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
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iclr_2018_SyhcXjy0Z | APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREVENT ATM FRAUD BY FACIAL DISGUISE IDENTIFICATION | The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem. All ... | rejected-papers | Reviewers are unanimous that this is a reject.
A "class project" level presentation.
Errors in methodology and presentation.
No author rebuttal or revision | train | [
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"The paper is relatively clear to follow, and implement. \n\nThe main concern is that this looks like a class project rather than a scientific paper. For a class project this could get an A in a ML class!\n\nIn particular, the authors take an already existing dataset, design a trivial convolutional neural network, ... | [
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iclr_2018_HkGcX--0- | Auxiliary Guided Autoregressive Variational Autoencoders | Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of b... | rejected-papers | To ensure that a VAE with a powerful autoregressive decoder does not ignore its latent variables, the authors propose adding an extra term to the ELBO, corresponding to a reconstruction with an auxiliary non-autoregressive decoder. This does indeed produce models that use latent variables and (with some tuning of the w... | test | [
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"My main problem is still that it's not clear what this model has to offer. The model is neither able to improve density estimation over PixelCNNs (while adding complexity), nor has it been shown to learn better representations (none of the evaluations seem appropriate to evaluate representations). Nevertheless, I ... | [
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iclr_2018_SkxqZngC- | A Bayesian Nonparametric Topic Model with Variational Auto-Encoders | Topic modeling of text documents is one of the most important tasks in representation learning. In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders. On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number ... | rejected-papers | The paper proposes a BNP topic model that uses a stick-breaking prior over document topics and performs VAE-style inference over them. Unfortunately, the novelty of this work is limited, as VAE-like inference for LDA-like models, inference with stick-breaking priors for VAEs, and placing a prior on the concentration pa... | train | [
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"\"topic modeling of text documents one of most important tasks\"\nDoes this claim have any backing?\n\n\"inference of HDP is more complicated and not easy to be applied to new models\" Really an artifact of the misguided nature of earlier work. The posterior for the $\\vec\\pi$ of a elements of DP or HDP can be m... | [
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iclr_2018_SJSVuReCZ | SHADE: SHAnnon DEcay Information-Based Regularization for Deep Learning | Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The originality of the approach is to define a prior based on conditional entropy, which explicitly decouples the learning of invariant represent... | rejected-papers | The proposed conditional variance regularizer looks interesting and the results show some promise. However, as the reviewers pointed out, the connection between the information-theoretic argument provided and the final form of the regularizer is too tenuous in its current form. Since this argument is central to the pap... | train | [
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"The authors propose a particular variance regularizer on activations and connect it to the conditional entropy of the activation given the class label. They also present some competitive results on CIFAR-10 and ImageNet.\n\nDespite some promising results, I found some issues with the paper. The main one is that th... | [
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iclr_2018_SJ3dBGZ0Z | LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures | Log-linear models models are widely used in machine learning, and in particular are ubiquitous in deep learning architectures in the form of the softmax. While exact inference and learning of these requires linear time, it can be done approximately in sub-linear time with strong concentrations guarantees. In this work,... | rejected-papers | The authors propose an efficient LSH-based method for computing unbiased gradients for softmax layers, building on (Mussmann et al. 2017). Given the somewhat incremental nature of the method, a thorough experimental evaluation is essential to demonstrating its value. The reviewers however found the experimental section... | train | [
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"The paper proposes to use LSH to approximate softmax, which greatly speeds up classification with large output space. The paper is overall well-written. However, similar ideas have been proposed before, such as \"Deep networks with large output spaces\" by Vijayanarasimhan et. al. (ICLR 2015). And this manuscript ... | [
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iclr_2018_BJRxfZbAW | The Context-Aware Learner | One important aspect of generalization in machine learning involves reasoning about previously seen data in new settings. Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario. To this end, we introduce the context-... | rejected-papers | The paper proposes augmenting Neural Statistician with a meta-context variable that specifies the partitioning of the latent context into the per-dataset and per-datapoint dimensions. This idea makes a lot of sense but the reviewers found the experimental section clearly insufficient to demonstrate its effectiveness co... | val | [
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"The authors propose an extension to the Neural Statistician which can model contexts with multiple partially overlapping features. This model can explain datasets by taking into account covariate structure needed to explain away factors of variation and it can also share this structure partially between datasets.\... | [
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iclr_2018_HkPCrEZ0Z | Combining Model-based and Model-free RL via Multi-step Control Variates | Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance. Model-based RL algorithms are sample-efficient on the other hand, while learning accurate global models of complex dynamic ... | rejected-papers | The paper has some potentially interesting ideas but it feels very preliminary. The experimental section in particular needs a lot more work. | train | [
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"The paper studies a combination of model-based and model-free RL. The idea is to train a forward predictive model which provides multi-step estimates to facilitate model-free policy learning. Some parts of the paper lack clarity and the empirical results need improvement to support the claims (see details below).... | [
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iclr_2018_SkZ-BnyCW | Learning Deep Generative Models With Discrete Latent Variables | There have been numerous recent advancements on learning deep generative models with latent variables thanks to the reparameterization trick that allows to train deep directed models effectively. However, since reparameterization trick only works on continuous variables, deep generative models with discrete latent var... | rejected-papers | The reviewers agreed that while this is a well-written paper, it is low on novelty and does not make a substantial enough contribution. They also pointed out that although the reported MNIST results are highly competitive, possibly due to the use of a powerful ResNet decoder, the CIFAR10/ImageNet results are underwhelm... | train | [
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"Summary of the paper:\nThe paper proposes to augment a variational auto encoder (VAE) with an binary restricted Boltzmann machine (RBM) in the role of the prior of the generative model. To yield a good initialisation of the parameters of the RBM and the inference network a special pertaining procedure is introduce... | [
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iclr_2018_HkbmWqxCZ | The Mutual Autoencoder: Controlling Information in Latent Code Representations | Variational autoencoders (VAE) learn probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. Beyond providing a good density model a VAE model assigns to each data instance a latent code. In many applications, this latent code provides a useful high-level summary of t... | rejected-papers | This is a well-written paper that aims to address an important problem. However, all the reviewers agreed that the experimental section is currently too weak for publication. They also made several good suggestions about improving the paper and the authors are encouraged to incorporate them before resubmitting. | train | [
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iclr_2018_ryb83alCZ | Towards Unsupervised Classification with Deep Generative Models | Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequatel... | rejected-papers | The authors propose a hierarchical VAE model with a discrete latent variable in the top-most layer for unsupervised learning of discriminative representations. While the reported results on the two flow cytometry datasets are encouraging, they are insufficient to draw strong conclusions about the general effectiveness... | train | [
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"This paper addresses the question of unsupervised clustering with high classification performance. They propose a deep variational autoencoder architecture with categorical latent variables at the deepest layer and propose to train it with modifications of the standard variational approach with reparameterization ... | [
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iclr_2018_SkERSm-0- | Preliminary theoretical troubleshooting in Variational Autoencoder | What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE? This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modelin... | rejected-papers | The reviewers agreed that the paper was too long (more than twice the recommended page limit not counting the appendix) and difficult to follow. They also pointed out that its central idea of learning the noise distribution in a VAE was not novel. While the shortened version uploaded by the authors looks like a step in... | train | [
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"This paper studies the importance of the noise modelling in Gaussian VAE. The original Gaussian VAE proposes to use the inference network for the noise that takes latent variables as inputs and outputs the variances, but most of the existing works on Gaussian VAE just use fixed noise probably because the inference... | [
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iclr_2018_r1kj4ACp- | Understanding Deep Learning Generalization by Maximum Entropy | Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent attention yet remains not fully explored. This paper attempts to provide an alternative understanding from the perspective of maximum entrop... | rejected-papers | The reviewers are in agreement, that the paper is a big hard to follow and incorrect in places, including some claims not supported by experiments. | train | [
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"Summary:\n\nThis paper presents a derivation which links a DNN to recursive application of\nmaximum entropy model fitting. The mathematical notation is unclear, and in\none cases the lemmas are circular (i.e. two lemmas each assume the other is\ncorrect for their proof). Additionally the main theorem requires comp... | [
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iclr_2018_B1X4DWWRb | Learning Weighted Representations for Generalization Across Designs | Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to machine learning applications. One example is the estimation of treatment effects from observational data, where a subtask is to predict the effect of a treatment on subjects that are systematically different ... | rejected-papers | The submission provides an interesting way to tackle the so-called distributional shift problem in machine learning. One familiar example is unsupervised domain adaptation. The main contribution of this work is deriving a bound on the generalization error/risk for a target domain as a combo of re-weighted empirical ris... | train | [
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"The paper proposes a novel way of causal inference in situations where in causal SEM notation the outcome Y = f(T,X) is a function of a treatment T and covariates X. The goal is to infer the treatment effect E(Y|T=1,X=x) - E(Y|T=0,X=x) for binary treatments at every location x. If the treatment effect can be learn... | [
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iclr_2018_HJ4IhxZAb | Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning | Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoret... | rejected-papers | In general, this seems like a sensible idea, but in my opinion the empirical results do not show a very compelling margin between using *entropy* as an active learning selection criterion vs the proposed methods. The difference is small enough that in practice it is very hard for me to believe that many researchers wou... | train | [
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"Sorry about the confusion, this was our oversight. We will correct the inaccurate sentence. \n\nWe also agree T-LSA is relevant for comparison, and we are running the experiment now and will add it to the final version. To contrast them explicitly, we expect MAP-GAL to perform better: (i) Due to non-myopic RL lear... | [
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iclr_2018_SktLlGbRZ | CyCADA: Cycle-Consistent Adversarial Domain Adaptation | Domain adaptation is critical for success in new, unseen environments.
Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.
Recent work has shown that generativ... | rejected-papers | I concur with two of the reviewers: the work is somewhat incremental in terms of technical novelty (it's effectively CycleGANs for domain adaptation with a couple of effective tricks) and the need/advantage of the cycle consistency loss is not demonstrated sufficiently. The only solid ablation evidence seems to the the... | train | [
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"This paper proposed a domain adaptation approach by extending the CycleGAN with 1) task specific loss functions and 2) loss imposed over both pixels and features. Experiments on digit recognition and semantic segmentation verify the effectiveness of the proposed method.\n\nStrengths:\n+ It is a natural and intuiti... | [
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iclr_2018_SyhRVm-Rb | Automatic Goal Generation for Reinforcement Learning Agents | Reinforcement learning (RL) is a powerful technique to train an agent to perform a task. However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set... | rejected-papers | In principle, the idea behind the submission is sound: use a generative model (GANs in this case) to learn to generate desirable "goals" (subsets of the state space) and use that instead of uniform sampling for goals. Overall I tend to agree with Reviewer 3 in that the current set of results is not convincing in terms ... | val | [
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iclr_2018_ryj0790hb | Incremental Learning through Deep Adaptation | Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added task, typically as... | rejected-papers | This work tackles an important problem of incremental learning and does so with extensive experimentation. As pointed out by two reviewers, the idea does seem novel and interesting, but the submission would require some rewriting before being potentially accepted at a venue like ICLR. I suggest focusing the paper more ... | train | [
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"This paper proposes to adapt convnet representations to new tasks while avoiding catastrophic forgetting by learning a per-task “controller” specifying weightings of the convolution-al filters throughout the network while keeping the filters themselves fixed.\n\n\nPros\n\nThe proposed approach is novel and broadly... | [
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iclr_2018_r1DPFCyA- | Discriminative k-shot learning using probabilistic models | This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural ne... | rejected-papers | This submission presents intriguingly good results on k-shot learning and I agree with the authors that the results are better than the presented previous work, and that the method is simple, so I took a deeper look into the paper despite the overall negative reviews. However, I think in its current form, the paper is ... | train | [
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"This paper presents a procedure to efficiently do K-shot learning in a classification setting by creating informative priors from information learned from a large, fully labeled dataset. Image features are learned using a standard convolutional neural network---the last layer form image features, while the last s... | [
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iclr_2018_rkeZRGbRW | Variance Regularizing Adversarial Learning | We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per updat... | rejected-papers | The reviewers found a number of short-comings in this work that would prevent it from being accepted at ICLR in its current form, both in terms of writing (not specifying the loss function), experiments that are too limited, and inconclusive comparisons with existing regularization techniques. I recommend the authors ... | train | [
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"This paper studies how the variance of the discriminator affect the gradient signal provided to the generator and therefore how it might limit its ability to learn the true data distribution.\n\nThe approach suggested in this paper models the output of the discriminator using a mixture of two Gaussians (one for “f... | [
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iclr_2018_H1BO9M-0Z | Lifelong Word Embedding via Meta-Learning | Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks. On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks. On the other hand, ma... | rejected-papers | While the problem of learning word embeddings for a new domain is important, the proposed method was found to be unclearly presented and missing a number of important baselines. The reviewers found the technical contribution to be of only limited value. | val | [
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"This paper presents a lifelong learning method for learning word embeddings. Given a new domain of interest, the method leverages previously seen domains in order to hopefully generate better embeddings compared to ones computed over just the new domain, or standard pre-trained embeddings.\n\nThe general problem ... | [
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iclr_2018_BJB7fkWR- | Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games | Many deep reinforcement learning approaches use graphical state representations,
this means visually distinct games that share the same underlying structure cannot
effectively share knowledge. This paper outlines a new approach for learning
underlying game state embeddings irrespective of the visual r... | rejected-papers | The reviewers have found that while the task of visual domain adaptation is meaningful to explore and improve, the proposed method is not sufficiently well-motivated, explained or empirically tested. | train | [
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"In this paper, the authors propose a new approach for learning underlying structure of visually distinct games.\n\nThe proposed approach combines convolutional layers for processing input images, Asynchronous Advantage Actor Critic for deep reinforcement learning task and adversarial approach to force the embeddin... | [
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iclr_2018_B1suU-bAW | Learning Covariate-Specific Embeddings with Tensor Decompositions | Word embedding is a useful approach to capture co-occurrence structures in a large corpus of text. In addition to the text data itself, we often have additional covariates associated with individual documents in the corpus---e.g. the demographic of the author, time and venue of publication, etc.---and we would like the... | rejected-papers | The reviewers agree that this paper provides a sensible mechanism for producing word embeddings that exploit correlating features in the data (e.g. texts written by the same author), but point to other work doing the same thing. The lack of direct comparison in the experimental section is troublesome, although it is en... | train | [
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"This paper produces word embedding tensors where the third order gives covariate information, via venue or author. The model is simple: tensor factorization, where the covariate can be viewed as warping the cosine distance to favor that covariate's more commonly cooccuring vocabulary (e.g. trump on hillary and cro... | [
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iclr_2018_S16FPMgRZ | Tensor Contraction & Regression Networks | Convolution neural networks typically consist of many convolutional layers followed by several fully-connected layers. While convolutional layers map between high-order activation tensors, the fully-connected layers operate on flattened activation vectors. Despite its success, this approach has notable drawbacks. Fla... | rejected-papers | This paper proposes methods for replacing parts of neural networks with tensors, the values of which are efficiently estimated through factorisation methods. The paper is well written and clear, but the two main objections from reviewers surround the novelty and evaluation of the method proposed. I am conscious that th... | test | [
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"In this paper, new layer architectures of neural networks using a low-rank representation of tensors are proposed. The main idea is assuming Tucker-type low-rank assumption for both a weight and an input. The performance is evaluated with toy data and Imagenet.\n\n[Clarity]\nThe paper is well written and easy to f... | [
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iclr_2018_rkGZuJb0b | Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz | The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on... | rejected-papers | This paper proposes a tree-structured tensor factorisation method for parameter reduction. The reviewers felt the paper was somewhat interesting, but agreed that more detail was needed in the method description, and that the experiments were on the whole uninformative. This seems like a promising research direction whi... | train | [
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"In the paper the authors suggest to use MERA tensorization technique for compressing neural networks. MERA itseld in a known framework in QM but not in ML. Although the idea seems to be fruitful and interesting I find the paper quite unclear. The most important part is section 2 which presents the methodology used... | [
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iclr_2018_B1bgpzZAZ | ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions | The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given \{\textit{passage, question}\} pair and select one of the n given options. The current state of the art model for this task first computes a query-aware representation for the passage and then \textit{selects... | rejected-papers | This paper provides a method for eliminating options in multiple-answer reading comprehension tasks, based on the contents of the text, in order to reduce the "answer space" a machine reading model must consider. While there's nothing wrong with this, conceptually, reviewers have questioned whether or not this is a par... | train | [
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"This paper gives an elaboration on the Gated Attention Reader (GAR) adding gates based on answer elimination in multiple choice reading comprehension. I found the formal presentation of the model reasonably clear the the empirical evaluation reasonably compelling.\n\nIn my opinion the main weakness of the paper i... | [
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iclr_2018_ryF-cQ6T- | Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures | The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning. Previous results used one-dime... | rejected-papers | This paper seeks to integrate tensor-based models from physics into machine learning architectures. The two main objections to this paper are first that, despite honest (I assume) efforts from the authors, it remains somewhat hard to understand without substantial background knowledge of physics. Second, that the exper... | train | [
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"Authors of this paper derived an efficient quantum-inspired learning algorithm based on a hierarchical representation that is known as tree tensor network, which is inspired by the multipartite entanglement renormalization ansatz approach where the tensors in the TN are kept to be unitary during training. Some obs... | [
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iclr_2018_HyHmGyZCZ | Comparison of Paragram and GloVe Results for Similarity Benchmarks | Distributional Semantics Models(DSM) derive word space from linguistic items
in context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. This
work concentrates on quality of word embeddings, improvement of wor... | rejected-papers | This paper proposes a method for refining distributional semantic representation at the lexical level. The reviews are fairly unanimous in that they found both the initial version of the paper, which was deemed quite rushed, and the substantial revision unworthy of publication in their current state. The weakness of bo... | train | [
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"The paper suggests taking GloVe word vectors, adjust them, and then use a non-Euclidean similarity function between them. The idea is tested on very small data sets (80 and 50 examples, respectively). The proposed techniques are a combination of previously published steps, and the new algorithm fails to reach stat... | [
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iclr_2018_SJlhPMWAW | GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders | Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sid... | rejected-papers | The authors present GraphVAE, a method for fitting a generative deep model, a variational autoencoder, to small graphs. Fitting deep learning models to graphs remains challenging (although there is relevant literature as brought up by the reviewers and anonymous comments) and this paper is a strong start.
