing to randomly group classes, and trains a classifier to predict the group membershi...	[ [ 6 ], [ 7 ], [ 1 ], [ 27 ], [ 3 ], [ 11, 18 ], [ 13 ], [ 23 ], [ 25 ], [ 29 ], [ 30 ], [ 2 ], [ 4 ], [ 5 ], [ 14 ], [ 19 ], [ 20 ], [ 24 ], [ 31 ], [ 32 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] } ], "category": [ ...	benchmark/PDF/ICLR2018_r1RQdCg0W.pdf	openreview	benchmark/MD/ICLR2018_r1RQdCg0W.md	ICLR 2018
ryiAv2xAZ	{ "title": "Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples", "abstract": "The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "Meta score: 6\n\nThe 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.\n\nPros:\n - clearly expressed idea, well-supported ...	[ "I have read authors' reply. In response to authors' comprehensive reply and feedback. I upgrade my score to 6.", "This paper presents a novel approach to calibrate classifiers for out of distribution samples. In additional to the original cross entropy loss, the “confidence loss” was proposed to guarantee the ...	[ [ 15 ], [ 20 ], [ 5 ], [ 8 ], [ 7 ], [ 21 ], [ 0 ], [ 16 ], [ 1 ], [ 4 ], [ 10 ], [ 18 ], [ 23 ], [ 24 ], [ 9 ], [ 13 ], [ 14 ], [ 17 ], [ 22 ], [ 2 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "ORIG...	benchmark/PDF/ICLR2018_ryiAv2xAZ.pdf	openreview	benchmark/MD/ICLR2018_ryiAv2xAZ.md	ICLR 2018
H1-nGgWC-	{ "title": "Gaussian Process Behaviour in Wide Deep Neural Networks", "abstract": "Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between Gaussian processes with a recursive kern...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": " A clearly written paper. While the practical relevance that came up in the review remains, the analysis and discussion is important for a deeper understanding of the deeper connections between these two important areas of machine learning." } ...	[ "The authors study the limiting behaviour for wide Bayesian neural networks, comparing to Gaussian processes.", "The paper is well written, and the experiments are enlightening. This work is a nice follow up to Neal (1994), and recent work considering similar results for neural networks with more than one hidden ...	[ [ 11 ], [ 16 ], [ 19 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 25 ], [ 26 ], [ 27 ], [ 28 ], [ 29 ], [ 30 ], [ 1 ], [ 10 ], [ 15 ], [ 24 ], [ 8 ], [ 3, 4 ], [ 7 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...	benchmark/PDF/ICLR2018_H1-nGgWC-.pdf	openreview	benchmark/MD/ICLR2018_H1-nGgWC-.md	ICLR 2018
r17Q6WWA-	{ "title": "Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection", "abstract": "Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains. While CNNs are particularly well-suited for capturing a proper hierarchy of conce...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...	[ [ { "role": "PC", "data": { "comment": "The experimental work in this paper leaves it just short of being suitable for acceptance.\nThe work needs more comparisons with prior work and other approaches.\nThe numerical ratings of the work by reviewers are just too low.\n" } } ] ]	[ "Pros:", "1. This paper proposed a new block which can aggregate features from different tasks. By doing this, it can take advantage of common information between related tasks and improve the generalization of target tasks.", "2. The achievement in this paper seems good, which is 24.31%.\nCons:", "1. The nov...	[ [ 6 ], [ 7 ], [ 0, 17 ], [ 2 ], [ 9, 13, 15 ], [ 3 ], [ 10 ], [ 12 ], [ 18 ], [ 19 ], [ 11 ], [ 16 ], [ 4 ], [ 1 ], [ 8 ], [ 5 ], [ 14 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "SI...	benchmark/PDF/ICLR2018_r17Q6WWA-.pdf	openreview	benchmark/MD/ICLR2018_r17Q6WWA-.md	ICLR 2018
rkEfPeZRb	{ "title": "Variance-based Gradient Compression for Efficient Distributed Deep Learning", "abstract": "Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, worker...	Invite to Workshop Track	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...	[ [ { "role": "PC", "data": { "comment": "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 pa...	[ "This paper proposes a variance-based gradient compression method to reduce the communication overhead of distributed deep learning. Experiments on real datasets are used for evaluation.", "The idea to adopt approximated variances of gradients to reduce communication cost seems to be interesting.", "However, th...	[ [ 9 ], [ 13 ], [ 10 ], [ 11 ], [ 14 ], [ 16 ], [ 23 ], [ 1, 22 ], [ 5 ], [ 8 ], [ 3 ], [ 4 ], [ 6 ], [ 7 ], [ 17 ], [ 18 ], [ 24 ], [ 15 ], [ 12 ], [ 19 ]...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, ...	benchmark/PDF/ICLR2018_rkEfPeZRb.pdf	openreview	benchmark/MD/ICLR2018_rkEfPeZRb.md	ICLR 2018
BkXmYfbAZ	{ "title": "Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering", "abstract": "Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The n...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "PROS:\n1. Clear, interesting idea.\n2. Largely convincing evaluation\n3. Good writing\n\nCONS:\n1. The model used in the evaluation is a Resnet-50 and could have been more convincing with a more SOTA model.\n2. There is some concern about the whether t...	[ "This paper proposes a new approach for multi-task learning.", "While previous approaches assumes the order of shared layers are the same between tasks, this paper assume the order can vary across tasks, and the (soft) order is learned during training. They show improved performance on a number of multi-task lea...	[ [ 15 ], [ 17 ], [ 18 ], [ 5 ], [ 19 ], [ 1, 4 ], [ 11 ], [ 3 ], [ 6, 7, 8 ], [ 22 ], [ 0, 21 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 24 ], [ 25 ], [ 27 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] }, { "role"...	benchmark/PDF/ICLR2018_BkXmYfbAZ.pdf	openreview	benchmark/MD/ICLR2018_BkXmYfbAZ.md	ICLR 2018
H1Xw62kRZ	{ "title": "Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis", "abstract": "Program synthesis is the task of automatically generating a program consistent with\na specification. Recent years have seen proposal of a number of neural approaches\nfor program synthesis, many of which adopt a s...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "Below is a summary of the pros and cons of the proposed paper:\n\nPros:\n* Proposes a novel method to tune program synthesizers to generate correct programs and prune search space, leading to better and more efficient synthesis\n* Shows small but subst...	[ "The authors consider the task of program synthesis in the Karel DSL. Their innovations are to use reinforcement learning to guide sequential generation of tokes towards a high reward output, incorporate syntax checking into the synthesis procedure to prune syntactically invalid programs.", "Finally they learn a ...	[ [ 3, 16 ], [ 4 ], [ 5 ], [ 1 ], [ 11 ], [ 17 ], [ 0, 8, 14 ], [ 9 ], [ 10, 13 ], [ 12 ], [ 6 ], [ 18 ], [ 19 ], [ 2 ], [ 7 ], [ 15 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4 ] }, { "role"...	benchmark/PDF/ICLR2018_H1Xw62kRZ.pdf	openreview	benchmark/MD/ICLR2018_H1Xw62kRZ.md	ICLR 2018
rkMt1bWAZ	{ "title": "Bias-Variance Decomposition for Boltzmann Machines", "abstract": "We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. Our decomposition leads to an interesting phenomenon that the variance does not necessarily increase when more parameters are includ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "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 a...	[ "Summary of the paper:\nThe paper derives a lower bound on the expected squared KL-divergence between a true distribution and the sample based maximum likelihood estimate (MLE) of that distribution modelled by an Boltzmann machine (BM) based on methods from information geometry. This KL-divergence is first split ...	[ [ 7 ], [ 8 ], [ 9 ], [ 11 ], [ 16 ], [ 18 ], [ 20 ], [ 6 ], [ 10 ], [ 12 ], [ 19 ], [ 22 ], [ 23 ], [ 1 ], [ 24 ], [ 25 ], [ 4 ], [ 2 ], [ 3, 5, 14 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-ANL" ] }, ...	benchmark/PDF/ICLR2018_rkMt1bWAZ.pdf	openreview	benchmark/MD/ICLR2018_rkMt1bWAZ.md	ICLR 2018
H1Yp-j1Cb	{ "title": "An Online Learning Approach to Generative Adversarial Networks", "abstract": "We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused b...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 8, ...	[ [ { "role": "PC", "data": { "comment": "This paper presents a GAN training algorithm motivated by online learning. The method is shown to converge to a mixed Nash equilibrium in the case of a shallow discriminator. In the initial version of the paper, reviewers had concerns about weak baseli...	[ "This is an interesting paper, exploring GAN dynamics using ideas from online learning, in particular the pioneering \"sparring\" follow-the-regularized leader analysis of Freund and Schapire (using what is listed here as Lemma 4). By restricting the discriminator to be a single layer, the maximum player plays over...	[ [ 2 ], [ 1 ], [ 3, 13 ], [ 12 ], [ 5 ], [ 11 ], [ 8 ], [ 10 ], [ 0, 7 ], [ 4 ], [ 6 ], [ 9 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect" ]	[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "QUAL-MET", "ORIG-ANL" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 3 ] } ], "cate...	benchmark/PDF/ICLR2018_H1Yp-j1Cb.pdf	openreview	benchmark/MD/ICLR2018_H1Yp-j1Cb.md	ICLR 2018
S1J2ZyZ0Z	{ "title": "Interpretable Counting for Visual Question Answering", "abstract": "Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of bo...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "Important problem and all reviewers recommend acceptance. I agree." } } ] ]	[ "Summary:\nThis work introduces a discrete and interpretable model for answering visually grounded counting questions. The proposed model executes a sequential decision process in which it 1) selects an image region to \"add to the count\" and then 2) updates the likelihood of selecting other regions based on their...	[ [ 38 ], [ 39 ], [ 15 ], [ 16 ], [ 5, 29 ], [ 3 ], [ 18 ], [ 0, 1 ], [ 28 ], [ 2 ], [ 31 ], [ 34 ], [ 35 ], [ 36 ], [ 37 ], [ 8 ], [ 9 ], [ 10 ], [ 12 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "ORIG...	benchmark/PDF/ICLR2018_S1J2ZyZ0Z.pdf	openreview	benchmark/MD/ICLR2018_S1J2ZyZ0Z.md	ICLR 2018
B1DmUzWAW	{ "title": "A Simple Neural Attentive Meta-Learner", "abstract": "Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a dist...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "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 ...	[ "The authors propose a model for sequence classification and sequential decision making. The model interweaves attention layers, akin to those used by Vaswani et al, with temporal convolution. The authors demonstrate superior performance on a variety of benchmark problems, including those for supervised classificat...	[ [ 13 ], [ 6 ], [ 12 ], [ 27 ], [ 8, 9 ], [ 2, 21 ], [ 0 ], [ 3 ], [ 20 ], [ 25 ], [ 26 ], [ 1 ], [ 4 ], [ 7 ], [ 10 ], [ 11 ], [ 18 ], [ 24 ], [ 19 ], [ ...	[ "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL...	benchmark/PDF/ICLR2018_B1DmUzWAW.pdf	openreview	benchmark/MD/ICLR2018_B1DmUzWAW.md	ICLR 2018
Skj8Kag0Z	{ "title": "Stabilizing Adversarial Nets with Prediction Methods", "abstract": "Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not mini...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4 ] }, "scores": { "Solid": null, "Presentation": null, "No...	[ [ { "role": "PC", "data": { "comment": "This paper provides a simple technique for stabilizing GAN training, and works over a variety of GAN models.\n\nOne of the reviewers expressed concerns with the value of the theory. I think that it would be worth emphasizing that similar arguments coul...	[ "This paper proposes a simple modification to the standard alternating stochastic gradient method for GAN training, which stabilizes training, by adding a prediction step.", "This is a clever and useful idea, and the paper is very well written. The proposed method is very clearly motivated, both intuitively and m...	[ [ 1 ], [ 3 ], [ 31 ], [ 36 ], [ 5 ], [ 10, 22 ], [ 17 ], [ 40 ], [ 6 ], [ 16, 24 ], [ 33 ], [ 2 ], [ 12, 13, 14, 15, 34, 38 ], [ 18, 25, 32 ], [ 19 ], [ 39 ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correc...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...	benchmark/PDF/ICLR2018_Skj8Kag0Z.pdf	openreview	benchmark/MD/ICLR2018_Skj8Kag0Z.md	ICLR 2018
HJw8fAgA-	{ "title": "Learning Dynamic State Abstractions for Model-Based Reinforcement Learning", "abstract": "A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed models that learn predictive and compact state ...	Reject	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "There was quite a bit of discussion about this paper but in the end the majority felt that, though the paper is interesting, the results are too limited and more needs to be done for publication.\n\nPROS:\n1. Good comparison of state space model variat...	[ "Summary:\nThis paper studies how to learn (hidden)-state-space models of environment dynamics, and integrate them with Imagination-Augmented Agents (I2A). The paper considers single-agent problems and tests on Ms Pacman etc.", "There are several variations of the hidden-state space [ds]SSM model: using det/stoch...	[ [ 21 ], [ 4 ], [ 17 ], [ 3 ], [ 23 ], [ 18 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 9 ], [ 11 ], [ 12 ], [ 14 ], [ 19 ], [ 20 ], [ 24 ], [ 25 ], [ 16 ], [ 26 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 4 ] } ], "categ...	benchmark/PDF/ICLR2018_HJw8fAgA-.pdf	openreview	benchmark/MD/ICLR2018_HJw8fAgA-.md	ICLR 2018
H1BLjgZCb	{ "title": "Generating Natural Adversarial Examples", "abstract": "Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially dif...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The paper proposes a method to generate adversaries close to the (training) data manifold using GANs rather than arbitrary adversaries. They show the effectiveness of their method in terms of human evaluation and success in fooling a deep network. The ...	[ "Summary:", "A method for creation of semantical adversary examples in suggested. The ‘semantic’ property is measured by building a latent space with mapping from this space to the observable (generator) and back (inverter). The generator is trained with a WGAN optimization. Semantic adversarials examples are the...	[ [ 5 ], [ 15 ], [ 16 ], [ 28 ], [ 11 ], [ 21 ], [ 10 ], [ 13 ], [ 17 ], [ 25 ], [ 3, 14 ], [ 20 ], [ 24 ], [ 26 ], [ 33 ], [ 22, 29 ], [ 23 ], [ 30 ], [ 30 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...	benchmark/PDF/ICLR2018_H1BLjgZCb.pdf	openreview	benchmark/MD/ICLR2018_H1BLjgZCb.md	ICLR 2018
H1VjBebR-	{ "title": "The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings", "abstract": "We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of pa...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "The reviewers were generally positive about this paper with a few caveats:\n\nPROS:\n1. Important and challenging topic to analyze and any progress on unsupervised learning is interesting.\n2. the paper is clear, although more formalization would help ...	[ "The paper addresses the problem of learning mappings between different domains without any supervision. It belongs to the recent family of papers based on GANs.", "The paper states three conjectures (predictions in the paper):", "1. GAN are sufficient to learn « semantic mappings » in an unsupervised way, if t...	[ [ 13 ], [ 11 ], [ 4 ], [ 10 ], [ 12, 32 ], [ 14, 18 ], [ 14 ], [ 25 ], [ 38 ], [ 39 ], [ 47 ], [ 48 ], [ 19 ], [ 22 ], [ 29 ], [ 7 ], [ 15 ], [ 23 ], [ 31 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 2 ] } ], "category": [ "QUAL-MET" ...	benchmark/PDF/ICLR2018_H1VjBebR-.pdf	openreview	benchmark/MD/ICLR2018_H1VjBebR-.md	ICLR 2018
HyjC5yWCW	{ "title": "Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm", "abstract": "Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent mo...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...	[ [ { "role": "PC", "data": { "comment": "R3 summarizes the reasons for the decision on this paper: \"The universal learning algorithm approximator result is a nice result, although I do not agree with the other reviewer that it is a \"significant contribution to the theoretical understanding...	[ "This paper studies the capacity of the model-agnostic meta-learning (MAML) framework as a universal learning algorithm approximator. Since a (supervised) learning algorithm can be interpreted as a map from a dataset and an input to an output, the authors define a universal learning algorithm approximator to be a u...	[ [ 5 ], [ 7 ], [ 16 ], [ 18 ], [ 19 ], [ 1 ], [ 15 ], [ 17 ], [ 20 ], [ 22 ], [ 6 ], [ 21 ], [ 23 ], [ 3 ], [ 4, 9, 25 ], [ 8 ], [ 11 ], [ 12 ], [ 2 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "ORIG...	benchmark/PDF/ICLR2018_HyjC5yWCW.pdf	openreview	benchmark/MD/ICLR2018_HyjC5yWCW.md	ICLR 2018
Hkc-TeZ0W	{ "title": "A Hierarchical Model for Device Placement", "abstract": "We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph oper...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...	[ [ { "role": "PC", "data": { "comment": "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 ...	[ "The paper seems clear enough and original enough. The idea of jointly forming groups of operations to colocate and figure out placement on devices seems to hold merit. Where the paper falls short is motivating the problem setting. Traditionally, for determining optimal execution plans, one may resort to cost-based...	[ [ 6 ], [ 8 ], [ 20 ], [ 22 ], [ 23 ], [ 24 ], [ 18 ], [ 0 ], [ 1 ], [ 4 ], [ 7 ], [ 3, 9 ], [ 5 ], [ 10 ], [ 15 ], [ 16 ], [ 19 ], [ 14 ], [ 21 ], [ 12 ],...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 15, 16, 17, 79, 80 ] } ], "category": [ "ORIG-PROB" ] }, { "senten...	benchmark/PDF/ICLR2018_Hkc-TeZ0W.pdf	openreview	benchmark/MD/ICLR2018_Hkc-TeZ0W.md	ICLR 2018
r1ZdKJ-0W	{ "title": "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking", "abstract": "Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn ...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...	[ [ { "role": "PC", "data": { "comment": "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 updat...	[ "This paper is well-written and easy follow. I didn't find serious concern and therefore suggest an acceptance.\nPros", "Methodology", "1. inductive ability: can generalize to unseen nodes without any further training", "2. personalized ranking: the model uses natural ranking that embeddings of closer nodes (...	[ [ 16 ], [ 15 ], [ 0 ], [ 10 ], [ 14 ], [ 21, 26 ], [ 23, 27 ], [ 6 ], [ 2 ], [ 18 ], [ 5 ], [ 7 ], [ 22 ], [ 4 ], [ 11 ], [ 13 ], [ 20 ], [ 17 ], [ 1 ], [ ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", ...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] } ], "category": [ "QU...	benchmark/PDF/ICLR2018_r1ZdKJ-0W.pdf	openreview	benchmark/MD/ICLR2018_r1ZdKJ-0W.md	ICLR 2018
HkNGsseC-	{ "title": "On the Expressive Power of Overlapping Architectures of Deep Learning", "abstract": "Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unle...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...	[ [ { "role": "PC", "data": { "comment": "The paper received scores of 8 (R1), 6 (R2), 6 (R3). R1's review is brief, and also is optimistic that these results demonstrated on ConvACs generalize to real convnets. R2 and R3 feel this might be a potential problem. R2 advocates weak accept and gi...	[ "The paper studies the expressive power provided by \"overlap\" in convolution layers of DNNs.", "Instead of ReLU networks with average/max pooling (as is standard in practice), the authors consider linear activations with product pooling. Such networks, which have been known as convolutional arithmetic circuits...	[ [ 9 ], [ 11 ], [ 10 ], [ 7 ], [ 1 ], [ 3 ], [ 6 ], [ 8 ], [ 2 ], [ 4 ], [ 0 ], [ 5 ] ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4 ] }, { ...	benchmark/PDF/ICLR2018_HkNGsseC-.pdf	openreview	benchmark/MD/ICLR2018_HkNGsseC-.md	ICLR 2018
ByBAl2eAZ	{ "title": "Parameter Space Noise for Exploration", "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a riche...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...	[ [ { "role": "PC", "data": { "comment": "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 ac...	[ "This paper explores the idea of adding parameter space noise in service of exploration. The paper is very well written and quite clear. It does a good job of contrasting parameter space noise to action space noise and evolutionary strategies.", "However, the results are weak. Parameter noise does better in some ...	[ [ 10 ], [ 0 ], [ 7 ], [ 21 ], [ 3 ], [ 6, 15 ], [ 19 ], [ 30 ], [ 8, 13 ], [ 25, 35 ], [ 27 ], [ 28 ], [ 29 ], [ 1 ], [ 2 ], [ 5 ], [ 11 ], [ 22 ], [ 24 ]...	[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...	[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author",...	benchmark/PDF/ICLR2018_ByBAl2eAZ.pdf	openreview	benchmark/MD/ICLR2018_ByBAl2eAZ.md	ICLR 2018
rJl63fZRb	{ "title": "Parametrized Hierarchical Procedures for Neural Programming", "abstract": "Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of...	Accept (Poster)	[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 6, ...	[ [ { "role": "PC", "data": { "comment": "This paper is somewhat incremental on recent prior work in a hot area; it has some weaknesses but does move the needle somewhat on these problems." } } ] ]	[ "In the paper titled \"Parameterized Hierarchical Procedures for Neural Programming\", the authors proposed \"Parametrized Hierarchical Procedure (PHP)\", which is a representation of a hierarchical procedure by differentiable parametrization. Each PHP is represented with two multi-layer perceptrons with ReLU activ...	[ [ 1 ], [ 9, 11 ], [ 16, 20, 23 ], [ 19 ], [ 3 ], [ 17 ], [ 7 ], [ 15 ], [ 10 ], [ 12 ], [ 13 ], [ 21 ], [ 2 ], [ 4, 6 ], [ 18, 22 ], [ 0 ], [ 5 ], [ 8 ], ...	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]	[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]	[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...	benchmark/PDF/ICLR2018_rJl63fZRb.pdf	openreview	benchmark/MD/ICLR2018_rJl63fZRb.md	ICLR 2018

