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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Learning to plan;Reinforcement Learning;Value Iteration;Navigation;Convnets | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Value Propagation Networks | null | null | 0 | 3 | Workshop | 4;2;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Automated Design;Gradient Descent | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | AUTOMATED DESIGN USING NEURAL NETWORKS AND GRADIENT DESCENT | null | null | 0 | 4.333333 | Workshop | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Musical audio;neural style transfer;Time-Frequency;Spectrogram | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | “Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | distributed representations;sentence embedding;representation learning;unsupervised learning;encoder-decoder;RNN | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks | null | null | 0 | 4.333333 | Workshop | 4;5;4 | null |
null | Microsoft Research, Canada; Ecole Polytechnique, Canada; Montreal Institute for Learning Algorithms (MILA), Canada | 2018 | 0 | null | null | 0 | null | null | null | null | null | Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Christopher Pal, Yoshua Bengio | https://iclr.cc/virtual/2018/poster/86 | generative rnns;long term dependencies;speech recognition;image captioning | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 6;7;8 | null | null | Twin Networks: Matching the Future for Sequence Generation | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Max-Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany | 2018 | 0 | null | null | 0 | null | null | null | null | null | Seong Joon Oh, Max Augustin, Mario Fritz, Bernt Schiele | https://iclr.cc/virtual/2018/poster/243 | black box;security;privacy;attack;metamodel;adversarial example;reverse-engineering;machine learning | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 5;7;7 | null | null | Towards Reverse-Engineering Black-Box Neural Networks | https://goo.gl/MbYfsv | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | University of Siegen; Technical University of Munich | 2018 | 0 | null | null | 0 | null | null | null | null | null | Thomas Frerix, Thomas Möllenhoff, Michael Moeller, Daniel Cremers | https://iclr.cc/virtual/2018/poster/202 | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Proximal Backpropagation | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | optimal control;reinforcement learning | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Towards Provable Control for Unknown Linear Dynamical Systems | null | null | 0 | 3.333333 | Workshop | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | structured attention;sentence matching | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 5;5;6 | null | null | STRUCTURED ALIGNMENT NETWORKS | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Named Entities;Neural methods;Goal oriented dialog | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 3;4;6 | null | null | A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | DenseNets;Tensor Analysis;Convolutional Arithmetic Circuits | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 4;4;5 | null | null | A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Generative Models;GANs | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Flexible Prior Distributions for Deep Generative Models | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Natural Language Processing;Machine Translation;Deep Learning;Data Augmentation | null | 0 | null | null | iclr | -0.654654 | 0 | null | main | 4.666667 | 3;5;6 | null | null | A cluster-to-cluster framework for neural machine translation | null | null | 0 | 3 | Withdraw | 4;2;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | dialogue generation;dialogue acts;open domain conversation;supervised learning;reinforcement learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 4;7;7 | null | null | Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Learning Topics using Semantic Locality | null | null | 0 | 4.333333 | Withdraw | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | target propagation;biologically-plausible learning;benchmark;neuroscience | null | 0 | null | null | iclr | 0.981981 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Assessing the scalability of biologically-motivated deep learning algorithms and architectures | null | null | 0 | 4 | Withdraw | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | MACHINE VS MACHINE: MINIMAX-OPTIMAL DEFENSE AGAINST ADVERSARIAL EXAMPLES | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Natural Language Processing;Deep Learning;Reasoning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Finding ReMO (Related Memory Object): A Simple neural architecture for Text based Reasoning | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | University of California, Los Angeles; École Normale Supérieure de Lyon; École Polytechnique Fédérale de Lausanne | 2018 | 0 | null | null | 0 | null | null | null | null | null | Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto | https://iclr.cc/virtual/2018/poster/286 | Universal perturbations;robustness;curvature | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Robustness of Classifiers to Universal Perturbations: A Geometric Perspective | null | null | 0 | 3.333333 | Poster | 3;4;3 | null |
