paper_id stringlengths 19 21 | paper_title stringlengths 8 170 | paper_abstract stringlengths 8 5.01k | paper_acceptance stringclasses 18
values | meta_review stringlengths 29 10k | label stringclasses 3
values | review_ids list | review_writers list | review_contents list | review_ratings list | review_confidences list | review_reply_tos list |
|---|---|---|---|---|---|---|---|---|---|---|---|
iclr_2018_SkF2D7g0b | Exploring the Space of Black-box Attacks on Deep Neural Networks | Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query a... | rejected-papers | The paper explores an increasingly important questions, especially showing the attack on existing APIs. The update to the paper has also improved it, but the paper is still not yet as impactful as it could be and needs much more comprehensive analysis to correctly appreciate its benefits and role. | train | [
"Syh_3H0VM",
"Hk96V1clf",
"rJGGOrcxz",
"B10Nn-jlf",
"Hkx9uTl4G",
"BkWKdLPGM",
"H1ddlLvzf",
"BJ0xxIwMM",
"H1FQ2HPfG",
"ry7lNQexM",
"ryDHmbY1G",
"SkYyvAX1G"
] | [
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"public",
"author",
"public"
] | [
"Thank you for your revised review. Regarding the higher value of distortion for SPSA, we would like to refer you to the second column of Table 2 titled 'Attack success'. The numbers in parentheses in this column provide the average distortion value for each type of attack. Since the earlier table (Table 1) of resu... | [
-1,
5,
6,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
-1,
4,
3,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"Hk96V1clf",
"iclr_2018_SkF2D7g0b",
"iclr_2018_SkF2D7g0b",
"iclr_2018_SkF2D7g0b",
"BJ0xxIwMM",
"iclr_2018_SkF2D7g0b",
"Hk96V1clf",
"rJGGOrcxz",
"B10Nn-jlf",
"ryDHmbY1G",
"SkYyvAX1G",
"iclr_2018_SkF2D7g0b"
] |
iclr_2018_r1RF3ExCb | Transformation Autoregressive Networks | The fundamental task of general density estimation has been of keen interest to machine learning. Recent advances in density estimation have either: a) proposed using a flexible model to estimate the conditional factors of the chain rule; or b) used flexible, non-linear transformations of variables of a simple base dis... | rejected-papers | This paper looks at building new density estimation methods and new methods for tranformations and autoregressive models. The request from reviewers for comparison improves the paper. These models have seen a wide range of applications and have been highly successful, needing the added benefits shown and their potenti... | train | [
"S1FCACYeG",
"By_sZWcgz",
"HkZ8Gb9eG",
"B1naFJL7z",
"SkEr0orXM",
"r1E9brOZf",
"r1A1-Bd-f",
"H1Ubxrd-f",
"Bkw504ubz"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author"
] | [
"The authors propose to combine nonlinear bijective transformations and flexible density models for density estimation. In terms of bijective change of variables transformations, they propose linear triangular transformations and recurrent transformations. They also propose to use as base transformation an autoregr... | [
5,
5,
8,
-1,
-1,
-1,
-1,
-1,
-1
] | [
3,
2,
4,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2018_r1RF3ExCb",
"iclr_2018_r1RF3ExCb",
"iclr_2018_r1RF3ExCb",
"SkEr0orXM",
"r1E9brOZf",
"S1FCACYeG",
"By_sZWcgz",
"HkZ8Gb9eG",
"iclr_2018_r1RF3ExCb"
] |
iclr_2018_rkONG0xAW | Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement | 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-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during trainin... | rejected-papers | 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 models of current practice, wider set of experiments and analysis, and ad... | train | [
"BkYwge9ef",
"SkMJBHOez",
"H11OyNqgM",
"HyA3vW57z",
"H1suSbq7z",
"HyJRxZ9mf"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author"
] | [
"There could be an interesting idea here, but the limitations and applicability of the proposed approach are not clear yet. More analysis should be done to clarify its potential. Besides, the paper seriously needs to be reworked. The text in general, but also the notation, should be improved.\n\nIn my opinion, the ... | [
6,
7,
5,
-1,
-1,
-1
] | [
3,
4,
3,
-1,
-1,
-1
] | [
"iclr_2018_rkONG0xAW",
"iclr_2018_rkONG0xAW",
"iclr_2018_rkONG0xAW",
"SkMJBHOez",
"BkYwge9ef",
"H11OyNqgM"
] |
iclr_2018_SJIA6ZWC- | Stochastic Hyperparameter Optimization through Hypernetworks | Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters. Our method trains a neural network to output approximately optima... | rejected-papers | The paper is interesting, and the update to the paper and additional experiments has already improved it in many ways, but the paper still does still not have as much impact as it could, by further strengthening the comparisons and usefulness in many of situations of current practice. | train | [
"Bk_UdcKxf",
"ryb9D_Bxf",
"r1dLqgZWM",
"BJH3BITQG",
"rJ6qV8pXf",
"ryTNBU6mM",
"Sygmdl-WG"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer"
] | [
"*Summary*\n\nThe paper proposes to use hyper-networks [Ha et al. 2016] for the tuning of hyper-parameters, along the lines of [Brock et al. 2017]. The core idea is to have a side neural network sufficiently expressive to learn the (large-scale, matrix-valued) mapping from a given configuration of hyper-parameters ... | [
6,
6,
6,
-1,
-1,
-1,
-1
] | [
4,
3,
1,
-1,
-1,
-1,
-1
] | [
"iclr_2018_SJIA6ZWC-",
"iclr_2018_SJIA6ZWC-",
"iclr_2018_SJIA6ZWC-",
"ryb9D_Bxf",
"r1dLqgZWM",
"Bk_UdcKxf",
"Bk_UdcKxf"
] |
iclr_2018_ByW5yxgA- | Multiscale Hidden Markov Models For Covariance Prediction | This paper presents a novel variant of hierarchical hidden Markov models (HMMs), the multiscale hidden Markov model (MSHMM), and an associated spectral estimation and prediction scheme that is consistent, finds global optima, and is computationally efficient. Our MSHMM is a generative model of multiple HMMs evolving at... | rejected-papers | The paper addresses and interesting problem, but the reviewers found that the paper is not as strong as it could be: improving the range of evaluated data (significantly improve the convincingness of the experiments, and clearly adressing any alternatives, their limitations and as baselines). | val | [
"HyUR-6Oez",
"Bk-DjW5ef",
"r1hXsPf-G",
"SJ7sJ_pmz",
"BkXd1O6mM",
"HJYNyupmG"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author"
] | [
"The paper focuses on a very particular HMM structure which involves multiple, independent HMMs. Each HMM emits an unobserved output with an explicit duration period. This explicit duration modelling captures multiple scale of temporal resolution. The actual observations are a weighted linear combination of the emi... | [
5,
6,
6,
-1,
-1,
-1
] | [
3,
4,
4,
-1,
-1,
-1
] | [
"iclr_2018_ByW5yxgA-",
"iclr_2018_ByW5yxgA-",
"iclr_2018_ByW5yxgA-",
"HyUR-6Oez",
"Bk-DjW5ef",
"r1hXsPf-G"
] |
iclr_2018_r1uOhfb0W | Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning | An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing.
In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks.
In the first stage, we r... | rejected-papers | This paper is interesting since it goes to showing the role of model averaging. The clarifications made improve the paper, but the impact of the paper is still not realised: the common confusion on the retraining can be re-examined, clarifications in the methodology and evaluation, and deeper contextulaisation of the w... | train | [
"B1A7YkceM",
"BJt3Bg5gM",
"Hy6mmeCgf",
"S14r4n5fz",
"B1OxK2cMG",
"HkmrOnqzG",
"BJJWN39zf",
"BkgBDh9GG",
"HysnNFwA-"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"public"
] | [
"The authors propose a procedure to generate an ensemble of sparse structured models. To do this, the authors propose to (1) sample models using SG-MCMC with group sparse prior, (2) prune hidden units with small weights, (3) and retrain weights by optimizing each pruned model. The ensemble is applied to MNIST class... | [
4,
6,
6,
-1,
-1,
-1,
-1,
-1,
-1
] | [
4,
3,
5,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2018_r1uOhfb0W",
"iclr_2018_r1uOhfb0W",
"iclr_2018_r1uOhfb0W",
"iclr_2018_r1uOhfb0W",
"HysnNFwA-",
"B1A7YkceM",
"Hy6mmeCgf",
"BJt3Bg5gM",
"iclr_2018_r1uOhfb0W"
] |
iclr_2018_HJJ0w--0W | Long-term Forecasting using Tensor-Train RNNs | We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error p... | rejected-papers | This paper address the increasingly studied problem of predictions over long-term horizons. Despite this, and the important updates from the authors, the paper is not yeat ready and improvements identified include more control over the fair comparisons, improved clarity in exposition. | train | [
"B1BulASgf",
"SJfyCxYgG",
"HJv0cb5xG"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"The paper proposes Tensor-Train RNN and Tensor-Train LSTM (TT-RNN/TLSTM), a RNN/LSTM architecture whose hidden unit at time t h_t is computed from the tensor-vector product between a tensor of weights and a concatenation of hidden units from the previous L time steps. The motivation is to incorporate previous hidd... | [
4,
5,
6
] | [
4,
3,
4
] | [
"iclr_2018_HJJ0w--0W",
"iclr_2018_HJJ0w--0W",
"iclr_2018_HJJ0w--0W"
] |
iclr_2018_Skx5txzb0W | A Boo(n) for Evaluating Architecture Performance | We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random ... | rejected-papers | The subject of model evaluation will always be a contentious one, and the reviewers were not yet fully-convinced by the discussion. The points you bring up at the end of your rresponse already point to directions for improvement as well as a greater degree of precision and control. | val | [
"H1otcvggM",
"BknlT5Bez",
"rynGrnpeM",
"rkvaNxw-G",
"H1Nhrlvbz",
"SyJ-SgPWG",
"HyRyZFL-G"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author"
] | [
"The authors propose a new measure to capture the inherent randomness of the performance of a neural net under different random initialisations and/or data inputs. Just reporting the best performance among many random realisations is clearly flawed yet still widely adopted. Instead, the authors propose to compute t... | [
4,
6,
4,
-1,
-1,
-1,
-1
] | [
4,
4,
4,
-1,
-1,
-1,
-1
] | [
"iclr_2018_Skx5txzb0W",
"iclr_2018_Skx5txzb0W",
"iclr_2018_Skx5txzb0W",
"H1otcvggM",
"rynGrnpeM",
"BknlT5Bez",
"iclr_2018_Skx5txzb0W"
] |
iclr_2018_SkwAEQbAb | A novel method to determine the number of latent dimensions with SVD | Determining the number of latent dimensions is a ubiquitous problem in machine
learning. In this study, we introduce a novel method that relies on SVD to discover
the number of latent dimensions. The general principle behind the method is to
compare the curve of singular values of the SVD decompositio... | rejected-papers | The paper addresses the important question of determining the intrinsic dimensionality, but there remain several issue, which make the paper not ready at this point: unclear exposition, lack of contextualisation of existing work and seemingly limited insights. The reviewers have provided many suggestions to improve the... | train | [
"ByPKCgNgG",
"r1N3gmtlz",
"HJAPXrtgM",
"B1hFqIXgM"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"public"
] | [
"The manuscript proposes to estimate the number of components in SVD by comparing the eigenvalues to those obtained on bootstrapped version of the input.\n\nThe paper has numerous flaws and is clearly below acceptance threshold for any scientific forum. Some of the more obvious issues, each alone sufficient for rej... | [
1,
2,
3,
-1
] | [
4,
5,
4,
-1
] | [
"iclr_2018_SkwAEQbAb",
"iclr_2018_SkwAEQbAb",
"iclr_2018_SkwAEQbAb",
"iclr_2018_SkwAEQbAb"
] |
iclr_2018_rJ5C67-C- | Hyperedge2vec: Distributed Representations for Hyperedges | Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are \enquote{sets of individuals}; \enquote{item sets} in pattern mining are sets; and for various types of ... | rejected-papers | While there are some interesting and novel aspects in this paper, none of the reviewers recommends acceptance. | train | [
"H1kAEtYlz",
"rJvDxGceG",
"S1teFU6gG",
"ryF8mLfNM",
"SyHasMG4M",
"HyhIPu67f",
"Sk1QH_aQM",
"Skqb4upmf"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author"
] | [
"The paper studies different methods for defining hypergraph embeddings, i.e. defining vectorial representations of the set of hyperedges of a given hypergraph. It should be noted that the framework does not allow to compute a vectorial representation of a set of nodes not already given as an hyperedge. A set of me... | [
5,
5,
5,
-1,
-1,
-1,
-1,
-1
] | [
3,
3,
4,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2018_rJ5C67-C-",
"iclr_2018_rJ5C67-C-",
"iclr_2018_rJ5C67-C-",
"SyHasMG4M",
"HyhIPu67f",
"H1kAEtYlz",
"rJvDxGceG",
"S1teFU6gG"
] |
iclr_2019_B1gabhRcYX | BA-Net: Dense Bundle Adjustment Networks | This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features th... | accepted-oral-papers | The first reviewer summarizes the contribution well: This paper combines [a CNN that computes both a multi-scale feature pyramid and a depth prediction, which is expressed as a linear combination of "depth bases"]. This is used to [define a dense re-projection error over the images, akin to that of dense or semi-dense ... | test | [
"r1x8O_Sw3X",
"SylPHRPDnQ",
"BkgvvbtzkN",
"H1ljP2vqAQ",
"r1xqEgFcCX",
"H1gAMXd90X",
"rkxhFe_qAm",
"HkeWLII90Q",
"SJx-VMJcnm"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"edit: the authors added several experiments (better evaluation of the predicted lambda, comparison with CodeSLAM), which address my concerns. I think the paper is much more convincing now. I am happy to increase my rating to clear accept.\n\nI also agree with the introduction of the Chi vector, and with the use of... | [
8,
7,
-1,
-1,
-1,
-1,
-1,
-1,
9
] | [
4,
4,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"iclr_2019_B1gabhRcYX",
"iclr_2019_B1gabhRcYX",
"rkxhFe_qAm",
"SJx-VMJcnm",
"r1x8O_Sw3X",
"r1x8O_Sw3X",
"SylPHRPDnQ",
"iclr_2019_B1gabhRcYX",
"iclr_2019_B1gabhRcYX"
] |
iclr_2019_B1l08oAct7 | Deterministic Variational Inference for Robust Bayesian Neural Networks | Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally effic... | accepted-oral-papers | The manuscript proposes deterministic approximations for Bayesian neural networks as an alternative to the standard Monte-Carlo approach. The results suggest that the deterministic approximation can be more accurate than previous methods. Some explicit contributions include efficient moment estimates and empirical Baye... | train | [
"H1eOIrXYhm",
"HyeV1yHgAm",
"HJxcV9EgRX",
"rJex4YNeCQ",
"H1g0a1ir2Q",
"rJexO5ZynQ"
] | [
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"The authors propose a new approach to perform deterministic variational inference for feed-forward BNN with specific nonlinear activation functions by approximating layerwise moments. Under certain conditions, the authors show that the proposed method achieves better performance than existing Monte Carlo variation... | [
7,
-1,
-1,
-1,
7,
7
] | [
3,
-1,
-1,
-1,
3,
5
] | [
"iclr_2019_B1l08oAct7",
"rJexO5ZynQ",
"H1g0a1ir2Q",
"H1eOIrXYhm",
"iclr_2019_B1l08oAct7",
"iclr_2019_B1l08oAct7"
] |
iclr_2019_B1l6qiR5F7 | Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks | Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information a... | accepted-oral-papers | This paper presents a substantially new way of introducing a syntax-oriented inductive bias into sentence-level models for NLP without explicitly injecting linguistic knowledge. This is a major topic of research in representation learning for NLP, so to see something genuinely original work well is significant. All thr... | train | [
"B1gIbtAKRm",
"BkgiNwT7h7",
"B1xh_mvdRX",
"HkgTokYQCX",
"Bkgp-SFxRQ",
"Skxnyrtx0m",
"SyeNaVYxCm",
"Bygp1Apv2m",
"H1ewsJDcjm"
] | [
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Regarding “LSTM’s performance consistently lags behind that of tree-based models”. \nOn sentence embedding tasks (e.g SNLI) and sequential labeling tasks (e.g sentiment analysis), TreeLSTM has shown better performance compared to vanilla LSTM. We’ve also updated the abstract according to the reviews.\n\nRegarding ... | [
-1,
7,
-1,
-1,
-1,
-1,
-1,
9,
8
] | [
-1,
3,
-1,
-1,
-1,
-1,
-1,
4,
4
] | [
"B1xh_mvdRX",
"iclr_2019_B1l6qiR5F7",
"BkgiNwT7h7",
"SyeNaVYxCm",
"H1ewsJDcjm",
"BkgiNwT7h7",
"Bygp1Apv2m",
"iclr_2019_B1l6qiR5F7",
"iclr_2019_B1l6qiR5F7"
] |
iclr_2019_B1xsqj09Fm | Large Scale GAN Training for High Fidelity Natural Image Synthesis | Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We ... | accepted-oral-papers | The paper proposes a set of tricks leading to a new SOTA for sampling high resolution images. It is clearly written and the presented contribution will be of high interest for practitioners. | train | [
"SJl68_Hx37",
"SkgkCbBm0Q",
"Syxd9-HXAQ",
"r1gI_-SQAm",
"BJeJx-H7RQ",
"rJx99xSXAX",
"S1gaWerP2X",
"HklmZ1xqhm",
"SkgcCLXypQ",
"Hkgd30pT27",
"BJgFGkiT2Q",
"rJgBuz5a3Q",
"rJlaYkcTnX",
"BklSXtmL2X",
"Sklp_OFLjm",
"SyesNhmUjm",
"Hke0IlKSim",
"S1xw0OrXqm",
"SJlWU-HGcm",
"rkxcudXfq7"... | [
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"public",
"public",
"public",
"author",
"public",
"author",
"public",
"author",
"public",
"public",
"author",
"public",
"author",
"public",
"author",
"public... | [
"This paper present extensions of the Self-Attention Generative Adversarial Network approach SAGAN, leading to impressive images generations conditioned on imagenet classes. \nThe key components of the approach are :\n- increasing the batch size by a factor 8\n- augmenting the width of the networks by 50% \nThese f... | [
9,
-1,
-1,
-1,
-1,
-1,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
4,
-1,
-1,
-1,
-1,
-1,
3,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2019_B1xsqj09Fm",
"SJl68_Hx37",
"r1gI_-SQAm",
"S1gaWerP2X",
"HklmZ1xqhm",
"iclr_2019_B1xsqj09Fm",
"iclr_2019_B1xsqj09Fm",
"iclr_2019_B1xsqj09Fm",
"iclr_2019_B1xsqj09Fm",
"iclr_2019_B1xsqj09Fm",
"rJgBuz5a3Q",
"rJlaYkcTnX",
"iclr_2019_B1xsqj09Fm",
"iclr_2019_B1xsqj09Fm",
"SyesNhmUjm"... |
iclr_2019_Bklr3j0cKX | Learning deep representations by mutual information estimation and maximization | This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representatio... | accepted-oral-papers | This paper proposes a new unsupervised learning approach based on maximizing the mutual information between the input and the representation. The results are strong across several image datasets. Essentially all of the reviewer's concerns were directly addressed in revisions of the paper, including additional experimen... | train | [
"rkgBfaIahQ",
"SkxJeJTX07",
"SJxEJLX2CQ",
"B1gEEtgAjm",
"ryxmvC2mC7",
"rJekflpQCX",
"SJxOvyaQCm",
"B1lMGywi6Q",
"SJxqzmcVp7",
"BkxA0Kt3nQ",
"rygYR8UMoQ",
"rklhZYMJom"
] | [
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"public"
] | [
"This paper proposes Deep InfoMax (DIM), for learning representations by maximizing the mutual information between the input and a deep representation. By structuring the network and objectives to encode input locality or priors on the representation, DIM learns features that are useful for downstream tasks without... | [
7,
-1,
-1,
9,
-1,
-1,
-1,
-1,
-1,
7,
-1,
-1
] | [
5,
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1
] | [
"iclr_2019_Bklr3j0cKX",
"rkgBfaIahQ",
"B1gEEtgAjm",
"iclr_2019_Bklr3j0cKX",
"iclr_2019_Bklr3j0cKX",
"B1gEEtgAjm",
"BkxA0Kt3nQ",
"rkgBfaIahQ",
"rkgBfaIahQ",
"iclr_2019_Bklr3j0cKX",
"rklhZYMJom",
"iclr_2019_Bklr3j0cKX"
] |
iclr_2019_ByeZ5jC5YQ | KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks | Feature selection is a pervasive problem. The discovery of relevant features can be as important for performing a particular task (such as to avoid overfitting in prediction) as it can be for understanding the underlying processes governing the true label (such as discovering relevant genetic factors for a disease). Ma... | accepted-oral-papers | The paper presents a novel strategy for statistically motivated feature selection i.e. aimed at controlling the false discovery rate. This is achieved by extending knockoffs to complex predictive models and complex distributions via (multiple) generative adversarial networks.