In weighing... | train | [
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iclr_2018_S1fcY-Z0- | Bayesian Hypernetworks | We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary net... | rejected-papers | This paper presents a new method for approximate Bayesian inference in neural networks. The reviewers all found the proposed idea interesting but originally had questions about its novelty (with regard to normalizing flows) and questioned the technical rigor of the approach. The authors did a good job of addressing t... | train | [
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iclr_2018_SkqV-XZRZ | Variational Bi-LSTMs | Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs), which model sequences along both forward and backward directions, generally perform bett... | rejected-papers | This paper proposes a method for performing stochastic variational inference for bidirectional LSTMs through introducing an additional latent variable that induces a dependence between the forward and backward directions. The authors demonstrate that their method achieves very strong empirical performance (log-likelih... | test | [
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iclr_2018_Bk6qQGWRb | Efficient Exploration through Bayesian Deep Q-Networks | We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at ... | rejected-papers | This work develops a methodology for exploration in deep Q-learning through Thompson sampling to learn to play Atari games. The major innovation is to perform a Bayesian linear regression on the last layer of the deep neural network mapping from frames to Q-values. This Bayesian linear regression allows for efficient... | val | [
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iclr_2018_By-IifZRW | Gaussian Process Neurons | We propose a method to learn stochastic activation functions for use in probabilistic neural networks.
First, we develop a framework to embed stochastic activation functions based on Gaussian processes in probabilistic neural networks.
Second, we analytically derive expressions for the propagation of means ... | rejected-papers | The authors propose the use of Gaussian processes as the prior over activation functions in deep neural networks. This is a purely mathematical paper in which the authors derive an efficient and scalable approach to their problem. The idea of having flexible distributions over activation functions is interesting and ... | val | [
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iclr_2018_BJlrSmbAZ | Bayesian Uncertainty Estimation for Batch Normalized Deep Networks | Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framework to handle uncerta... | rejected-papers | This paper shows that batch normalization can be cast as approximate inference in deep neural networks. This is an appealing result as batch normalization is used in practice in a wide variety of models. The reviewers found the paper well written and easy to understand and were motivated by underlying idea. However... | train | [
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"This paper proposes an approximate method to construct Bayesian uncertainty estimates in networks trained with batch normalization.\n\nThere is a lot going on in this paper. Although the overall presentation is clean, there are few key shortfalls (see below). Overall, the reported functionality is nice, although t... | [
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iclr_2018_SknC0bW0- | Continuous-fidelity Bayesian Optimization with Knowledge Gradient | While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multi-fidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on ... | rejected-papers | This paper combines multiple existing ideas in Bayesian optimization (continuous-fidelity, use of gradient information and knowledge gradient) to develop their proposed cfKG method. While the methodology seems neat and effective, the reviewers (and AC) found that the presented approach was not quite novel enough in li... | test | [
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"This paper studies hyperparameter-optimization by Bayesian optimization, using the Knowledge Gradient framework and allowing the Bayesian optimizer to tune fideltiy against cost.\n\nThere’s nothing majorly wrong with this paper, but there’s also not much that is exciting about it. As the authors point out very cle... | [
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iclr_2018_rk8R_JWRW | Gating out sensory noise in a spike-based Long Short-Term Memory network | Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network. While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed... | rejected-papers | The reviewers agreed that the paper was somewhat preliminary in terms of the exposition and empirical work. They all find the underlying problem quite interesting and challenging (i.e. spiking recurrent networks). However, the manuscript failed to motivate the approach. In particular, everyone agrees that spiking ne... | train | [
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iclr_2018_SyxCqGbRZ | Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks | Sepsis is a life-threatening complication from infection and a leading cause of mortality in hospitals. While early detection of sepsis improves patient outcomes, there is little consensus on exact treatment guidelines, and treating septic patients remains an open problem. In this work we present a new deep reinforc... | rejected-papers | This paper brings recent innovations in reinforcement learning to bear on a tremendously important application, treating sepsis. The reviewers were all compelled by the application domain but thought that the technical innovation in the work was low. While ICLR welcomes application papers, in this instance the review... | train | [
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"I appreciate the authors' in-depth and very thoughtful responses to all of the reviews. I really REALLY like this work, and contrary to the other (IMO, overly negative) reviews, I feel that it fits at ICLR, which has recent history of accepting very solid clinical application work, even without significant methods... | [
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iclr_2018_rkTBjG-AZ | DeepArchitect: Automatically Designing and Training Deep Architectures | In deep learning, performance is strongly affected by the choice of architecture
and hyperparameters. While there has been extensive work on automatic hyperpa-
rameter optimization for simple spaces, complex spaces such as the space of deep
architectures remain largely unexplored. As a result, the cho... | rejected-papers | This paper introduces a framework for specifying the model search space for exploring over the space of architectures and hyperparameters in deep learning models (often referred to as architecture search). Optimizing over complex architectures is a challenging problem that has received significant attention as deep le... | train | [
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"The author present a language for expressing hyperparameters (HP) of a network. This language allows to define a tree structure search space to cover the case where some HP variable exists only if some previous HP variable took some specific value. Using this tool, they explore the depth of the network, when to ap... | [
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iclr_2018_HyBbjW-RW | Open Loop Hyperparameter Optimization and Determinantal Point Processes | Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampl... | rejected-papers | The idea of using the determinant of the covariance matrix over inputs to select experiments to run is a foundational concept of experimental design. Thus it is natural to think about extending such a strategy to sequential model based optimization for the hyperparameters of machine learning models, using recent advan... | train | [
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iclr_2018_H1Nyf7W0Z | Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation | Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL). However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency. We point out that it is difficult to resolve a... | rejected-papers | The reviewers agreed that this paper is not quite ready for publication at ICLR. One of the reviewers thought the paper was well written and easy to follow while the two others said the opposite. One of the main criticisms was issues with the composition. The paper seems to lack a clear formal explanation of the pro... | train | [
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iclr_2018_ryk77mbRZ | Noise-Based Regularizers for Recurrent Neural Networks | Recurrent neural networks (RNNs) are powerful models for sequential data. They can approximate arbitrary computations, and have been used successfully in domains such as text and speech. However, the flexibility of RNNs makes them susceptible to overfitting and regularization is important. We develop a noise-based regu... | rejected-papers | This paper proposes a regularizer for recurrent neural networks, based on injecting random noise into the hidden unit activations. In general the reviewers thought that the paper was well written and easy to understand. However, the major concern among the reviewers was a lack of empirical evidence that the method wo... | train | [
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"The authors of the paper advocate injecting noise into the activations of recurrent networks for regularisation. This is done by replacing the deterministic units with stochastic ones.\n\nThe paper has several issues with respect to the method and related work. \n\n- The paper needs to mention [Graves 2011], whic... | [
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iclr_2018_r1vccClCb | Neighbor-encoder | We propose a novel unsupervised representation learning framework called neighbor-encoder in which domain knowledge can be trivially incorporated into the learning process without modifying the general encoder-decoder architecture. In contrast to autoencoder, which reconstructs the input data, neighbor-encoder reconstr... | rejected-papers | The paper proposes a form of autoencoder that learns to predict the neighbors of a given input vector rather than the input itself. The idea is nice but there are some reviewer concerns about insufficient evaluation and the effect of the curse of dimensionality. The revised paper does address some questions and inclu... | train | [
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"This paper describes a generalization of autoencoders that are trained to reconstruct a close neighbor of its input, instead of merely the input itself. Experiments on 3 datasets show that this yields better representations in terms of post hoc classification with a linear classifier or clustering, compared to a r... | [
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iclr_2018_SkYMnLxRW | Weighted Transformer Network for Machine Translation | State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et. al. (2017) propose a new architecture that avoids recurrence and convolution completely. Instead, it uses only self-attention and feed-forward layers. While th... | rejected-papers | The paper proposes a modification to the Transformer network, which mostly consists in changing how the attention heads are combined. The contribution is incremental, and its novelty is limited. The results demonstrate an improvement over the baseline at the cost of a more complicated training procedure with more hyper... | train | [
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"This paper describes an extension to the recently introduced Transformer networks which shows better convergence properties and also improves results on standard machine translation benchmarks. \n\nThis is a great paper -- it introduces a relatively simple extension of Transformer networks which only adds very few... | [
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iclr_2018_rJBiunlAW | Training RNNs as Fast as CNNs | Common recurrent neural network architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU) architecture, a recurrent unit that simplifies the computation and exposes more parallelism. In SRU, the majority of computation ... | rejected-papers | The paper presents Simple Recurrent Unit, which is characterised by the lack of state-to-gates connections as used in conventional recurrent architectures. This allows for efficient implementation, and leads to results competitive with the recurrent baselines, as shown on several benchmarks.