HJNGGmZ0Z

{ "title": "What is image captioning made of?", "abstract": "We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Paper reviewed by three experts who have provided detailed feedback. All three recommend rejection, and this AC sees no reason to overrule their recommendation. " } } ] ]

[ "This paper analyzes the effect of image features on image captioning. The authors propose to use a model similar to that of Vinyals et al., 2015 and change the image features it is conditioned on. The MSCOCO captioning and Flickr30K datasets are used for evaluation.\nIntroduction", "- The introduction to the pap...

[ [ 10 ], [ 11 ], [ 1 ], [ 2 ], [ 24 ], [ 13 ], [ 26 ], [ 29 ], [ 37 ], [ 27 ], [ 4, 17 ], [ 7 ], [ 12 ], [ 14, 18 ], [ 19 ], [ 21 ], [ 31 ], [ 32 ], [ 33 ], ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_HJNGGmZ0Z.pdf

openreview

benchmark/MD/ICLR2018_HJNGGmZ0Z.md

ICLR 2018

HJr4QJ26W

{ "title": "Improving image generative models with human interactions", "abstract": "GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The reviewers agree that the idea of incorporating humans in the training of generative adversarial networks is interesting and worthwhile exploring. However, they felt that the paper fell short in providing strong support for their approach. The AC ag...

[ "+ Quality:\nThe paper discusses an interesting direction of incorporating humans in the training of a generative adversarial networks in the hope of improving generated samples. I personally find this exciting/refreshing and will be useful in the future of machine learning.", "However, the paper shows only preli...

[ [ 32 ], [ 7 ], [ 8 ], [ 14 ], [ 19 ], [ 31, 35 ], [ 33 ], [ 34 ], [ 36 ], [ 9 ], [ 27 ], [ 16 ], [ 21 ], [ 2 ], [ 5 ], [ 6 ], [ 13, 30 ], [ 18 ], [ 22 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...

benchmark/PDF/ICLR2018_HJr4QJ26W.pdf

openreview

benchmark/MD/ICLR2018_HJr4QJ26W.md

ICLR 2018

B1NOXfWR-

{ "title": "Neural Task Graph Execution", "abstract": "In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and depend...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Paper presents and interesting new direction, but the evaluation leaves many questions open, and situation with respect to state of the art is lacking" } } ] ]

[ "In the context of multitask reinforcement learning, this paper considers the problem of learning behaviours when given specifications of subtasks and the relationship between them, in the form of a task graph. The paper presents a neural task graph solver (NTS), which encodes this as a recursive-reverse-recursive ...

[ [ 6 ], [ 27 ], [ 5 ], [ 7 ], [ 13 ], [ 15 ], [ 33 ], [ 22 ], [ 21 ], [ 1 ], [ 3 ], [ 16 ], [ 24 ], [ 30 ], [ 0 ], [ 2, 12 ], [ 10 ], [ 11, 29 ], [ 32 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_B1NOXfWR-.pdf

openreview

benchmark/MD/ICLR2018_B1NOXfWR-.md

ICLR 2018

Byd-EfWCb

{ "title": "Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks", "abstract": "Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. Introducing the concept of an optimal representation space, we provide a si...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "this submission has two results; (1) it defines what it means for the optimal representation is, although this is rather uninteresting that it simply says that if the representation from a model is going to be used based on some given metric, the cost ...

[ "The authors provide some theoretical justification for why simple log-linear decoders perform better than RNN decoders for various unsupervised sentence similarity tasks. They also provide a simple method for improving the performance of RNN based models. Please find below my comments/questions/suggestions:", "1...

[ [ 9 ], [ 1 ], [ 11, 16, 33 ], [ 25 ], [ 5, 6, 7 ], [ 8 ], [ 10 ], [ 24 ], [ 27 ], [ 34 ], [ 37 ], [ 38 ], [ 32 ], [ 3 ], [ 20 ], [ 21 ], [ 22 ], [ 26 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_Byd-EfWCb.pdf

openreview

benchmark/MD/ICLR2018_Byd-EfWCb.md

ICLR 2018

BJ4prNx0W

{ "title": "Learning what to learn in a neural program", "abstract": "Learning programs with neural networks is a challenging task, addressed by a long line of existing work. It is difficult to learn neural networks which will generalize to problem instances that are much larger than those used during training. Fur...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "This paper is novel, but relatively incremental and relatively niche; the reviewers (despite discussion) are still unsure why this approach is needed." } } ] ]

[ "Quality\nThe paper is well-written and clear, and includes relevant comparisons to previous work (NPI and recursive NPI).", "Clarity\nThe paper is clearly written.", "Originality\nTo my knowledge the method proposed in this work is novel. It is the first to study constructing minimal training sets for NPI give...

[ [ 0 ], [ 1 ], [ 11 ], [ 7 ], [ 2 ], [ 3 ], [ 15 ], [ 12 ], [ 8 ], [ 16, 17 ], [ 14 ], [ 4 ], [ 5, 9 ], [ 6 ], [ 10 ], [ 13 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT", "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_BJ4prNx0W.pdf

openreview

benchmark/MD/ICLR2018_BJ4prNx0W.md

ICLR 2018

r15kjpHa-

{ "title": "Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing", "abstract": "In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "All reviewers are unanimous that the paper is below threshold for acceptance. The authors have not provided rebuttals, but merely perfunctory generic responses.\n\nI think the most important criticism is that the approach is \"very ad-hoc.\" I would ...

[ "The authors suggest using a mixture of shared and individual rewards within a MARL environment to induce cooperation among independent agents. They show that on their specific application this can lead to a better overall global performance than purely sharing the global signal, or using just the independent rewar...

[ [ 2 ], [ 3 ], [ 6 ], [ 7 ], [ 8 ], [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 22 ], [ 23 ], [ 1, 5 ], [ 4 ], [ 9 ], [ 20 ], [ 17 ], [ 19 ], [ 21 ],...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_r15kjpHa-.pdf

openreview

benchmark/MD/ICLR2018_r15kjpHa-.md

ICLR 2018

BJj6qGbRW

{ "title": "Few-Shot Learning with Graph Neural Networks", "abstract": "We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-p...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 7, ...

[ [ { "role": "PC", "data": { "comment": "All reviewers agree that the proposed method is novel and experiments do a good job in establishing its value for few-shot learning. Most the concerns raised by the reviewers on experimental protocols have been addressed in the author response and revi...

[ "This paper studies the problem of one-shot and few-shot learning using the Graph Neural Network (GNN) architecture that has been proposed and simplified by several authors. The data points form the nodes of the graph with the edge weights being learned, using ideas similar to message passing algorithms similar to ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_BJj6qGbRW.pdf

openreview

benchmark/MD/ICLR2018_BJj6qGbRW.md

ICLR 2018

BkPrDFgR-

{ "title": "Piecewise Linear Neural Networks verification: A comparative study", "abstract": "The success of Deep Learning and its potential use in many important safety-\ncritical applications has motivated research on formal verification of Neural Net-\nwork (NN) models. Despite the reputation of learned NN model...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "All three reviewers are in agreement that this paper is not ready for ICLR in its current state. Given the pros/cons, the committee feels the paper is not ready for acceptance in its current form." } } ] ]

[ "The paper studies methods for verifying neural nets through their piecewise", "linear structure. The authors survey different methods from the literature,", "propose a novel one, and evaluate them on a set of benchmarks.", "A major drawback of the evaluation of the different approaches is that", "everythin...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5, 6, ...

benchmark/PDF/ICLR2018_BkPrDFgR-.pdf

openreview

benchmark/MD/ICLR2018_BkPrDFgR-.md

ICLR 2018

BJypUGZ0Z

{ "title": "Accelerating Neural Architecture Search using Performance Prediction", "abstract": "Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that standard frequentist reg...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes to use simple regression models for predicting the accuracy of a neural network based on its initial training curve, architecture, and hyper-parameters; this can be used for speeding up architecture search. While this is an interesti...