The reviewers and ACs noted weakness in t... | train | [
"S1lT0N1cTQ",
"Hkx5lH19T7",
"B1xSOmkcpQ",
"B1gBqxk9pm",
"H1eAulfwpm",
"HklHlOUPnQ",
"HkeTawVrhX"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"\nA5: This was indeed an oversight and we will correct the text. We will change “trivial” to “well-known” and hopefully that will make clearer our point. The asterisk will be removed as we do not feel it helps provide any clarity.\n\nA6: The citations found in Table 1 are in fact citations to relevant PubMed liter... | [
-1,
-1,
-1,
-1,
6,
10,
7
] | [
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"HklHlOUPnQ",
"HklHlOUPnQ",
"HkeTawVrhX",
"H1eAulfwpm",
"iclr_2019_ByeZ5jC5YQ",
"iclr_2019_ByeZ5jC5YQ",
"iclr_2019_ByeZ5jC5YQ"
] |
iclr_2019_Byg3y3C9Km | Learning Protein Structure with a Differentiable Simulator | The Boltzmann distribution is a natural model for many systems, from brains to materials and biomolecules, but is often of limited utility for fitting data because Monte Carlo algorithms are unable to simulate it in available time. This gap between the expressive capabilities and sampling practicalities of energy-based... | accepted-oral-papers | This paper presents a differentiable simulator for protein structure prediction that can be trained end-to-end. It makes several contributions to this research area. Particularly training a differentiable sampling simulator could be of interest to a wider community.
The main criticism comes from the clarity for the ma... | train | [
"SJlUZjqEim",
"B1lRoQMLTX",
"HylvOjIjCQ",
"r1etop_q0X",
"SkxK8JG507",
"HJlD-yM90X",
"rJeaJ0bc0X",
"Hyxj9pbc0Q",
"Skx-IPBvTQ",
"Hyl_nFNX6m",
"Hkgk3b343m",
"ryeM3Sm4im",
"ByeS8UUb5X",
"Ske1rVBZcX",
"B1eOXxVb9X"
] | [
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"public",
"author",
"public"
] | [
"Post-rebuttal revision: The authors have adressed my concerns sufficiently. The paper still has issues with presentation, and weak comparisons to earlier methods. However, the field is currently rapidly developing, and comparing to earlier works is often difficult. I believe the Langevin-based prediction is a sign... | [
6,
7,
-1,
-1,
-1,
-1,
-1,
-1,
6,
-1,
7,
-1,
-1,
-1,
-1
] | [
3,
5,
-1,
-1,
-1,
-1,
-1,
-1,
5,
-1,
3,
-1,
-1,
-1,
-1
] | [
"iclr_2019_Byg3y3C9Km",
"iclr_2019_Byg3y3C9Km",
"r1etop_q0X",
"SkxK8JG507",
"SJlUZjqEim",
"Hkgk3b343m",
"B1lRoQMLTX",
"Skx-IPBvTQ",
"iclr_2019_Byg3y3C9Km",
"Hkgk3b343m",
"iclr_2019_Byg3y3C9Km",
"ByeS8UUb5X",
"Ske1rVBZcX",
"B1eOXxVb9X",
"iclr_2019_Byg3y3C9Km"
] |
iclr_2019_Bygh9j09KX | ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness | Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on... | accepted-oral-papers | This paper proposes a hypothesis about the kinds of visual information for which popular neural networks are most selective. It then proposes a series of empirical experiments on synthetically modified training sets to test this and related hypotheses. The main conclusions of the paper are contained in the title, and... | train | [
"BklS3JIeR7",
"r1lJ3Mke0X",
"S1eErXNKC7",
"rJlAnqlH2X",
"HylJK7nYAm",
"HJxSI4x527",
"BkeaMFAK3m",
"r1xmGpubRX",
"HygXM-Yb07",
"SygjL7t-0m",
"S1g75tNxTQ",
"B1x2vGlKhm",
"BJghHSvNh7",
"Bkea9yCbiQ"
] | [
"author",
"public",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"public",
"author",
"public"
] | [
"Thanks for your interest in our work!\n\nAs mentioned in the paper, we used the PyTorch implementation from [1]. The degree of stylization (parameter \"alpha\" in the implementation) was kept at the default value of 1.0; it might be interesting to explore whether a lower coefficient still nudges a model towards a ... | [
-1,
-1,
-1,
8,
-1,
7,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
-1,
-1,
-1,
4,
-1,
4,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"r1lJ3Mke0X",
"iclr_2019_Bygh9j09KX",
"iclr_2019_Bygh9j09KX",
"iclr_2019_Bygh9j09KX",
"SygjL7t-0m",
"iclr_2019_Bygh9j09KX",
"iclr_2019_Bygh9j09KX",
"HJxSI4x527",
"BkeaMFAK3m",
"rJlAnqlH2X",
"HJxSI4x527",
"BJghHSvNh7",
"Bkea9yCbiQ",
"iclr_2019_Bygh9j09KX"
] |
iclr_2019_H1xSNiRcF7 | Smoothing the Geometry of Probabilistic Box Embeddings | There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs. Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models... | accepted-oral-papers | The manuscript presents a promising new algorithm for learning geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures. The manuscript builds on the build on the box lattice model, extending prior work by relaxing the box embeddings via Gaussian convolutions. This is shown to ... | val | [
"H1xPEJOVsm",
"Bye2jf25Rm",
"rJeo2-39Rm",
"SylyqW25Cm",
"BkejJghqAQ",
"rJglCnOshm",
"SylZ79N5hm",
"HylK0EWqhX",
"r1lircRdnQ"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"Post-rebuttal revision: All my concerns were adressed by the authors. This is a great paper and should be accepted.\n\n------\n\nThe paper presents smoothing probabilistic box embeddings with softplus functions, which make the optimization landscape continuous, while also presenting the theoretical background of t... | [
7,
-1,
-1,
-1,
-1,
8,
8,
-1,
-1
] | [
3,
-1,
-1,
-1,
-1,
3,
4,
-1,
-1
] | [
"iclr_2019_H1xSNiRcF7",
"H1xPEJOVsm",
"SylyqW25Cm",
"SylZ79N5hm",
"rJglCnOshm",
"iclr_2019_H1xSNiRcF7",
"iclr_2019_H1xSNiRcF7",
"r1lircRdnQ",
"iclr_2019_H1xSNiRcF7"
] |
iclr_2019_HJx54i05tX | On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training | We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena when the depth becomes large. This, in particular, provides quantitative answers and in... | accepted-oral-papers | This paper analyzes random auto encoders in the infinite dimension limit with an assumption that the weights are tied in the encoder and decoder. In the limit the paper is able to show the random auto encoder transformation as doing an approximate inference on data. The paper is able to obtain principled initializatio... | train | [
"rJeRWADXyE",
"B1lULFW9hm",
"S1lmapk71V",
"SJe6Z_6dAm",
"B1lLKO6dAX",
"rJehwu6dRm",
"BkeCavpORQ",
"rklt1LauAQ",
"SygkNeB92Q",
"Skeq2IQ7h7"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for your reply. We are happy to know that.",
"This work applies infinite width limit random network framework (a.k.a. Mean field analysis) to study deep autoencoders when weights are tied between encoder and decoder. Random network analysis allows to have exact analysis of asymptotic behaviour where th... | [
-1,
8,
-1,
-1,
-1,
-1,
-1,
-1,
9,
8
] | [
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4
] | [
"S1lmapk71V",
"iclr_2019_HJx54i05tX",
"BkeCavpORQ",
"B1lULFW9hm",
"Skeq2IQ7h7",
"Skeq2IQ7h7",
"B1lULFW9hm",
"SygkNeB92Q",
"iclr_2019_HJx54i05tX",
"iclr_2019_HJx54i05tX"
] |
iclr_2019_HkNDsiC9KQ | Meta-Learning Update Rules for Unsupervised Representation Learning | A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations us... | accepted-oral-papers | The reviewers all agree that the idea is interesting, the writing clear and the experiments sufficient.
To improve the paper, the authors should consider better discussing their meta-objective and some of the algorithmic choices. | train | [
"SJeJvkj5hX",
"rJgRNO1Kp7",
"HJeD-O1Yam",
"r1eK0P1F67",
"Bkgckkbah7",
"r1eIOmju3m"
] | [
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"This work brings a novel meta-learning approach that learns unsupervised learning rules for learning representations across different modalities, datasets, input permutation, and neural network architectures. The meta-objectives consist of few shot learning scores from several supervised tasks. The idea of using m... | [
8,
-1,
-1,
-1,
8,
8
] | [
3,
-1,
-1,
-1,
4,
3
] | [
"iclr_2019_HkNDsiC9KQ",
"r1eIOmju3m",
"SJeJvkj5hX",
"Bkgckkbah7",
"iclr_2019_HkNDsiC9KQ",
"iclr_2019_HkNDsiC9KQ"
] |
iclr_2019_HygBZnRctX | Transferring Knowledge across Learning Processes | In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that... | accepted-oral-papers | This paper proposes an approach for learning to transfer knowledge across multiple tasks. It develops a principled approach for an important problem in meta-learning (short horizon bias). Nearly all of the reviewer's concerns were addressed throughout the discussion phase. The main weakness is that the experimental set... | val | [
"Hye_eUxDk4",
"BkemR3cMkE",
"rkezle-C0X",
"HJgKnIJY27",
"BkextEnBCQ",
"Byxo0m2rRm",
"Bkevhwv927",
"Skgx-yRE0Q",
"SyxtTCp4Am",
"SJgQTudgAX",
"BkeSD__xR7",
"HJeAEd_eR7",
"SylzzddgRm",
"HyeuqwdeCm",
"HkeqwPulCQ",
"H1xWiy_q2Q"
] | [
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"Dear reviewer,\n \nThank you for taking the time to consider our rebuttal and revised manuscript.\n \nYou raise good points and we will address these in a final version of the paper; we have added a sentence following the stabilizer describing how it affects the meta gradient, and to answer your question about the... | [
-1,
-1,
-1,
8,
-1,
-1,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6
] | [
-1,
-1,
-1,
3,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"rkezle-C0X",
"H1xWiy_q2Q",
"HJeAEd_eR7",
"iclr_2019_HygBZnRctX",
"iclr_2019_HygBZnRctX",
"Skgx-yRE0Q",
"iclr_2019_HygBZnRctX",
"SyxtTCp4Am",
"HyeuqwdeCm",
"iclr_2019_HygBZnRctX",
"H1xWiy_q2Q",
"SylzzddgRm",
"HJgKnIJY27",
"HkeqwPulCQ",
"Bkevhwv927",
"iclr_2019_HygBZnRctX"
] |
iclr_2019_HylzTiC5Km | GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING | The unconditional generation of high fidelity images is a longstanding benchmark
for testing the performance of image decoders. Autoregressive image models
have been able to generate small images unconditionally, but the extension of
these methods to large images where fidelity can be more readily ass... | accepted-oral-papers | All reviewers recommend acceptance, with two reviewers in agreement that the results represent a significant advance for autoregressive generative models. The AC concurs.
| val | [
"Bkx8AurWeN",
"rJgX0gv814",
"SJeStxgqRm",
"HJgrrcCO2X",
"H1gOyezcaX",
"H1llqKX56m",
"r1xv8KGcp7",
"H1lNzuZ9a7",
"Bkgl4P-cpm",
"B1eI3IJ5pQ",
"rkeURDYKpQ",
"S1gjDP-927",
"HkxN569T2X",
"Bke6VkQAY7",
"SJgqbZTaYQ"
] | [
"public",
"public",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"https://arxiv.org/abs/1109.4389 seems to be another relevant reference for AR models using multiple scales",
"Dear authors:\n\nThank you for your really interesting and impressive ideas. The idea is really amazing and experimental results are sound. Generating 256x256 imagenet images in the auto-regressive manne... | [
-1,
-1,
-1,
9,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
10,
7,
-1,
-1
] | [
-1,
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
5,
3,
-1,
-1
] | [
"Bke6VkQAY7",
"iclr_2019_HylzTiC5Km",
"iclr_2019_HylzTiC5Km",
"iclr_2019_HylzTiC5Km",
"HJgrrcCO2X",
"r1xv8KGcp7",
"rkeURDYKpQ",
"S1gjDP-927",
"S1gjDP-927",
"HkxN569T2X",
"S1gjDP-927",
"iclr_2019_HylzTiC5Km",
"iclr_2019_HylzTiC5Km",
"SJgqbZTaYQ",
"iclr_2019_HylzTiC5Km"
] |
iclr_2019_S1x4ghC9tQ | Temporal Difference Variational Auto-Encoder | To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-... | accepted-oral-papers | The reviewers agree that this is a novel paper with a convincing evaluation. | train | [
"BJxUrnv_AQ",
"SyxSfnP_0Q",
"BJgAOovOC7",
"rkeaQHUJam",
"rJeR1S-ThQ",
"BkgnEnawnm"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for your review and comments. We clarified our intuitive derivation of the loss in section A. It is indeed difficult to compare the jumpy TD-VAE model to other models, as there is little work that studies such models. We updated the appendix to explain how a model similar to jumpy TD-VAE provides an appr... | [
-1,
-1,
-1,
8,
9,
7
] | [
-1,
-1,
-1,
4,
4,
5
] | [
"BkgnEnawnm",
"rJeR1S-ThQ",
"rkeaQHUJam",
"iclr_2019_S1x4ghC9tQ",
"iclr_2019_S1x4ghC9tQ",
"iclr_2019_S1x4ghC9tQ"
] |
iclr_2019_S1xq3oR5tQ | A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs | The vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whe... | accepted-oral-papers | The paper advocates neuroscience-based V1 models to adapt CNNs. The results of the simulations are convincing from a neuroscience-perspective. The reviewers equivocally recommend publication. | train | [
"r1eiULeC3Q",
"H1gQhLTipX",
"rkls0Uasp7",
"BJx4_OpjpQ",
"B1xQQuaiam",
"rkxrOwpjpQ",
"HJgMwJe5hm",
"Hyg8wO1rhQ"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"EDIT: On the basis of revisions made to the paper, which significantly augment the results, the authors note: \"the call for papers explicitly mentions applications in neuroscience as within the scope of the conference\" which clarifies my other concern. For both of these reasons, I have changed my prior rating.\n... | [
8,
-1,
-1,
-1,
-1,
-1,
8,
8
] | [
5,
-1,
-1,
-1,
-1,
-1,
5,
3
] | [
"iclr_2019_S1xq3oR5tQ",
"r1eiULeC3Q",
"H1gQhLTipX",
"B1xQQuaiam",
"Hyg8wO1rhQ",
"HJgMwJe5hm",
"iclr_2019_S1xq3oR5tQ",
"iclr_2019_S1xq3oR5tQ"
] |
iclr_2019_SkVhlh09tX | Pay Less Attention with Lightweight and Dynamic Convolutions | Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. ... | accepted-oral-papers | Very solid work, recognized by all reviewers as worthy of acceptance. Additional readers also commented and there is interest in the open source implementation that the authors promise to provide. | train | [
"SyexV29PJN",
"H1glXkLRhm",
"SkgRkkYAAQ",
"BkxEwCe6pQ",
"BJxVasJp6m",
"B1lA_ikT6m",
"r1gUWo16Tm",
"Bkx6Dmk6Tm",
"SygyHzyaTX",
"BJlW-fk667",
"BklnHDDcT7",
"S1xedHZ_pX",
"rygh1-1upQ",
"Byg-bBXZaX",
"H1gJoj8C3X",
"Byx0nlaKnX",
"Hkgbd38ChQ"
] | [
"public",
"official_reviewer",
"public",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"public",
"public",
"public",
"public",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"I found your work very interesting, but there are some recent works that are closely related to your work, which take a sentence as input and generate convolutional kernels that are further applied on the sentence, but with a different granularity. I think those works are definitely worth comparing to.\n\nmissing ... | [
-1,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
8,
8,
-1
] | [
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
-1
] | [
"iclr_2019_SkVhlh09tX",
"iclr_2019_SkVhlh09tX",
"Bkx6Dmk6Tm",
"Byg-bBXZaX",
"Byx0nlaKnX",
"H1glXkLRhm",
"H1gJoj8C3X",
"rygh1-1upQ",
"S1xedHZ_pX",
"BklnHDDcT7",
"iclr_2019_SkVhlh09tX",
"iclr_2019_SkVhlh09tX",
"iclr_2019_SkVhlh09tX",
"iclr_2019_SkVhlh09tX",
"iclr_2019_SkVhlh09tX",
"iclr_... |
iclr_2019_r1lYRjC9F7 | Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset | Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using no... | accepted-oral-papers | All reviewers agree that the presented audio data augmentation is very interesting, well presented, and clearly advancing the state of the art in the field. The authors’ rebuttal clarified the remaining questions by the reviewers. All reviewers recommend strong acceptance (oral presentation) at ICLR. I would like to re... | test | [
"SklV7Ix9aX",
"rJllkIgcTQ",
"H1eFiHgqTQ",
"rklS4Hl5am",
"BJl9uwaQ67",
"B1efz6dgpX",
"S1gnFxZjnX"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for your review and comments.\n\n* Eq (1) this is really the joint distribution between audio and notes, not the marginal of audio\n\nThank you for catching the mistake. We have updated the equation to include the marginalizing integral through the expectation over notes: P(audio) = E_{notes} [ P(audio|n... | [
-1,
-1,
-1,
-1,
8,
8,
8
] | [
-1,
-1,
-1,
-1,
5,
2,
4
] | [
"S1gnFxZjnX",
"B1efz6dgpX",
"BJl9uwaQ67",
"iclr_2019_r1lYRjC9F7",
"iclr_2019_r1lYRjC9F7",
"iclr_2019_r1lYRjC9F7",
"iclr_2019_r1lYRjC9F7"
] |
iclr_2019_r1xlvi0qYm | Learning to Remember More with Less Memorization | Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize t... | accepted-oral-papers | Well-written paper that motivates through theoretical analysis new memory writing methods in memory augmented neural networks. Extensive experimental analysis support and demonstrate the advantages of the new solutions over other recurrent architectures.