The submission lacks novel... | train | [
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iclr_2018_HJOQ7MgAW | Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum | Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates. We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models ... | rejected-papers | The paper performs an ablation analysis on LSTM, showing that the gating component is the most important. There is little novelty in the analysis, and in its current form, its impact is rather limited. | train | [
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iclr_2018_SkffVjUaW | Building effective deep neural networks one feature at a time | Successful training of convolutional neural networks is often associated with suffi-
ciently deep architectures composed of high amounts of features. These networks
typically rely on a variety of regularization and pruning techniques to converge
to less redundant states. We introduce a novel bottom-up... | rejected-papers | Regarding clarity, while the paper definitely needs work if it is to be resubmitted to an ML venue, different revisions would be appropriate for a physics audience. And given the above comment, any suggested changes are likely to be superfluous. | train | [
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"The authors propose an approach to dynamically adjust the feature map depth of a fully convolutional neural network. The work formulates a measure of self-resemblance, to determine when to stop increasing the feature dimensionality at each convolutional layer. The experimental section evaluates this method on MNIS... | [
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iclr_2018_SJmAXkgCb | DNN Feature Map Compression using Learned Representation over GF(2) | In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field fo... | rejected-papers | The paper presents a technique for feature map compression at inference time. As noted by reviewers, the main concern is that the method is applied to one NN architecture (SqueezeNet), which severely limits its impact and applicability to better performing state-of-the-art models. | train | [
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iclr_2018_SJn0sLgRb | Data Augmentation by Pairing Samples for Images Classification | Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, fli... | rejected-papers | The paper proposes a data augmentation technique for image classification which consists in averaging two input images and using the label of one of them. The method is shown to outperform the baseline on the image classification task, the but evaluation doesn’t extend beyond that (to other tasks or alternative augment... | train | [
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iclr_2018_Sy3fJXbA- | Connectivity Learning in Multi-Branch Networks | While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads... | rejected-papers | The paper proposes a method for learning connectivity in neural networks, evaluated on the ResNeXt architecture. The novelty of the method is rather limited, and even though the method has been shown to improve on the ResNeXt baselines on CIFAR-100 and ImageNet classification tasks (which is encouraging), it should hav... | train | [
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iclr_2018_rJoXrxZAZ | HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models | This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
models for raw audio waveform generation. As an example, we propose
a hybrid model that combines an autoregressive network named WaveNet and a
conventional LSTM model to address speech synthesis. Instead of generating
... | rejected-papers | The paper presents a hybrid architecture which combines WaveNet and LSTM for speeding-up raw audio generation. The novelty of the method is limited, as it’s a simple combination of existing techniques. The practical impact of the approach is rather questionable since the generated audio has significantly lower MOS scor... | test | [
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iclr_2018_S1NHaMW0b | ShakeDrop regularization | This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning.
While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not o... | rejected-papers | The paper proposes a regularisation technique based on Shake-Shake which leads to the state of the art performance on the CIFAR-10 and CIFAR-100 dataset. Despite good results on CIFAR, the novelty of the method is low, justification for the method is not provided, and the impact of the method on tasks beyond CIFAR clas... | test | [
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iclr_2018_rJ6iJmWCW | POLICY DRIVEN GENERATIVE ADVERSARIAL NETWORKS FOR ACCENTED SPEECH GENERATION | In this paper, we propose the generation of accented speech using generative adversarial
networks. Through this work we make two main contributions a) The
ability to condition latent representations while generating realistic speech samples
b) The ability to efficiently generate long speech samples by... | rejected-papers | The paper proposes a method for accented speech generation using GANs.
The reviewers have pointed out the problems in the justification of the method (e.g. the need for using policy gradients with a differentiable objective) as well as its evaluation. | train | [
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iclr_2018_H1cKvl-Rb | UCB EXPLORATION VIA Q-ENSEMBLES | We show how an ensemble of Q∗-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the Q-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments sh... | rejected-papers | The idea studied here is interesting, if incremental. The empirical results are not particularly stellar, but it's clear that the authors have done their best to provide reproducible and defensible results. A few sticking points: a) The use of the term 'UCB', as mentioned in an anonymous comment, is somewhat misleading... | train | [
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iclr_2018_B16yEqkCZ | Avoiding Catastrophic States with Intrinsic Fear | Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward s... | rejected-papers | This paper presents an interesting idea that is related to imitation learning, safe exploration,
and intrinsic motivation. However, in its current state the paper needs improvement in clarity. There are also some concerns about the number of hyperparameters involved. Finally, the experimental results are not completely... | test | [
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"I've slightly increased my score to reflect the improvements made by the authors. Theorem 1 seems to have been corrected. Unfortunately, the bound now indicates that the average reward is within lambda * epsilon * (R_max - R_min) of the optimal average reward (where lambda can be arbitrarily large). This does not ... | [
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iclr_2018_BJ7d0fW0b | Faster Reinforcement Learning with Expert State Sequences | Imitation learning relies on expert demonstrations. Existing approaches often re- quire that the complete demonstration data, including sequences of actions and states are available. In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state se... | rejected-papers | This paper proposes a simple idea for using expert data to improve a deep RL agent's performance. Its main flaw is the lack of justification for the specific techniques used. The empirical evaluation is also fairly limited.