[ "This paper explores the use of simple models for predicting the final", "validation performance of a neural network, from intermediate values", "during training. It uses support vector regression to show that a", "relatively small number of samples of hyperparameters, architectures,", "and validation time...

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[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ] } ], "category": [ "QUAL-CMP" ] },...

benchmark/PDF/ICLR2018_BJypUGZ0Z.pdf

openreview

benchmark/MD/ICLR2018_BJypUGZ0Z.md

ICLR 2018

S1uxsye0Z

{ "title": "Adaptive Dropout with Rademacher Complexity Regularization", "abstract": "We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-ad...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "AC", "data": { "comment": "Are the authors aware of the work by David McAllister using PAC-Bayes bounds to analyze dropout? Last I saw, it was not mentioned in the paper. IT seems like important related work. Could the authors, very quickly (!), comment as to the relationship...

[ "This paper studies the adjustment of dropout rates which is a useful tool to prevent the overfitting of deep neural networks. The authors derive a generalization error bound in terms of dropout rates. Based on this, the authors propose a regularization framework to adaptively select dropout rates. Experimental res...

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[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_S1uxsye0Z.pdf

openreview

benchmark/MD/ICLR2018_S1uxsye0Z.md

ICLR 2018

S1TgE7WR-

{ "title": "Covariant Compositional Networks For Learning Graphs", "abstract": "Most existing neural networks for learning graphs deal with the issue of permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that ...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "This is a good contribution, with the potential to become extremely good and significant if presentation is substantially improved.\nAll reviewers comment on the lack of clarity of the paper, especially concerning its central contributions (Section 4 a...

[ "Thank you for your contribution to ICLR. The paper covers a very interesting topic and presents some though-provoking ideas.", "The paper introduces \"covariant compositional networks\" with the purpose of learning graph representations. An example application also covered in the experimental section is graph cl...

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[ "correct", "incorrect", "correct", "correct", "incorrect", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct"...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4, 5 ] }, { ...

benchmark/PDF/ICLR2018_S1TgE7WR-.pdf

openreview

benchmark/MD/ICLR2018_S1TgE7WR-.md

ICLR 2018

rJk51gJRb

{ "title": "Adversarial Policy Gradient for Alternating Markov Games", "abstract": "Policy gradient reinforcement learning has been applied to two-player alternate-turn zero-sum games, e.g., in AlphaGo, self-play REINFORCE was used to improve the neural net model after supervised learning. In this paper, we emphasi...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...

[ [ { "role": "PC", "data": { "comment": "The reviewers agree that the paper is below threshold for acceptance in the main track (one with very low confidence), but they favor submitting the paper to the workshop track.\n\nThe paper considers policy gradient methods for two-player zero-sum Alt...

[ "This paper is outside of my area of expertise, so I'll just provide a light review:", "- the idea of assuming that the opponent will take the worst possible action is reasonable in widely used in classic search, so making value functions follow this intuition seems sensible,", "- but somehow I wonder if this i...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-MET" ] }, ...

benchmark/PDF/ICLR2018_rJk51gJRb.pdf

openreview

benchmark/MD/ICLR2018_rJk51gJRb.md

ICLR 2018

ByzvHagA-

{ "title": "Disentangled activations in deep networks", "abstract": "Deep neural networks have been tremendously successful in a number of tasks.\nOne of the main reasons for this is their capability to automatically\nlearn representations of data in levels of abstraction,\nincreasingly disentangling the data as th...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "The novelty of the paper is limited and it lacks on comparisons with relevant baselines, as pointed out by the reviewers. " } } ] ]

[ "The authors propose a penalization term that enforces decorrelation between the dimensions of the representation", "They show that it can be included as additional term in cost functions to train generic models.", "The idea is simple and it seems to work for the presented examples.", "However, they talk abou...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL-EXP"...

benchmark/PDF/ICLR2018_ByzvHagA-.pdf

openreview

benchmark/MD/ICLR2018_ByzvHagA-.md

ICLR 2018

r1NYjfbR-

{ "title": "Generative networks as inverse problems with Scattering transforms", "abstract": "Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4 ] }, "scores": { "Solid": null, "Presentation": null, "No...

[ [ { "role": "PC", "data": { "comment": "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, wa...

[ "After a first manuscript that needed majors edits, the revised version", "offers an interesting GAN approach based the scattering transform.", "Approach is well motivated with proper references to the recent literature.", "Experiments are not state of the art but clearly demonstrate that the", "proposed ap...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QU...

benchmark/PDF/ICLR2018_r1NYjfbR-.pdf

openreview

benchmark/MD/ICLR2018_r1NYjfbR-.md

ICLR 2018

BJk7Gf-CZ

{ "title": "Global Optimality Conditions for Deep Neural Networks", "abstract": "We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, and despite recent progress, our understanding of this l...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "Understanding global optimality conditions for deep nets even in the restricted case of linear layers is a valuable contribution. Please add clarifications to ways in which the paper goes beyond the results of Kawaguchi'16, which was the main concern e...

[ "Summary:\nThe paper gives theoretical results regarding the existence of local minima in the objective function of deep neural networks.\nIn particular:", "- in the case of deep linear networks, they characterize whether a critical point is a global optimum or a saddle point by a simple criterion. This improves ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, {...

benchmark/PDF/ICLR2018_BJk7Gf-CZ.pdf

openreview

benchmark/MD/ICLR2018_BJk7Gf-CZ.md

ICLR 2018

SyL9u-WA-

{ "title": "Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization", "abstract": "Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs). In this paper, we presen...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...

[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clearly written paper.\n+ Good theoretical analysis of the expressivity of the proposed model.\n+ Efficient model update is appealing.\n+ Reviewers appreciated the addition of results on the copy and adding tasks in Appendix C.\n\nCons:\n- Eva...

[ "This paper proposed a new parametrization scheme for weight matrices in neural network based on the Householder reflectors to solve the gradient vanishing and exploding problems in training. The proposed method improved two previous papers:", "1) stronger expressive power than Mahammedi et al. (2017),", "2) f...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-CMP" ] }, ...

benchmark/PDF/ICLR2018_SyL9u-WA-.pdf

openreview

benchmark/MD/ICLR2018_SyL9u-WA-.md

ICLR 2018

HJcSzz-CZ

{ "title": "Meta-Learning for Semi-Supervised Few-Shot Classification", "abstract": "In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized m...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "The paper extends the earlier work on Prototypical networks to semi-supervised setting. Reviewers largely agree that the paper is well-written. There are some concerns on the incremental nature of the paper wrt to the novelty aspect but in the light of...

[ "This paper is an extension of the “prototypical network” which will be published in NIPS 2017. The classical few-shot learning has been limited to using the unlabeled data, while this paper considers employing the unlabeled examples available to help train each episode. The paper solves a new semi-supervised situa...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-PROB", "QUAL-REP", "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { ...

benchmark/PDF/ICLR2018_HJcSzz-CZ.pdf

openreview

benchmark/MD/ICLR2018_HJcSzz-CZ.md

ICLR 2018

BkUHlMZ0b

{ "title": "Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach", "abstract": "The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, li...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper proposes a new metric to evaluate the robustness of neural networks to adversarial attacks. This metric comes with theoretical guarantees and can be efficiently computed on large-scale neural networks.\n\nReviewers were generally positive ab...

[ "Summary\nThe authors present CLEVER, an algorithm which consists in evaluating the (local) Lipschitz constant of a trained network around a data point. This is used to compute a lower-bound on the minimal perturbation of the data point needed to fool the network.", "The method proposed in the paper already exist...

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[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_BkUHlMZ0b.pdf

openreview

benchmark/MD/ICLR2018_BkUHlMZ0b.md

ICLR 2018

Byk4My-RZ

{ "title": "Flexible Prior Distributions for Deep Generative Models", "abstract": "We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper presents a method for learning more flexible prior distributions for GANs by learning another distribution on top of the latent codes for training examples. It's reminiscent of layerwise training of deep generative models. This seems like a ...

[ "Summary:\nThe paper proposes to learn new priors for latent codes z for GAN training. for this the paper shows that there is a mismatch between the gaussian prior and an estimated of the latent codes of real data by reversal of the generator . To fix this the paper proposes to learn a second GAN to learn the pri...

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[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...

benchmark/PDF/ICLR2018_Byk4My-RZ.pdf

openreview

benchmark/MD/ICLR2018_Byk4My-RZ.md

ICLR 2018

rkONG0xAW

{ "title": "Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement", "abstract": "This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Byt...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...

[ [ { "role": "PC", "data": { "comment": "This is an interesting paper and addresses an important problem of neural networks with memory constrains. New experiments have been added that add to the paper, but the full impact of the paper is not yet realised, needing further exploration of model...

[ "The idea of this work is fairly simple. Two main problems exist in end devices for deep learning: power and memory. There have been a series of works showing how to discretisize neural networks. This work, discretisize a NN incrementally. It does so in the following way: First, we train the network with the memory...

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[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4 ] }, { "role": "Au...

benchmark/PDF/ICLR2018_rkONG0xAW.pdf

openreview

benchmark/MD/ICLR2018_rkONG0xAW.md

ICLR 2018

H1eJxngCW

{ "title": "DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension", "abstract": "We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC c...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overa...

[ [ { "role": "PC", "data": { "comment": "This is a (question answering) dataset paper with some baseline models.\n\nThe evaluation metric seems far from ideal and not quite ready for prime-time yet. They use F1 and Exact Match - these metrics make sense for extractive question answering syste...

[ "This paper presents a useful dataset for testing reading comprehension while avoiding significant lexical overlap between question and document. The paper rightly mentions that existing reading comprehension datasets (e.g. SQuAD) where the current methods are already performing at the human level largely due to la...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-EXP" ]...

benchmark/PDF/ICLR2018_H1eJxngCW.pdf

openreview

benchmark/MD/ICLR2018_H1eJxngCW.md

ICLR 2018

BkfEzz-0-

{ "title": "Neuron as an Agent", "abstract": "Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\\em Neuron as an A...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...

[ [ { "role": "PC", "data": { "comment": "\nThe reviewers have significantly different views, with one strongly negative,\none strongly positive, and one borderline negative. However, all three\nreviews seem to regard the NaaA framework as a very interesting and novel approach to training neu...

[ "In this paper, the authors present a novel way to look at a neural network such that each neuron (node) in the network is an agent working to optimize its reward. The paper shows that by appropriately defining the neuron level reward function, the model can learn a better policy in different tasks.", "For exampl...

[ [ 38 ], [ 1 ], [ 5 ], [ 30 ], [ 32 ], [ 34 ], [ 35 ], [ 4, 14 ], [ 24 ], [ 2 ], [ 7 ], [ 8 ], [ 9, 21 ], [ 10 ], [ 11 ], [ 12 ], [ 15 ], [ 16 ], [ 18 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 5 ] }, { ...

benchmark/PDF/ICLR2018_BkfEzz-0-.pdf

openreview

benchmark/MD/ICLR2018_BkfEzz-0-.md

ICLR 2018

HyyP33gAZ

{ "title": "Activation Maximization Generative Adversarial Nets", "abstract": "Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label inf...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "The authors investigate various class aware GANs and provide extensive analysis of their ability to address mode collapse and sample quality issues. Based on this analysis they propose an extension called Activation Maximization-GAN which tries to push...

[ "I thank the authors for the thoughtful responses and updated manuscript.", "Although the manuscript is improved, I still feel it is unfocused and may be substantially improved, thus my review score remains unchanged.", "The authors describe a new version of a generative adversarial network (GAN) for generating...

[ [ 17 ], [ 1 ], [ 4 ], [ 16 ], [ 18 ], [ 0 ], [ 12 ], [ 14 ], [ 15 ], [ 23 ], [ 25 ], [ 13 ], [ 19 ], [ 22 ], [ 7 ], [ 9 ], [ 2 ], [ 5 ], [ 8 ], [ 11 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] }, { "role"...

benchmark/PDF/ICLR2018_HyyP33gAZ.pdf

openreview

benchmark/MD/ICLR2018_HyyP33gAZ.md

ICLR 2018

ryCM8zWRb

{ "title": "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations", "abstract": "RNNs have been shown to be excellent models for sequential data and in particular for session-based user behavior. The use of RNNs provides impressive performance benefits over classical methods in session-based ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "While the use of RNNs for building session-based recommender systems is certainly an important class of applications, the main strength of the paper is to propose and benchmark practical modifications to prior RNN-based systems that lead to performance...

[ "This is an interesting paper that analyzes existing loss functions for session-based recommendations. Based on the result of these analysis the authors propose two novel losses functions which add a weighting to existing ranking-based loss functions. These novelties are meant to improve issues related to vanishing...

[ [ 7, 9 ], [ 16 ], [ 1 ], [ 6 ], [ 14 ], [ 15 ], [ 4 ], [ 0, 10 ], [ 11 ], [ 12 ], [ 19 ], [ 5 ], [ 13 ], [ 2 ], [ 18 ], [ 17 ], [ 3 ], [ 8 ] ]

[ "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-MTH", "ORIG-ANL", "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], ...

benchmark/PDF/ICLR2018_ryCM8zWRb.pdf

openreview

benchmark/MD/ICLR2018_ryCM8zWRb.md

ICLR 2018

S1Euwz-Rb

{ "title": "Compositional Attention Networks for Machine Reasoning", "abstract": "We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural networks are effective at learning and general...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "PROS:\n1. Good results on CLEVER datasets\n2. Writing is clear\n3. The MAC unit is novel and interesting.\n4. Ablation experiments are helpful\n\nCONS:\nThe authors overstate the degree to which they are doing \"sound\" and \"transparent\" reasoning. ...

[ "This paper describes a new model architecture for machine reasoning.", "In contrast\nto previous approaches that explicitly predict a question-specific module", "network layout, the current paper introduces a monolithic feedforward network", "with iterated rounds of attention and memory. On a few variants of...