Reviewers suggested extension and clarification of the analysis p... | train | [
"HyxFDRZuCX",
"B1lqiDbOAQ",
"Byl6cSbdC7",
"SygH6Dzjpm",
"BJxv6enuhm",
"SJeG8ByF6m",
"Bklj1SJKa7",
"rJeuIPUjnm"
] | [
"official_reviewer",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer"
] | [
"Thanks for your responses and paper revisions. I still agree this is a nicely conducted piece of research and will retain my score of 8.\n\nRe. (1) & (2) I see, in most cases when people perform the copy task they train on programatically generated sequences that essentially cannot be overfit to (e.g. because the ... | [
-1,
-1,
-1,
7,
7,
-1,
-1,
8
] | [
-1,
-1,
-1,
3,
4,
-1,
-1,
4
] | [
"SJeG8ByF6m",
"iclr_2019_r1xlvi0qYm",
"SygH6Dzjpm",
"iclr_2019_r1xlvi0qYm",
"iclr_2019_r1xlvi0qYm",
"rJeuIPUjnm",
"BJxv6enuhm",
"iclr_2019_r1xlvi0qYm"
] |
iclr_2019_rJEjjoR9K7 | Learning Robust Representations by Projecting Superficial Statistics Out | Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superficial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classifier. Buildin... | accepted-oral-papers | The paper presents a new approach for domain generalization whereby the original supervised model is trained with an explicit objective to ignore so called superficial statistics present in the training set but which may not be present in future test sets. The paper proposes using a differentiable variant of gray-level... | train | [
"S1xWkt7QCX",
"BJet3_XXRQ",
"S1gEYuQXC7",
"SJxqbOQmRX",
"rJxbWynhhm",
"H1ehlWduhm",
"HJee7cfwh7"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for the strong positive assessment of our work. We’re glad that you appreciated the originality of our approach, the value of our new datasets, and the quality of our exposition. We will continue to improve the draft in the camera-ready version.",
"Thanks for a detailed review. We are grateful both for... | [
-1,
-1,
-1,
-1,
7,
7,
9
] | [
-1,
-1,
-1,
-1,
4,
4,
3
] | [
"HJee7cfwh7",
"H1ehlWduhm",
"rJxbWynhhm",
"iclr_2019_rJEjjoR9K7",
"iclr_2019_rJEjjoR9K7",
"iclr_2019_rJEjjoR9K7",
"iclr_2019_rJEjjoR9K7"
] |
iclr_2019_rJVorjCcKQ | Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware | As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the... | accepted-oral-papers | The authors propose a new method of securely evaluating neural networks.
The reviewers were unanimous in their vote to accept. The paper is very well written, the idea is relatively simple, and so it is likely that this would make a nice presentation. | train | [
"Hkgpl17URQ",
"BJl-sAfICQ",
"Hyx47Cf80Q",
"SylJsazLRm",
"rkgtvmXc2m",
"r1eyl4tHhQ",
"HJg2YxyGoQ"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"In response to the below reviews, we have made the following main changes to our paper:\n\n- As suggested by the second reviewer, we have moved some of the content from the Appendix back to the main body. These include the microbenchmark results, as well as a discussion of the challenges in extending Slalom to DNN... | [
-1,
-1,
-1,
-1,
7,
7,
9
] | [
-1,
-1,
-1,
-1,
3,
2,
4
] | [
"iclr_2019_rJVorjCcKQ",
"HJg2YxyGoQ",
"r1eyl4tHhQ",
"rkgtvmXc2m",
"iclr_2019_rJVorjCcKQ",
"iclr_2019_rJVorjCcKQ",
"iclr_2019_rJVorjCcKQ"
] |
iclr_2019_rJgMlhRctm | The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision | We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene represe... | accepted-oral-papers | Strong paper in an interesting new direction.
More work should be done in this area. | train | [
"r1g6tF8F3X",
"BylnXIUC0Q",
"HJeHGTF5nX",
"Bkx7KxOjpX",
"r1xJIbv5A7",
"ryxKPxw9Rm",
"rJxoZ-_ipQ",
"SJgbnnCgRX",
"rJl4slOsa7",
"SyxhAx_jpm",
"rJx2mlOjTQ",
"Sklo1V_znQ"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer"
] | [
"To achieve the state-of-the-art on the CLEVR and the variations of this, the authors propose a method to use object-based visual representations and a differentiable quasi-symbolic executor. Since the semantic parser for a question input is not differentiable, they use REINFORCE algorithm and a technique to reduce... | [
6,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
9
] | [
4,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
5
] | [
"iclr_2019_rJgMlhRctm",
"r1xJIbv5A7",
"iclr_2019_rJgMlhRctm",
"r1g6tF8F3X",
"SJgbnnCgRX",
"iclr_2019_rJgMlhRctm",
"iclr_2019_rJgMlhRctm",
"Bkx7KxOjpX",
"r1g6tF8F3X",
"Sklo1V_znQ",
"HJeHGTF5nX",
"iclr_2019_rJgMlhRctm"
] |
iclr_2019_rJl-b3RcF7 | The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to tr... | accepted-oral-papers | The authors posit and investigate a hypothesis -- the “lottery ticket hypothesis” -- which aims to explain why overparameterized neural networks are easier to train than their sparse counterparts. Under this hypothesis, randomly initialized dense networks are easier to train because they contain a larger number of “win... | train | [
"S1xmvZRayE",
"Hkelbn3I14",
"r1l-QxArJ4",
"ryggsG-VkV",
"HJeDy85anQ",
"HygUFDOTAX",
"Bkg5UpU52m",
"ryemP68v2m",
"SkloQowaAm",
"BygUAeW5R7",
"BJeHlbWcCX",
"SylwJEW9Cm",
"H1gQ-4Z5CQ",
"ryg2lfbcRQ",
"SygdQnlqRm",
"r1xZ0Ag5Cm",
"BylPD1gKT7"
] | [
"author",
"public",
"author",
"public",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"public"
] | [
"\n\nWe have an update with several further experiments that examine the relationship between SNIP and our paper.\n\nWe have simplified our pruning mechanism to prune weights globally (instead of per-layer) with otherwise the same pruning technique. For our three main networks (MNIST, Resnet-18, and VGG-19), we fin... | [
-1,
-1,
-1,
-1,
5,
-1,
9,
9,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
-1,
-1,
-1,
-1,
4,
-1,
4,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"Hkelbn3I14",
"r1l-QxArJ4",
"ryggsG-VkV",
"iclr_2019_rJl-b3RcF7",
"iclr_2019_rJl-b3RcF7",
"ryg2lfbcRQ",
"iclr_2019_rJl-b3RcF7",
"iclr_2019_rJl-b3RcF7",
"SylwJEW9Cm",
"HJeDy85anQ",
"HJeDy85anQ",
"ryemP68v2m",
"ryemP68v2m",
"Bkg5UpU52m",
"iclr_2019_rJl-b3RcF7",
"SygdQnlqRm",
"HJeDy85an... |
iclr_2019_rJxgknCcK7 | FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models | A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these models requires restricting their architectures to allow cheap computation of Jacobian determinants. Alternatively, the Jacobian t... | accepted-oral-papers | This paper proposes the use of recently propose neural ODEs in a flow-based generative model.
As the paper shows, a big advantage of a neural ODE in a generative flow is that an unbiased estimator of the log-determinant of the mapping is straightforward to construct. Another advantage, compared to earlier published f... | train | [
"Sylsscjbe4",
"ryxeNAsF14",
"r1xI56j2R7",
"HklkaJG92m",
"Hylm9LQd3X",
"H1xNi4_oT7",
"rJglGJOi6m",
"rJlBrCwsp7",
"rklhnnPj6X",
"ryeYYxV9n7",
"Byg3817r5Q"
] | [
"author",
"public",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"public"
] | [
"Thank for you pointing this out. We will update the camera-ready version with the correct results if our paper is accepted. ",
"It looks like the authors are not reporting the most up-to-date likelihoods using TANs (as per the Table 1 in official ICML paper http://proceedings.mlr.press/v80/oliva18a.html ). Hence... | [
-1,
-1,
-1,
7,
7,
-1,
-1,
-1,
-1,
7,
-1
] | [
-1,
-1,
-1,
4,
3,
-1,
-1,
-1,
-1,
4,
-1
] | [
"ryxeNAsF14",
"iclr_2019_rJxgknCcK7",
"rklhnnPj6X",
"iclr_2019_rJxgknCcK7",
"iclr_2019_rJxgknCcK7",
"Byg3817r5Q",
"Hylm9LQd3X",
"HklkaJG92m",
"ryeYYxV9n7",
"iclr_2019_rJxgknCcK7",
"iclr_2019_rJxgknCcK7"
] |
iclr_2019_ryGs6iA5Km | How Powerful are Graph Neural Networks? | Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been pro... | accepted-oral-papers | Graph neural networks are an increasingly popular topic of research in machine learning, and this paper does a good job of studying the representational power of some newly proposed variants. The framing of the problem in terms of the WL test, and the proposal of the GIN architecture is a valuable contribution. Through... | train | [
"rkl2Q1Qi6X",
"B1xYlDERRX",
"SJeYuLH41V",
"HJgMSgUqhQ",
"BygALwN0CX",
"B1et5yXJ14",
"H1gRfJQJy4",
"rkxt80KARX",
"rJxY7atRCX",
"BkgrFw3iRQ",
"S1egpyLCAX",
"H1xW3wVA0X",
"rkeW9FDnnQ",
"S1ljyieA0m",
"BJgIGNTP27",
"BkxHNhu607",
"rJx9PavpA7",
"BJeRhEhiRX",
"ryeaD73iRX",
"SJxgqg2N0m"... | [
"public",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"public",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"public",
"public",
... | [
"I do not think that Equation (4.1) is as powerful as the 1-WL. Consider the two labeled graphs \n\nr -- g\n| |\ng -- r\n\nand \n\nr -- g\n| |\nr -- g\n\nwith node color \"g\" and \"r\". Clearly, the 1-WL can distinguish between these two graphs. Howeover, when using (4.1) with an 1-hot encoding of the labels... | [
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
-1,
8,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-... | [
-1,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
5,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-... | [
"iclr_2019_ryGs6iA5Km",
"S1ljyieA0m",
"S1ljyieA0m",
"iclr_2019_ryGs6iA5Km",
"rJx9PavpA7",
"rJxY7atRCX",
"rkxt80KARX",
"BygALwN0CX",
"H1xW3wVA0X",
"BJeRhEhiRX",
"BkgrFw3iRQ",
"BkxHNhu607",
"iclr_2019_ryGs6iA5Km",
"H1xLhQUEA7",
"iclr_2019_ryGs6iA5Km",
"Byg6WVL4Rm",
"B1l2k48VCQ",
"rye... |
iclr_2019_B1G5ViAqFm | Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation | Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and informat... | accepted-poster-papers | 1. Describe the strengths of the paper. As pointed out by the reviewers and based on your expert opinion.
- The paper tackles an interesting and challenging problem with a novel approach.
- The method gives improves improved performance for the surface reconstruction task.
2. Describe the weaknesses of the paper. A... | test | [
"HyxCDlbK0Q",
"r1gpWlWFC7",
"H1xYOJWKAQ",
"HJlzBRwC2m",
"BJxMFSeonm",
"SJlRpk09hQ"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We thank you for your review and feedback, and we hope to be able to address your concerns below.\n\nOur paper addresses the issue of handling irregular domains, with possibly mixed topologies in the context of deep learning, and propose an optimal spectral sampling scheme for constructing a volumetric representat... | [
-1,
-1,
-1,
5,
7,
4
] | [
-1,
-1,
-1,
3,
4,
3
] | [
"SJlRpk09hQ",
"BJxMFSeonm",
"HJlzBRwC2m",
"iclr_2019_B1G5ViAqFm",
"iclr_2019_B1G5ViAqFm",
"iclr_2019_B1G5ViAqFm"
] |
iclr_2019_B1G9doA9F7 | Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation | Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.
However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source... | accepted-poster-papers | The authors propose a method for low-resource domain adaptation where the number of examples available in the target domain are limited. The proposed method modifies the basic approach in a CycleGAN by augmenting it with a “content” (task-specific) loss, instead of the standard reconstruction error. The authors also de... | train | [
"rkgVnNv52Q",
"HJxLgpUNJE",
"HJxHpfhH2m",
"SJgRrjSf14",
"Byez806lJE",
"Syl3KFjyJN",
"BylYeKiykE",
"HyeIY_i1y4",
"rkxI_hF3RX",
"HklKo3NW6Q",
"B1xgX-Y0am",
"BJxNk5FwCQ",
"H1gOeZtCpQ",
"ByxZHgYCam",
"SylfAxKCpQ"
] | [
"official_reviewer",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author"
] | [
"The authors propose an extension of cycle-consistent adversarial adaptation methods in order to tackle domain adaptation in settings where a limited amount of supervised target data is available (though they also validate their model in the standard unsupervised setting as well). The method appears to be a natural... | [
6,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
8,
-1,
-1,
-1,
-1,
-1
] | [
4,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
2,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2019_B1G9doA9F7",
"H1gOeZtCpQ",
"iclr_2019_B1G9doA9F7",
"Byez806lJE",
"HyeIY_i1y4",
"ByxZHgYCam",
"rkxI_hF3RX",
"rkxI_hF3RX",
"BJxNk5FwCQ",
"iclr_2019_B1G9doA9F7",
"HJxHpfhH2m",
"B1xgX-Y0am",
"rkgVnNv52Q",
"iclr_2019_B1G9doA9F7",
"HklKo3NW6Q"
] |
iclr_2019_B1GAUs0cKQ | Variance Networks: When Expectation Does Not Meet Your Expectations | Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different k... | accepted-poster-papers | The authors describe a very counterintuitive type of layer: one with mean zero Gaussian weights. They show that various Bayesian deep learning algorithms tend to converge to layers of this variety. This work represents a step forward in our understanding of bayesian deep learning methods and potentially may shine light... | val | [
"SyeR8Podp7",
"ByloevouTQ",
"Hkx_V8sdam",
"SkxrImCK2Q",
"r1e0vhrKhX",
"HygV2aqO3Q"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for your review and your questions!\n\n> (1) My main concern is verification. Most of the comparisons are between variance layer (zero-mean) and conventional binary dropout, while the main argument of the paper is that it’s better to set Gaussian posterior’s mean to zero. So in all the experiments the pa... | [
-1,
-1,
-1,
6,
6,
6
] | [
-1,
-1,
-1,
3,
4,
4
] | [
"HygV2aqO3Q",
"r1e0vhrKhX",
"SkxrImCK2Q",
"iclr_2019_B1GAUs0cKQ",
"iclr_2019_B1GAUs0cKQ",
"iclr_2019_B1GAUs0cKQ"
] |
iclr_2019_B1GMDsR5tm | Initialized Equilibrium Propagation for Backprop-Free Training | Deep neural networks are almost universally trained with reverse-mode automatic differentiation (a.k.a. backpropagation). Biological networks, on the other hand, appear to lack any mechanism for sending gradients back to their input neurons, and thus cannot be learning in this way. In response to this, Scellier & Bengi... | accepted-poster-papers | The paper investigates a novel initialisation method to improve Equilibrium Propagation. In particular, the results are convincing, but the reviewers remain with small issues here and there.
An issue with the paper is the biological plausibility of the approach. Nonetheless publication is recommended. | train | [
"rJxVRXrulV",
"ryeI_q3v3Q",
"Hyl4OzIIJN",
"Syx_vW0HkE",
"rkeWkpvBk4",
"r1gcB3JH14",
"H1l2Nbf50Q",
"B1gzOC6Wa7",
"HkxVPjhFAQ",
"rkenvhdtA7",
"rkxaD5dY07",
"rye_pK_YAm",
"H1xeru_K07",
"r1eOy7xKhQ"
] | [
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"Have bumped score to 7, in anticipation of the final improvements from this thread being included in the camera ready.",
"This paper presents an improvement on the local/derivative-free learning algorithm equilibrium propagation. Specifically, it trains a feedforward network to initialize the iterative optimizat... | [
-1,
7,
-1,
-1,
-1,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
8
] | [
-1,
5,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
5
] | [
"Hyl4OzIIJN",
"iclr_2019_B1GMDsR5tm",
"Syx_vW0HkE",
"rkeWkpvBk4",
"r1gcB3JH14",
"rkenvhdtA7",
"HkxVPjhFAQ",
"iclr_2019_B1GMDsR5tm",
"rye_pK_YAm",
"ryeI_q3v3Q",
"r1eOy7xKhQ",
"B1gzOC6Wa7",
"iclr_2019_B1GMDsR5tm",
"iclr_2019_B1GMDsR5tm"
] |
iclr_2019_B1MXz20cYQ | Explaining Image Classifiers by Counterfactual Generation | When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision? Producing an answer requires marginalizing over images that could have been seen but weren'... | accepted-poster-papers | Important problem (explainable AI); sensible approach, one of the first to propose a method for the counter-factual question (if this part of the input were different, what would the network have predicted). Initially there were some concerns by the reviewers but after the author response and reviewer discussion, all t... | train | [
"rJxUUoj8g4",
"S1edEowWgN",
"rJxRJWdayV",
"H1x2bX-p1V",
"B1eKk_WRaX",
"HkguvdW0a7",
"HJlz9u-RaQ",
"H1g195-CT7",
"BklNCIGhh7",
"SklSwVYo37",
"SkephArKjX"
] | [
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"As we mentioned above, Fan et al. is orthogonal to our work. We highly recommend you to reread our manuscript to understand the scope of our work.",
"Fan et al. is used in saliency prediction and seems to achieve good accuracy as reported in other papers:\nhttps://openreview.net/forum?id=BJxbYoC9FQ\n\n",
"Good... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
5,
7,
5
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
3,
5
] | [
"S1edEowWgN",
"HkguvdW0a7",
"H1x2bX-p1V",
"B1eKk_WRaX",
"iclr_2019_B1MXz20cYQ",
"BklNCIGhh7",
"SklSwVYo37",
"SkephArKjX",
"iclr_2019_B1MXz20cYQ",
"iclr_2019_B1MXz20cYQ",
"iclr_2019_B1MXz20cYQ"
] |
iclr_2019_B1VZqjAcYX | SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY | Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility... | accepted-poster-papers | This method proposes a criterion (SNIP) to prune neural networks before training. The pro is that SNIP can find the architecturally important parameters in the network without full training. The con is that SNIP only evaluated on small datasets (mnist, cifar, tiny-imagenet) and it's uncertain if the same heuristic wor... | train | [
"HkgD860RJN",
"BJlFsBZA14",
"HkeMgJ6LyN",
"r1ecBMxwTm",
"rygEepnLJV",
"Skx0ADeVkE",
"rye-Cr95Am",
"Bygrtyec3X",
"HylRy09FCX",
"SygWrMoHRX",
"HkeeUt2G0X",
"rylC4nnfCQ",
"rJgNZnhzCX",
"S1gwW9hGRX",
"HkxRgu-z0X",
"BylnY5EPTX",
"SJlJlW6g6m",
"Hkx-JZ3vh7",
"SJxr7w6B3Q",
"H1e1b9pz3Q"... | [
"author",
"public",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"public",
"official_reviewer",
"author",
"official_reviewer",
"author",
"public",
... | [
"\nWe believe that the comparison is misleading since [1] and SNIP focus on different (orthogonal) aspects of network pruning, and we elaborate this below.\n- SNIP focuses on finding a subnetwork at single-shot with a mini-batch of data, and shows that the subnetwork can be trained in the standard way. There are no... | [
-1,
-1,
-1,
8,
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
9,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
-1,
-1,
-1,
5,
-1,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"BJlFsBZA14",
"rygEepnLJV",
"rygEepnLJV",
"iclr_2019_B1VZqjAcYX",
"Skx0ADeVkE",
"rJgNZnhzCX",
"HylRy09FCX",
"iclr_2019_B1VZqjAcYX",
"SygWrMoHRX",
"S1gwW9hGRX",
"Hkx-JZ3vh7",
"r1ecBMxwTm",
"r1ecBMxwTm",
"BylnY5EPTX",
"iclr_2019_B1VZqjAcYX",
"SJlJlW6g6m",
"Bygrtyec3X",
"iclr_2019_B1V... |
iclr_2019_B1e0X3C9tQ | Diagnosing and Enhancing VAE Models | Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In ... | accepted-poster-papers | The reviewers acknowledge the value of the careful analysis of Gaussian encoder/decoder VAE presented in the paper. The proposed algorithm shows impressive FID scores that are comparable to those obtained by state of the art GANs. The paper will be a valuable addition to the ICLR program.