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"SIGNIFICANCE AND ORIGINALITY:\n\nThe authors propose to accelerate the learning of complex tasks by exploiting traces of experts.\nUnlike the most common form of imitation learning or behavioral cloning, the authors \nformulate their solution in the case where the expert’s state trajectory is observable, \nbut the... | [
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iclr_2018_HkpRBFxRb | Learning to Mix n-Step Returns: Generalizing Lambda-Returns for Deep Reinforcement Learning | Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using the next state's value function. lambd... | rejected-papers | This is an interesting paper, but was quite difficult to follow. As they stand, the empirical results are not altogether convincing nor warrant acceptance. | train | [
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"SUMMARY\nThe major contribution of the paper is a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. These CARs are used in the A3C algorithm. The weights are based on the confidence of the... | [
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iclr_2018_HyunpgbR- | Structured Exploration via Hierarchical Variational Policy Networks | Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample ef... | rejected-papers | The reviewers feel there are two issues that make this paper fall short of acceptance: first, the
lack of a clear emphasis and focus (evidenced by the significant revisions) and second, a lack of
comparison to similar, existing methods for multi-agent reinforcement learning. | train | [
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"This paper proposes an approach to improve exploration in multiagent reinforcement learning by allowing the policies of the individual agents to be conditioned on an external coordination signal \\lambda. In order to find such parametrized policies, the approach combines deep RL with a variational inference approa... | [
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iclr_2018_BJvWjcgAZ | Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update | We propose Episodic Backward Update - a new algorithm to boost the performance of a deep reinforcement learning agent by fast reward propagation. In contrast to the conventional use of the replay memory with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state into... | rejected-papers | The reviewers agree the proposed idea is relatively incremental, and the paper itself does not do an exemplary job in other areas to make up for this. | train | [
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"This paper proposes a new variant of DQN where the DQN targets are computed on a full episode by a « backward » update (i.e. from end to start of episode). The targets’ update rule is similar to a regular tabular Q-learning update with high learning rate beta: this allows faster propagation of rewards obtained at ... | [
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iclr_2018_Sy_MK3lAZ | PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE | Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
discrete or continuous space. Motivated by the project of design Game AI for King of Glory
(KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
hybrid act... | rejected-papers | The idea studied here is fairly incremental and the empirical evaluation could be improved. | train | [
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"This paper examines a modified NN architecture and algorithm (P-DQN) for learning in hybrid discrete/continuous action spaces. The authors come up with a clever way of modifying the architecture of parameterized-action-space DDPG (as in Hausknecht & Stone 16) in such a way that the actor only outputs values for th... | [
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iclr_2018_S1GDXzb0b | Model-based imitation learning from state trajectories | Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are available. We present a model-based imitation learning method that can learn en... | rejected-papers | The paper is hard to follow at times. The heuristic reward has little justification -- not clear how
this would extend to other domains. Lack of empirical comparisons (see e.g. Hester et al., Deep Q-Learning from Demonstrations, 2017). | train | [
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"Thank you for the overall encouraging review. We address some of the concerns in the following,\n\nQ : Not clear that method converges on all problems. \nA: Yes it does not converge on all dynamics models. Currently, the main drawback of the method is that it cannot model complex dynamics models like raw video tra... | [
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iclr_2018_HyDAQl-AW | Time Limits in Reinforcement Learning | In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed amount of time, or (ii) ... | rejected-papers | The reviewers agree that this paper suffers from a lack of novelty and does not make sufficient contributions to warrant acceptance. | train | [
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"Summary: This paper explores how to handle two practical issues in reinforcement learning. The first is including time remaining in the state, for domains where episodes are cut-off before a terminal state is reached in the usual way. The second idea is to allow bootstrapping at episode boundaries, but cutting off... | [
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iclr_2018_rkc_hGb0Z | A dynamic game approach to training robust deep policies | We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later expose... | rejected-papers | The reviewers are unanimous that the paper is not sufficiently clear and could be improved with better empirical results. | test | [
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"The authors propose to incorporate elements of robust control into guided policy search, in order to devise a method that is resilient to perturbations and (presumably) model mismatch.\n\nThe idea behind the method and the discussion in the introduction and related work is interesting and worthwhile, and I think t... | [
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iclr_2018_rkvDssyRb | Multi-Advisor Reinforcement Learning | We consider tackling a single-agent RL problem by distributing it to n learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local planni... | rejected-papers | The reviewers agree this is an interesting paper with interesting ideas, but is not ready for publication in its current shape. In particular, there is a need for strong empirical results. | train | [
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"The paper presents Multi-Advisor RL (MAd-RL), a formalized view of many forms of performing RL by training multiple learners, then aggregating their results into a single decision-making agent. Previous work and citations are plentiful and complete, and the field of study is a promising approach to RL. Through M... | [
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iclr_2018_rJIgf7bAZ | An inference-based policy gradient method for learning options | In the pursuit of increasingly intelligent learning systems, abstraction plays a vital role in enabling sophisticated decisions to be made in complex environments. The options framework provides formalism for such abstraction over sequences of decisions. However most models require that options be given a priori, pres... | rejected-papers | The reviewers are unanimous that this is an interesting paper, but that ultimately the empirical results are not sufficiently promising to warrant the added complexity. | train | [
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iclr_2018_Bk-ofQZRb | TD Learning with Constrained Gradients | Temporal Difference Learning with function approximation is known to be unstable. Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize. However, in practice, TD-learning with neural networks requires various tricks like using a targe... | rejected-papers | The reviewers agree this paper is not yet ready for publication. | train | [
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"Summary: This paper tackles the issue of combining TD learning methods with function approximation. The proposed algorithm constrains the gradient update to deal with the fact that canonical TD with function approximation ignores the impact of changing the weights on the target of the TD learning rule. Results wit... | [
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iclr_2018_SyF7Erp6W | Learning to play slot cars and Atari 2600 games in just minutes | Machine learning algorithms for controlling devices will need to learn quickly, with few trials. Such a goal can be attained with concepts borrowed from continental philosophy and formalized using tools from the mathematical theory of categories. Illustrations of this approach are presented on a cyberphysical system: t... | rejected-papers | This paper does not seem completely appropriate for ICLR. | val | [
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"The authors argue that many machine learning systems need a large amount of data and long training times. To mend those shortcomings their proposed algorithm takes the novel approach of combining mathematical category theory and continental philosophy. Instead of computation units, the concept of entities and a ... | [
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iclr_2018_rJ3fy0k0Z | Deterministic Policy Imitation Gradient Algorithm | The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous control tasks. However, GAIL requires a huge number of interactions with enviro... | rejected-papers | All of the reviewers found some aspects of the formulation and experiments interesting, but they found the paper hard to read and understand. Some of the components of the technique such as the state screening function (SSF) seem ad-hoc and heuristic without much justification. Please improve the exposition and remove ... | train | [
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iclr_2018_B1mSWUxR- | Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML | Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a number of impressive empirical successes. RAML incorporates task-specific reward by performing maximum-likelihood updates on candidate outpu... | rejected-papers | There are some interesting ideas discussed in the paper, but the reviewers expressed difficulty understanding the motivation and the theoretical results. The experiments do not seem convincing in showing that SQDML achieves significant gains. Overall, the the paper needs either stronger and clearer theoretical results,... | train | [
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"This paper dives deeper into understand reward augmented maximum likelihood training. Overall, I feel that the paper is hard to understand and that it would benefit from more clarity, e.g., section 3.3 states that decoding from the softmax q-distribution is similar to the Bayes decision rule. Please elaborate on t... | [
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iclr_2018_rk3b2qxCW | Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus | In recent years deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces. In this ... | rejected-papers | The paper has some interesting ideas around auto-regressive policies and estimating their entropy for exploration. The use of autoregressive policies in RL is not particularly novel, and the estimate of entropy for such models is straightforward. Finally, the experiments focus on very simple tasks. | train | [
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iclr_2018_SyPMT6gAb | Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization | Off-policy learning, the task of evaluating and improving policies using historic data collected from a logging policy, is important because on-policy evaluation is usually expensive and has adverse impacts. One of the major challenge of off-policy learning is to derive counterfactual estimators that also has low varia... | rejected-papers | The reviewers agree that the paper studies and interesting problem with an interesting approach. The reviewers raised some concerns regarding the theoretical and empirical results. The authors have made changes to the paper, but given the theoretical nature of the paper and the extent of changes, another review is need... | train | [
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"Dear reviewer,\n\nThanks a lot for the inspiring comments and below are our point-by-point correspondence and hope the revision can address these concerns and make the paper more solid.\n\n- (Citations formatting) We have fixed the missing parenthesis for end-of-sentence citations. We apologize for the inconvenien... | [
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iclr_2018_By5ugjyCb | PACT: Parameterized Clipping Activation for Quantized Neural Networks | Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. To address this cost, a number of quantization schemeshave been proposed - but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations. This paper pr... | rejected-papers | All of the reviewers agree that the experimental results are promising and the proposed activation function enables a decent degree of quantization. However, the main concern with the approach is its limited novelty compared to previous work on clipped activation functions.
minor comments:
- Even though PACT is very s... | test | [
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"The authors have addressed my concerns, and clarified a misunderstanding of the baseline that I had, which I appreciate. I do think that it is a solid contribution with thorough experiments. I still keep my original rating of the paper because the method presented is heavily based on previous works, which limits t... | [
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iclr_2018_HJDV5YxCW | Heterogeneous Bitwidth Binarization in Convolutional Neural Networks | Recent work has shown that performing inference with fast, very-low-bitwidth
(e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate
results. However, although 2-bit approximated networks have been shown to
be quite accurate, 1 bit approximations, which are twice as fas... | rejected-papers | All of the reviewers find the approach interesting, but they have reservations regarding the practical impact and empirical evaluation. The paper needs improvement both on the motivation and on the experimental results by including more baseline methods and neural architectures.