null | Department of Computer Science, Stanford University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Aditi Raghunathan, Jacob Steinhardt, Percy Liang | https://iclr.cc/virtual/2018/poster/116 | adversarial examples;certificate of robustness;convex relaxations | null | 0 | null | null | iclr | 1 | 0 | null | main | 7 | 5;8;8 | null | null | Certified Defenses against Adversarial Examples | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;regularization | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | Achieving Strong Regularization for Deep Neural Networks | https://github.com/(anonymized) | null | 0 | 4 | Reject | 5;5;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Dialog Systems;Language Generation | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Placeholder | null | null | 0 | 0 | Withdraw | null | null |
null | Department of Statistics, Columbia University; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | gonzalo mena, David Belanger, Scott Linderman, Jasper Snoek | https://iclr.cc/virtual/2018/poster/183 | Permutation;Latent;Sinkhorn;Inference;Optimal Transport;Gumbel;Softmax;Sorting | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7 | 6;7;8 | null | null | Learning Latent Permutations with Gumbel-Sinkhorn Networks | null | null | 0 | 3.333333 | Poster | 2;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | generative adversarial networks;Wasserstein;GAN;generalization;theory | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | Towards a Testable Notion of Generalization for Generative Adversarial Networks | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Adversarial Training;Privacy Protection;Random Subspace | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Censoring Representations with Multiple-Adversaries over Random Subspaces | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | University of Washington, Seattle | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tianyi Zhou, Jeff Bilmes | https://iclr.cc/virtual/2018/poster/276 | machine teaching;deep learning;minimax;curriculum learning;submodular;diversity | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | Paper under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Training Deep Models;Non-convex Optimization;Local and Global Equivalence;Local Openness | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learning Deep Models: Critical Points and Local Openness | null | null | 0 | 4 | Workshop | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | representation learning;disentanglement;regularization | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Disentangled activations in deep networks | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | language modeling;NCE;self-normalization | null | 0 | null | null | iclr | -0.720577 | 0 | null | main | 3.666667 | 2;3;6 | null | null | A Matrix Approximation View of NCE that Justifies Self-Normalization | null | null | 0 | 4 | Withdraw | 4;5;3 | null |
null | University of Cambridge; University of Cambridge, Uber AI Labs | 2018 | 0 | null | null | 0 | null | null | null | null | null | Alexander Matthews, Jiri Hron, Mark Rowland, Richard E Turner, Zoubin Ghahramani | https://iclr.cc/virtual/2018/poster/161 | Gaussian Processes;Bayesian Deep Learning;Theory of Deep Neural Networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Gaussian Process Behaviour in Wide Deep Neural Networks | https://github.com/widedeepnetworks/widedeepnetworks | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | invariance;cnn;gan;infogan;transformation | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.333333 | 2;4;4 | null | null | Parametrizing filters of a CNN with a GAN | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | graph convolutional neural networks;graph-structured data;semi-classification | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | Topology Adaptive Graph Convolutional Networks | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning theory;architecture selection;algebraic topology | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.666667 | 3;4;4 | null | null | On Characterizing the Capacity of Neural Networks Using Algebraic Topology | null | null | 0 | 5 | Reject | 5;5;5 | null |
null | Product Architecture Group, Intel, OR; Parallel Computing Lab, Intel Labs, SC; Parallel Computing Lab, Intel Labs, India; Software Services Group, Intel, OR | 2018 | 0 | null | null | 0 | null | null | null | null | null | Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep K Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov | https://iclr.cc/virtual/2018/poster/52 | deep learning training;reduced precision;imagenet;dynamic fixed point | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Mixed Precision Training of Convolutional Neural Networks using Integer Operations | null | null | 0 | 3.333333 | Poster | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Label Propagation;Depthwise separable convolution;Graph and geometric convolution | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5 | 4;5;6 | null | null | Learning Graph Convolution Filters from Data Manifold | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | distributed;deep learning;straggler | null | 0 | null | null | iclr | 1 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Faster Distributed Synchronous SGD with Weak Synchronization | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | GAN;graphs;random walks;implicit generative models | null | 0 | null | null | iclr | -0.944911 | 0 | null | main | 5.666667 | 4;6;7 | null | null | GraphGAN: Generating Graphs via Random Walks | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | Department of Computer Science, ETH Zurich | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann | https://iclr.cc/virtual/2018/poster/117 | Deep Generative Models;GANs | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Semantic Interpolation in Implicit Models | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | sequence-to-sequence recurrent networks;compositionality;systematicity;generalization;language-driven navigation | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks | null | null | 0 | 4 | Workshop | 3;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Nuisance variation;transform learning;image embeddings | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Correcting Nuisance Variation using Wasserstein Distance | null | null | 0 | 3.666667 | Reject | 5;3;3 | null |