| val | [
"Bkg4rgm93Q",
"rygm8MFA0X",
"BJxjvk7nCm",
"H1xarJXnCm",
"S1exXym2Cm",
"Bye1byCKCX",
"HJxVsagYnm",
"Sye7L4k1AQ",
"ryg--PcTTQ",
"SyxF01pF6X",
"Bkletw5FT7",
"ByeNgv9KTQ",
"H1xC7LAvaQ",
"S1low-VDpX",
"BJxGPNr4TX",
"B1xK2VH4T7",
"ByebcVS4TX",
"BJlRhQrETQ",
"BJ-97B4pQ",
"B1eNhzHVTX",... | [
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"public",
"author",
"public",
"author",
"author",
"public",
"public",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"This paper proposed a two-stage VAE method to generate high-quality samples and avoid blurriness. It is accomplished by utilizing a VAE structure on the observation and latent variable separately. The paper exploited a collection of interesting properties of VAE and point out the problem existed in the generative ... | [
6,
-1,
-1,
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
9
] | [
3,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"iclr_2019_B1e0X3C9tQ",
"iclr_2019_B1e0X3C9tQ",
"Bye1byCKCX",
"Bye1byCKCX",
"Bye1byCKCX",
"BJ-97B4pQ",
"iclr_2019_B1e0X3C9tQ",
"ryg--PcTTQ",
"SyxF01pF6X",
"Bkletw5FT7",
"S1low-VDpX",
"H1xC7LAvaQ",
"iclr_2019_B1e0X3C9tQ",
"iclr_2019_B1e0X3C9tQ",
"HJxVsagYnm",
"HJxVsagYnm",
"HJxVsagYnm... |
iclr_2019_B1exrnCcF7 | Disjoint Mapping Network for Cross-modal Matching of Voices and Faces | We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different... | accepted-poster-papers | All reviewers agree that the proposed method interesting and well presented. The authors' rebuttal addressed all outstanding raised issues. Two reviewers recommend clear accept and the third recommends borderline accept. I agree with this recommendation and believe that the paper will be of interest to the audience att... | val | [
"r1gPRr2jT7",
"r1lCSghsam",
"ByxLt0iipQ",
"r1e5d52hhQ",
"Hkxxm_Qsh7",
"HkxvRZesnQ"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We sincerely appreciate the review for the recognition of our novelty and many valuable suggestions.\n\nOur main contribution mainly lies in proposing a cross modal matching framework called DIMNet, which learns a shared representation for different modalities by mapping them individually to their common covariate... | [
-1,
-1,
-1,
7,
6,
7
] | [
-1,
-1,
-1,
4,
3,
4
] | [
"Hkxxm_Qsh7",
"HkxvRZesnQ",
"r1e5d52hhQ",
"iclr_2019_B1exrnCcF7",
"iclr_2019_B1exrnCcF7",
"iclr_2019_B1exrnCcF7"
] |
iclr_2019_B1ffQnRcKX | Automatically Composing Representation Transformations as a Means for Generalization | A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leve... | accepted-poster-papers |
pros:
- the paper is well-written and presents a nice framing of the composition problem
- good comparison to prior work
- very important research direction
cons:
- from an architectural standpoint the paper is somewhat incremental over Routing Networks [Rosenbaum et al]
- as Reviewers 2 and 3 point out, the experime... | train | [
"r1gbHlap3X",
"BJl1SmLj3m",
"SygCi7pr14",
"BJeiNE7TaQ",
"H1ei6m7TaX",
"Byg3F7Qap7",
"B1eifeX9n7",
"r1x-kZyT2X",
"Hkx25xk6hQ",
"SJg_qTBs3X",
"HJlzO5a1nm"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"author",
"public",
"public"
] | [
"Summary: This paper is about trying to learn a function from typed input-output data so that it can generalize to test data with an input-output type that it hasn't seen during training. It should be able to use \"analogy\" (if we want to translate from French to Spanish but don't know how to do so directly, we sh... | [
7,
9,
-1,
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1
] | [
2,
4,
-1,
-1,
-1,
-1,
3,
-1,
-1,
-1,
-1
] | [
"iclr_2019_B1ffQnRcKX",
"iclr_2019_B1ffQnRcKX",
"Byg3F7Qap7",
"Hkx25xk6hQ",
"B1eifeX9n7",
"r1gbHlap3X",
"iclr_2019_B1ffQnRcKX",
"SJg_qTBs3X",
"HJlzO5a1nm",
"HJlzO5a1nm",
"iclr_2019_B1ffQnRcKX"
] |
iclr_2019_B1fpDsAqt7 | Visual Reasoning by Progressive Module Networks | Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn – most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for ea... | accepted-poster-papers | Important problem (modular & interpretable approaches for VQA and visual reasoning); well-written manuscript, sensible approach. Paper was reviewed by three experts. Initially there were some concerns but after the author response and reviewer discussion, all three unanimously recommend acceptance. | train | [
"SkeIsnWJlN",
"HygakjZyl4",
"SJgyyPTv3m",
"SkeHNjEqRQ",
"BkgO1UGq07",
"Hygs-DpF0X",
"S1enb1hOCm",
"BylkHTN_RX",
"rygVoa6DRQ",
"H1xozmAE0m",
"SyeoH17BTQ",
"SJlY63MSpX",
"Sklfd2fSpX",
"H1gUmqkh3Q",
"r1eaptK5hm"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"We have added the GT captions experiment in the 'plug-and-play architecture' paragraph in Section 4.1.\n\nThank you again for your great suggestion!",
"Thanks for the response! It is interesting that the GT captions can help improve the VQA performance, please incorporate the results and update the manuscripts a... | [
-1,
-1,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6,
7
] | [
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
5
] | [
"HygakjZyl4",
"SJlY63MSpX",
"iclr_2019_B1fpDsAqt7",
"BkgO1UGq07",
"Hygs-DpF0X",
"S1enb1hOCm",
"BylkHTN_RX",
"rygVoa6DRQ",
"SyeoH17BTQ",
"iclr_2019_B1fpDsAqt7",
"SJgyyPTv3m",
"r1eaptK5hm",
"H1gUmqkh3Q",
"iclr_2019_B1fpDsAqt7",
"iclr_2019_B1fpDsAqt7"
] |
iclr_2019_B1g30j0qF7 | Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes | There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but b... | accepted-poster-papers | There has been a recent focus on proving the convergence of Bayesian fully connected networks to GPs. This work takes these ideas one step further, by proving the equivalence in the convolutional case.
All reviewers and the AC are in agreement that this is interesting and impactful work. The nature of the topic is suc... | val | [
"SklzQ5QfgN",
"Skluw57MeN",
"SygaK6RU14",
"SJgkvoCrJE",
"BkxQiXRYhX",
"BJgxlnn4yV",
"BJejjc3VkV",
"SkgZdQ0t37",
"rJx7Qcjq07",
"HJgaDeh9R7",
"S1lesx2c0X",
"Hkes7gncAm",
"r1eI1gn5Cm",
"ryeVtyn907",
"Bkg_Xk290X",
"BygZgkn5RQ",
"BJl_qRsqCQ",
"BygYYYicRX",
"H1xWncjq0Q",
"ryl9t5scRQ"... | [
"author",
"author",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"------------------------------------------------------------------------------------\n>>> - Demonstrate through some sample figures that GP-CNN with pooling achieves invariance while GP-CNN with out pooling fail to capture it.\n\nThank you for the suggestion, we are working on and are planning to include covarian... | [
-1,
-1,
7,
-1,
7,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6
] | [
-1,
-1,
3,
-1,
2,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"SygaK6RU14",
"SygaK6RU14",
"iclr_2019_B1g30j0qF7",
"rke_hxhHhX",
"iclr_2019_B1g30j0qF7",
"BkxQiXRYhX",
"SkgZdQ0t37",
"iclr_2019_B1g30j0qF7",
"rke_hxhHhX",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"SkgZdQ0t37",
"rke_hxhHhX",
... |
iclr_2019_B1gTShAct7 | Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference | Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off ... | accepted-poster-papers | Pros:
- novel method for continual learning
- clear, well written
- good results
- no need for identified tasks
- detailed rebuttal, new results in revision
Cons:
- experiments could be on more realistic/challenging domains
The reviewers agree that the paper should be accepted. | train | [
"rye41gla2Q",
"HkxQpF3zk4",
"B1xXbsgkJ4",
"SkeOiAJ9RQ",
"rJl0zU-cCQ",
"SJlIlAJqCm",
"Bkxpap1cAm",
"H1l7x3R_h7",
"B1eZ4nh_nm",
"ByxHXBo8h7",
"Hyxl43qlnQ"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer"
] | [
"The transfer/ interference perspective of lifelong learning is well motivated, and combining the meta-learning literature with the continual learning literature (applying reptile twice), even if seems obvious, wasn't explored before. In addition, this paper shows that a lot of gain can be obtained if one uses more... | [
6,
-1,
-1,
-1,
-1,
-1,
-1,
8,
7,
-1,
-1
] | [
5,
-1,
-1,
-1,
-1,
-1,
-1,
4,
5,
-1,
-1
] | [
"iclr_2019_B1gTShAct7",
"SJlIlAJqCm",
"rJl0zU-cCQ",
"H1l7x3R_h7",
"B1eZ4nh_nm",
"Bkxpap1cAm",
"rye41gla2Q",
"iclr_2019_B1gTShAct7",
"iclr_2019_B1gTShAct7",
"Hyxl43qlnQ",
"iclr_2019_B1gTShAct7"
] |
iclr_2019_B1gstsCqt7 | Sparse Dictionary Learning by Dynamical Neural Networks | A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical system’s evolution and/or limit points in the associated state space can correspond to numerical solutions to certain mathematical optimiza... | accepted-poster-papers | While there has been lots of previous work on training dictionaries for sparse coding, this work tackles the problem of doing son in a purely local way. While previous work suggests that the exact computation of gradient addressed in the paper is not necessarily critical, as noted by reviewers, all reviewers agree that... | train | [
"S1xN4BLApX",
"Hyl2hXI067",
"rJgVjEUATX",
"rklBey5vTQ",
"SJe04G-g67",
"ryxykzEyam"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Figure 2 serves to illustrate our theoretical results and shows how the algorithm is run in practice. We revised the caption of Figure 2, providing a more detailed and clear description.\n\nWe indeed cited and discussed the early \"similarity matching\" work (Hu et al. 2014) in our original submission. In our upda... | [
-1,
-1,
-1,
6,
9,
8
] | [
-1,
-1,
-1,
4,
4,
4
] | [
"ryxykzEyam",
"iclr_2019_B1gstsCqt7",
"rklBey5vTQ",
"iclr_2019_B1gstsCqt7",
"iclr_2019_B1gstsCqt7",
"iclr_2019_B1gstsCqt7"
] |
iclr_2019_B1lKS2AqtX | Eidetic 3D LSTM: A Model for Video Prediction and Beyond | Spatiotemporal predictive learning, though long considered to be a promising self-supervised feature learning method, seldom shows its effectiveness beyond future video prediction. The reason is that it is difficult to learn good representations for both short-term frame dependency and long-term high-level relations. W... | accepted-poster-papers | Strengths: Strong results on future frame video prediction using a 3D convolutional network. Use of future video prediction to jointly learn auxiliary tasks shown to to increase performance. Good ablation study.
Weaknesses: Comparisons with older action recognition methods. Some concerns about novelty, the main contri... | val | [
"HJeJHrJc27",
"BJgyWXsuh7",
"rJl_P_YA0m",
"S1gVVTLVhX",
"SyxImDCiA7",
"ryeBsAat0Q",
"ByVd51AFRm",
"HyxkqaatRm",
"r1e-K3aF0Q"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author"
] | [
"AFTER REBUTTAL:\n\nThis is an overall good work, and I do think proves its point. The results on the TaxiBJ dataset (not TatxtBJ, please correct the name in the paper) are compelling, and the concerns regarding some of the text explainations have been corrected.\n\n-----\n\nThe proposed model uses a 3D-CNN with a ... | [
7,
7,
-1,
7,
-1,
-1,
-1,
-1,
-1
] | [
5,
4,
-1,
4,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2019_B1lKS2AqtX",
"iclr_2019_B1lKS2AqtX",
"ryeBsAat0Q",
"iclr_2019_B1lKS2AqtX",
"ByVd51AFRm",
"BJgyWXsuh7",
"S1gVVTLVhX",
"HJeJHrJc27",
"iclr_2019_B1lKS2AqtX"
] |
iclr_2019_B1lnzn0ctQ | ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA | Deep neural networks based on unfolding an iterative algorithm, for example, LISTA (learned iterative shrinkage thresholding algorithm), have been an empirical success for sparse signal recovery. The weights of these neural networks are currently determined by data-driven “black-box” training. In this work, we propose ... | accepted-poster-papers | This is a well executed paper that makes clear contributions to the understanding of unrolled iterative optimization and soft thresholding for sparse signal recovery with neural networks. | train | [
"HJen7j87JN",
"HklQ7zppRQ",
"B1xca8C4nX",
"rkxXkC3DTQ",
"r1xH4AhDpX",
"HyltGkJFiQ",
"ryxQcC8jp7",
"rJey90hw6m",
"r1lP83nDpX",
"SyxX7z5uhQ"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer"
] | [
"[Opening is okay]\n\nPoints 1 and 2: There is no word \"tree\" or \"graph\", no \"beta\" or \"$\\beta$\", in our paper. We are confused and think they may refer to another paper. Could you kindly clarify?\n\n3: This is great suggestion. The matrix W is the solution of a convex quadratic program subject to linear c... | [
-1,
-1,
7,
-1,
-1,
9,
-1,
-1,
-1,
10
] | [
-1,
-1,
4,
-1,
-1,
5,
-1,
-1,
-1,
5
] | [
"HklQ7zppRQ",
"rkxXkC3DTQ",
"iclr_2019_B1lnzn0ctQ",
"B1xca8C4nX",
"B1xca8C4nX",
"iclr_2019_B1lnzn0ctQ",
"HyltGkJFiQ",
"HyltGkJFiQ",
"SyxX7z5uhQ",
"iclr_2019_B1lnzn0ctQ"
] |
iclr_2019_B1lz-3Rct7 | Three Mechanisms of Weight Decay Regularization | Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L2 regularization.
Literal weight decay has been shown to outperform L2 regularization for... | accepted-poster-papers | Reviewers are in a consensus and recommended to accept after engaging with the authors. Please take reviewers' comments into consideration to improve your submission for the camera ready.
| train | [
"SyxswZfEkV",
"B1eBMzYupm",
"B1g5xlXqnm",
"rygRc8IopQ",
"SJgG4O8op7",
"HklYSxUoTX",
"rygtlWdRnm",
"Skldcfu0hm",
"rJx5uduA2m",
"rJx3XFFv2Q",
"B1eS7cTdhm",
"rJlhD5wRnQ"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"public",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author"
] | [
"The authors have taken my comment into account in the new revision of the paper and adequately addressed issues pointed out by other reviewers. So, I keep my rating unchanged.",
"Q1: Agreed\n\nQ2: You are right about weight decay on gamma only affecting the complexity of the model due to the last layer which can... | [
-1,
-1,
6,
-1,
-1,
-1,
-1,
-1,
-1,
7,
7,
-1
] | [
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
5,
4,
-1
] | [
"Skldcfu0hm",
"rJx5uduA2m",
"iclr_2019_B1lz-3Rct7",
"HklYSxUoTX",
"B1eBMzYupm",
"iclr_2019_B1lz-3Rct7",
"B1g5xlXqnm",
"B1eS7cTdhm",
"rJx3XFFv2Q",
"iclr_2019_B1lz-3Rct7",
"iclr_2019_B1lz-3Rct7",
"iclr_2019_B1lz-3Rct7"
] |
iclr_2019_B1xJAsA5F7 | Learning Multimodal Graph-to-Graph Translation for Molecule Optimization | We view molecule optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph.... | accepted-poster-papers | The revisions made by the authors convinced the reviewers to all recommend accepting this paper. Therefore, I am recommending acceptance as well. I believe the revisions were important to make since I concur with several points in the initial reviews about additional baselines. It is all too easy to add confusion to th... | test | [
"HylNHt4h0X",
"r1gHailcAQ",
"SyeFxjBc37",
"B1lysCy5CX",
"r1xXOkQO07",
"Syl_q2s_67",
"SyghQTiupQ",
"H1xuZ76rTm",
"Skl19Q6Hpm",
"SkgWrRs_Tm",
"Hkl9bvO52Q",
"HkgKBKlq2m"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for your insightful comments again! They are very helpful!",
"Thank you for updating the paper. I've updated the score as well.",
"Update:\nThe score has been updated to reflect the authors' great efforts in improving the manuscript. This reviewer would suggest to accept the paper now.\n\n\nOld Revie... | [
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
6
] | [
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4
] | [
"r1gHailcAQ",
"B1lysCy5CX",
"iclr_2019_B1xJAsA5F7",
"r1xXOkQO07",
"H1xuZ76rTm",
"HkgKBKlq2m",
"HkgKBKlq2m",
"SyeFxjBc37",
"SyeFxjBc37",
"Hkl9bvO52Q",
"iclr_2019_B1xJAsA5F7",
"iclr_2019_B1xJAsA5F7"
] |
iclr_2019_B1xVTjCqKQ | A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery | In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery. First, real-world signals can seldom be described as perfectly sparse vectors in a known basis, and traditionally used random measurement schemes are seldom optimal for sensing them. Second, existi... | accepted-poster-papers | This paper studies deep convolutional architectures to perform compressive sensing of natural images, demonstrating improved empirical performance with an efficient pipeline.