| train | [
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"This paper suggests a method for varying the degree of quantization in a neural network during the forward propagation phase.\n\nThough this is an important direction to investigate, there are several issues:\n\n1. Comparison with previous results is misleading:\na.\t1-bit weights and floating point activations: R... | [
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iclr_2018_SJgf6Z-0W | Predicting Multiple Actions for Stochastic Continuous Control | We introduce a new approach to estimate continuous actions using actor-critic algorithms for reinforcement learning problems. Policy gradient methods usually predict one continuous action estimate or parameters of a presumed distribution (most commonly Gaussian) for any given state which might not be optimal as it may ... | rejected-papers | All of the reviewers agree that the paper presents strong experimental results on continuous control benchmarks. The reviewers raised concerns regarding the analysis of the behavior of the algorithm, the possible impact of the technique, and requested more references and comparison with related work. The paper has sign... | train | [
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"The relationship with SVG0, is that both are off-policy stochastic algorithms learned with the reparametrization trick. Currently the comparisons you have are with DDPG (deterministic, off-policy), A3C(stochastic, on-policy) and MAPG(stochastic, on-policy). So it is difficult to separate which gains are simply due... | [
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iclr_2018_r1BRfhiab | The Principle of Logit Separation | We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is to identify only whether the given example belongs to a specific class, which can be different in different applications of the classifier. For instance, this is the case in an image search engin... | rejected-papers | All of the reviewers have found some aspects of the formulation interesting, but they raised concerns regarding the practical use of the experimental setup.
| train | [
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"The paper is well-written which makes it easy to understand its main\nthrust - choosing loss functions so that at test time one can\naccurately (and speedily) determine whether an example is in a given\nclass, ie loss functions which are aligned with the \"Principle of Logit\nSeparation (PoLS)\". \n\nWhen the \"Pr... | [
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iclr_2018_SJD8YjCpW | Balanced and Deterministic Weight-sharing Helps Network Performance | Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network. But understanding how weight-sharing can be used effectively in general is a topic that has not been studied extensively. Che... | rejected-papers | An empirical study of weight sharing for neural networks is interesting, but all of the reviewers found the experiments insufficient without enough baseline comparisons. | test | [
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"The manuscript advocates to study the weight sharing in a more systematic way by proposing ArbNets which defines the weight sharing function as a hash function. In this framework, any existing neural network architectures, including CNN and RNN, could be incorporated into ArbNets.\n\nThe manuscript is not well wri... | [
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iclr_2018_ByL48G-AW | Simple Nearest Neighbor Policy Method for Continuous Control Tasks | We design a new policy, called a nearest neighbor policy, that does not require any optimization for simple, low-dimensional continuous control tasks. As this policy does not require any optimization, it allows us to investigate the underlying difficulty of a task without being distracted by optimization difficulty of ... | rejected-papers | Evaluating simple baselines for continuous control is important and nearest neighbor search methods are interesting. However, the reviewers think that the paper lacks citation and comparison to some prior work and evaluation on more challenging benchmarks. | train | [
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"Thanks for the author's response. As with the other reviewers, I continue to believe this is more suited for a workshop submission.\n\nAs I cited in my review (and hopefully this also addresses the follow-up comment), I don't believe there are recent, accepted papers which only use these simple tasks (except for s... | [
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iclr_2018_rkw-jlb0W | Deep Lipschitz networks and Dudley GANs | Generative adversarial networks (GANs) have enjoyed great success, however often suffer instability during training which motivates many attempts to resolve this issue. Theoretical explanation for the cause of instability is provided in Wasserstein GAN (WGAN), and wasserstein distance is proposed to stablize the traini... | rejected-papers | Dear authors,
While the reviewers appreciated your analysis, they all expressed concerns about the significance of the paper. Indeed, given the plethora of GAN variants, it would have been good to get stronger evidence about the advantages of the Dudley GAN. Even though I agree it is difficult to provide a clean compa... | train | [
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"Ensuring Lipschitz condition in neural nets is essential of stablizing GANs. This paper proposes two contraint-based optimzation to ensure the Lips condtions , and these proposed approaches maintain suffcient capacity, as well as expressiveness of the network. A simple theoritical result is given by emprical risk... | [
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iclr_2018_SJtChcgAW | Cheap DNN Pruning with Performance Guarantees | Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy. However most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain cla... | rejected-papers | Dear authors,
While the reviewers appreciated the idea, the significant loss of accuracy was a concern. Even though you made significant changes to the submission, it is unfortunately unrealistic to ask the reviewers to do another review of a heavily modified version in such a short amount of time.
Thus, I cannot acc... | train | [
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"The manuscript mainly presents a cheap pruning algorithm for dense layers of DNNs. The proposed algorithm is an improvement of Net-Trim (Aghasi et al., 2016), which is to enforce the weights to be sparse.\n\nThe main contribution of this manuscript is that the non-convex optimization problem in (Aghasi et al., 201... | [
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iclr_2018_Sy-tszZRZ | Bounding and Counting Linear Regions of Deep Neural Networks | In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout. We investigate the complexity of such networks by studying the number of linear regions of the PWL function. Typicall... | rejected-papers | Dear authors,
The reviewers appreciated your work and recognized the importance of theoretical work to understand the behaviour of deep nets. That said, the improvement over existing work (especially Montufar, 2017) is minor. This, combined with the limited attraction of such work, means that the paper will not be acc... | val | [
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"This paper investigates the complexity of neural networks with piecewise linear activations by studying the number of linear regions of the representable functions. It builds on previous works Montufar et al. (2014) and Raghu et al. (2017) and presents improved bounds on the maximum number of linear regions. It al... | [
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iclr_2018_H1l8sz-AW | Improving generalization by regularizing in L2 function space | Learning rules for neural networks necessarily include some form of regularization. Most regularization techniques are conceptualized and implemented in the space of parameters. However, it is also possible to regularize in the space of functions. Here, we propose to measure networks in an L2 Hilbert space, and test a ... | rejected-papers | Dear authors,
Despite the desirable goal, that is to move away from regularization in parameter space toward regularization in function space, the reviewers all thought that the paper was not convincing enough, both in the choice of the particular regularization and in the experimental section.
While I appreciate tha... | train | [
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"\nGENERAL IMPRESSION:\n\nOverall, the revised version of the paper is greatly improved. The new derivation of the method yields a much simpler interpretation, although the relation to the natural gradient remains weak (see below). The experimental evaluation is now far more solid. Multiple data sets and network ar... | [
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iclr_2018_ry831QWAb | BLOCK-NORMALIZED GRADIENT METHOD: AN EMPIRICAL STUDY FOR TRAINING DEEP NEURAL NETWORK | In this paper, we propose a generic and simple strategy for utilizing stochastic gradient information in optimization. The technique essentially contains two consecutive steps in each iteration: 1) computing and normalizing each block (layer) of the mini-batch stochastic gradient; 2) selecting appropriate step size to ... | rejected-papers | The paper proposes to study the impact of normalizing the gradient for each layer before applying existing techniques such as SG + momentum, Adam or AdaGrad. The study is done on a reasonable number of datasets and, after the reviewers' comments, confidence intervals have been added, although Table 1 puts results in b... | train | [
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"This paper proposes a family of first-order stochastic optimization schemes based on (1) normalizing (batches of) stochastic gradient descents and (2) choosing from a step size updating scheme. The authors argue that iterative first-order optimization algorithms can be interpreted as a choice of an update directi... | [
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iclr_2018_H1pri9vTZ | Deep Function Machines: Generalized Neural Networks for Topological Layer Expression | In this paper we propose a generalization of deep neural networks called deep function machines (DFMs). DFMs act on vector spaces of arbitrary (possibly infinite) dimension and we show that a family of DFMs are invariant to the dimension of input data; that is, the parameterization of the model does not directly hinge ... | rejected-papers | The idea of extending deep nets to infinite dimensional inputs is interesting but, as the reviewers noted, the execution does not have the quality we can expect from an ICLR publication. I encourage the authors to consider the meaningful comments that were made and modify the paper accordingly. | train | [
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"This paper extends the framework of neural networks for finite-dimension to the case of infinite-dimension setting, called deep function machines. This theory seems to be interesting and might have further potential in applications.",
"The main idea of this paper is to replace the feedforward summation\ny = f(W*... | [
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iclr_2018_rJma2bZCW | Three factors influencing minima in SGD | We study the statistical properties of the endpoint of stochastic gradient descent (SGD). We approximate SGD as a stochastic differential equation (SDE) and consider its Boltzmann Gibbs equilibrium distribution under the assumption of isotropic variance in loss gradients.. Through this analysis, we find that three fact... | rejected-papers | Dear authors,
The reviewers agreed that the theoretical part lacked novelty and that the paper should focus on its experimental part which at the moment is not strong enough to warrant publication.