null | Facebook AI Research; Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Guillaume Lample, , Marc'Aurelio Ranzato, , Hervé Jégou | https://iclr.cc/virtual/2018/poster/336 | unsupervised learning;machine translation;multilingual embeddings;parallel dictionary induction;adversarial training | null | 0 | null | null | iclr | -0.777714 | 0 | null | main | 6.666667 | 3;8;9 | null | null | Word translation without parallel data | https://github.com/facebookresearch/MUSE | null | 0 | 4 | Poster | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;machine learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates | null | null | 0 | 3.333333 | Reject | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Robotics;Artificial Intelligence;Computer Vision | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 2.333333 | 2;2;3 | null | null | TOWARDS ROBOT VISION MODULE DEVELOPMENT WITH EXPERIENTIAL ROBOT LEARNING | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | University of California, Irvine, CA 92697, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Zhengli Zhao, Dheeru Dua, Sameer Singh | https://iclr.cc/virtual/2018/poster/142 | adversarial examples;generative adversarial networks;interpretability;image classification;textual entailment;machine translation | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Generating Natural Adversarial Examples | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Lifelong learning;meta learning;word embedding | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | Lifelong Word Embedding via Meta-Learning | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Batch Normalized;Convolutional Neural Networks;Displaced Rectifier Linear Unit;Comparative Study | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | Enhancing Batch Normalized Convolutional Networks using Displaced Rectifier Linear Units: A Systematic Comparative Study | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | program induction;HCI;deep learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Learning to Infer Graphics Programs from Hand-Drawn Images | null | null | 0 | 3.333333 | Reject | 4;2;4 | null |
null | New York University, New York, NY 10003; New York Genome Center, New York, NY 10003, USA; Weill Cornell Medicine, Meyer Cancer Center, New York, NY 10065; Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10065; Weill Cornell Medicine, Division of Hematology and Medical Oncology, New York, NY 10065 | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | somatic mutation;variant calling;cancer;liquid biopsy;early detection;convolution;deep learning;machine learning;lung cancer;error suppression;mutect | null | 0 | null | null | iclr | 0.693375 | 0 | null | main | 5.666667 | 4;5;8 | null | null | Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy | null | null | 0 | 3.666667 | Workshop | 3;4;4 | null |
null | DeepMind | 2018 | 0 | null | null | 0 | null | null | null | null | null | Daniel Horgan, John Quan, David Budden, Gabriel Barth-maron, Matteo Hessel, Hado van Hasselt, David Silver | https://iclr.cc/virtual/2018/poster/134 | deep learning;reinforcement learning;distributed systems | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 7.333333 | 6;7;9 | null | null | Distributed Prioritized Experience Replay | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep learning;model compression | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5.666667 | 5;6;6 | null | null | WSNet: Learning Compact and Efficient Networks with Weight Sampling | null | null | 0 | 4 | Workshop | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | TESLA: Task-wise Early Stopping and Loss Aggregation for Dynamic Neural Network Inference | null | null | 0 | 2.666667 | Reject | 2;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Real time strategy;latent space;forward model;monte carlo tree search;reinforcement learning;planning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Latent forward model for Real-time Strategy game planning with incomplete information | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | University of Toronto and Vector Institute | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse | https://iclr.cc/virtual/2018/poster/240 | meta-learning; optimization; short-horizon bias. | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7 | 6;7;8 | null | null | Understanding Short-Horizon Bias in Stochastic Meta-Optimization | https://github.com/renmengye/meta-optim-public | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Object detection;Visual Tracking;Loss function;Region Proposal Network;Network compression | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Tracking Loss: Converting Object Detector to Robust Visual Tracker | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neural network;reinforcement learning;natural language processing;machine translation;alpha-divergence | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation | null | null | 0 | 3 | Reject | 1;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Post-training for Deep Learning | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | hyper-parameters;optimization | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Online Hyper-Parameter Optimization | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | exploration;intrinsic motivation;reinforcement learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Curiosity-driven Exploration by Bootstrapping Features | null | null | 0 | 0 | Withdraw | null | null |