Reviewers reached a consensus that this is an interesting contribution that advances data-driven methods for compressed sensing, despite some do... | test | [
"SygHVHWiyV",
"Byl6y31r37",
"BkgimTEqRm",
"HklY22V9RQ",
"B1gQwnV9R7",
"Bylk_jNcC7",
"S1lGNoV90m",
"HkgjfHOahX",
"SJgsRkfb3m"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"I think the authors have addressed all my comments and I recommend acceptance. ",
"Quality & Clarity:\nThis is a nice paper with clear explanations and justifications. The experiments seem a little shakey.\n\nOriginality & Significance:\nI'm personally not familiar enough to say the theoretical work is original,... | [
-1,
7,
-1,
-1,
-1,
-1,
-1,
8,
6
] | [
-1,
3,
-1,
-1,
-1,
-1,
-1,
4,
3
] | [
"Bylk_jNcC7",
"iclr_2019_B1xVTjCqKQ",
"SJgsRkfb3m",
"Byl6y31r37",
"Byl6y31r37",
"HkgjfHOahX",
"HkgjfHOahX",
"iclr_2019_B1xVTjCqKQ",
"iclr_2019_B1xVTjCqKQ"
] |
iclr_2019_B1xWcj0qYm | On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data | Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrar... | accepted-poster-papers | This paper studies the task of learning a binary classifier from only unlabeled data. They first provide a negative result, i.e., they show it is impossible to learn an unbiased estimator from a set of unlabeled data. Then they provide an empirical risk minimization method which works when given two sets of unlabeled d... | train | [
"ryxRqB96R7",
"HJgRrkQw07",
"BJe2DbrUR7",
"r1enCpTGCQ",
"S1gL7qwf07",
"rkxn-AIbC7",
"SyeJk0I-A7",
"rJlyh6UW07",
"H1leKaUW0Q",
"rJgzB6LbAX",
"r1gZR2ahaQ",
"rkx7Fs-ham",
"BJgWwjZ3T7",
"rJeZEjbhTX",
"rJepWiW3pm",
"rkeeoqWha7",
"ryxIBivipQ",
"BkgahCMcpQ",
"H1xTSAEqnm"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"The authors have responded to my questions, and I have no other comment to make.",
"Thank you for your many insightful clarifications and expanding your experiments. I look forward to seeing more work in the future!",
"We would like to thank all reviewers for their helpful comments! We have now updated our sub... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
8,
8,
7
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
3,
3,
4
] | [
"S1gL7qwf07",
"rJgzB6LbAX",
"iclr_2019_B1xWcj0qYm",
"r1gZR2ahaQ",
"ryxIBivipQ",
"rJgzB6LbAX",
"rJgzB6LbAX",
"rJgzB6LbAX",
"rJgzB6LbAX",
"BkgahCMcpQ",
"iclr_2019_B1xWcj0qYm",
"rkeeoqWha7",
"rkeeoqWha7",
"rkeeoqWha7",
"rkeeoqWha7",
"H1xTSAEqnm",
"iclr_2019_B1xWcj0qYm",
"iclr_2019_B1x... |
iclr_2019_B1xY-hRctX | Neural Logic Machines | We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifier... | accepted-poster-papers |
pros:
- The paper presents an interesting forward chaining model which makes use of meta-level expansions and reductions on predicate arguments in a neat way to reduce complexity. As Reviewer 3 points out, there are a number of other papers from the neuro-symbolic community that learn relations (logic tensor networks... | train | [
"r1ee4DycyV",
"HJxOd0tDJE",
"S1e1Do49Cm",
"rklvumwmCQ",
"r1e_izwX0m",
"S1l4CCI66Q",
"rJxii0IT6Q",
"H1g19nL6Tm",
"rylWbydT3Q",
"rkgpGkN52Q",
"r1gMP1TKnQ"
] | [
"author",
"public",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thanks for your pointers to the related papers. We will discuss them in the next version of our paper.",
"... although it is not a differentiable model or even a neural model, the idea of learning to sort infinite arrays from short examples has been explored in the \"Generalized Planning\" literature, for exampl... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6,
7,
5
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
2,
5
] | [
"HJxOd0tDJE",
"iclr_2019_B1xY-hRctX",
"r1e_izwX0m",
"rkgpGkN52Q",
"rylWbydT3Q",
"rJxii0IT6Q",
"r1gMP1TKnQ",
"iclr_2019_B1xY-hRctX",
"iclr_2019_B1xY-hRctX",
"iclr_2019_B1xY-hRctX",
"iclr_2019_B1xY-hRctX"
] |
iclr_2019_B1xf9jAqFQ | Neural Speed Reading with Structural-Jump-LSTM | Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts ... | accepted-poster-papers | The authors obtain nice speed improvements by learning to skip and jump over input words when processing text with an LSTM. At some points the reviewers considered the work incremental since similar ideas have already been explored, but at the end two of the reviewers ended up endorsing the paper with strong support. | test | [
"H1liRmli2Q",
"BygoH5N51N",
"S1eAQNnPnm",
"rygL7FSlRQ",
"r1xklFrl07",
"S1gJqdreRm",
"SylYFwwu2m"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer"
] | [
"The paper proposes a Structural-Jump-LSTM model to speed up machine reading, which is an extension of the previous speed reading models, such as LSTM-Jump, Skim-LSTM and LSTM-Shuffle. The major difference, as claimed by the authors, is that the proposed model has two agents instead of one. One agent decides whethe... | [
7,
-1,
7,
-1,
-1,
-1,
5
] | [
5,
-1,
4,
-1,
-1,
-1,
4
] | [
"iclr_2019_B1xf9jAqFQ",
"S1gJqdreRm",
"iclr_2019_B1xf9jAqFQ",
"S1eAQNnPnm",
"SylYFwwu2m",
"H1liRmli2Q",
"iclr_2019_B1xf9jAqFQ"
] |
iclr_2019_B1xhQhRcK7 | Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures | This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent e... | accepted-poster-papers |
* Strengths
The paper addresses a timely topic, and reviewers generally agreed that the approach is reasonable and the experiments are convincing. Reviewers raised a number of specific concerns (which could be addressed in a revised version or future work), described below.
* Weaknesses
Some reviewers were concerne... | train | [
"r1ggQ1XjCX",
"BJex-sbiRm",
"H1l_8M3gRm",
"H1lea0SWpQ",
"Byxi9c4fpm",
"r1lgl9Iz6X",
"r1xLqdIM6X",
"BJxm6c4GaQ",
"BJl92-Odhm",
"B1lWFTj03X",
"H1guoXzK37",
"ByeinoLypQ"
] | [
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author"
] | [
"Thanks for clarifying your concerns.\n\nWe understand the high-level question raised here to be: “when should practitioners deploying a system in the real world test this system with the FPP rather than VMC”? In short, the answer is *always*. \n\nFirst, for risk estimation, by mixing the FPP and VMC estimates, we ... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6,
6,
6,
-1
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
3,
3,
-1
] | [
"BJex-sbiRm",
"r1xLqdIM6X",
"iclr_2019_B1xhQhRcK7",
"BJl92-Odhm",
"B1lWFTj03X",
"H1guoXzK37",
"H1guoXzK37",
"B1lWFTj03X",
"iclr_2019_B1xhQhRcK7",
"iclr_2019_B1xhQhRcK7",
"iclr_2019_B1xhQhRcK7",
"BJl92-Odhm"
] |
iclr_2019_BJG0voC9YQ | Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search | Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for ... | accepted-poster-papers | see my comment to the authors below | train | [
"Ske2pWsvg4",
"Skl3eOkblN",
"SklAR5DLpm",
"rJxsxWflC7",
"ryxd2ezlRQ",
"H1l_SlGxCX",
"H1xnN1GxAm",
"Bye_P5EZT7",
"B1lQbh_c37"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"We thank the area chair for pointing out the references, we will add them to our\nmanuscript. As stated in the response to the reviewers, we agree that our\nexperiments test our algorithm only in the idealized setting of known transition\nand reward kernels and unknown initial state. We will change the wording in ... | [
-1,
-1,
7,
-1,
-1,
-1,
-1,
7,
7
] | [
-1,
-1,
2,
-1,
-1,
-1,
-1,
3,
3
] | [
"Skl3eOkblN",
"iclr_2019_BJG0voC9YQ",
"iclr_2019_BJG0voC9YQ",
"B1lQbh_c37",
"Bye_P5EZT7",
"SklAR5DLpm",
"iclr_2019_BJG0voC9YQ",
"iclr_2019_BJG0voC9YQ",
"iclr_2019_BJG0voC9YQ"
] |
iclr_2019_BJe-DsC5Fm | signSGD via Zeroth-Order Oracle | In this paper, we design and analyze a new zeroth-order (ZO) stochastic optimization algorithm, ZO-signSGD, which enjoys dual advantages of gradient-free operations and signSGD. The latter requires only the sign information of gradient estimates but is able to achieve a comparable or even better convergence speed tha... | accepted-poster-papers | This is a solid paper that proposes and analyzes a sound approach to zero order optimization, covering a variants of a simple base algorithm. After resolving some issues during the response period, the reviewers concluded with a unanimous recommendation of acceptance. Some concerns regarding the necessity for such al... | train | [
"S1lxUJxU6X",
"Skemq-kZkV",
"rkxd0tneyN",
"HyxK1V69n7",
"ryex1VPEC7",
"r1xqTty5CQ",
"SJgTdFJc0Q",
"r1e7SFJcCQ",
"SJewbwwVC7",
"rJxHBMwE07",
"SJxAFZDECm",
"SklDel3Vam",
"Hyl5D-vj3X",
"r1xwxNwtnQ"
] | [
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"public",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for pointing out the concurrent ICLR submission, which focused on the first-order Byzantine setting. The authors agreed that the extra unimodal symmetric assumption can improve the theoretical convergence bound. And indeed we showed that in the zeroth-order setting, this conclusion holds (Corollary 2). M... | [
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
8,
6
] | [
-1,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
2
] | [
"SklDel3Vam",
"rkxd0tneyN",
"ryex1VPEC7",
"iclr_2019_BJe-DsC5Fm",
"HyxK1V69n7",
"SJgTdFJc0Q",
"r1e7SFJcCQ",
"ryex1VPEC7",
"r1xwxNwtnQ",
"Hyl5D-vj3X",
"iclr_2019_BJe-DsC5Fm",
"iclr_2019_BJe-DsC5Fm",
"iclr_2019_BJe-DsC5Fm",
"iclr_2019_BJe-DsC5Fm"
] |
iclr_2019_BJe0Gn0cY7 | Preventing Posterior Collapse with delta-VAEs | Due to the phenomenon of “posterior collapse,” current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires altering the training objective. We develop an alternative that utilizes the most powerful generative models as decoders, optimize the var... | accepted-poster-papers | Strengths: The proposed method is relatively principled. The paper also demonstrates a new ability: training VAEs with autoregressive decoders that have meaningful latents. The paper is clear and easy to read.
Weaknesses: I wasn't entirely convinced by the causal/anticausal formulation, and it's a bit unfortunate ... | train | [
"SyegyQ0LCm",
"S1eyafA807",
"BJxg9M0LCQ",
"BJgVLMCIAm",
"SJxpwu6A2m",
"Byl_786uhQ",
"r1ixv5dnX",
"HJlopG40cX",
"BJl4aRZTq7"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"We thank all the reviewers for their valuable feedback. All three reviewers agree that the paper is clear and well-written. R1 and R2 highlighted the convincing results of learning useful representations with autoregressive decoders and noted our extensive experiments. R3 was concerned about experiments demonstrat... | [
-1,
-1,
-1,
-1,
6,
7,
6,
-1,
-1
] | [
-1,
-1,
-1,
-1,
3,
4,
3,
-1,
-1
] | [
"iclr_2019_BJe0Gn0cY7",
"r1ixv5dnX",
"Byl_786uhQ",
"SJxpwu6A2m",
"iclr_2019_BJe0Gn0cY7",
"iclr_2019_BJe0Gn0cY7",
"iclr_2019_BJe0Gn0cY7",
"BJl4aRZTq7",
"iclr_2019_BJe0Gn0cY7"
] |
iclr_2019_BJe1E2R5KX | Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees | Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algo... | accepted-poster-papers | This paper proposes model-based reinforcement learning algorithms that have theoretical guarantees. These methods are shown to good results on Mujuco benchmark tasks. All of the reviewers have given a reasonable score to the paper, and the paper can be accepted. | train | [
"Hkx29sJxC7",
"rJxCLWG_Rm",
"rJgrRkSDTX",
"rygz4okeAQ",
"rJlACiJXa7",
"r1xWIcFhTm",
"H1g1w5th6X",
"r1gl8iJX6X",
"H1e69okXam",
"HJeBddsh37",
"SyxTcTgq3Q"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"We’ve added a paragraph below Theorem 3.1 and Appendix G, which contains a finite sample complexity results. We can obtain an approximate local maximum in $O(1/\\epsilon)$ iterations with sample complexity (in the number of trajectories) that is linear in the number of parameters and accuracy $\\epsilon$ and is lo... | [
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
6,
6
] | [
-1,
-1,
4,
-1,
-1,
-1,
-1,
-1,
-1,
4,
2
] | [
"SyxTcTgq3Q",
"H1g1w5th6X",
"iclr_2019_BJe1E2R5KX",
"iclr_2019_BJe1E2R5KX",
"HJeBddsh37",
"rJgrRkSDTX",
"rJgrRkSDTX",
"SyxTcTgq3Q",
"HJeBddsh37",
"iclr_2019_BJe1E2R5KX",
"iclr_2019_BJe1E2R5KX"
] |
iclr_2019_BJeOioA9Y7 | Knowledge Flow: Improve Upon Your Teachers | A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves ‘knowledge’ from mult... | accepted-poster-papers | The authors have taken inspiration from recent publications that demonstrate transfer learning over sequential RL tasks and have proposed a method that trains individual learners from experts using layerwise connections, gradually forcing the features to distill into the student with a hard-coded annealing of coeffieci... | train | [
"Bkl3Lcd80m",
"rklBpc_LC7",
"r1liuBG50X",
"r1lPW7iI07",
"BkgiFx9nnQ",
"BJlDEqbKA7",
"ByeCKHTOA7",
"SkgAysOIRm",
"rkgVy1xs2m",
"Byx-vLP5hQ"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Updated: Changed section numbers to fit latest revision.\n---------------------------------------------------------------------------\nWe thank the reviewer for time and feedback.\n\nRe 1: Use teachers with different architectures from the student. \nIn additional experiments, following the suggestion of the revie... | [
-1,
-1,
-1,
-1,
6,
-1,
-1,
-1,
8,
7
] | [
-1,
-1,
-1,
-1,
3,
-1,
-1,
-1,
5,
4
] | [
"Byx-vLP5hQ",
"rkgVy1xs2m",
"BJlDEqbKA7",
"SkgAysOIRm",
"iclr_2019_BJeOioA9Y7",
"rklBpc_LC7",
"r1lPW7iI07",
"BkgiFx9nnQ",
"iclr_2019_BJeOioA9Y7",
"iclr_2019_BJeOioA9Y7"
] |
iclr_2019_BJeWUs05KQ | Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information | The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. ... | accepted-poster-papers | This paper proposes an approach for imitation learning from unsegmented demonstrations. The paper addresses an important problem and is well-motivated. Many of the concerns about the experiments have been addressed with follow-up comments. We strongly encourage the authors to integrate the new results and additional li... | val | [
"S1gRVWWc0m",
"HkehIf6w0Q",
"rJxs7IUPAQ",
"rklVVJy5pX",
"SJx_MRAKaX",
"ryeTN6CYpm",
"H1l3BLbJpm",
"rJel6IwA3Q",
"Ske4Ltaws7"
] | [
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"For completeness, here is the table of results on the FetchPickandPlace-v1 environment with results of the VAE baseline included:\n\nDirected Info GAIL + L2 loss: Mean = -9.47, Std dev. = 4.84\nGAIL + L2 loss: Mean = -12. 05, Std dev. = 4.94\nDirected-Info GAIL: Mean = -11.74, Std dev. = 5.87\nGAIL: Mean = -13.29,... | [
-1,
-1,
-1,
-1,
-1,
-1,
6,
6,
8
] | [
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"rJxs7IUPAQ",
"iclr_2019_BJeWUs05KQ",
"SJx_MRAKaX",
"H1l3BLbJpm",
"rJel6IwA3Q",
"Ske4Ltaws7",
"iclr_2019_BJeWUs05KQ",
"iclr_2019_BJeWUs05KQ",
"iclr_2019_BJeWUs05KQ"
] |
iclr_2019_BJej72AqF7 | A Max-Affine Spline Perspective of Recurrent Neural Networks | We develop a framework for understanding and improving recurrent neural networks (RNNs) using max-affine spline operators (MASOs). We prove that RNNs using piecewise affine and convex nonlinearities can be written as a simple piecewise affine spline operator. The resulting representation provides several new perspectiv... | accepted-poster-papers | While the reformulation of RNNs is not practical as it is missing sigmoids and tanhs that are common in LSTMs it does provide an interesting analysis of traditional RNNs and a technique that's novel for many in the ICLR community.
| train | [
"SkePFUZVam",
"HyeMiQWEaQ",
"S1eTiBWVpm",
"B1eby2B5n7",
"B1e0FnM93X",
"r1e_41DDhQ"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We thank the reviewer for their careful reading and constructive suggestions. We agree that the MASO framework sheds new light on the inner workings of RNNs. We have made significant simplifications and revisions to the mathematical notation, particularly in Sections 1.1, 1.2, and 2, that should address most of yo... | [
-1,
-1,
-1,
6,
6,
6
] | [
-1,
-1,
-1,
3,
3,
3
] | [
"r1e_41DDhQ",
"B1eby2B5n7",
"B1e0FnM93X",
"iclr_2019_BJej72AqF7",
"iclr_2019_BJej72AqF7",
"iclr_2019_BJej72AqF7"
] |
iclr_2019_BJemQ209FQ | Learning to Navigate the Web | Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of... | accepted-poster-papers | All reviewers (including those with substantial expertise in RL) were solid in their praise for this paper that is also tackling an interesting application that is much less well studied but deserves attention.