Regarding the theoretical part, here are the main concerns:
- Even though it is used in previous works, the continuous t... | train | [
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"The paper investigates how the learning rate and mini-batch size in SGD impacts the optima that the SGD algorithm finds.\nEmpirically, the authors argue that it was observed that larger learning rates converge to minima which are more wide,\nand that smaller learning rates more often lead to convergence to minima ... | [
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iclr_2018_rk3mjYRp- | Diffusing Policies : Towards Wasserstein Policy Gradient Flows | Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence. We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region). This is done in the discrete and continuous multi-armed bandit settin... | rejected-papers | Dear authors,
The authors all agreed that this was an interesting topic but that the novelty, either theoretical or empirical, was lacking. This, the paper cannot be accepted to ICLR in its current state but I encourage the authors to make the recommended updates and to push their idea further. | train | [
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"The paper ‘Diffusing policies: Towards Wasserstein policy gradient flows’ explores \nthe connections between reinforcement learning and the theory of quadratic optimal transport (i.e.\nusing the Wasserstein_2 as a regularizer of an iterative problem that converges toward\nan optimal policy). Following a classical ... | [
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iclr_2018_HyxjwgbRZ | Convergence rate of sign stochastic gradient descent for non-convex functions | The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates. Since signSGD carries out one-bit quantization of the gradients, it is extremely practical for distributed optimization where gradients need to be aggregated from different processors. For the first t... | rejected-papers | Dear authors,
After carefully reading the reviews, the rebuttal, and going through the paper, I regret to inform you that this paper does not meet the requirements for publication at ICLR.
While the variance analysis is definitely of interest, the reality of the algorithm does not match the claims. The theoretical ra... | train | [
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"In what sense is the result far worse than Alistarh et al.?\n\nWe have now validated empirically that for resnet-20 on cifar-10, the squared gradient 1-norm dominates the squared gradient 2-norm by a factor O(d). Also the stochastic gradient variance is O(d).\n\nThe closest thing to our result in Alistarh et al. i... | [
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] |
iclr_2018_B1uvH_gC- | Parametric Manifold Learning Via Sparse Multidimensional Scaling | We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We employ Siamese networks to solve the problem of least squares multidimensional scaling for generating mappings that preserve geodesic distances on the manifold. In ... | rejected-papers | Dear authors,
Thank you for your submission to ICLR. Sadly, the reviewers were not convinced by the novelty of your approach nor by its experimental results. Thus, your paper cannot be accepted to ICLR. | train | [
"Bku1giNxf",
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"B1-O-VImM",
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"The paper describes a manifold learning method that adapts the old ideas of multidimensional scaling, with geodesic distances in particular, to neural networks. The goal is to switch from a non-parametric to a parametric method and hence to have a straightforward out-of-sample extension.\n\nThe paper has several m... | [
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] |
iclr_2018_Sk0pHeZAW | Sparse Regularized Deep Neural Networks For Efficient Embedded Learning | Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prevent them from being deployed effectively on embedded platforms, limiting their applications. This work ... | rejected-papers | Dear authors,
I agree with the reviewers that the paper tries to do several things at once and the results are not that convincing. Overall, this work is mostly incremental, which is fine if there is no issue in the execution. Thus, I regret to inform you that this paper will not be accepted to ICLR. | train | [
"SyPaSBDxz",
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"Summary: \nPaper proposes the compression method Delicate-SVRG-cumulative-L1 (combining minibatch SVRG with cumulative L1 regularization) which can significantly reduce the number of weights without affecting the test accuracy. Paper provides numerical experiments for MNIST and CIRAR10 on LeNet-300-100 and LeNet-5... | [
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iclr_2018_r1ISxGZRb | Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning | Deep lifelong learning systems need to efficiently manage resources to scale to large numbers of experiences and non-stationary goals. In this paper, we explore the relationship between lossy compression and the resource constrained lifelong learning problem of function transferability. We demonstrate that lossy episod... | rejected-papers | The reviewers were uniformly unimpressed with the contributions of this paper. The method is somewhat derivative and the paper is quite long and lacks clarity. Moreover, the tactic of storing autoencoder variables rather than full samples is clearly an improvement, but it still does not allow the method to scale to a t... | val | [
"ryfA9SYez",
"S1iEoBnlf",
"B1GkSWIWM",
"ryAT4O6QM",
"rJ9c-u67z",
"HyMjedamG",
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] | [
"official_reviewer",
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"The paper proposes an architecture for efficient deep lifelong learning. The key idea is to use recollection generator (autoencoder) to remember the previously processed data in a compact representation. Then when training a reasoning model, recollections generated from the recollection generator are used with rea... | [
5,
5,
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"ryfA9SYez",
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] |
iclr_2018_S14EogZAZ | Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning | Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artificial agent can autonomously acquire this intuition through interaction with the environment. We created a synthetic block stacking environm... | rejected-papers | The authors present a toy stacking task where the goal is to stack blocks to match a given configuration, and a method that is a slightly modified DQN algorithm where the target configuration is observed by the network as well as the current state. There are a few problems with this paper. First, the method lacks novel... | train | [
"HJUMdjteM",
"H1uUNm9ef",
"B171xj6eM"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"The authors propose a model for learning physical interaction skills through trial and error. They use end-to-end deep reinforcement learning - the DQN model - including the task goal as an input in order to to improve generalization over several tasks, and shaping the reward depending on the visual differences be... | [
5,
4,
5
] | [
4,
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3
] | [
"iclr_2018_S14EogZAZ",
"iclr_2018_S14EogZAZ",
"iclr_2018_S14EogZAZ"
] |
iclr_2018_rJssAZ-0- | TRL: Discriminative Hints for Scalable Reverse Curriculum Learning | Deep reinforcement learning algorithms have proven successful in a variety of domains. However, tasks with sparse rewards remain challenging when the state space is large. Goal-oriented tasks are among the most typical problems in this domain, where a reward can only be received when the final goal is accomplished. In ... | rejected-papers | The paper proposes an extension to the reverse curriculum RL approach which uses a discriminator to label states as being on a goal trajectory or off the goal trajectory. The paper is well-written, with good empirical results on a number of task domains. However, the method relies on a number of assumptions on the abil... | train | [
"B129GzFxf",
"r1Kg9atxz",
"BkFL6KCxf",
"S1-osRMEM",
"ryCOkjmmz",
"rJRVQimmf",
"Bkq9MWEmM"
] | [
"official_reviewer",
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] | [
"This paper proposes a new method for reverse curriculum generation by gradually reseting the environment in phases and classifying states that tend to lead to success. It additionally proposes a mechanism for learning from human-provided \"key states\".\n\nThe ideas in this paper are quite nice, but the paper has ... | [
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] |
iclr_2018_BkeC_J-R- | Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control | Reinforcement learning methods have recently achieved impressive results on a wide range of control problems. However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution. This limitation largely prohibits their usage for complex input ... | rejected-papers | The proposed method combines supervised pretraining given some expert data and further uses the supervision to regularize the Q-updates to prevent the agent from exploring 'nonsense' directions. There a significant problems with the paper: the approach is not novel, the assumption of large amounts of expert data is pro... | train | [
"BJCXSFZgz",
"SJmcBU_ez",
"Bk05KWcgz",
"HkJ5oC37M",
"rJyGRRhmM",
"Sku3FC27f",
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] | [
"official_reviewer",
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"This paper proposes leveraging labelled controlled data to accelerate reinforcement-based learning of a control policy. It provides two main contributions: pre-training the policy network of a DDPG agent in a supervised manner so that it begins in reasonable state-action distribution and regalurizing the Q-update... | [
4,
5,
3,
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-1,
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"iclr_2018_BkeC_J-R-",
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] |
iclr_2018_HktXuGb0- | Reward Estimation via State Prediction | Reinforcement learning typically requires carefully designed reward functions in order to learn the desired behavior. We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.... | rejected-papers | The paper presents a method for learning from expert state trajectories using a similarity metric in a learned feature space. The approach uses only the states, not the actions of the expert. The reviewers were variously dissatisfied with the novelty, the theoretical presentation, and the robustness of the approach. Th... | train | [