[ [ 16 ], [ 37 ], [ 12 ], [ 13 ], [ 14 ], [ 15 ], [ 21 ], [ 28 ], [ 38 ], [ 4 ], [ 1 ], [ 2 ], [ 3, 6 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 11 ], [ 17 ], [ 22 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6, ...

benchmark/PDF/ICLR2018_S1Euwz-Rb.pdf

openreview

benchmark/MD/ICLR2018_S1Euwz-Rb.md

ICLR 2018

S1WRibb0Z

{ "title": "Expressive power of recurrent neural networks", "abstract": "Deep neural networks are surprisingly efficient at solving practical tasks,\nbut the theory behind this phenomenon is only starting to catch up with\nthe practice. Numerous works show that depth is the key to this efficiency.\nA certain class ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...

[ [ { "role": "PC", "data": { "comment": "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 ...

[ "In this paper, the expressive power of neural networks characterized by tensor train (TT) decomposition, a chain-type tensor decomposition, is investigated. Here, the expressive power refers to the rank of tensor decomposition, i.e., the number of latent components. The authors compare the complexity of TT-type ne...

[ [ 18 ], [ 1 ], [ 5 ], [ 10 ], [ 13 ], [ 14 ], [ 8 ], [ 2 ], [ 4 ], [ 15 ], [ 7 ], [ 11 ], [ 17 ], [ 6 ], [ 16 ], [ 0 ], [ 3 ], [ 9 ], [ 12 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...

benchmark/PDF/ICLR2018_S1WRibb0Z.pdf

openreview

benchmark/MD/ICLR2018_S1WRibb0Z.md

ICLR 2018

rk49Mg-CW

{ "title": "Stochastic Variational Video Prediction", "abstract": "Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural image...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "Not quite enough for an oral but a very solid poster." } } ] ]

[ "Quality: above threshold\nClarity: above threshold, but experiment details are missing.\nOriginality: slightly above threshold.", "Significance: above threshold", "Pros:\nThis paper proposes a stochastic variational video prediction model. It can be used for prediction in optionally available external action c...

[ [ 20 ], [ 21 ], [ 4 ], [ 8 ], [ 6 ], [ 22 ], [ 0 ], [ 1 ], [ 2 ], [ 7 ], [ 10 ], [ 12, 14, 19 ], [ 13 ], [ 18 ], [ 15 ], [ 3 ], [ 11 ], [ 17 ], [ 9 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4 ] }, { ...

benchmark/PDF/ICLR2018_rk49Mg-CW.pdf

openreview

benchmark/MD/ICLR2018_rk49Mg-CW.md

ICLR 2018

S1DWPP1A-

{ "title": "Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration", "abstract": "Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been sho...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "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...

[ "This paper introduces a representation learning step in the Intrinsically Motivated Exploration Process (IMGEP) framework.", "Though this work is far from my expertise fields I find it quite easy to read and a good introduction to IMGEP.", "Nevertheless I have some major concerns that prevent me from giving a...

[ [ 22 ], [ 29 ], [ 42 ], [ 37 ], [ 38 ], [ 7 ], [ 25 ], [ 27 ], [ 31 ], [ 32 ], [ 36 ], [ 39, 43 ], [ 40 ], [ 44 ], [ 6 ], [ 9 ], [ 41 ], [ 24 ], [ 11 ], [ 1...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5 ] }, ...

benchmark/PDF/ICLR2018_S1DWPP1A-.pdf

openreview

benchmark/MD/ICLR2018_S1DWPP1A-.md

ICLR 2018

BJ8c3f-0b

{ "title": "Auto-Encoding Sequential Monte Carlo", "abstract": "We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the effic...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "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...

[ "Overall:", "I had a really hard time reading this paper because I found the writing to be quite confusing.", "For this reason I cannot recommend publication as I am not sure how to evaluate the paper’s contribution.", "Summary\nThe authors study state space models in the unsupervised learning case. We have a...

[ [ 12 ], [ 35 ], [ 13 ], [ 38 ], [ 0 ], [ 2 ], [ 3, 6 ], [ 4 ], [ 10 ], [ 15 ], [ 16 ], [ 17 ], [ 28 ], [ 33 ], [ 36 ], [ 37 ], [ 29 ], [ 31, 32 ], [ 7, 9 ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 3, 4, 5 ] ...

benchmark/PDF/ICLR2018_BJ8c3f-0b.pdf

openreview

benchmark/MD/ICLR2018_BJ8c3f-0b.md

ICLR 2018

B1ZZTfZAW

{ "title": "Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs", "abstract": "Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Overall I agree with the assessment of R1 that the paper touches on many interesting issues (deep learning for time series, privacy-respecting ML, simulated-to-real-world adaptation) but does not make a strong contribution to any of these. Especially w...

[ "The authors propose to use synthetic data generated by GANs as a replacement for personally identifiable data in training ML models for privacy-sensitive applications such as medicine.", "In particular it demonstrates adversarial training of a recurrent generator for an ICU monitoring multidimensional time serie...

[ [ 20 ], [ 10 ], [ 1, 15 ], [ 5 ], [ 7 ], [ 2 ], [ 8 ], [ 12 ], [ 13 ], [ 18 ], [ 19 ], [ 17 ], [ 11 ], [ 16 ], [ 0 ], [ 3 ], [ 4 ], [ 6 ], [ 9 ], [ 14 ], ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 4 ] }, { ...

benchmark/PDF/ICLR2018_B1ZZTfZAW.pdf

openreview

benchmark/MD/ICLR2018_B1ZZTfZAW.md

ICLR 2018

SJTB5GZCb

{ "title": "Extending the Framework of Equilibrium Propagation to General Dynamics", "abstract": "The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward pha...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": " + interesting novel extension of equilibrium propagation, as a biologically more plausible alternative to backpropagation, with encouraging initial experimental validation.\n - currently lacks theoretical guarantees regarding convergence of the algor...

[ "The manuscript discusses a learning algorithm that is based on the equilibrium propagation method, which can be applied to networks with asymmetric connections. This extension is interesting, but the results seem to be incomplete and missing necessary additional analyses.", "Therefore, I do not recommend accepta...

[ [ 4 ], [ 11 ], [ 0 ], [ 2 ], [ 5 ], [ 8 ], [ 10, 16 ], [ 12, 17 ], [ 13 ], [ 14 ], [ 15 ], [ 3 ], [ 6 ], [ 7 ], [ 9 ], [ 18 ], [ 1 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 25, 26, 27, 28 ] } ], "category": [ "QUAL-EXP", "QUAL-MET", "QUAL-STA" ]...

benchmark/PDF/ICLR2018_SJTB5GZCb.pdf

openreview

benchmark/MD/ICLR2018_SJTB5GZCb.md

ICLR 2018

rybDdHe0Z

{ "title": "Sequence Transfer Learning for Neural Decoding", "abstract": "A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. In typical applications, decoders are constructed for individual subjects and with limited data leading to restr...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper tries to establish that LSTMs are suitable for modeling neural signals from the brain. However, the committee and most reviewers find that results are inconclusive. Results are mixed across subjects. We think it would have been far more i...

[ "The paper describes an approach to use LSTM’s for finger classification based on ECOG. and a transfer learning extension of which two variations exists. From the presented results, the LSTM model is not an improvement over a basic linear model. The transfer learning models performs better than subject specific mod...

[ [ 41 ], [ 1 ], [ 35 ], [ 2 ], [ 7 ], [ 12, 17 ], [ 19 ], [ 20 ], [ 3 ], [ 4, 22 ], [ 8 ], [ 9 ], [ 11 ], [ 13 ], [ 25 ], [ 32 ], [ 34 ], [ 36 ], [ 37 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...

benchmark/PDF/ICLR2018_rybDdHe0Z.pdf

openreview

benchmark/MD/ICLR2018_rybDdHe0Z.md

ICLR 2018

Sy5OAyZC-

{ "title": "On the Use of Word Embeddings Alone to Represent Natural Language Sequences", "abstract": "To construct representations for natural language sequences, information from two main sources needs to be captured: (i) semantic meaning of individual words, and (ii) their compositionality. These two types of in...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This work presents a strong baseline model for several NLP-ish tasks such as document classification, sentence classification, representation learning based NLI, and text matching. In terms of originality, reviewers found that \"there is not much contr...

[ "This paper empirically investigates the differences realized by using compositional functions over word embeddings as compared to directly operating the word embeddings.", "That is, the authors seek to explore the advantages afforded by RNN/CNN based models that induce intermediate semantic representations of te...

[ [ 22 ], [ 10 ], [ 11 ], [ 16 ], [ 25 ], [ 2 ], [ 26 ], [ 24 ], [ 7, 14 ], [ 3 ], [ 4 ], [ 5 ], [ 6, 13 ], [ 9 ], [ 15 ], [ 17 ], [ 18 ], [ 19 ], [ 21 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_Sy5OAyZC-.pdf

openreview

benchmark/MD/ICLR2018_Sy5OAyZC-.md

ICLR 2018

B1EPYJ-C-

{ "title": "Federated Learning: Strategies for Improving Communication Efficiency", "abstract": "Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slo...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...

[ [ { "role": "PC", "data": { "comment": "The authors study the problem of reducing uplink communication costs in training a ML model where the training data is distributed over many clients. The reviewers consider the problem interesting, but have concerns about the extent of the novelty of...

[ "This paper proposes several client-server neural network gradient update strategies aimed at reducing uplink usage while maintaining prediction performance. The main approaches fall into two categories: structured, where low-rank/sparse updates are learned, and sketched, where full updates are either sub-sampled ...

[ [ 15 ], [ 1, 8 ], [ 19 ], [ 11 ], [ 22 ], [ 3 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 2, 5 ], [ 7 ], [ 10 ], [ 23 ], [ 4 ], [ 21 ], [ 6 ], [ 18 ], [ 0 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_B1EPYJ-C-.pdf

openreview

benchmark/MD/ICLR2018_B1EPYJ-C-.md

ICLR 2018

BJuWrGW0Z

{ "title": "Dynamic Neural Program Embeddings for Program Repair", "abstract": "Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization. However, most existing program embeddings are base...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...

[ [ { "role": "PC", "data": { "comment": "PROS:\n\n1. Interesting and clearly useful idea\n2. The paper is clearly written.\n3. This work doesn't seem that original from an algorithmic point of view since Reed & De Freitas (2015) and Cai et. al (2017) among others have considered using executi...

[ "The authors present 3 architectures for learning representations of programs from execution traces. In the variable trace embedding, the input to the model is given by a sequence of variable values. The state trace embedding combines embeddings for variable traces using a second recurrent encoder. The dependency e...

[ [ 3 ], [ 4 ], [ 1 ], [ 0, 7 ], [ 2, 14 ], [ 15 ], [ 8 ], [ 6 ], [ 13 ], [ 16 ], [ 11 ], [ 17 ], [ 9 ], [ 10 ], [ 5 ], [ 12 ], [ 18 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_BJuWrGW0Z.pdf

openreview

benchmark/MD/ICLR2018_BJuWrGW0Z.md

ICLR 2018

ry-TW-WAb

{ "title": "Variational Network Quantization", "abstract": "In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced...

Accept (Poster)

[ [ { "role": "Author", "data": { "value": { "comment": [ 0, 1, 2, 3, 4, 5, 6 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper...

[ [ { "role": "PC", "data": { "comment": "The paper presents a variational Bayesian approach for quantising neural network weights and makes interesting and useful steps in this increasingly popular area of deep learning." } } ] ]

[ "The authors would like to correct four typos in the current version of the manuscript:", "-) Table 1: Percentage of non-zero weights for Soft Weight-Sharing (P&Q) is 0.5 (not 3 as reported in the table) and bits for Deep Compression is 5 - 8 (not 10 - 13 as reported in the table)", "-) Last paragraph before 4....

[ [ 13 ], [ 14 ], [ 20 ], [ 1 ], [ 11 ], [ 12 ], [ 16 ], [ 18 ], [ 21 ], [ 22 ], [ 2 ], [ 4 ], [ 5 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 19 ], [ 3 ], [ 0 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 7 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 8 ] } ], "category": [ "CLAR-WRT", "ORIG...

benchmark/PDF/ICLR2018_ry-TW-WAb.pdf

openreview

benchmark/MD/ICLR2018_ry-TW-WAb.md

ICLR 2018

ByeqORgAW

{ "title": "Proximal Backpropagation", "abstract": "We propose proximal backpropagation (ProxProp) as a novel algorithm that takes implicit instead of explicit gradient steps to update the network parameters during neural network training. Our algorithm is motivated by the step size limitation of explicit gradient ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clear, well-written paper that tackles an interesting problem.\n+ Interesting potential connections to other approaches in the literature such as Carreira-Perpiñán and Wang, 2014 and Taylor et al., 2016.\n+ Paper shows good understanding of th...

[ "This work proposes to replace the gradient step for updating the network parameters to a proximal step (implicit gradient) so that a large stepsize can be taken.", "Then to make it fast, the implicit step is approximated using conjugate gradient method because the step is solving a quadratic problem.", "The th...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] }, { "role": "Au...

benchmark/PDF/ICLR2018_ByeqORgAW.pdf

openreview

benchmark/MD/ICLR2018_ByeqORgAW.md

ICLR 2018

BJInEZsTb

{ "title": "Learning Representations and Generative Models for 3D Point Clouds", "abstract": "Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "This paper compares autoencoder and GAN-based methods for 3D point cloud representation and generation, as well as new (and welcome) metrics for quantitatively evaluating generative models. The experiments form a good but still a bit too incomplete ex...

[ "This paper introduces a generative approach for 3D point clouds. More specifically, two Generative Adversarial approaches are introduced: Raw point cloud GAN, and Latent-space GAN (r-GAN and l-GAN as referred to in the paper).", "In addition, a GMM sampling + GAN decoder approach to generation is also among the ...

[ [ 8 ], [ 18 ], [ 11 ], [ 6 ], [ 16 ], [ 2, 15 ], [ 4 ], [ 1 ], [ 13 ], [ 14 ], [ 17 ], [ 7 ], [ 9 ], [ 3 ], [ 19 ], [ 5 ], [ 12 ], [ 0 ], [ 10 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL-EXP"...

benchmark/PDF/ICLR2018_BJInEZsTb.pdf

openreview

benchmark/MD/ICLR2018_BJInEZsTb.md

ICLR 2018

HknbyQbC-

{ "title": "Generating Adversarial Examples with Adversarial Networks", "abstract": "Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results.\n...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper presents AdvGAN: a GAN that is trained to generate adversarial examples against a convolutional network. The motivation for this method is unclear: the proposed attack does not outperform simpler attack methods such as Carlini-Wagner attack. ...

[ "I thank the authors for the thoughtful response and rebuttal. The authors have substantially updated their manuscript and improved the presentation.", "Re: Speed. I brought up this point because this was a bulleted item in the Introduction in the earlier version of the manuscript. In the revised manuscript, this...