| train | [
"SylnSTaghX",
"Bklaz-qtRm",
"SJeSplqYR7",
"HJlCjlqKAX",
"BJexNkcF0Q",
"ryejZrK9h7",
"HkxIVs6PsQ"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"UPDATE:\n\nThank you to the authors for a comprehensive response. I have increased my score based on these changes. I apologize for the misunderstanding about ArXiV papers and indeed the authors are correct on that point. Thank you as well for reporting the learning speeds. As you mentioned, they confirm our i... | [
8,
-1,
-1,
-1,
-1,
7,
7
] | [
3,
-1,
-1,
-1,
-1,
3,
3
] | [
"iclr_2019_BJemQ209FQ",
"HkxIVs6PsQ",
"HJlCjlqKAX",
"SylnSTaghX",
"ryejZrK9h7",
"iclr_2019_BJemQ209FQ",
"iclr_2019_BJemQ209FQ"
] |
iclr_2019_BJfIVjAcKm | Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability | We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models - weight sparsity a... | accepted-poster-papers | This paper introduced a concept called ReLU stability to motivate regularization and enable fast verification. Most of the analysis was presented empirically on two simple datasets and with low-performing models. I feel theoretical analysis and more comprehensive and realistic empirical studies would make the paper str... | train | [
"rkgwBQQKCQ",
"HJlXjAodT7",
"rkxpILquA7",
"Bke0ohlmoQ",
"SklV2h91A7",
"BJlht5PcaX",
"Hkgl-jv5p7",
"rkefO5yq67",
"H1e9xkx5TQ",
"ryl5Ywpd6Q",
"ByxrBih_aX",
"H1lQYCj_6Q",
"H1gHm0i_pm",
"Skxfe0sdpX",
"Skg1L6iuTm",
"SylfYwH5hX",
"rkeN2o7FhQ"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"public",
"public",
"author",
"public",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"We would like to thank all reviewers and commenters for their suggestions on improving the manuscript. We have revised our submission based on the feedback we received, and uploaded our revision.",
"We thank the reviewer for their helpful comments. We are glad you found the paper pleasant to read!\n\nWe agree th... | [
-1,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
8,
7
] | [
-1,
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
2,
3
] | [
"iclr_2019_BJfIVjAcKm",
"SylfYwH5hX",
"SklV2h91A7",
"iclr_2019_BJfIVjAcKm",
"Hkgl-jv5p7",
"rkefO5yq67",
"H1e9xkx5TQ",
"ryl5Ywpd6Q",
"rkefO5yq67",
"ByxrBih_aX",
"H1gHm0i_pm",
"rkeN2o7FhQ",
"Skxfe0sdpX",
"Skg1L6iuTm",
"Bke0ohlmoQ",
"iclr_2019_BJfIVjAcKm",
"iclr_2019_BJfIVjAcKm"
] |
iclr_2019_BJfOXnActQ | Learning to Learn with Conditional Class Dependencies | Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning. Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data... | accepted-poster-papers | The reviewers think that incorporating class conditional dependencies into the metric space of a few-shot learner is a sufficiently good idea to merit acceptance. The performance isn’t necessarily better than the state-of-the-art approaches like LEO, but it is nonetheless competitive. One reviewer suggests incorporatin... | train | [
"r1xVbbV9RQ",
"H1x2TeE90m",
"B1gPog4c0Q",
"Hyg7wl45AQ",
"HJxo10uFh7",
"ByxxsLKVn7",
"H1lyuO0foX"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for the very detailed and constructive comments.\n\n1. The motivation\n1.1 How the metric space is trained?\nThe metric space is trained in a pre-training step and it is not updated while training the base-learner. The embeddings obtained from the metric space is different from other popular pre-training... | [
-1,
-1,
-1,
-1,
6,
8,
4
] | [
-1,
-1,
-1,
-1,
3,
3,
5
] | [
"H1lyuO0foX",
"ByxxsLKVn7",
"HJxo10uFh7",
"iclr_2019_BJfOXnActQ",
"iclr_2019_BJfOXnActQ",
"iclr_2019_BJfOXnActQ",
"iclr_2019_BJfOXnActQ"
] |
iclr_2019_BJfYvo09Y7 | Hierarchical Visuomotor Control of Humanoids | We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, ... | accepted-poster-papers | A hierarchical method is presented for developing humanoid motion control,
using low-level control fragments, egocentric visual input, recurrent high-level control.
It is likely the first demonstration of 3D humanoids learning to do memory-enabled tasks using only
proprioceptive and head-based ego-centric vision. The u... | test | [
"r1gObOE-Am",
"HJg6uB8Pam",
"HkeENL21C7",
"S1xdTHWs6m",
"BketCjOKp7",
"HJxPPo_FTm",
"rJlfZ-ut6m",
"Sygyex8D67",
"SJxBek8vTX",
"r1lL-jzbTm",
"Hke4Tu33nX",
"BkgV2s7537",
"r1e-UMBoiQ",
"BkltQstIi7"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"public",
"author",
"author",
"public",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"The authors claim that this method improves upon the earlier work by substantially decreasing the amount of manual curation needed, however I still cannot see any real difference in the level of manual work required. This method as well as the earlier work (Peng et al. 2017 and Peng et al 2018) use existing pre-cl... | [
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
8,
6,
-1,
-1
] | [
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
4,
-1,
-1
] | [
"BketCjOKp7",
"iclr_2019_BJfYvo09Y7",
"iclr_2019_BJfYvo09Y7",
"iclr_2019_BJfYvo09Y7",
"HJxPPo_FTm",
"HJg6uB8Pam",
"r1e-UMBoiQ",
"BkgV2s7537",
"Hke4Tu33nX",
"Hke4Tu33nX",
"iclr_2019_BJfYvo09Y7",
"iclr_2019_BJfYvo09Y7",
"BkltQstIi7",
"iclr_2019_BJfYvo09Y7"
] |
iclr_2019_BJg4Z3RqF7 | Unsupervised Adversarial Image Reconstruction | We address the problem of recovering an underlying signal from lossy, inaccurate observations in an unsupervised setting. Typically, we consider situations where there is little to no background knowledge on the structure of the underlying signal, no access to signal-measurement pairs, nor even unpaired signal-measurem... | accepted-poster-papers | This paper proposes a GAN-based method to recover images from a noisy version of it. The paper builds upon existing works on AmbientGAN and CS-GAN. By combining the two approaches, the work finds a new method that performs better than existing approaches.
The paper clearly has new interesting ideas which have been exe... | test | [
"HkgUbQ_WgN",
"BJguc0OE0Q",
"BJlcvR_VRm",
"H1xoXRON0m",
"H1xo8T_4RQ",
"SklY001CnQ",
"BylSgJYp2Q",
"B1lziIvI3m"
] | [
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We have released the code used in this paper : https://github.com/UNIR-Anonymous/UNIR",
"Thank you for your feedback. We have taken note of your comments and have been actively working to take them into account.\nYou raised two main questions , one concerning the measurement process and the second one concerning... | [
-1,
-1,
-1,
-1,
-1,
6,
8,
4
] | [
-1,
-1,
-1,
-1,
-1,
3,
4,
3
] | [
"iclr_2019_BJg4Z3RqF7",
"SklY001CnQ",
"BylSgJYp2Q",
"B1lziIvI3m",
"iclr_2019_BJg4Z3RqF7",
"iclr_2019_BJg4Z3RqF7",
"iclr_2019_BJg4Z3RqF7",
"iclr_2019_BJg4Z3RqF7"
] |
iclr_2019_BJg9DoR9t7 | Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds | Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \emph{learning from crowds without knowing the information structure among the crowds a priori, when some people of the crowds make highly corr... | accepted-poster-papers | This paper proposes an interesting approach to leveraging crowd-sourced labels, along with an ML model learned from the data itself.
The reviewers were unanimous in their vote to accept. | train | [
"rygf8BMF37",
"BklLgntDhX",
"B1g5uv1N07",
"B1xLvvU0pQ",
"B1e5ewz9pQ",
"Syxqx4gdp7",
"SJg2zPgOpQ",
"SkgHhklOTQ",
"HylQebguTX",
"HygvOxIC3Q"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"Update after feedback: I would like to thank the authors for their detailed answers, it would be great to see some revisions in the paper also though (except new experimental results).\nEspecially thank you for providing details of a training procedure which I was missing in the initial draft. I hope to see them i... | [
6,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6
] | [
4,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"iclr_2019_BJg9DoR9t7",
"iclr_2019_BJg9DoR9t7",
"rygf8BMF37",
"B1e5ewz9pQ",
"SJg2zPgOpQ",
"rygf8BMF37",
"BklLgntDhX",
"iclr_2019_BJg9DoR9t7",
"HygvOxIC3Q",
"iclr_2019_BJg9DoR9t7"
] |
iclr_2019_BJgK6iA5KX | AutoLoss: Learning Discrete Schedule for Alternate Optimization | Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss,... | accepted-poster-papers | The paper suggests using meta-learning to tune the optimization schedule of alternative optimization problems. All of the reviewers agree that the paper is worthy of publication at ICLR. The authors have engaged with the reviewers and improved the paper since the submission. I asked the authors to address the rest of t... | train | [
"BJgG8g-Kx4",
"SkxBNIaEeV",
"Bke9KJljRX",
"B1lSm_S9AX",
"BkgdU-e16m",
"SJl_vpqYRQ",
"rkgn_XcxRm",
"ByxRxkQFpX",
"BJlq-BGFa7",
"rylM2EzFpX",
"ByeGCYgKTX",
"BklC6-auT7",
"rked778JaQ",
"HklV1vNp2m"
] | [
"author",
"public",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for pointing us to your work [1], which studies the similar topic concurrently with us. Both works focus on designing methods to introducing dynamicas into objectives/loss functions. Specifically, [1] tries to directly cast the objective function as a a learnable neural network (learned by measuring the ... | [
-1,
-1,
-1,
-1,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
7
] | [
-1,
-1,
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
3
] | [
"SkxBNIaEeV",
"iclr_2019_BJgK6iA5KX",
"SJl_vpqYRQ",
"rkgn_XcxRm",
"iclr_2019_BJgK6iA5KX",
"ByeGCYgKTX",
"rylM2EzFpX",
"iclr_2019_BJgK6iA5KX",
"rked778JaQ",
"rked778JaQ",
"BkgdU-e16m",
"HklV1vNp2m",
"iclr_2019_BJgK6iA5KX",
"iclr_2019_BJgK6iA5KX"
] |
iclr_2019_BJgLg3R9KQ | Learning what and where to attend | Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate th... | accepted-poster-papers | This paper presents a large-scale annotation of human-derived attention maps for ImageNet dataset. This annotation can be used for training more accurate and more interpretable attention models (deep neural networks) for object recognition. All reviewers and AC agree that this work is clearly of interest to ICLR and th... | train | [
"r1eVYyoh3Q",
"H1gDHhddR7",
"Sklu7PO_C7",
"Bke5rI__CX",
"ryg0WL__07",
"HyeS0BduCm",
"HJxxoO1ram",
"S1xOmfJSTQ",
"rylRhZkSam",
"S1gAO-ySTQ",
"S1gU756h3X",
"HygHbsciim"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"\nSUMMARY\n\nThis paper argues that most recent gains in visual recognition are due to the use of visual attention mechanisms in deep convolutional networks (DCNs). According to the authors; the networks learn where to focus through a weak form of supervision based on image class labels. This paper introduces a da... | [
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
6,
8
] | [
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
3
] | [
"iclr_2019_BJgLg3R9KQ",
"HyeS0BduCm",
"HygHbsciim",
"r1eVYyoh3Q",
"S1gU756h3X",
"iclr_2019_BJgLg3R9KQ",
"HygHbsciim",
"r1eVYyoh3Q",
"S1gU756h3X",
"iclr_2019_BJgLg3R9KQ",
"iclr_2019_BJgLg3R9KQ",
"iclr_2019_BJgLg3R9KQ"
] |
iclr_2019_BJgRDjR9tQ | ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS | Robust estimation under Huber's ϵ-contamination model has become an important topic in statistics and theoretical computer science. Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability. In this paper, we es... | accepted-poster-papers |
* Strengths
This paper presents a very interesting connection between GANs and robust estimation in the presence of corrupted training data. The conceptual ideas are novel and can likely be extended in many further directions. I would not be surprised if this opens up a new line of research.
* Weaknesses
The paper ... | test | [
"rkx90sfIJ4",
"rklmCB5rk4",
"rkef55Kc0Q",
"SyeWL0ScAm",
"B1xPE0H507",
"rkx0ascORX",
"SJgijaKR6Q",
"S1lJt6YR6X",
"ryeV9nKRpQ",
"ByluFd5mpm",
"rkxfaWqihQ",
"B1e-NIOq3Q"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you for the question. \n\nYes, the statement is a bit confusing. In the formulation $\\min_Q\\max_{\\tilde{Q}}$, notice that $\\min_Q$ is before $\\max_{\\tilde{Q}}$. Thus, the class that we maximize over $\\tilde{Q}$ is allowed to depend on $Q$. To be specific, for example in location estimation (Propositio... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
5,
7
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
5,
5
] | [
"rklmCB5rk4",
"iclr_2019_BJgRDjR9tQ",
"SJgijaKR6Q",
"rkx0ascORX",
"rkx0ascORX",
"S1lJt6YR6X",
"ByluFd5mpm",
"rkxfaWqihQ",
"B1e-NIOq3Q",
"iclr_2019_BJgRDjR9tQ",
"iclr_2019_BJgRDjR9tQ",
"iclr_2019_BJgRDjR9tQ"
] |
iclr_2019_BJg_roAcK7 | INVASE: Instance-wise Variable Selection using Neural Networks | The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems. While global feature selection has been a well-studied problem for quite some time, only recently has the paradigm of instance-wise feature s... | accepted-poster-papers | This manuscript proposes a new algorithm for instance-wise feature selection. To this end, the selection is achieved by combining three neural networks trained via an actor-critic methodology. The manuscript highlight that beyond prior work, this strategy enables the selection of a different number of features for each... | val | [
"SkgNuvW707",
"BkehMR6shm",
"Byl06-RnTQ",
"H1lgbw8K3X",
"S1xeFW7oT7",
"SylXL-XspQ",
"r1eVBWms6Q",
"Hke-ey7oTQ",
"rkxFiB2Tom"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"I would like to thank the authors for clarifying my concerns in details, especially for the first point. I think this is a straightforward idea that relaxes the need for a predefined k in L2X and has good performance. I have updated my score accordingly.",
"This paper proposes an instance-wise feature selection ... | [
-1,
6,
-1,
6,
-1,
-1,
-1,
-1,
6
] | [
-1,
3,
-1,
4,
-1,
-1,
-1,
-1,
3
] | [
"Hke-ey7oTQ",
"iclr_2019_BJg_roAcK7",
"r1eVBWms6Q",
"iclr_2019_BJg_roAcK7",
"rkxFiB2Tom",
"H1lgbw8K3X",
"H1lgbw8K3X",
"BkehMR6shm",
"iclr_2019_BJg_roAcK7"
] |
iclr_2019_BJgklhAcK7 | Meta-Learning with Latent Embedding Optimization | Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these ... | accepted-poster-papers | This work builds on MAML by (1) switching from a single underlying set of parameters to a distribution in a latent lower-dimensional space, and (2) conditioning the initial parameter of each subproblem on the input data.
All reviewers agree that the solid experimental results are impressive, with careful ablation studi... | train | [
"BJx4Oa-Be4",
"BJl60tXjJN",
"SkeNDEQoJN",
"H1gS9SOFk4",
"HJlZ5J93hm",
"rkgrGgvgRX",
"BygPCp8lRQ",
"H1ec3GPg0m",
"ryxI0Wwx0X",
"S1xuqePgAQ",
"SJler0LgAQ",
"ryeVsW1Ha7",
"r1gYWGRjnQ",
"ryeYsYpe2m",
"SyxZkTAacm",
"B1gQ7PdK5Q"
] | [
"public",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"public",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"Hi, \n\nMay I ask what happens if the relation net is removed? How much will it affect the performance?",
"Thanks for your constructive comments! We are happy to address any remaining concerns.",
"Thank you for helping us improve the paper! We are in the process of open-sourcing our code and embeddings.",
"T... | [
-1,
-1,
-1,
-1,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
5,
8,
-1,
-1
] | [
-1,
-1,
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
5,
-1,
-1
] | [
"rkgrGgvgRX",
"rkgrGgvgRX",
"H1gS9SOFk4",
"BygPCp8lRQ",
"iclr_2019_BJgklhAcK7",
"r1gYWGRjnQ",
"HJlZ5J93hm",
"iclr_2019_BJgklhAcK7",
"ryeYsYpe2m",
"ryeVsW1Ha7",
"BygPCp8lRQ",
"r1gYWGRjnQ",
"iclr_2019_BJgklhAcK7",
"iclr_2019_BJgklhAcK7",
"B1gQ7PdK5Q",
"iclr_2019_BJgklhAcK7"
] |
iclr_2019_BJgqqsAct7 | Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach | Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be ``compressed to much smaller representations. The purpose of this paper is to connect... | accepted-poster-papers | The paper combines PAC-Bayes bound with network compression to derive a generalization bound for large-scale neural nets such as ImageNet. The approach is novel and interesting and the paper is well-written. The authors provided detailed replies and improvements in response to reviewers questions, and all reviewers ag... | train | [
"HklR-CdX67",
"BygfLTdX67",
"BJl6K3uXa7",
"BklTXaUjnm",
"H1gYrD3Sn7",
"SkxzLGFojm",
"ryll1c-6oQ",
"HJxGoDyTjm"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer"
] | [
"Thank you for your careful reading and detailed questions and comments. .\n\n0. We have added a remark following Theorem 2.1 noting that this form is relatively complicated, explaining the reason we use it, and providing references to a unified treatment of the different PAC-Bayes bounds. In particular, Laviolett... | [
-1,
-1,
-1,
6,
6,
8,
-1,
-1
] | [
-1,
-1,
-1,
4,
5,
4,
-1,
-1
] | [
"BklTXaUjnm",
"SkxzLGFojm",
"H1gYrD3Sn7",
"iclr_2019_BJgqqsAct7",
"iclr_2019_BJgqqsAct7",
"iclr_2019_BJgqqsAct7",
"HJxGoDyTjm",
"iclr_2019_BJgqqsAct7"
] |
iclr_2019_BJl6AjC5F7 | Learning to Represent Edits | We introduce the problem of learning distributed representations of edits. By combining a
"neural editor" with an "edit encoder", our models learn to represent the salient
information of an edit and can be used to apply edits to new inputs.
We experiment on natural language and source code edit data. ... | accepted-poster-papers | This paper investigates learning to represent edit operations for two domains: text and source code. The primary contributions of the paper are in the specific task formulation and the new dataset (for source code edits). The technical novelty is relatively weak.