"S1ucldOlf",
"S1qg275gM",
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"r1WNJ7KfM",
"B1TekQFMG",
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] | [
"official_reviewer",
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"The authors propose to solve the inverse reinforcement learning problem of inferring the reward function from observations of a behaving agent, i.e. trajectories, albeit without observing state-action pairs as is common in IRL but only with the state sequences. This is an interesting problem setting. But, apparent... | [
4,
5,
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] | [
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iclr_2018_BJgVaG-Ab | AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION | An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new ta... | rejected-papers | The authors make an argument for constructing an MDP from the formal structures of temporal logic and associated finite state automata and then applying RL to learn a policy for the MDP. This does not provide a solution for low-level skill composition, because there are discontinuities between states, but does provide ... | val | [
"ryRVwuOeM",
"SJnC0yKez",
"Syp3P75gz",
"BJlOg2YXz",
"r1i7y2FQG",
"Sk3PqjK7f"
] | [
"official_reviewer",
"official_reviewer",
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"The paper argues for structured task representations (in TLTL) and shows how these representations can be used to reuse learned subtasks to decrease learning time.\n\nOverall, the paper is sloppily put together, so it's a little difficult to assess the completeness of the ideas. The problem being solved is not lit... | [
5,
3,
4,
-1,
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] | [
4,
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] | [
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"ryRVwuOeM",
"SJnC0yKez",
"Syp3P75gz"
] |
iclr_2018_rJFOptp6Z | Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification | Knowledge distillation is a potential solution for model compression. The idea is to make a small student network imitate the target of a large teacher network, then the student network can be competitive to the teacher one. Most previous studies focus on model distillation in the classification task, where they propos... | rejected-papers | The authors propose a distillation-based approach that is applied to transfer knowledge from a classification network to non-classification tasks (face alignment and verification). The writing is very imprecise - for instance repeatedly referring to a 'simple trick' rather than actually defining the procedure - and the... | train | [
"B1736j_gz",
"SkX-5ijlG",
"rJR-8EAgG"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"The paper proposes knowledge distillation on two very specific non-classification tasks. I find the scope of the paper is quite limited and the approach seems hard to generalize to other tasks. There is also very limited technical contribution. I think the paper might be a better fit in conferences on faces such a... | [
3,
5,
3
] | [
4,
5,
4
] | [
"iclr_2018_rJFOptp6Z",
"iclr_2018_rJFOptp6Z",
"iclr_2018_rJFOptp6Z"
] |
iclr_2018_Sktm4zWRb | Soft Value Iteration Networks for Planetary Rover Path Planning | Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers. The key challenging task in... | rejected-papers | The authors have proposed a 'soft' version of VIN which is differentiable, where the cost function is trained by behavior cloning / imitation learning from expert/computer trajectories. The method is applied to a toy problem and to real historical data from mars rovers. The paper does not acknowledge nor compare agains... | train | [
"Bknbc_kxG",
"Sksl-n_xf",
"HJTvyeceM",
"BkJ2XCpQz",
"HyB1eAp7M",
"rk130paQM"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author"
] | [
"Summary:\n\nThe Value-Iteration-Network (VIN) architecture is modified to have a softmax loss function at the end. This is termed SVIN. It is then applied in a behavior cloning manner to the task of rover path planning from start to goal from overhead imagery.\n\nSimulation results on binary obstacle maps and usin... | [
3,
3,
4,
-1,
-1,
-1
] | [
5,
3,
4,
-1,
-1,
-1
] | [
"iclr_2018_Sktm4zWRb",
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"iclr_2018_Sktm4zWRb",
"Bknbc_kxG",
"Sksl-n_xf",
"HJTvyeceM"
] |
iclr_2018_S1xDcSR6W | Hybed: Hyperbolic Neural Graph Embedding | Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into ... | rejected-papers | This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection. | train | [
"HyiISdgef",
"rynh2mGgf",
"SyVv9AjWG",
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"author",
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"public",
"official_reviewer",
"official_reviewer",
"author",
"author",
"public",
"author",
... | [
"== Preamble ==\n\nAs promised, I have read the updated paper from scratch and this is my revised review. My original review is kept below for reference. My original review had rating \"4: Ok but not good enough - rejection\".\n\n== Updated review ==\n\nThe revised improves upon the original submission in several w... | [
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"Sy6BFU_eG",
"... |
iclr_2018_SJd0EAy0b | Generalized Graph Embedding Models | Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we con... | rejected-papers | This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection. | train | [
"SJ5CeLYef",
"S12o7fqlM",
"Byp8oT3xf"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"The paper is well-written and provides sufficient background on the knowledge graph tasks. The current state-of-the-art models are mentioned and the approach is evaluated against them. The proposed model is rather simple so it is really surprising that the proposed model performs on par or even outperforms existin... | [
6,
4,
3
] | [
4,
4,
4
] | [
"iclr_2018_SJd0EAy0b",
"iclr_2018_SJd0EAy0b",
"iclr_2018_SJd0EAy0b"
] |
iclr_2018_S1viikbCW | TCAV: Relative concept importance testing with Linear Concept Activation Vectors | Despite neural network’s high performance, the lack of interpretability has been the main bottleneck for its safe usage in practice. In domains with high stakes (e.g., medical diagnosis), gaining insights into the network is critical for gaining trust and being adopted. One of the ways to improve interpretability of a ... | rejected-papers | This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection. | train | [
"ryetNfcxG",
"rkMtrl6bz",
"H1EFxgC-f",
"BykC2IA-G",
"SJCVW6WfM",
"r1C4WcxzM",
"SyqwWceMM",
"BJT3eceMG",
"BkAu19xzM",
"H18MkJAWf"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author",
"author",
"author",
"public"
] | [
"Summary\n---\nThis paper proposes the use of Concept Activation Vectors (CAVs) for interpreting deep models. It shows how concept activation vectors can be used to provide explanations where the user provides a concept (e.g., red) as a set of training examples and then the method provides explanations like \"If th... | [
4,
4,
5,
3,
-1,
-1,
-1,
-1,
-1,
-1
] | [
4,
3,
2,
5,
-1,
-1,
-1,
-1,
-1,
-1
] | [
"iclr_2018_S1viikbCW",
"iclr_2018_S1viikbCW",
"iclr_2018_S1viikbCW",
"iclr_2018_S1viikbCW",
"SyqwWceMM",
"iclr_2018_S1viikbCW",
"r1C4WcxzM",
"BykC2IA-G",
"H18MkJAWf",
"iclr_2018_S1viikbCW"
] |
iclr_2018_ryZ3KCy0W | Link Weight Prediction with Node Embeddings | Application of deep learning has been successful in various domains such as im-
age recognition, speech recognition and natural language processing. However,
the research on its application in graph mining is still in an early stage. Here we
present the first generic deep learning approach to the grap... | rejected-papers | This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection. | train | [
"SJFHMtueG",
"H1Q3f2_ef",
"BJ-Rv1neG"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"Although this paper aims at an interesting and important task, the reviewer does not feel it is ready to be published.\nBelow are some detailed comments:\n\nPros\n- Numerous public datasets are used for the experiments\n- Good introductions for some of the existing methods.\nCons\n- The novelty is limited. The bas... | [
3,
3,
4
] | [
4,
5,
3
] | [
"iclr_2018_ryZ3KCy0W",
"iclr_2018_ryZ3KCy0W",
"iclr_2018_ryZ3KCy0W"
] |
iclr_2018_BJhxcGZCW | Generative Discovery of Relational Medical Entity Pairs | Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies. To promote these benefits, it is desired to effectively expand the scale of high-quality yet novel relational medical entity pairs that embody ... | rejected-papers | The authors seem to miss important related literature for their comparison.
They also tuned hyperparameters and tested on the same validation set.
They should split between train/validation/test.
Reviews are just too low across the board to accept. | train | [
"Bym8Y7aXf",
"r1hITk7ez",
"H1_4279lf",
"SJyOXNclf",
"S1Gt1m6mM",
"HJ-9qfp7M"
] | [
"author",
"official_reviewer",
"official_reviewer",
"official_reviewer",
"author",
"author"
] | [
"Thanks for your review. \n\n1.\tThe medical entity pairs generated by proposed model can be used to expand an existing knowledge graph with new entities as vertexes and relations as edges in a generative fashion. However, the KB completion task and the proposed entity pair discovery task share different objectives... | [
-1,
4,
4,
2,
-1,
-1
] | [
-1,
3,
4,
5,
-1,
-1
] | [
"SJyOXNclf",
"iclr_2018_BJhxcGZCW",
"iclr_2018_BJhxcGZCW",
"iclr_2018_BJhxcGZCW",
"H1_4279lf",
"r1hITk7ez"
] |
iclr_2018_ryA-jdlA- | A closer look at the word analogy problem | Although word analogy problems have become a standard tool for evaluating word vectors, little is known about why word vectors are so good at solving these problems. In this paper, I attempt to further our understanding of the subject, by developing a simple, but highly accurate generative approach to solve the word an... | rejected-papers | This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection. | train | [
"Hkkq0dDlM",
"B1oFM1FeG",
"ByWUtfoef"
] | [
"official_reviewer",
"official_reviewer",
"official_reviewer"
] | [
"This paper proposes a new method for solving the analogy task, which can potentially provide some insight as to why word2vec recovers word analogies.\n\nIn my view, there are three main issues with this paper: (1) the assumptions it makes about our understanding of the analogy phenomenon; (2) the authors' understa... | [
2,
3,
3
] | [
5,
4,
4
] | [
"iclr_2018_ryA-jdlA-",
"iclr_2018_ryA-jdlA-",
"iclr_2018_ryA-jdlA-"
] |
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