[ [ 9 ], [ 12 ], [ 16 ], [ 1, 4, 15 ], [ 2, 5 ], [ 10 ], [ 6 ], [ 7 ], [ 0 ], [ 3 ], [ 8 ], [ 11 ], [ 13 ], [ 14 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]

[ "correct", "correct", "incorrect", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_HknbyQbC-.pdf

openreview

benchmark/MD/ICLR2018_HknbyQbC-.md

ICLR 2018

By9iRkWA-

{ "title": "Phase Conductor on Multi-layered Attentions for Machine Comprehension", "abstract": "Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and self-matching attention model. Our research proposes phase conductor ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Generally solid engineering work but a bit lacking in terms of novelty and some issues with clarity. At the end of the day the empirical gains are not sufficient for acceptance - the results are state-of-the-art relative to published work, but not in t...

[ "Summary: The paper introduces \"Phase Conductor\", which consists of two phases, context-question attention phase and context-context (self) attention phase. Each phase has multiple layers of attention, for which the paper uses a novel way to fuse the layers, and context-question attention uses different question ...

[ [ 11 ], [ 19 ], [ 20 ], [ 21 ], [ 4 ], [ 9 ], [ 10 ], [ 12 ], [ 23 ], [ 3 ], [ 5, 13 ], [ 18 ], [ 8 ], [ 15 ], [ 16, 17 ], [ 24 ], [ 1 ], [ 2 ], [ 6 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-SOT" ] }, ...

benchmark/PDF/ICLR2018_By9iRkWA-.pdf

openreview

benchmark/MD/ICLR2018_By9iRkWA-.md

ICLR 2018

ByOnmlWC-

{ "title": "Policy Optimization by Genetic Distillation ", "abstract": "Genetic algorithms have been widely used in many practical optimization problems.\nInspired by natural selection, operators, including mutation, crossover\nand selection, provide effective heuristics for search and black-box optimization.\nHowe...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "At least two of the reviewers found the proposed approach novel and interesting and worthy of publication at ICLR. The reviewers raised concerns regarding the paper's terminology, which may lead to some misunderstanding. I agree that upon a quick skim,...

[ "This is a highly interesting paper that proposes a set of methods that combine ideas from imitation learning, evolutionary computation and reinforcement learning in a novel way. It combines the following ingredients:", "a) a population-based setup for RL", "b) a pair-selection and crossover operator", "c) a ...

[ [ 11 ], [ 12 ], [ 23, 25, 28 ], [ 29 ], [ 34 ], [ 0 ], [ 6, 15, 27 ], [ 7 ], [ 13 ], [ 14 ], [ 17 ], [ 21 ], [ 22 ], [ 30 ], [ 31 ], [ 32 ], [ 35 ], [ 9 ], ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "ORIG-COM" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, {...

benchmark/PDF/ICLR2018_ByOnmlWC-.pdf

openreview

benchmark/MD/ICLR2018_ByOnmlWC-.md

ICLR 2018

SJa9iHgAZ

{ "title": "Residual Connections Encourage Iterative Inference", "abstract": "Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of f...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...

[ [ { "role": "PC", "data": { "comment": "The paper presents an interesting view of ResNets and the findings should be of broad interest. R1 did not update their score/review, but I am satisfied with the author response, and recommend this paper for acceptance. " } } ] ]

[ "This paper investigates residual networks (ResNets) in an empirical way. The authors argue that shallow layers are responsible for learning important feature representations, while deeper layers focus on refining the features. They validate this point by performing a series of lesion study on ResNet.", "Overall,...

[ [ 16 ], [ 12 ], [ 2 ], [ 11 ], [ 14 ], [ 17 ], [ 3 ], [ 9, 10 ], [ 1 ], [ 15 ], [ 0 ], [ 4 ], [ 7 ], [ 8 ], [ 5 ], [ 6 ], [ 13 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-EXP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_SJa9iHgAZ.pdf

openreview

benchmark/MD/ICLR2018_SJa9iHgAZ.md

ICLR 2018

ByJHuTgA-

{ "title": "On the State of the Art of Evaluation in Neural Language Models", "abstract": "Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing codebases...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 7, ...

[ [ { "role": "PC", "data": { "comment": "this submission demonstrates an existing loop-hole (?) in rushing out new neural language models by carefully (and expensively) running hyperparameter tuning of baseline approaches. i feel this is an important contribution, but as pointed out by some r...

[ "The submitted manuscript describes an exercise in performance comparison for neural language models under standardization of the hyperparameter tuning and model selection strategies and costs. This type of study is important to give perspective to non-standardized performance scores reported across separate publi...

[ [ 9 ], [ 10 ], [ 12 ], [ 15 ], [ 2, 3 ], [ 4 ], [ 5, 6 ], [ 11 ], [ 14 ], [ 16 ], [ 7 ], [ 8 ], [ 13 ], [ 0 ], [ 1 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, ...

benchmark/PDF/ICLR2018_ByJHuTgA-.pdf

openreview

benchmark/MD/ICLR2018_ByJHuTgA-.md

ICLR 2018

B13EC5u6W

{ "title": "Thinking like a machine — generating visual rationales through latent space optimization", "abstract": "Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes a semi-supervised method to make deep learning more interpretable and at the same time be accurate on small datasets. The main idea is to learn dense representations from unlabelled data and then use those for building classifiers on...

[ "* This paper models images with a latent code representation, and then tries to modify the latent code to minimize changes in image space, while changing the classification label. As the authors indicate, it lies in the space of algorithms looking to modify the image while changing the label (e.g. LIME etc).", "...

[ [ 8, 31 ], [ 16 ], [ 20, 34, 45 ], [ 30 ], [ 32 ], [ 3 ], [ 4 ], [ 11 ], [ 12, 21 ], [ 13 ], [ 15 ], [ 19 ], [ 40 ], [ 36 ], [ 28 ], [ 29 ], [ 6 ], [ 7 ], [...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-SOT" ] }, ...

benchmark/PDF/ICLR2018_B13EC5u6W.pdf

openreview

benchmark/MD/ICLR2018_B13EC5u6W.md

ICLR 2018

HyIFzx-0b

{ "title": "BinaryFlex: On-the-Fly Kernel Generation in Binary Convolutional Networks", "abstract": "In this work we present BinaryFlex, a neural network architecture that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes using a set of orthogonal bases that combine to form convolution kernels for CNNs leading to a significant reduction of memory usage. The main concerns raised by the reviewers were 1) clarity; 2) issues with writing and presentation ...

[ "The paper presents a binary neural network architecture that operated on predefined orthogonal binary basis. The binary filters that are used as basis are generated using Orthogonal Variable Spreading Factor.", "Because the filters are weighted combinations of predefined basis, only the weights need to be traine...

[ [ 2 ], [ 18 ], [ 23 ], [ 1 ], [ 19 ], [ 3 ], [ 10 ], [ 14 ], [ 15 ], [ 17 ], [ 20 ], [ 21 ], [ 22 ], [ 24 ], [ 25 ], [ 28 ], [ 7 ], [ 4 ], [ 5 ], [ 9 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_HyIFzx-0b.pdf

openreview

benchmark/MD/ICLR2018_HyIFzx-0b.md

ICLR 2018

BkA7gfZAb

{ "title": "Stable Distribution Alignment Using the Dual of the Adversarial Distance", "abstract": "Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often ...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...

[ [ { "role": "PC", "data": { "comment": "All the reviewers noted that the dual formulation, as presented, only applies to the logistic family of classifiers. The kernelization is of course something that *can* be done, as argued by the authors, but is not in fact approached in the submission,...

[ "The paper deals with “fixing GANs at the computational level”, in a similar sprit to f-GANs and WGANs. The fix is very specific and restricted. It relies on the logistic regression model as the discriminator, and the dual formulation of logistic regression by Jaakkola and Haussler.\nComments:", "1) Experiments a...

[ [ 3 ], [ 17 ], [ 11, 16 ], [ 18 ], [ 19, 22 ], [ 20 ], [ 5 ], [ 6 ], [ 7 ], [ 10, 15 ], [ 21, 23 ], [ 28 ], [ 1, 14 ], [ 9, 13 ], [ 0 ], [ 8 ], [ 12, 29 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 5 ] } ], "category": [ "QUAL-MET" ] }, { "sentences": [ { "role": "Reviewer 1", "dat...

benchmark/PDF/ICLR2018_BkA7gfZAb.pdf

openreview

benchmark/MD/ICLR2018_BkA7gfZAb.md

ICLR 2018

rkmu5b0a-

{ "title": "MGAN: Training Generative Adversarial Nets with Multiple Generators", "abstract": "We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "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 shar...

[ "The present manuscript attempts to address the problem of mode collapse in GANs using a constrained mixture distribution for the generator, and an auxiliary classifier which predicts the source mixture component, plus a loss term which encourages diversity amongst components.", "All told the proposed method is q...

[ [ 17 ], [ 25 ], [ 10 ], [ 7 ], [ 9 ], [ 11 ], [ 16 ], [ 20, 29 ], [ 21 ], [ 23, 27 ], [ 1 ], [ 22 ], [ 5 ], [ 6 ], [ 18 ], [ 3 ], [ 19 ], [ 24 ], [ 26, 28, ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_rkmu5b0a-.pdf

openreview

benchmark/MD/ICLR2018_rkmu5b0a-.md

ICLR 2018

SJA7xfb0b

{ "title": "Sobolev GAN", "abstract": "We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) restricted to a Sobolev ball defined with respect to a dominant measure mu. We show that the Sob...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper provides a useful analysis of the role of gradient penalties and the performance of the proposed approach in semi-supervised cases." } } ] ]

[ "The paper deals with the increasingly popular GAN approach to constructing generative models. Following the first formulation of GANs in 2014, it was soon realized that the training dynamics was highly unstable, leading to significant difficulties in achieving stable results. The paper by Arjovsky et al (2017) pro...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "incorrect", "incorrect", "incorrect", "incorrect", "correct", "correct", "corr...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6 ...

benchmark/PDF/ICLR2018_SJA7xfb0b.pdf

openreview

benchmark/MD/ICLR2018_SJA7xfb0b.md

ICLR 2018

HyRnez-RW

{ "title": "Multi-Mention Learning for Reading Comprehension with Neural Cascades", "abstract": "Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to t...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ...

[ [ { "role": "PC", "data": { "comment": "The authors did a good job addressing reviewer concerns and analyzing and testing their model on interesting datasets with convincing results." } } ] ]

[ "The authors present a scalable model for questioning answering that is able to train on long documents. On the TriviaQA dataset, the proposed model achieves state of the art results on both domains (wikipedia and web). The formulation of the model is straight-forward, however I am skeptical about whether the resul...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "incorrect", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correc...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] }, { "role": "Author", "data": [ 13, 14, 15, 16, 17, 65, 74 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_HyRnez-RW.pdf

openreview

benchmark/MD/ICLR2018_HyRnez-RW.md

ICLR 2018

B1EA-M-0Z

{ "title": "Deep Neural Networks as Gaussian Processes", "abstract": "It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian infer...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper presents several theoretical results linking deep, wide neural networks to GPs. It even includes illuminating experiments.\n\nMany of the results were already developed in earlier works. However, many at ICLR may be unaware of these links, ...

[ "Neal (1994) showed that a one hidden layer Bayesian neural network, under certain conditions, converges to a Gaussian process as the number of hidden units approaches infinity. Neal (1994) and Williams (1997) derive the resulting kernel functions for such Gaussian processes when the neural networks have certain tr...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, ...

benchmark/PDF/ICLR2018_B1EA-M-0Z.pdf

openreview

benchmark/MD/ICLR2018_B1EA-M-0Z.md

ICLR 2018

rkLyJl-0-

{ "title": "Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks", "abstract": "Progress in deep learning is slowed by the days or weeks it takes to train large models. The natural solution of using more hardware is limited by diminishing returns, and leads to inefficient use of additional...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "Pros:\n+ Clearly written paper.\n+ Easily implemented algorithm that appears to have excellent scaling properties and can even improve on validation error in some cases.\n+ Thorough evaluation against the state of the art.\n\nCons:\n- No theoretical gu...

[ "This paper presents a new 2nd-order algorithm that implicitly uses curvature information, and it shows the intuition behind the approximation schemes in the algorithms and also validates the heuristics in various experiments. The method involves using Neumann Series and Richardson iteration to avoid Hessian-vecto...

[ [ 10 ], [ 3 ], [ 13 ], [ 11 ], [ 9, 16 ], [ 1 ], [ 2 ], [ 4 ], [ 5 ], [ 14 ], [ 17 ], [ 6 ], [ 8, 12 ], [ 0 ], [ 7 ], [ 15 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_rkLyJl-0-.pdf

openreview

benchmark/MD/ICLR2018_rkLyJl-0-.md

ICLR 2018

ry018WZAZ

{ "title": "Deep Active Learning for Named Entity Recognition", "abstract": "Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that th...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "The reviewers liked this paper quite a bit. The novelty seems modest and the results are limited to a fairly simple NER task, but there is nothing wrong with the paper, hence recommending acceptance." } } ] ]

[ "Summary:\nThis paper applies active learning to a deep neural model (CNN-CNN-LSTM) for named-entity recognition, which allows the model to match state-of-the-art performance with about 25% of the full training data.", "Strength:\nThe paper is relatively easy to follow. Experiments show significant reduction of ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "SIGN...

benchmark/PDF/ICLR2018_ry018WZAZ.pdf

openreview

benchmark/MD/ICLR2018_ry018WZAZ.md

ICLR 2018

HJWLfGWRb

{ "title": "Matrix capsules with EM routing", "abstract": "A capsule is a group of neurons whose outputs represent different properties of the same entity. Each layer in a capsule network contains many capsules. We describe a version of capsules in which each capsule has a logistic unit to represent the presence of...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "Authors present a new multi-layered capsule network architecture, implemented an EM routing procedure, and introduced \"Coordinate Addition\". Capsule architectures are gaining interest because of their ability to achieve equivariance of parts, and em...

[ "The paper proposes a novel architecture for capsule networks. Each capsule has a logistic unit representing the presence of an entity plus a 4x4 pose matrix representing the entity/viewer relationship. This new representation comes with a novel iterative routing scheme, based on the EM algorithm.", "Evaluated on...