Pros:
The paper introduces a new dataset for source cod... | val | [
"B1l0SBlOJE",
"B1xPkXivJV",
"H1eeIsRo3Q",
"Sye616mI0m",
"HJgMTr4LAm",
"HkebczdFCm",
"HklnK9E8RX",
"rkeXF8NUCQ",
"H1gbD8dQ6Q",
"Hyg1fVtXaQ",
"rJeUUtum6X",
"r1g6GvdXp7",
"HylSf_0a27",
"HkeCSi493X"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Thanks for clarifying! We certainly agree that this needs to be stated as prominently as possible and we will make changes to state this more prominently and clearly in the next version of the paper.",
"Thank you for the updates!\n\nIn agreement with R3's concerns, I do think it's important to state (prominently... | [
-1,
-1,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
6
] | [
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
4
] | [
"B1xPkXivJV",
"rkeXF8NUCQ",
"iclr_2019_BJl6AjC5F7",
"iclr_2019_BJl6AjC5F7",
"rJeUUtum6X",
"HJgMTr4LAm",
"r1g6GvdXp7",
"Hyg1fVtXaQ",
"iclr_2019_BJl6AjC5F7",
"HkeCSi493X",
"H1eeIsRo3Q",
"HylSf_0a27",
"iclr_2019_BJl6AjC5F7",
"iclr_2019_BJl6AjC5F7"
] |
iclr_2019_BJl6TjRcY7 | Neural Probabilistic Motor Primitives for Humanoid Control | We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it i... | accepted-poster-papers | Strengths: One-shot physics-based imitation at a scale and with efficiency not seen before.
Clear video, paper, and related work.
Weaknesses described include: the description of a secondary contribution (LFPC)
takes up too much space (R1,4); results are not compelling (R1,4); prior art in graphics and robotics (R2... | train | [
"HyxwKQnF1N",
"ryevUebMRm",
"SygmMXpJAm",
"Sylz2k9o6Q",
"SJlewPYtpX",
"HJlsxPYYam",
"HkllTLYYaQ",
"HJxBuUYY6m",
"S1eS0BTpnX",
"S1gTWJZ6hX",
"HJeCcTtms7"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We are reaching the end of the discussion period.\nThere remain mixed opinions on the paper.\nAny further thoughts from R2 and R3? Stating pros + cons and summarizing any change in opinion would be very useful.\nThe main contribution is centred around one-shot imitation as well as reuse of low-level motor behavior... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
3,
6,
4
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7",
"HJeCcTtms7",
"S1gTWJZ6hX",
"S1eS0BTpnX",
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7",
"iclr_2019_BJl6TjRcY7"
] |
iclr_2019_BJlgNh0qKQ | Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder | Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embed... | accepted-poster-papers | This paper proposes a method for unsupervised learning that uses a latent variable generative model for semi-supervised dependency parsing. The key learning method consists of making perturbations to the logits going into a parsing algorithm, to make it possible to sample within the variational auto-encoder framework. ... | val | [
"r1gciHq_pQ",
"SJlN1H5up7",
"ByxR94q_p7",
"SylgfKWC27",
"HJlknmEq2X",
"Bkgmgia43Q",
"rJgRXtqO3Q",
"HJebIHYwh7"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer"
] | [
"Thank you for your comments and for finding the method novel and interesting.\n\nWe would like first to clarify that we are not making claiming that our method is appropriate in the high resource scenario (i.e. full in-domain English PTB parsing). However, large datasets are available only for a few languages, so... | [
-1,
-1,
-1,
8,
7,
5,
-1,
-1
] | [
-1,
-1,
-1,
4,
3,
3,
-1,
-1
] | [
"Bkgmgia43Q",
"HJlknmEq2X",
"SylgfKWC27",
"iclr_2019_BJlgNh0qKQ",
"iclr_2019_BJlgNh0qKQ",
"iclr_2019_BJlgNh0qKQ",
"HJebIHYwh7",
"iclr_2019_BJlgNh0qKQ"
] |
iclr_2019_BJluy2RcFm | Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs | We consider a simple and overarching representation for permutation-invariant functions of sequences (or set functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allow... | accepted-poster-papers | AR1 is concerned about whether higher-order interactions are modeled explicitly and if pi-SGD convergence conditions can be easily satisfied. AR2 is concerned that basic JP has been conceptually discussed in the literature and \pi-SGD is not novel because it was realized by Hamilton et al. (2017) and Moore & Neville (2... | train | [
"B1ey5ZDp2X",
"Skgp6BtKAm",
"BklEWNrmC7",
"SJxGsKnlA7",
"r1x2XvP107",
"B1gpO5L10X",
"B1eC2L8J0m",
"B1gYh_tiTm",
"HJlOsS79pm",
"HklWWHbbT7",
"S1xNEC4q2X"
] | [
"official_reviewer",
"author",
"author",
"public",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"In this paper, the authors presented a new pooling method called Janossy Pooling (JP), which is designed to better capture high-order information by addressing two limitations of existing works - fixed pooling function and fixed-size inputs. The studied problem is important and the motivation is clear, where the i... | [
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
8
] | [
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4
] | [
"iclr_2019_BJluy2RcFm",
"BklEWNrmC7",
"SJxGsKnlA7",
"iclr_2019_BJluy2RcFm",
"B1ey5ZDp2X",
"S1xNEC4q2X",
"HklWWHbbT7",
"HJlOsS79pm",
"S1xNEC4q2X",
"iclr_2019_BJluy2RcFm",
"iclr_2019_BJluy2RcFm"
] |
iclr_2019_BJlxm30cKm | An Empirical Study of Example Forgetting during Deep Neural Network Learning | Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ``forgetting event'' to have occ... | accepted-poster-papers | This paper is an analysis of the phenomenon of example forgetting in deep neural net training. The empirical study is the first of its kind and features convincing experiments with architectures that achieve near state-of-the-art results. It shows that a portion of the training set can be seen as support examples. The ... | test | [
"S1gAKB7QC7",
"rklya7Xm0Q",
"rkl2xZmXCQ",
"SyxkXUThiQ",
"SkgKResg0X",
"rygcHkH53m",
"S1lWe2ceRQ",
"ryeU8m5xRQ",
"Skx4CMsha7",
"BJgqy28cTQ",
"ryxipBQcpX",
"B1eemyXqp7"
] | [
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author"
] | [
"Thanks for your review and suggestions, your suggested additional experiments have strengthened the paper and we will acknowledge them in the paper, if accepted. Applying some of our results towards solving catastrophic forgetting is one of the promising directions we hope to investigate in the future. One of the ... | [
-1,
-1,
-1,
7,
-1,
8,
-1,
-1,
-1,
9,
-1,
-1
] | [
-1,
-1,
-1,
4,
-1,
4,
-1,
-1,
-1,
5,
-1,
-1
] | [
"SkgKResg0X",
"BJgqy28cTQ",
"S1lWe2ceRQ",
"iclr_2019_BJlxm30cKm",
"ryxipBQcpX",
"iclr_2019_BJlxm30cKm",
"ryeU8m5xRQ",
"Skx4CMsha7",
"B1eemyXqp7",
"iclr_2019_BJlxm30cKm",
"SyxkXUThiQ",
"rygcHkH53m"
] |
iclr_2019_BJx0sjC5FX | RNNs implicitly implement tensor-product representations | Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structure... | accepted-poster-papers | AR1 seeks the paper to be more standalone and easier to read. As this comment comes from the reviewer who is very experienced in tensor models, it is highly recommended that the authors make further efforts to make the paper easier to follow. AR2 is concerned about the manually crafted role schemes and alignment discr... | train | [
"H1gY4uvYJE",
"SklIQYXqCQ",
"HyxC1Km907",
"SkeR9OQ50X",
"rJl1E7lHTX",
"r1eg-zgrT7",
"rkgLvpJraX",
"S1x9a0e2hX",
"S1l7y8esh7",
"HJgWEWbPnQ"
] | [
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We have created an anonymized webpage with interactive demos to accompany this paper. The page can be found here:\nhttps://tpdn-iclr.github.io/tpdn-demo/tpr_demo.html",
"Thank you again for the suggestions. We have uploaded a new version of the paper that incorporates the changes discussed in our response.",
"... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
6,
6
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"iclr_2019_BJx0sjC5FX",
"rJl1E7lHTX",
"r1eg-zgrT7",
"rkgLvpJraX",
"HJgWEWbPnQ",
"S1l7y8esh7",
"S1x9a0e2hX",
"iclr_2019_BJx0sjC5FX",
"iclr_2019_BJx0sjC5FX",
"iclr_2019_BJx0sjC5FX"
] |
iclr_2019_BJxgz2R9t7 | Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach | Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circu... | accepted-poster-papers | This paper introduces a new graph neural network architecture designed to learn to solve Circuit SAT problems, a fundamental problem in computer science. The key innovation is the ability to to use the DAG structure as an input, as opposed to typical undirected (factor graph style) representations of SAT problems. The ... | train | [
"BkekmsMohm",
"HklvKK14aX",
"Skx8EY1Nam",
"rJgGgF1V6Q",
"SJg8puyN6m",
"HyeLzuJVTX",
"rJxGBgb527",
"HyevmThdhX"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"The paper proposes a graph neural network architecture that is designed to use the DAG structure in the input to learn to solve Circuit SAT problems. Unlike graph neural nets for undirected graphs, the proposed network propagates information according to the edge directions, using a deep sets representation to agg... | [
6,
-1,
-1,
-1,
-1,
-1,
8,
7
] | [
5,
-1,
-1,
-1,
-1,
-1,
4,
3
] | [
"iclr_2019_BJxgz2R9t7",
"HyevmThdhX",
"rJxGBgb527",
"BkekmsMohm",
"BkekmsMohm",
"iclr_2019_BJxgz2R9t7",
"iclr_2019_BJxgz2R9t7",
"iclr_2019_BJxgz2R9t7"
] |
iclr_2019_BJxh2j0qYm | Dynamic Channel Pruning: Feature Boosting and Suppression | Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and supp... | accepted-poster-papers | The authors propose a dynamic inference technique for accelerating neural network prediction with minimal accuracy loss. The method are simple and effective. The paper is clear and easy to follow. However, the real speedup on CPU/GPU is not demonstrated beyond the theoretical FLOPs reduction. Reviewers are also concern... | train | [
"BJlgEOGIl4",
"SJgBir11l4",
"H1g7fwG3JE",
"SkgSXSGny4",
"B1l4hUboJE",
"B1e6h0GUy4",
"Bkgib72i2X",
"rJxv5KuXRm",
"SJevI4GXRQ",
"Bkx2UnWm0m",
"rye68ibXCm",
"rklHZy28TX",
"S1euMaPJ67",
"SJlk4ADkaQ",
"B1eUMSd16X"
] | [
"author",
"official_reviewer",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"author",
"author",
"official_reviewer"
] | [
"We tested VGG-16 and ResNet-18 with FBS against their respective baselines, the experiments were repeated 1000 times and we recorded the average wall-clock time results for each model.\n\nThe VGG-16 baseline observed on average 520.80 ms for each inference. FBS was applied to VGG-16 and reduced the amount of comp... | [
-1,
-1,
-1,
-1,
-1,
-1,
6,
7,
-1,
-1,
-1,
6,
-1,
-1,
7
] | [
-1,
-1,
-1,
-1,
-1,
-1,
4,
5,
-1,
-1,
-1,
3,
-1,
-1,
4
] | [
"SJgBir11l4",
"SkgSXSGny4",
"rJxv5KuXRm",
"B1l4hUboJE",
"iclr_2019_BJxh2j0qYm",
"SJevI4GXRQ",
"iclr_2019_BJxh2j0qYm",
"iclr_2019_BJxh2j0qYm",
"Bkgib72i2X",
"B1eUMSd16X",
"rklHZy28TX",
"iclr_2019_BJxh2j0qYm",
"Bkgib72i2X",
"Bkgib72i2X",
"iclr_2019_BJxh2j0qYm"
] |
iclr_2019_BJxhijAcY7 | signSGD with Majority Vote is Communication Efficient and Fault Tolerant | Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine cou... | accepted-poster-papers | The Reviewers noticed that the paper undergone many editions and raise concern about the content. They encourage improving experimental section further and strengthening the message of the paper. | test | [
"SkgZHKcaJE",
"B1eUDnU037",
"HkgS6925hX",
"r1lMfYylTQ",
"r1lxIcJxpm",
"SJg-hh75C7",
"SJg5q27qAX",
"r1g4OhQqRQ",
"Byx4HczqC7",
"S1eaXqJgpX",
"SygCGdygTQ",
"BJezmXgjt7",
"r1eEYTEq2m"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"Dear AC and AnonReviewer1,\n\nThe reviewers’ scores show a consensus to accept. Still, AnonReviewer1 raises important points that we want to address here.\n\n1. QSGD precision. We agree, thanks for pointing it out. We are running experiments on 2 and 4bit QSGD and will add these to the paper.\n\n2. Bulyan. We disa... | [
-1,
6,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7
] | [
-1,
5,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"iclr_2019_BJxhijAcY7",
"iclr_2019_BJxhijAcY7",
"iclr_2019_BJxhijAcY7",
"B1eUDnU037",
"HkgS6925hX",
"r1eEYTEq2m",
"HkgS6925hX",
"B1eUDnU037",
"iclr_2019_BJxhijAcY7",
"r1eEYTEq2m",
"iclr_2019_BJxhijAcY7",
"iclr_2019_BJxhijAcY7",
"iclr_2019_BJxhijAcY7"
] |
iclr_2019_BJxssoA5KX | Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces | We introduce an approach to model surface properties governing bounces in everyday scenes. Our model learns end-to-end, starting from sensor inputs, to predict post-bounce trajectories and infer
two underlying physical properties that govern bouncing - restitution and effective collision normals. Our model, Boun... | accepted-poster-papers | This paper proposes a novel dataset of bouncing balls and a way to learn the dynamics of the balls when colliding. The reviewers found the paper well-written, tackling an interesting and hard problem in a novel way. The main concern (that I share with one of the reviewers) is about the fact that the paper proposes both... | train | [
"ryeBDwsMs7",
"SJloGnWRnQ",
"Hyl5ady1JV",
"rkgYYukJ1N",
"S1lJ8Ylo0Q",
"SJe2hHXj6Q",
"H1gfIEmspm",
"r1eXfEXiaX",
"ByekAQQo6Q",
"HkegBTQcnm"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"Paper summary:\nThe paper proposes to predict bouncing behavior from visual data. The model has two main components: (1) Physics Interface Module, which predicts the output trajectory from a given incoming trajectory and the physical properties of the contact surface. (2) Visual Interface Module, which predicts th... | [
8,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7
] | [
4,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"iclr_2019_BJxssoA5KX",
"iclr_2019_BJxssoA5KX",
"rkgYYukJ1N",
"S1lJ8Ylo0Q",
"r1eXfEXiaX",
"ryeBDwsMs7",
"HkegBTQcnm",
"ByekAQQo6Q",
"SJloGnWRnQ",
"iclr_2019_BJxssoA5KX"
] |
iclr_2019_BJxvEh0cFQ | K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning | We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks. The basic approach is to learn a model patch - a small set of parameters - that will specialize to each task, instead of fine-tuning the last layer or the entire network. For instance, we show that l... | accepted-poster-papers | Reviewers largely agree that the proposed method for finetuning the deep neural networks is interesting and empirical results clearly show the benefits over finetuning only the last layer. I recommend acceptance. | train | [
"S1eTI1PuAX",
"BJgEyp8gAQ",
"SJgssRVq3X",
"SJeD6ZMT6Q",
"B1lPdZz6a7",
"H1lFC1M6p7",
"BygmSOC2hm",
"S1e7BbGqj7"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"Thanks to the authors for their reply. I am satisfied with the current state of the paper and tend to keep my score.",
"Several changes have been made to my comments, thanks for pointing out the mistakes. ",
"This paper explored the means of tuning the neural network models using less parameters. The authors e... | [
-1,
-1,
6,
-1,
-1,
-1,
7,
8
] | [
-1,
-1,
3,
-1,
-1,
-1,
5,
4
] | [
"SJeD6ZMT6Q",
"B1lPdZz6a7",
"iclr_2019_BJxvEh0cFQ",
"S1e7BbGqj7",
"SJgssRVq3X",
"BygmSOC2hm",
"iclr_2019_BJxvEh0cFQ",
"iclr_2019_BJxvEh0cFQ"
] |
iclr_2019_BJzbG20cFQ | Towards Metamerism via Foveated Style Transfer | The problem of visual metamerism is defined as finding a family of perceptually
indistinguishable, yet physically different images. In this paper, we propose our
NeuroFovea metamer model, a foveated generative model that is based on a mixture
of peripheral representations and style transfer forward-pa... | accepted-poster-papers | 1. Describe the strengths of the paper. As pointed out by the reviewers and based on your expert opinion.
- The problem is well-motivated and related work is thoroughly discussed
- The evaluation is compelling and extensive.
2. Describe the weaknesses of the paper. As pointed out by the reviewers and based on your e... | train | [
"S1gispPmRX",
"r1e9ZnwmA7",
"ryx7JhwmRX",
"BygPHovmC7",
"HyeGxiD7C7",
"rJeH99PXA7",
"B1li_tDmAm",
"HJedMX8Na7",
"rJxupYl0hm",
"Byxt733Fhm"
] | [
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We’d like thank all reviewers for the feedback and assessment of our paper. We hope to have individually addressed all your concerns. We have uploaded a modified version of our paper where we have addresses such concerns, re-arranged figures, and fixed minor typos and corrections. These include:\n\nMoving Figure 1... | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
8,
7
] | [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
5
] | [
"iclr_2019_BJzbG20cFQ",
"ryx7JhwmRX",
"BygPHovmC7",
"Byxt733Fhm",
"rJeH99PXA7",
"rJxupYl0hm",
"HJedMX8Na7",
"iclr_2019_BJzbG20cFQ",
"iclr_2019_BJzbG20cFQ",
"iclr_2019_BJzbG20cFQ"
] |
iclr_2019_BkG5SjR5YQ | Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator | Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensiona... | accepted-poster-papers | The submission evaluates maximum mean discrepancy estimators for post selection inference.
It combines two contributions: (i) it proposes an incomplete u-statistic estimator for MMD, (ii) it evaluates this and existing estimators in a post selection inference setting.
The method extends the post selection inference ap... | train | [
"H1lpAZgWAQ",
"S1xJY1jeC7",
"HJeK_589a7",
"S1lGXvh8T7",
"ryeN2LnIaX",
"Syl46BhIpX",
"BkxV1bD02Q",
"HkgHB2j23X",
"SyghNyEchQ"
] | [
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"We really appreciate your feedback. We have already fixed typos. ",
"A few additional typos to fix:\n-Section 1: 2nd paragraph: 'i.e. higher order' -> 'i.e., higher order',\n-Section 2: 1st paragraph: 'larges score' -> 'largest score',\n-Section 3.3.: last paragraph: 'see theoretical analysis section' -> 'see Se... | [
-1,
-1,
-1,
-1,
-1,
-1,
6,
5,
8
] | [
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"S1xJY1jeC7",
"HJeK_589a7",
"S1lGXvh8T7",
"SyghNyEchQ",
"HkgHB2j23X",
"BkxV1bD02Q",
"iclr_2019_BkG5SjR5YQ",
"iclr_2019_BkG5SjR5YQ",
"iclr_2019_BkG5SjR5YQ"
] |
iclr_2019_BkG8sjR5Km | Emergent Coordination Through Competition | We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from rando... | accepted-poster-papers | The paper studies population-based training for MARL with co-play, in MuJoCo (continuous control) soccer. It shows that (long term) cooperative behaviors can emerge from simple rewards, shaped but not towards cooperation.