[ [ 20 ], [ 3, 14, 19 ], [ 10 ], [ 5 ], [ 13 ], [ 18 ], [ 21 ], [ 7 ], [ 15 ], [ 2 ], [ 1 ], [ 12, 16 ], [ 8 ], [ 9 ], [ 4 ], [ 6, 17 ], [ 0 ], [ 11 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "QUAL-EXP", "QUAL...

benchmark/PDF/ICLR2018_HJWLfGWRb.pdf

openreview

benchmark/MD/ICLR2018_HJWLfGWRb.md

ICLR 2018

S1p31z-Ab

{ "title": "Deep contextualized word representations", "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word ve...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 6, ...

[ [ { "role": "PC", "data": { "comment": "This is a good paper that presents state-of-the-art results on a number of challenging NLP tasks. The idea is fairly simple and clean, I therefore expect it to get adopted in the community. It also seems to work across several tasks, which is nice. At ...

[ "The authors learn token embeddings that use surrounding context by concatenating representations obtained by training a bidirectional language model, very similar to Peters et al. 2017. They learn a distribution of weights for each layer of embeddings of the pre-trained bi-lm language model. These embeddings impro...

[ [ 3 ], [ 6 ], [ 1 ], [ 2 ], [ 10 ], [ 4 ], [ 5 ], [ 7 ], [ 9 ], [ 11 ], [ 12 ], [ 13 ], [ 0 ], [ 8 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "incorrect", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "incorrect" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_S1p31z-Ab.pdf

openreview

benchmark/MD/ICLR2018_S1p31z-Ab.md

ICLR 2018

HyZoi-WRb

{ "title": "Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference", "abstract": "The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The authors analyze the IWAE bound as an estimator of the marginal log-likelihood and show how to reduce its bias by using the jackknife. They then evaluate the effect of using the resulting estimator (JVI) for training and evaluating VAEs on MNIST. Th...

[ "[After author feedback]", "I think this is an interesting paper and recommend acceptance. My remaining main comments are described in the response to author feedback below.\n[Original review]", "The authors introduce jackknife variational inference (JVI), a method for debiasing Monte Carlo objectives such as t...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4, 5, 6 ...

benchmark/PDF/ICLR2018_HyZoi-WRb.pdf

openreview

benchmark/MD/ICLR2018_HyZoi-WRb.md

ICLR 2018

SJlhPMWAW

{ "title": "GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders", "abstract": "Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative mode...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overa...

[ [ { "role": "PC", "data": { "comment": "The authors present GraphVAE, a method for fitting a generative deep model, a variational autoencoder, to small graphs. Fitting deep learning models to graphs remains challenging (although there is relevant literature as brought up by the reviewers an...

[ "This work proposed an interesting graph generator using a variational autoencoder. The work should be interesting to researchers in the various areas.", "However, the work can only work on small graphs. The search space of small graph generation is usually very small, is there any other traditional methods can w...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...

benchmark/PDF/ICLR2018_SJlhPMWAW.pdf

openreview

benchmark/MD/ICLR2018_SJlhPMWAW.md

ICLR 2018

HymuJz-A-

{ "title": "Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks", "abstract": "The robust and efficient recognition of visual relations in images is a hallmark of biological vision. Here, we argue that, despite recent progress in visual recognition, modern machine vision algorithms are severely limite...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...

[ [ { "role": "PC", "data": { "comment": "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 thi...

[ "Quality\nThis paper demonstrates that convolutional and relational neural networks fail to solve visual relation problems by training networks on artificially generated visual relation data. This points at important limitations of current neural network architectures where architectures depend mainly on rote memor...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct"...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "ORIG-NEG" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "dat...

benchmark/PDF/ICLR2018_HymuJz-A-.pdf

openreview

benchmark/MD/ICLR2018_HymuJz-A-.md

ICLR 2018

B1n8LexRZ

{ "title": "Generalizing Hamiltonian Monte Carlo with Neural Networks", "abstract": "We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "This paper presents a learned inference architecture which generalizes HMC. It defines a parameterized family of MCMC transition operators which share the volume preserving structure of HMC updates, which allows the acceptance ratio to be computed effi...

[ "In this work, the authors propose a procedure for tuning the parameters of an HMC algorithm (I guess, if I have understood correctly).", "I think this paper has a good and strong point: this work points out the difficulties in choosing properly the parameters in a HMC method (such as the step and the number of i...

[ [ 11 ], [ 4 ], [ 10 ], [ 0 ], [ 2 ], [ 13 ], [ 14 ], [ 15 ], [ 16 ], [ 17 ], [ 18 ], [ 19 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 24 ], [ 25 ], [ 1 ], [ 5 ], [...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 12, 13 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1...

benchmark/PDF/ICLR2018_B1n8LexRZ.pdf

openreview

benchmark/MD/ICLR2018_B1n8LexRZ.md

ICLR 2018

Syhr6pxCW

{ "title": "PixelNN: Example-based Image Synthesis", "abstract": "We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models des...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes a novel method for conditional image generation which is based on nearest neighbor matching for transferring high-frequency statistics. The evaluation is carried out on several image synthesis tasks, where the technique is shown to p...

[ "Overall I like the paper and the results look nice in a diverse set of datasets and tasks such as edge-to-image, super-resolution, etc.", "Unlike the generative distribution sampling of GANs, the method provides an interesting compositional scheme, where the low frequencies are regressed and the high frequencies...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2 ] } ], "category": [ "SIGN-BRD" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3, 4, 5 ] ...

benchmark/PDF/ICLR2018_Syhr6pxCW.pdf

openreview

benchmark/MD/ICLR2018_Syhr6pxCW.md

ICLR 2018

H1meywxRW

{ "title": "DCN+: Mixed Objective And Deep Residual Coattention for Question Answering", "abstract": "Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a ...

Accept (Poster)

[ [ { "role": "Author", "data": { "value": { "comment": [ 0 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 1, 2, ...

[ [ { "role": "PC", "data": { "comment": "This is an interesting paper that provides modeling improvements over several strong baselines and presents SOTA on Squad. One criticism of the paper is that it evaluates only on Squad, which is somewhat of an artificial task, but we think for publica...

[ "In Equation 17 (page 5), we made a typo in that we did not include the regularization terms $$\\log \\sigma_{ce}^2 + \\log \\sigma_{rl}^2$$.", "Summary:\nThis paper proposed an extension of the dynamic coattention network (DCN) with deeper residual layers and self-attention. It also introduced a mixed objective ...

[ [ 4 ], [ 2 ], [ 1 ], [ 6 ], [ 5 ], [ 0 ], [ 3 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 2, 3 ] } ], "category": [ "QUAL-MET"...

benchmark/PDF/ICLR2018_H1meywxRW.pdf

openreview

benchmark/MD/ICLR2018_H1meywxRW.md

ICLR 2018

ByS1VpgRZ

{ "title": "cGANs with Projection Discriminator", "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. \nThis approach is in contrast with most fra...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes a simple modification to conditional GANs, where the discriminator involves an inner product term between the condition vector y and the feature vector of x. This formulation is reasonable and well motivated from popular models (e.g....

[ "I thank the authors for the thoughtful response and updated manuscript. After reading through both, my review score remains unchanged.", "The authors describe a new variant of a generative adversarial network (GAN) for generating images. This model employs a 'projection discriminator' in order to incorporate ima...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_ByS1VpgRZ.pdf

openreview

benchmark/MD/ICLR2018_ByS1VpgRZ.md

ICLR 2018

SkhQHMW0W

{ "title": "Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training", "abstract": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network in...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "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...

[ "I think this is a good work that I am sure will have some influence in the near future. I think it should be accepted and my comments are mostly suggestions for improvement or requests for additional information that would be interesting to have.", "Generally, my feeling is that this work is a little bit too den...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "inc...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT" ] }, ...

benchmark/PDF/ICLR2018_SkhQHMW0W.pdf

openreview

benchmark/MD/ICLR2018_SkhQHMW0W.md

ICLR 2018

HyiAuyb0b

{ "title": "TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning", "abstract": "Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators. Th...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This is an interesting piece of work that provides solid evidence on the topic of bootstrapping in deep reinforcement learning." } } ] ]

[ "This paper includes several controlled empirical studies comparing MC and TD methods in predicting of value function with complex DNN function approximators. Such comparison has been carried out both in theory and practice for simple low dimensional environments with linear (and RKHS) value function approximation ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1 ] } ], "category": [ "N/A" ] }, { ...

benchmark/PDF/ICLR2018_HyiAuyb0b.pdf

openreview

benchmark/MD/ICLR2018_HyiAuyb0b.md

ICLR 2018

BydjJte0-

{ "title": "Towards Reverse-Engineering Black-Box Neural Networks", "abstract": "Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain propri...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Novel way of analyzing neural networks to predict NN attributes such as architecture, training method, batch size etc. And the method works surprisingly good on the MNIST and ImageNet." } } ] ]

[ "-----UPDATE------\nHaving read the responses from the authors, and the other reviews, I am happy with my rating and maintain that this paper should be accepted.", "In this paper, the authors trains a large number of MNIST classifier networks with differing attributes (batch-size, activation function, no. layers ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 2 ] }, { "role": "Author", "data": [ 49 ] } ], "category": [ "CLAR-WRT", "QUAL-EXP" ] }, { "sentences": [ { "r...

benchmark/PDF/ICLR2018_BydjJte0-.pdf

openreview

benchmark/MD/ICLR2018_BydjJte0-.md

ICLR 2018

SJiHXGWAZ

{ "title": "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting", "abstract": "Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, ...

[ [ { "role": "PC", "data": { "comment": "The paper received highly diverging scores: 5 (R1) ,9 (R2), 4(R3). Both R1 and R3 complained about the comparisons to related methods. R3 suggested some kNN and GP baselines, while R1 mentioned concurrent work using deepnets for trafffic prediction.\n\...

[ "The paper proposes to build a graph where the edge weight is defined using the road network distance which is shown to be more realistic than the Euclidean distance. The defined diffusion convolution operation is essentially conducting random walks over the road segment graph. To avoid the expensive matrix operati...

[ [ 10 ], [ 11 ], [ 12 ], [ 13 ], [ 9 ], [ 7 ], [ 8 ], [ 1 ], [ 2 ], [ 3 ], [ 5 ], [ 14 ], [ 4 ], [ 0 ], [ 6 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "incorrect", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_SJiHXGWAZ.pdf

openreview

benchmark/MD/ICLR2018_SJiHXGWAZ.md

ICLR 2018

B14TlG-RW

{ "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension", "abstract": " Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "AC", "data": { "comment": "Authors, \n\nPlease post a rebuttal for this work. Discussion period ends Jan 5th. " } }, { "role": "Author", "data": { "value": { "comment": "Dear Area Chair,\n\nWe have submitted the rebuttal and revision. ...

[ "This paper proposes two contributions: first, applying CNNs+self-attention modules instead of LSTMs, which could result in significant speedup and good RC performance;", "second, enhancing the RC model training with passage paraphrases generated by a neural paraphrasing model, which could improve the RC performa...

[ [ 1 ], [ 5 ], [ 6 ], [ 11 ], [ 13, 16 ], [ 23 ], [ 24 ], [ 3 ], [ 2 ], [ 4 ], [ 7, 21 ], [ 9 ], [ 10, 20 ], [ 14 ], [ 19 ], [ 18 ], [ 15 ], [ 22 ], [ 8, ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3, 4 ] }, { ...

benchmark/PDF/ICLR2018_B14TlG-RW.pdf

openreview

benchmark/MD/ICLR2018_B14TlG-RW.md

ICLR 2018

SkqV-XZRZ

{ "title": "Variational Bi-LSTMs", "abstract": "Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs), which model sequences along both for...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "This paper proposes a method for performing stochastic variational inference for bidirectional LSTMs through introducing an additional latent variable that induces a dependence between the forward and backward directions. The authors demonstrate that ...

[ "*Quality*\nThe paper is easy to parse, with clear diagrams and derivations at the start. The problem context is clearly stated, as is the proposed model.", "The improvements in terms of average log-likelihood are clear. The model does improve over state-of-the-art in some cases, but not all.", "Based on the pr...

[ [ 8 ], [ 26 ], [ 0 ], [ 3 ], [ 4 ], [ 12 ], [ 16, 17 ], [ 22 ], [ 23 ], [ 25 ], [ 13 ], [ 1 ], [ 5 ], [ 6 ], [ 7 ], [ 15 ], [ 18 ], [ 27 ], [ 10 ], [ 11 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2, 3, 5 ] }, {...

benchmark/PDF/ICLR2018_SkqV-XZRZ.pdf

openreview

benchmark/MD/ICLR2018_SkqV-XZRZ.md

ICLR 2018

r1VVsebAZ

{ "title": "Synthesizing realistic neural population activity patterns using Generative Adversarial Networks", "abstract": "The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simu...

Accept (Poster)

[ [ { "role": "Author", "data": { "value": { "comment": [ 0, 1, 2, 3, 4, 5, 6 ] } } } ], [ { "role": "Reviewer", "data": { "summary_of_the_paper...

[ [ { "role": "PC", "data": { "comment": "This paper proposes a novel application of generative adversarial networks to model neural spiking activity. Their technical contribution, SpikeGAN, generates neural spikes that accurately match the statistics of real recorded spiking behavior from a ...

[ "We provide below a few comments aiming at correcting/clarifying some sentences:", "* First sentence in Section 2.1. should read: We adapted the Generative Adversarial Networks described by Goodfellow et al. (2014) to produce samples that simulate the spiking activity of a population of N neurons as binary vector...

[ [ 16 ], [ 20 ], [ 21 ], [ 33 ], [ 7 ], [ 11 ], [ 13 ], [ 22 ], [ 25 ], [ 1, 23 ], [ 32 ], [ 9, 30 ], [ 10 ], [ 27 ], [ 15 ], [ 18, 19 ], [ 28 ], [ 29 ], [ 3...

[ "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 7, 8 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 9 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_r1VVsebAZ.pdf

openreview

benchmark/MD/ICLR2018_r1VVsebAZ.md

ICLR 2018

BkM27IxR-

{ "title": "Learning to Optimize Neural Nets", "abstract": "Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimiz...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The presented work is a good attempt to expand the work of Li and Malik to the high-dimensional, stochastic setting. Given the reviewer comments, I think the paper would benefit from highlighting the comparatively novel aspects, and in particular doing...

[ "[Main comments]", "* I would advice the authors to explain in more details in the intro", "what's new compared to Li & Malik (2016) and Andrychowicz et al. (2016).", "It took me until section 3.5 to figure it out.", "* If I understand correctly, the only new part compared to Li & Malik (2016) is", "secti...

[ [ 3 ], [ 21 ], [ 22 ], [ 0 ], [ 1 ], [ 2 ], [ 7 ], [ 8 ], [ 9, 10 ], [ 11 ], [ 19 ], [ 23 ], [ 27 ], [ 28 ], [ 24 ], [ 4 ], [ 15 ], [ 17 ], [ 18 ], [ 26 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1, 2, 3 ] }, { "role": "Author", "data": [ 31, 32, 33, 34, 35, 36, 37 ] } ...

benchmark/PDF/ICLR2018_BkM27IxR-.pdf

openreview

benchmark/MD/ICLR2018_BkM27IxR-.md

ICLR 2018

BJ_UL-k0b

{ "title": "Recasting Gradient-Based Meta-Learning as Hierarchical Bayes", "abstract": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-lea...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Pros:\n+ The paper introduces a non-trivial interpretation of MAML as hierarchical Bayesian learning and uses this perspective to develop a new variation of MAML that accounts for curvature information.\n\nCons:\n- Relatively small gains over MAML on m...

[ "Summary\nThe paper presents an interesting view on the recently proposed MAML formulation of meta-learning (Finn et al). The main contribution is a) insight into the connection between the MAML procedure and MAP estimation in an equivalent linear hierarchical Bayes model with explicit priors, b) insight into the c...

[ [ 4 ], [ 22 ], [ 9 ], [ 18 ], [ 26 ], [ 27 ], [ 33 ], [ 35 ], [ 40 ], [ 3 ], [ 10 ], [ 11 ], [ 13 ], [ 21 ], [ 28 ], [ 29 ], [ 30 ], [ 31 ], [ 32 ], [ 34 ], ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "SIGN-BRD" ] }, ...

benchmark/PDF/ICLR2018_BJ_UL-k0b.pdf

openreview

benchmark/MD/ICLR2018_BJ_UL-k0b.md

ICLR 2018

rylSzl-R-

{ "title": "On Unifying Deep Generative Models", "abstract": "Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct p...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This is a thought-provoking paper that places GANs and VAEs in a single framework and, motivated by this perspective, proposes several novel extensions to them. The reviewers made several good suggestions for improving the paper and the authors are exp...

[ "Update 1/11/18:\nI'm happy with the comments from the authors. I think the explanation of non-saturating vs saturating objective is nice, and I've increased the score.", "Note though: I absolutely expect a revision at camera-ready if the paper gets accepted (we did not get one).", "Original review:\nThe paper ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 1, 2 ] } ], "category": [ "N/A"...

benchmark/PDF/ICLR2018_rylSzl-R-.pdf

openreview

benchmark/MD/ICLR2018_rylSzl-R-.md

ICLR 2018

rye7IMbAZ

{ "title": " Explicit Induction Bias for Transfer Learning with Convolutional Networks", "abstract": "In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.\nWhen using fine-tuning, the underlying assumption is that the pre-trained model extra...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper addresses the question of how to regularize when starting from a pre-trained convolutional network in the context of transfer learning. The authors propose to regularize toward the parameters of the pre-trained model and study multiple regu...

[ "The paper proposes an analysis on different adaptive regularization techniques for deep transfer learning.", "Specifically it focuses on the use of an L2-SP condition that constraints the new parameters to be close to the", "ones previously learned when solving a source task.", "+ The paper is easy to read a...

[ [ 11 ], [ 1 ], [ 2 ], [ 8 ], [ 13 ], [ 18 ], [ 3 ], [ 4 ], [ 6, 15 ], [ 9 ], [ 10 ], [ 14 ], [ 16 ], [ 12 ], [ 7 ], [ 0 ], [ 5 ], [ 17 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_rye7IMbAZ.pdf

openreview

benchmark/MD/ICLR2018_rye7IMbAZ.md

ICLR 2018

SyzKd1bCW

{ "title": "Backpropagation through the Void: Optimizing control variates for black-box gradient estimation", "abstract": "Gradient-based optimization is the foundation of deep learning and reinforcement learning.\nEven when the mechanism being optimized is unknown or not differentiable, optimization using high-var...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This is an interesting and well-written paper introducing two unbiased gradient estimators for optimizing expectations of black box functions. LAX can handle functions of both continuous and discrete random variables, while RELAX is specialized to func...

[ "This paper introduces LAX/RELAX, a method to reduce the variance of the REINFORCE gradient estimator. The method builds on and is directly inspired by REBAR.", "Similarly to REBAR, RELAX is an unbiased estimator, and the idea is to introduce a control variate that leverages the reparameterization gradient.", "...

[ [ 12 ], [ 22 ], [ 25 ], [ 34 ], [ 35 ], [ 2 ], [ 4 ], [ 9 ], [ 24 ], [ 27 ], [ 3 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 10 ], [ 11 ], [ 13 ], [ 14, 44 ], [ 17 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4 ] }, { ...

benchmark/PDF/ICLR2018_SyzKd1bCW.pdf

openreview

benchmark/MD/ICLR2018_SyzKd1bCW.md

ICLR 2018

HkGcX--0-

{ "title": "Auxiliary Guided Autoregressive Variational Autoencoders", "abstract": "Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "AC", "data": { "comment": "Hi, I'm a Master's student in Computer Science taking part in the ICLR reproducibility challenge, and I have a few questions regarding implementation. \n\nFirst, you mention that \"We allow each layer of the pixel-CNN to take additional input using non...

[ "Summary:\nThis paper attempts to solve the problem of meaningfully combining variational autoencoders (VAEs) and PixelCNNs. It proposes to do this by simultaneously optimizing a VAE with PixelCNN++ decoder, and a VAE with factorial decoder. The model is evaluated in terms of log-likelihood (with no improvement ove...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]

[ "correct", "incorrect", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] }, { "role": "Author", "data": [ ...

benchmark/PDF/ICLR2018_HkGcX--0-.pdf

openreview

benchmark/MD/ICLR2018_HkGcX--0-.md

ICLR 2018

HkwVAXyCW

{ "title": "Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks", "abstract": "Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficul...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper explores what might be characterized as an adaptive form of ZoneOut.\nWith the improvements and clarifications added to the paper during the rebuttal the paper could be accepted.\n" } } ] ]

[ "UPDATE: Following the author's response I've increased my score from 5 to 6. The revised paper includes many of the additional references that I suggested, and the author response clarified my confusion over the Charades experiments; their results are indeed close to state-of-the-art on Charades activity localizat...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "incorrect", "correct"...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 26, 27, 28, 29, 30, 31, 32 ] } ], "category": [ "QUAL-CMP"...

benchmark/PDF/ICLR2018_HkwVAXyCW.pdf

openreview

benchmark/MD/ICLR2018_HkwVAXyCW.md

ICLR 2018

SylJ1D1C-

{ "title": "PDE-Net: Learning PDEs from Data", "abstract": "Partial differential equations (PDEs) play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc. PDEs are commonly derived based on physical laws or empirical observations. However, ...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...

[ [ { "role": "PC", "data": { "comment": "This paper studies the approximation and integration of partial differential equations using convolutional neural networks. By constraining CNN filters to have prescribed vanishing moments, the authors interpret CNN-based temporal prediction in terms o...

[ "The paper explores the use of deep learning machinery for the purpose of identifying dynamical systems specified by PDEs.", "The paper advocates the following approach:", "One assumes a dynamic PDE system involving differential operators up to a given order. Each differential operator term is approximated by a...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 3 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_SylJ1D1C-.pdf

openreview

benchmark/MD/ICLR2018_SylJ1D1C-.md

ICLR 2018

By4HsfWAZ

{ "title": "Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge", "abstract": "We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive para...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "This paper proposes to use data-driven deep convolutional architectures for modeling advection diffusion. It is well motivated and comes with convincing numerical experiments.\nReviewers agreed that this is a worthy contribution to ICLR with the potent...

[ "The paper ‘Deep learning for Physical Process: incorporating prior physical knowledge’ proposes", "to question the use of data-intensive strategies such as deep learning in solving physical", "inverse problems that are traditionally solved through assimilation strategies. They notably show", "how physical pr...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3, 4, 5 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 6 ...

benchmark/PDF/ICLR2018_By4HsfWAZ.pdf

openreview

benchmark/MD/ICLR2018_By4HsfWAZ.md

ICLR 2018

HJIoJWZCZ

{ "title": "Adversarial Dropout Regularization", "abstract": "We present a domain adaptation method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by ``fooling'' a special do...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "The general consensus is that this method provides a practical and interesting approach to unsupervised domain adaptation. One reviewer had concerns with comparing to state of the art baselines, but those have been addressed in the revision.\n\nThere w...

[ "(Summary)\nThis paper is about learning discriminative features for the target domain in unsupervised DA problem. The key idea is to use a critic which randomly drops the activations in the logit and maximizes the sensitivity between two versions of discriminators.\n(Pros)", "The approach proposed in section 3.2...

[ [ 16 ], [ 3 ], [ 15 ], [ 11 ], [ 13 ], [ 14 ], [ 1 ], [ 2 ], [ 12 ], [ 9 ], [ 17 ], [ 7 ], [ 8 ], [ 10 ], [ 19 ], [ 4 ], [ 5 ], [ 18 ], [ 0 ], [ 6 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, ...

benchmark/PDF/ICLR2018_HJIoJWZCZ.pdf

openreview

benchmark/MD/ICLR2018_HJIoJWZCZ.md

ICLR 2018

BywyFQlAW

{ "title": "Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity", "abstract": "We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "The submission formulates self paced learning as a specific iterative mini-max optimization, which incorporates both a risk minimization step and a submodular maximization for selecting the next training examples.\n\nThe strengths of the paper lie prim...

[ "Overview:\nThis paper proposes an approach to curriculum learning, where subsets of examples to train on are chosen during the training process. The proposed method is based on a submodular set function over the examples, which is intended to capture diversity of the included examples and is added to the training ...

[ [ 7 ], [ 4 ], [ 6 ], [ 15 ], [ 9 ], [ 10 ], [ 17 ], [ 5 ], [ 3 ], [ 12 ], [ 16 ], [ 1 ], [ 11 ], [ 13 ], [ 14 ], [ 18 ], [ 19 ], [ 20 ], [ 0 ], [ 8 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-MET" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_BywyFQlAW.pdf

openreview

benchmark/MD/ICLR2018_BywyFQlAW.md

ICLR 2018

r1RQdCg0W

{ "title": "MACH: Embarrassingly parallel $K$-class classification in $O(d\\log{K})$ memory and $O(K\\log{K} + d\\log{K})$ time, instead of $O(Kd)$", "abstract": "We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that requ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "There is a very nice discussion with one of the reviewers on the experiments, that I think would need to be battened down in an ideal setting. I'm also a bit surprised at the lack of discussion or comparison to two seemingly highly related papers:\n\n1...

[ "The manuscript proposes an efficient hashing method, namely MACH, for softmax approximation in the context of large output space, which saves both memory and computation.", "In particular, the proposed MACH uses 2-universal hashing to randomly group classes, and trains a classifier to predict the group membershi...

[ [ 6 ], [ 7 ], [ 1 ], [ 27 ], [ 3 ], [ 11, 18 ], [ 13 ], [ 23 ], [ 25 ], [ 29 ], [ 30 ], [ 2 ], [ 4 ], [ 5 ], [ 14 ], [ 19 ], [ 20 ], [ 24 ], [ 31 ], [ 32 ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] } ], "category": [ ...

benchmark/PDF/ICLR2018_r1RQdCg0W.pdf

openreview

benchmark/MD/ICLR2018_r1RQdCg0W.md

ICLR 2018

ryiAv2xAZ

{ "title": "Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples", "abstract": "The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "Meta score: 6\n\nThe 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.\n\nPros:\n - clearly expressed idea, well-supported ...

[ "I have read authors' reply. In response to authors' comprehensive reply and feedback. I upgrade my score to 6.", "This paper presents a novel approach to calibrate classifiers for out of distribution samples. In additional to the original cross entropy loss, the “confidence loss” was proposed to guarantee the ...

[ [ 15 ], [ 20 ], [ 5 ], [ 8 ], [ 7 ], [ 21 ], [ 0 ], [ 16 ], [ 1 ], [ 4 ], [ 10 ], [ 18 ], [ 23 ], [ 24 ], [ 9 ], [ 13 ], [ 14 ], [ 17 ], [ 22 ], [ 2 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "ORIG...

benchmark/PDF/ICLR2018_ryiAv2xAZ.pdf

openreview

benchmark/MD/ICLR2018_ryiAv2xAZ.md

ICLR 2018

H1-nGgWC-

{ "title": "Gaussian Process Behaviour in Wide Deep Neural Networks", "abstract": "Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between Gaussian processes with a recursive kern...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": " A clearly written paper. While the practical relevance that came up in the review remains, the analysis and discussion is important for a deeper understanding of the deeper connections between these two important areas of machine learning." } ...

[ "The authors study the limiting behaviour for wide Bayesian neural networks, comparing to Gaussian processes.", "The paper is well written, and the experiments are enlightening. This work is a nice follow up to Neal (1994), and recent work considering similar results for neural networks with more than one hidden ...

[ [ 11 ], [ 16 ], [ 19 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 25 ], [ 26 ], [ 27 ], [ 28 ], [ 29 ], [ 30 ], [ 1 ], [ 10 ], [ 15 ], [ 24 ], [ 8 ], [ 3, 4 ], [ 7 ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...

benchmark/PDF/ICLR2018_H1-nGgWC-.pdf

openreview

benchmark/MD/ICLR2018_H1-nGgWC-.md

ICLR 2018

r17Q6WWA-

{ "title": "Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection", "abstract": "Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains. While CNNs are particularly well-suited for capturing a proper hierarchy of conce...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5 ] }, "scores": { "Solid": null, "Presentation": null...

[ [ { "role": "PC", "data": { "comment": "The experimental work in this paper leaves it just short of being suitable for acceptance.\nThe work needs more comparisons with prior work and other approaches.\nThe numerical ratings of the work by reviewers are just too low.\n" } } ] ]

[ "Pros:", "1. This paper proposed a new block which can aggregate features from different tasks. By doing this, it can take advantage of common information between related tasks and improve the generalization of target tasks.", "2. The achievement in this paper seems good, which is 24.31%.\nCons:", "1. The nov...

[ [ 6 ], [ 7 ], [ 0, 17 ], [ 2 ], [ 9, 13, 15 ], [ 3 ], [ 10 ], [ 12 ], [ 18 ], [ 19 ], [ 11 ], [ 16 ], [ 4 ], [ 1 ], [ 8 ], [ 5 ], [ 14 ] ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "SI...

benchmark/PDF/ICLR2018_r17Q6WWA-.pdf

openreview

benchmark/MD/ICLR2018_r17Q6WWA-.md

ICLR 2018

rkEfPeZRb

{ "title": "Variance-based Gradient Compression for Efficient Distributed Deep Learning", "abstract": "Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, worker...