The paper is overall well written and includes a thorough study/ablation. The weaknesses are the ... | train | [
"rygaShhcn7",
"BJlGC9UhpX",
"HkgfkJPnaX",
"Sylf0sIn6X",
"BJl-oTGeaX",
"Skx1dt70hX"
] | [
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer"
] | [
"This paper introduces a new multiagent research environment---a simplified version of 2x2 RoboSoccer using the MuJoCo physics engine with spherical players that can rotate laterally, move forwards / backwards, and jump.\n\nThe paper deploys a fine-tuned version of population-based sampling on top of a stochastic v... | [
6,
-1,
-1,
-1,
7,
7
] | [
3,
-1,
-1,
-1,
3,
3
] | [
"iclr_2019_BkG8sjR5Km",
"BJl-oTGeaX",
"rygaShhcn7",
"Skx1dt70hX",
"iclr_2019_BkG8sjR5Km",
"iclr_2019_BkG8sjR5Km"
] |
iclr_2019_BkMiWhR5K7 | Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors | We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and demonstrate that the current state-of-the-art methods are optimal in a natural s... | accepted-poster-papers | This paper is on the problem of adversarial example generation in the setting where the predictor is only accessible via function evaluations with no gradients available. The associated problem can be cast as a blackbox optimization problem wherein finite difference and related gradient estimation techniques can be use... | train | [
"BJx1HlJJpQ",
"S1eCfrzsA7",
"H1gTyuSq0Q",
"BkglK_U5C7",
"SJg2WDBcR7",
"rkgBbCf90X",
"B1xIAu-qhm",
"BJg_v6iOCQ",
"SJeCGji_A7",
"SyglTvjdR7",
"rklvTWsO07",
"rke9oWZuam",
"rJgK3ebOpX",
"B1ead6lupQ",
"B1lDhBt52X"
] | [
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"This paper formulates the black-box adversarial attack as a gradient estimation\nproblem, and provide some theoretical analysis to show the optimality of an\nexisting gradient estimation method (Neural Evolution Strategies) for black-box\nattacks.\n\nThis paper also proposes two additional methods to reduce the nu... | [
7,
-1,
-1,
-1,
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
8
] | [
5,
-1,
-1,
-1,
-1,
-1,
3,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
2
] | [
"iclr_2019_BkMiWhR5K7",
"BkglK_U5C7",
"SJg2WDBcR7",
"SJg2WDBcR7",
"rkgBbCf90X",
"B1ead6lupQ",
"iclr_2019_BkMiWhR5K7",
"B1xIAu-qhm",
"B1lDhBt52X",
"BJx1HlJJpQ",
"iclr_2019_BkMiWhR5K7",
"B1xIAu-qhm",
"B1lDhBt52X",
"BJx1HlJJpQ",
"iclr_2019_BkMiWhR5K7"
] |
iclr_2019_BkN5UoAqF7 | Sample Efficient Imitation Learning for Continuous Control | The goal of imitation learning (IL) is to enable a learner to imitate expert behavior given expert demonstrations. Recently, generative adversarial imitation learning (GAIL) has shown significant progress on IL for complex continuous tasks. However, GAIL and its extensions require a large number of environment interact... | accepted-poster-papers | The paper proposes a simple method for improving the sample efficiency of GAIL, essentially a way of turning inverse reinforcement learning into classification. As reviewers noted, the method is based on a simple idea with potentially broad applicability.
Concerns were raised about the multiple components of the syste... | train | [
"rye5EKn1pm",
"B1lQQqme6X",
"BkgMjKRznX",
"Sklwhthhnm"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"This paper proposed an imitation learning algorithm that achieves competitive results with GAIL, while requiring significantly fewer interactions with the environment.\n\nI like the method proposed in this paper. It seems similar to ideas in this concurrent submission: https://openreview.net/forum?id=B1excoAqKQ\n\... | [
7,
5,
5,
5
] | [
5,
4,
5,
5
] | [
"iclr_2019_BkN5UoAqF7",
"iclr_2019_BkN5UoAqF7",
"iclr_2019_BkN5UoAqF7",
"iclr_2019_BkN5UoAqF7"
] |
iclr_2019_Bke4KsA5FX | Generative Code Modeling with Graphs | Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. Ou... | accepted-poster-papers | This paper presents an interesting method for code generation using a graph-based generative approach. Empirical evaluation shows that the method outperforms relevant baselines (PHOG).
There is consensus among reviewers that the methods are novel and is worth acceptance to ICLR. | train | [
"S1x0nNKgCm",
"rJgMmnfxC7",
"SyxeYOdu3Q",
"HJl5-iMxAX",
"Byen_drhpX",
"Ske3KQBnT7",
"rkxpzWG36m",
"H1eHUfG36m",
"HJeYe4p9p7",
"Skly0QT9pm",
"S1gsoQ69Tm",
"S1ggSfa567",
"S1gbGzT5aX",
"rygKSlpcp7",
"rkeutuqqnm",
"Hyl3dbvq2X",
"SJeXntTY37"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"author"
] | [
"The new figure 2 is indeed much clearer. Thanks!",
"Looking forward to revisions",
"The paper proposes a code completion task that given the rest of a program, predicts the content of an expression. This task has similarity to code completion tasks in the code editor of an IDE. The paper proposes an interestin... | [
-1,
-1,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
7,
-1
] | [
-1,
-1,
5,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
-1
] | [
"S1gbGzT5aX",
"HJl5-iMxAX",
"iclr_2019_Bke4KsA5FX",
"SyxeYOdu3Q",
"H1eHUfG36m",
"rkxpzWG36m",
"HJeYe4p9p7",
"Skly0QT9pm",
"SyxeYOdu3Q",
"SyxeYOdu3Q",
"rygKSlpcp7",
"Hyl3dbvq2X",
"rkeutuqqnm",
"iclr_2019_Bke4KsA5FX",
"iclr_2019_Bke4KsA5FX",
"iclr_2019_Bke4KsA5FX",
"iclr_2019_Bke4KsA5F... |
iclr_2019_BkeStsCcKQ | Critical Learning Periods in Deep Networks | Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill. The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network. Deficit... | accepted-poster-papers | Irrespective of their taste for comparisons of neural networks to biological organisms, all reviewers agree that the empirical observations in this paper are quite interesting and well presented. While some reviewers note that the paper is not making theoretical contributions, the empirical results in themselves are in... | train | [
"rJxHyW8rRX",
"rJlxyjwX07",
"Bkgba5DmCQ",
"H1ebz8D7Cm",
"ryguIWkf6Q",
"SJg8cOOka7",
"HJgH4ifFi7",
"SJlyF5rTnQ",
"BJeHWCM6hQ"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"public"
] | [
"We are thankful to the reviewer for their positive assessment of our paper. In fact, we share the same sentiment, as we articulate in the Conclusion, that one should resist the temptation to build too much on structural correspondences between such diverse systems. By showing these data we mostly wanted to emphasi... | [
-1,
-1,
-1,
-1,
9,
8,
6,
-1,
-1
] | [
-1,
-1,
-1,
-1,
4,
4,
5,
-1,
-1
] | [
"ryguIWkf6Q",
"Bkgba5DmCQ",
"SJg8cOOka7",
"HJgH4ifFi7",
"iclr_2019_BkeStsCcKQ",
"iclr_2019_BkeStsCcKQ",
"iclr_2019_BkeStsCcKQ",
"BJeHWCM6hQ",
"iclr_2019_BkeStsCcKQ"
] |
iclr_2019_BkeU5j0ctQ | CEM-RL: Combining evolutionary and gradient-based methods for policy search | Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstab... | accepted-poster-papers | This paper combines two different types of existing optimization methods, CEM/CMA-ES and DDPG/TD3, for policy optimization. The approach resembles ERL but demonstrates good better performance on a variety of continuous control benchmarks. Although I feel the novelty of the paper is limited, the provided promising resu... | test | [
"H1g8kU290X",
"Ske_YvI527",
"Ske3D7Jqh7",
"SJeaaoUYAX",
"rJgh-YoWAQ",
"SyefCujZRm",
"rkxM5djb0Q",
"r1lJmuibAQ",
"BJgACvi-CQ",
"Syev33W527"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer"
] | [
"The rebuttal provided by the authors is convincing.",
"The contributions of this paper are in the domain of policy search, where the authors combine evolutionary and gradient-based methods. Particularly, they propose a combination approach based on cross-entropy method (CEM) and TD3 as an alternative to existing... | [
-1,
6,
7,
-1,
-1,
-1,
-1,
-1,
-1,
7
] | [
-1,
3,
5,
-1,
-1,
-1,
-1,
-1,
-1,
4
] | [
"Ske_YvI527",
"iclr_2019_BkeU5j0ctQ",
"iclr_2019_BkeU5j0ctQ",
"SyefCujZRm",
"Ske3D7Jqh7",
"rkxM5djb0Q",
"Syev33W527",
"Ske_YvI527",
"iclr_2019_BkeU5j0ctQ",
"iclr_2019_BkeU5j0ctQ"
] |
iclr_2019_BkedznAqKQ | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | We propose Lanczos network (LanczosNet) which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution.
Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation o... | accepted-poster-papers | The reviewers unanimously agreed that the paper was a significant advance in the field of machine learning on graph-structured inputs. They commented particularly on the quality of the research idea, and its depth of development. The results shared by the researchers are compelling, and they also report optimal hyperpa... | train | [
"SJghoMfW0Q",
"SklJrmGZA7",
"BJeBk7f-0Q",
"r1eh_ffWAm",
"S1lEn5RRhQ",
"ryxJEZ4Rhm",
"r1llrOIv2Q"
] | [
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thanks for the comments! We have not tried Arnoldi algorithm since we only deal with undirected graphs in the current applications which have symmetric graph Laplacians. Unlike Lanczos algorithm which has error bounds and monotonic convergence properties, Arnoldi algorithm is not well understood since eigenvalues ... | [
-1,
-1,
-1,
-1,
7,
7,
8
] | [
-1,
-1,
-1,
-1,
3,
5,
4
] | [
"S1lEn5RRhQ",
"r1llrOIv2Q",
"ryxJEZ4Rhm",
"iclr_2019_BkedznAqKQ",
"iclr_2019_BkedznAqKQ",
"iclr_2019_BkedznAqKQ",
"iclr_2019_BkedznAqKQ"
] |
iclr_2019_BkfbpsAcF7 | Excessive Invariance Causes Adversarial Vulnerability | Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We sh... | accepted-poster-papers | This paper studies the roots of the existence of adversarial perspective from a new perspective. This perspective is quite interesting and thought-provoking. However, some of the contributions rely on fairly restrictive assumptions and/or are not properly evaluated.
Still, overall, this paper should be a valuable add... | val | [
"rkl8B7OVJ4",
"HklfNANAh7",
"ByeDB22aRQ",
"Bkeye2iT0X",
"r1e1SFU_2m",
"SyeLf8916Q",
"Byx23FoQaQ",
"ByeqLhs7TX",
"BklM_OiQ6m",
"HJeo0yhma7",
"B1xjee27aX"
] | [
"author",
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"author"
] | [
"We were glad to see your positive feedback.\n\nIndeed we agree some open questions (summarized below in point (II)) remain. Yet, we hope that our efforts to prove the underlying principles of our objective sparks future analysis how/when our optimality assumptions (discussed below in point (I)) can be achieved and... | [
-1,
6,
-1,
-1,
6,
7,
-1,
-1,
-1,
-1,
-1
] | [
-1,
4,
-1,
-1,
2,
4,
-1,
-1,
-1,
-1,
-1
] | [
"ByeDB22aRQ",
"iclr_2019_BkfbpsAcF7",
"Bkeye2iT0X",
"B1xjee27aX",
"iclr_2019_BkfbpsAcF7",
"iclr_2019_BkfbpsAcF7",
"iclr_2019_BkfbpsAcF7",
"SyeLf8916Q",
"r1e1SFU_2m",
"HklfNANAh7",
"HJeo0yhma7"
] |
iclr_2019_Bkg2viA5FQ | Hindsight policy gradients | A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to e... | accepted-poster-papers | The paper generalizes the concept of "hindsight", i.e. the recycling of data from trajectories in a goal-based system based on the goal state actually achieved, to policy gradient methods.
This was an interesting paper in that it scored quite highly despite all three reviewers mentioning incrementality or a relative l... | test | [
"Hyx3e4Pc3X",
"BJxW_kWc3Q",
"B1enK-g7Am",
"SkgYmj3gCQ",
"HklK6CgeCm",
"rygTDRlx0X",
"B1l7mFA06m",
"BJlHmDRC6Q",
"HJeizrR0pQ",
"HJxEna5ETQ",
"SyefN6lMcQ",
"BkxBfwLbcQ"
] | [
"official_reviewer",
"official_reviewer",
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"author",
"public"
] | [
"The authors present HPG, which applies the hindsight formulation already applied to off-policy RL algorithms (hindsight experience replay, HER, Andrychowicz et al., 2017) to policy gradients.\nBecause the idea is not new, and formulating HPG from PG is so straightforward (simply tie the dynamical model over goals)... | [
7,
7,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
7,
-1,
-1
] | [
4,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
4,
-1,
-1
] | [
"iclr_2019_Bkg2viA5FQ",
"iclr_2019_Bkg2viA5FQ",
"iclr_2019_Bkg2viA5FQ",
"HJeizrR0pQ",
"BJlHmDRC6Q",
"B1l7mFA06m",
"HJxEna5ETQ",
"Hyx3e4Pc3X",
"BJxW_kWc3Q",
"iclr_2019_Bkg2viA5FQ",
"BkxBfwLbcQ",
"iclr_2019_Bkg2viA5FQ"
] |
iclr_2019_Bkg3g2R9FX | Adaptive Gradient Methods with Dynamic Bound of Learning Rate | Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Rec... | accepted-poster-papers | The paper was found to be well-written and conveys interesting idea. However the AC notices a large body of clarifications that were provided to the reviewers (regarding the theory, experiments, and setting in general) that need to be well addressed in the paper. | train | [
"Bke-32cM1N",
"ryl12OxuAX",
"BylLNcbdAX",
"S1eizWWuCQ",
"BJgABOx_C7",
"S1lEtdgdAQ",
"SJef8P3thQ",
"BkeFPweF3m",
"rkg0-SM-3m",
"rkg7oagJn7",
"Skx8lvomim",
"H1glD5ZAcm",
"Byg-RFWA5X",
"Hklkd3A25X",
"SklPiVVncm",
"r1lJqtvjcm",
"HkxJNxD55X",
"S1goTjL5qX"
] | [
"official_reviewer",
"author",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"public",
"author",
"author",
"public",
"author",
"public",
"author",
"public"
] | [
"I thank the reviewers for their response, and I keep my score.",
"\n[About details and extra experiments you asked for]\n\n>>> Am I correct in saying that with t=100 (i.e., the 100th iteration), the \\eta s constrain the learning rates to be in a tight bound around 0.1? If beta=0.9, then \\eta_l(1) = 0.1 - 0.1 /... | [
-1,
-1,
-1,
-1,
-1,
-1,
7,
4,
6,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
-1,
-1,
-1,
-1,
-1,
-1,
4,
5,
4,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"BylLNcbdAX",
"S1lEtdgdAQ",
"rkg0-SM-3m",
"SJef8P3thQ",
"BkeFPweF3m",
"BJgABOx_C7",
"iclr_2019_Bkg3g2R9FX",
"iclr_2019_Bkg3g2R9FX",
"iclr_2019_Bkg3g2R9FX",
"Skx8lvomim",
"iclr_2019_Bkg3g2R9FX",
"Byg-RFWA5X",
"Hklkd3A25X",
"iclr_2019_Bkg3g2R9FX",
"r1lJqtvjcm",
"iclr_2019_Bkg3g2R9FX",
... |
iclr_2019_Bkg6RiCqY7 | Decoupled Weight Decay Regularization | L2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L2 regularization (oft... | accepted-poster-papers | Evaluating this paper is somewhat awkward because it has already been through multiple reviewing cycles, and in the meantime, the trick has already become widely adopted and inspired interesting follow-up work. Much of the paper is devoted to reviewing this follow-up work. I think it's clearly time for this to be made ... | train | [
"Bkx6qDs50m",
"rJxk4OZ5AQ",
"HJlCOfb90X",
"HylQ0bbqR7",
"rJl_LZZcA7",
"B1xxyZZqCm",
"rkeDkABcnm",
"rJlYWZMYhm",
"rkgKJ4AXhX"
] | [
"author",
"official_reviewer",
"author",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Thank you very much for your positive evaluation! We have fixed the typo and updated the paper. ",
"1) This completely clears up my concern.\n\n2) It seems that we largely share the same opinion here. After some more reflection, I think that this proposition does bring some good to the paper by attempting to for... | [
-1,
-1,
-1,
-1,
-1,
-1,
6,
7,
5
] | [
-1,
-1,
-1,
-1,
-1,
-1,
4,
4,
4
] | [
"rJxk4OZ5AQ",
"rJl_LZZcA7",
"iclr_2019_Bkg6RiCqY7",
"rkgKJ4AXhX",
"rJlYWZMYhm",
"rkeDkABcnm",
"iclr_2019_Bkg6RiCqY7",
"iclr_2019_Bkg6RiCqY7",
"iclr_2019_Bkg6RiCqY7"
] |
iclr_2019_Bkg8jjC9KQ | Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile | Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards effici... | accepted-poster-papers | This paper investigates the usage of the extragradient step for solving saddle-point problems with non-monotone stochastic variational inequalities, motivated by GANs. The authors propose an assumption weaker/diffrerent than the pseudo-monotonicity of the variational inequality for their convergence analysis (that they... | test | [
"r1x7Pw_4yE",
"HJxro3I4kV",
"rkxGKGDY6m",
"r1lVGZvtTm",
"SkxF31vKpm",
"HkxnI7tn3Q",
"H1xUfVr9hX",
"SyeTm7oIhm"
] | [
"author",
"official_reviewer",
"author",
"author",
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Many thanks for the extra round of feedback and the encouraging remarks! We reply to the points you raised below:\n\n1. Regarding the example of a coherent problem with a general convex solution set.\n\nAgain, for simplicity, focus on the optimization case, i.e., the minimization of a function f:X->R (X convex). I... | [
-1,
-1,
-1,
-1,
-1,
7,
6,
5
] | [
-1,
-1,
-1,
-1,
-1,
3,
5,
5
] | [
"HJxro3I4kV",
"r1lVGZvtTm",
"SyeTm7oIhm",
"H1xUfVr9hX",
"HkxnI7tn3Q",
"iclr_2019_Bkg8jjC9KQ",
"iclr_2019_Bkg8jjC9KQ",
"iclr_2019_Bkg8jjC9KQ"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.