Invite to Workshop Track

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7 ] }, "scores": { "Solid": null, ...

[ [ { "role": "PC", "data": { "comment": "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 pa...

[ "This paper proposes a variance-based gradient compression method to reduce the communication overhead of distributed deep learning. Experiments on real datasets are used for evaluation.", "The idea to adopt approximated variances of gradients to reduce communication cost seems to be interesting.", "However, th...

[ [ 9 ], [ 13 ], [ 10 ], [ 11 ], [ 14 ], [ 16 ], [ 23 ], [ 1, 22 ], [ 5 ], [ 8 ], [ 3 ], [ 4 ], [ 6 ], [ 7 ], [ 17 ], [ 18 ], [ 24 ], [ 15 ], [ 12 ], [ 19 ]...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-MTH" ] }, ...

benchmark/PDF/ICLR2018_rkEfPeZRb.pdf

openreview

benchmark/MD/ICLR2018_rkEfPeZRb.md

ICLR 2018

BkXmYfbAZ

{ "title": "Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering", "abstract": "Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The n...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "PROS:\n1. Clear, interesting idea.\n2. Largely convincing evaluation\n3. Good writing\n\nCONS:\n1. The model used in the evaluation is a Resnet-50 and could have been more convincing with a more SOTA model.\n2. There is some concern about the whether t...

[ "This paper proposes a new approach for multi-task learning.", "While previous approaches assumes the order of shared layers are the same between tasks, this paper assume the order can vary across tasks, and the (soft) order is learned during training. They show improved performance on a number of multi-task lea...

[ [ 15 ], [ 17 ], [ 18 ], [ 5 ], [ 19 ], [ 1, 4 ], [ 11 ], [ 3 ], [ 6, 7, 8 ], [ 22 ], [ 0, 21 ], [ 12 ], [ 13 ], [ 14 ], [ 16 ], [ 24 ], [ 25 ], [ 27 ], [ ...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "incorrect", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 3 ] }, { "role"...

benchmark/PDF/ICLR2018_BkXmYfbAZ.pdf

openreview

benchmark/MD/ICLR2018_BkXmYfbAZ.md

ICLR 2018

H1Xw62kRZ

{ "title": "Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis", "abstract": "Program synthesis is the task of automatically generating a program consistent with\na specification. Recent years have seen proposal of a number of neural approaches\nfor program synthesis, many of which adopt a s...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "Below is a summary of the pros and cons of the proposed paper:\n\nPros:\n* Proposes a novel method to tune program synthesizers to generate correct programs and prune search space, leading to better and more efficient synthesis\n* Shows small but subst...

[ "The authors consider the task of program synthesis in the Karel DSL. Their innovations are to use reinforcement learning to guide sequential generation of tokes towards a high reward output, incorporate syntax checking into the synthesis procedure to prune syntactically invalid programs.", "Finally they learn a ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2, 4 ] }, { "role"...

benchmark/PDF/ICLR2018_H1Xw62kRZ.pdf

openreview

benchmark/MD/ICLR2018_H1Xw62kRZ.md

ICLR 2018

rkMt1bWAZ

{ "title": "Bias-Variance Decomposition for Boltzmann Machines", "abstract": "We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. Our decomposition leads to an interesting phenomenon that the variance does not necessarily increase when more parameters are includ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "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 a...

[ "Summary of the paper:\nThe paper derives a lower bound on the expected squared KL-divergence between a true distribution and the sample based maximum likelihood estimate (MLE) of that distribution modelled by an Boltzmann machine (BM) based on methods from information geometry. This KL-divergence is first split ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "ORIG-ANL" ] }, ...

benchmark/PDF/ICLR2018_rkMt1bWAZ.pdf

openreview

benchmark/MD/ICLR2018_rkMt1bWAZ.md

ICLR 2018

H1Yp-j1Cb

{ "title": "An Online Learning Approach to Generative Adversarial Networks", "abstract": "We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused b...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 8, ...

[ [ { "role": "PC", "data": { "comment": "This paper presents a GAN training algorithm motivated by online learning. The method is shown to converge to a mixed Nash equilibrium in the case of a shallow discriminator. In the initial version of the paper, reviewers had concerns about weak baseli...

[ "This is an interesting paper, exploring GAN dynamics using ideas from online learning, in particular the pioneering \"sparring\" follow-the-regularized leader analysis of Freund and Schapire (using what is listed here as Lemma 4). By restricting the discriminator to be a single layer, the maximum player plays over...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect" ]

[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0, 1 ] } ], "category": [ "QUAL-MET", "ORIG-ANL" ] }, { "sentences": [ { "role": "Reviewer 2", "data": [ 3 ] } ], "cate...

benchmark/PDF/ICLR2018_H1Yp-j1Cb.pdf

openreview

benchmark/MD/ICLR2018_H1Yp-j1Cb.md

ICLR 2018

S1J2ZyZ0Z

{ "title": "Interpretable Counting for Visual Question Answering", "abstract": "Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of bo...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "Important problem and all reviewers recommend acceptance. I agree." } } ] ]

[ "Summary:\nThis work introduces a discrete and interpretable model for answering visually grounded counting questions. The proposed model executes a sequential decision process in which it 1) selects an image region to \"add to the count\" and then 2) updates the likelihood of selecting other regions based on their...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "ORIG-MTH" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "ORIG...

benchmark/PDF/ICLR2018_S1J2ZyZ0Z.pdf

openreview

benchmark/MD/ICLR2018_S1J2ZyZ0Z.md

ICLR 2018

B1DmUzWAW

{ "title": "A Simple Neural Attentive Meta-Learner", "abstract": "Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a dist...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "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 ...

[ "The authors propose a model for sequence classification and sequential decision making. The model interweaves attention layers, akin to those used by Vaswani et al, with temporal convolution. The authors demonstrate superior performance on a variety of benchmark problems, including those for supervised classificat...

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[ "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "QUAL-CMP" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] } ], "category": [ "QUAL...

benchmark/PDF/ICLR2018_B1DmUzWAW.pdf

openreview

benchmark/MD/ICLR2018_B1DmUzWAW.md

ICLR 2018

Skj8Kag0Z

{ "title": "Stabilizing Adversarial Nets with Prediction Methods", "abstract": "Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not mini...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4 ] }, "scores": { "Solid": null, "Presentation": null, "No...

[ [ { "role": "PC", "data": { "comment": "This paper provides a simple technique for stabilizing GAN training, and works over a variety of GAN models.\n\nOne of the reviewers expressed concerns with the value of the theory. I think that it would be worth emphasizing that similar arguments coul...

[ "This paper proposes a simple modification to the standard alternating stochastic gradient method for GAN training, which stabilizes training, by adding a prediction step.", "This is a clever and useful idea, and the paper is very well written. The proposed method is very clearly motivated, both intuitively and m...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correc...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...

benchmark/PDF/ICLR2018_Skj8Kag0Z.pdf

openreview

benchmark/MD/ICLR2018_Skj8Kag0Z.md

ICLR 2018

HJw8fAgA-

{ "title": "Learning Dynamic State Abstractions for Model-Based Reinforcement Learning", "abstract": "A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed models that learn predictive and compact state ...

Reject

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "There was quite a bit of discussion about this paper but in the end the majority felt that, though the paper is interesting, the results are too limited and more needs to be done for publication.\n\nPROS:\n1. Good comparison of state space model variat...

[ "Summary:\nThis paper studies how to learn (hidden)-state-space models of environment dynamics, and integrate them with Imagination-Augmented Agents (I2A). The paper considers single-agent problems and tests on Ms Pacman etc.", "There are several variations of the hidden-state space [ds]SSM model: using det/stoch...

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benchmark/PDF/ICLR2018_HJw8fAgA-.pdf

openreview

benchmark/MD/ICLR2018_HJw8fAgA-.md

ICLR 2018

H1BLjgZCb

{ "title": "Generating Natural Adversarial Examples", "abstract": "Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially dif...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The paper proposes a method to generate adversaries close to the (training) data manifold using GANs rather than arbitrary adversaries. They show the effectiveness of their method in terms of human evaluation and success in fooling a deep network. The ...

[ "Summary:", "A method for creation of semantical adversary examples in suggested. The ‘semantic’ property is measured by building a latent space with mapping from this space to the observable (generator) and back (inverter). The generator is trained with a WGAN optimization. Semantic adversarials examples are the...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 2 ] }, { "role": "Author", ...

benchmark/PDF/ICLR2018_H1BLjgZCb.pdf

openreview

benchmark/MD/ICLR2018_H1BLjgZCb.md

ICLR 2018

H1VjBebR-

{ "title": "The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings", "abstract": "We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of pa...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "The reviewers were generally positive about this paper with a few caveats:\n\nPROS:\n1. Important and challenging topic to analyze and any progress on unsupervised learning is interesting.\n2. the paper is clear, although more formalization would help ...

[ "The paper addresses the problem of learning mappings between different domains without any supervision. It belongs to the recent family of papers based on GANs.", "The paper states three conjectures (predictions in the paper):", "1. GAN are sufficient to learn « semantic mappings » in an unsupervised way, if t...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "c...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer", "data": [ 2 ] } ], "category": [ "QUAL-MET" ...

benchmark/PDF/ICLR2018_H1VjBebR-.pdf

openreview

benchmark/MD/ICLR2018_H1VjBebR-.md

ICLR 2018

HyjC5yWCW

{ "title": "Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm", "abstract": "Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent mo...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...

[ [ { "role": "PC", "data": { "comment": "R3 summarizes the reasons for the decision on this paper: \"The universal learning algorithm approximator result is a nice result, although I do not agree with the other reviewer that it is a \"significant contribution to the theoretical understanding...

[ "This paper studies the capacity of the model-agnostic meta-learning (MAML) framework as a universal learning algorithm approximator. Since a (supervised) learning algorithm can be interpreted as a map from a dataset and an input to an output, the authors define a universal learning algorithm approximator to be a u...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "cor...

[ "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "ORIG...

benchmark/PDF/ICLR2018_HyjC5yWCW.pdf

openreview

benchmark/MD/ICLR2018_HyjC5yWCW.md

ICLR 2018

Hkc-TeZ0W

{ "title": "A Hierarchical Model for Device Placement", "abstract": "We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph oper...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] },...

[ [ { "role": "PC", "data": { "comment": "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 ...

[ "The paper seems clear enough and original enough. The idea of jointly forming groups of operations to colocate and figure out placement on devices seems to hold merit. Where the paper falls short is motivating the problem setting. Traditionally, for determining optimal execution plans, one may resort to cost-based...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", ...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] }, { "role": "Author", "data": [ 15, 16, 17, 79, 80 ] } ], "category": [ "ORIG-PROB" ] }, { "senten...

benchmark/PDF/ICLR2018_Hkc-TeZ0W.pdf

openreview

benchmark/MD/ICLR2018_Hkc-TeZ0W.md

ICLR 2018

r1ZdKJ-0W

{ "title": "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking", "abstract": "Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn ...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] }, "scores...

[ [ { "role": "PC", "data": { "comment": "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 updat...

[ "This paper is well-written and easy follow. I didn't find serious concern and therefore suggest an acceptance.\nPros", "Methodology", "1. inductive ability: can generalize to unseen nodes without any further training", "2. personalized ranking: the model uses natural ranking that embeddings of closer nodes (...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "incorrect", "correct", "correct", ...

[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] } ], "category": [ "QU...

benchmark/PDF/ICLR2018_r1ZdKJ-0W.pdf

openreview

benchmark/MD/ICLR2018_r1ZdKJ-0W.md

ICLR 2018

HkNGsseC-

{ "title": "On the Expressive Power of Overlapping Architectures of Deep Learning", "abstract": "Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unle...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6, 7, 8 ] }, "scores": { ...

[ [ { "role": "PC", "data": { "comment": "The paper received scores of 8 (R1), 6 (R2), 6 (R3). R1's review is brief, and also is optimistic that these results demonstrated on ConvACs generalize to real convnets. R2 and R3 feel this might be a potential problem. R2 advocates weak accept and gi...

[ "The paper studies the expressive power provided by \"overlap\" in convolution layers of DNNs.", "Instead of ReLU networks with average/max pooling (as is standard in practice), the authors consider linear activations with product pooling. Such networks, which have been known as convolutional arithmetic circuits...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct" ]

[ "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "incorrect", "correct", "correct", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0, 1, 2, 3 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 4 ] }, { ...

benchmark/PDF/ICLR2018_HkNGsseC-.pdf

openreview

benchmark/MD/ICLR2018_HkNGsseC-.md

ICLR 2018

ByBAl2eAZ

{ "title": "Parameter Space Noise for Exploration", "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a riche...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1, 2, 3, 4, 5, 6 ] }, "scores": { "Solid": null, "Pres...

[ [ { "role": "PC", "data": { "comment": "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 ac...

[ "This paper explores the idea of adding parameter space noise in service of exploration. The paper is very well written and quite clear. It does a good job of contrasting parameter space noise to action space noise and evolutionary strategies.", "However, the results are weak. Parameter noise does better in some ...

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[ { "sentences": [ { "role": "Reviewer 1", "data": [ 0 ] } ], "category": [ "CLAR-WRT" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1, 2 ] }, { "role": "Author",...

benchmark/PDF/ICLR2018_ByBAl2eAZ.pdf

openreview

benchmark/MD/ICLR2018_ByBAl2eAZ.md

ICLR 2018

rJl63fZRb

{ "title": "Parametrized Hierarchical Procedures for Neural Programming", "abstract": "Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of...

Accept (Poster)

[ [ { "role": "Reviewer", "data": { "summary_of_the_paper": null, "value": { "review": [ 0, 1 ] }, "scores": { "Solid": null, "Presentation": null, "Novelty": null, "Overall": 6, ...

[ [ { "role": "PC", "data": { "comment": "This paper is somewhat incremental on recent prior work in a hot area; it has some weaknesses but does move the needle somewhat on these problems." } } ] ]

[ "In the paper titled \"Parameterized Hierarchical Procedures for Neural Programming\", the authors proposed \"Parametrized Hierarchical Procedure (PHP)\", which is a representation of a hierarchical procedure by differentiable parametrization. Each PHP is represented with two multi-layer perceptrons with ReLU activ...

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[ "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "correct", "incorrect", "correct", "correct" ]

[ { "sentences": [ { "role": "Reviewer", "data": [ 0 ] } ], "category": [ "N/A" ] }, { "sentences": [ { "role": "Reviewer 1", "data": [ 1 ] } ], "category": [ "CLAR-WRT", "QUAL...

benchmark/PDF/ICLR2018_rJl63fZRb.pdf

openreview

benchmark/MD/ICLR2018_rJl63fZRb.md

ICLR 2018