null | Microsoft Research, Montreal; Unknown; Element AI, Montreal; Montreal Institute for Learning Algorithms (MILA), Montreal | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Joao Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher Pal | https://iclr.cc/virtual/2018/poster/2 | deep learning;complex-valued neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 4;7;8 | null | null | Deep Complex Networks | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | interpreting convolutional neural networks;nearest neighbors;generative adversarial networks | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Do Convolutional Neural Networks act as Compositional Nearest Neighbors? | null | null | 0 | 4.333333 | Withdraw | 3;5;5 | null |
null | Baidu Research, Sunnyvale USA; National Engineering Laboratory for Deep Learning Technology and Applications, Beijing China | 2018 | 0 | null | null | 0 | null | null | null | null | null | Haonan Yu, Haichao Zhang, Wei Xu | https://iclr.cc/virtual/2018/poster/275 | grounded language learning and generalization;zero-shot language learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Interactive Grounded Language Acquisition and Generalization in a 2D World | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | kernel methods;low-rank approximation;quadrature rules;random features | null | 0 | null | null | iclr | 0.981981 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Quadrature-based features for kernel approximation | null | null | 0 | 4 | Reject | 3;4;5 | null |
null | Facebook Research; Coordinated Science Lab, Department of ECE, University of Illinois at Urbana-Champaign | 2018 | 0 | null | null | 0 | null | null | null | null | null | R. Srikant, Shiyu Liang, Yixuan Li | https://iclr.cc/virtual/2018/poster/264 | Neural networks;out-of-distribution detection | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7 | 6;6;9 | null | null | Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks | null | null | 0 | 3.333333 | Poster | 4;3;3 | null |
null | Facebook AI Research & The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel; The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tomer Galanti, Lior Wolf, Sagie Benaim | https://iclr.cc/virtual/2018/poster/154 | Unsupervised learning;cross-domain mapping;Kolmogorov complexity;Occam's razor | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | null | null | 0 | 3.333333 | Poster | 4;2;4 | null |
null | Toyota Technological Institute at Chicago, Chicago, IL, 60637, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Lifu Tu, Kevin Gimpel | https://iclr.cc/virtual/2018/poster/75 | Approximate Inference Networks;Structured Prediction;Multi-Label Classification;Sequence Labeling | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7 | 5;7;9 | null | null | Learning Approximate Inference Networks for Structured Prediction | null | null | 0 | 4 | Poster | 3;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | multitask learning;lifelong learning;online learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 3 | 2;3;4 | null | null | Lifelong Learning with Output Kernels | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | Deepmind; Google; University of Oxford; Microsoft Research | 2018 | 0 | null | null | 0 | null | null | null | null | null | Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli | https://iclr.cc/virtual/2018/poster/294 | Program Synthesis;Reinforcement Learning;Language Model | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | OpenAI; University of Amsterdam, TNO, Intelligent Imaging; University of Amsterdam, CIFAR | 2018 | 0 | null | null | 0 | null | null | null | null | null | Christos Louizos, Max Welling, Diederik Kingma | https://iclr.cc/virtual/2018/poster/222 | Sparsity;compression;hard and soft attention. | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Learning Sparse Neural Networks through L_0 Regularization | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | ETH Zürich | 2018 | 0 | null | null | 0 | null | null | null | null | null | Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, Andreas Krause | https://iclr.cc/virtual/2018/poster/301 | Generative Adversarial Networks;GANs;online learning | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 6.666667 | 5;7;8 | null | null | An Online Learning Approach to Generative Adversarial Networks | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Recurrent neural network;Vanishing and exploding gradients;Parameter efficiency;Kronecker matrices;Soft unitary constraint | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6 | 5;6;7 | null | null | Kronecker Recurrent Units | null | null | 0 | 4 | Workshop | 4;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | distributed representation;sentence embedding;structure;technical documents;sentence embedding;out-of-vocabulary | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;5;7 | null | null | UNSUPERVISED SENTENCE EMBEDDING USING DOCUMENT STRUCTURE-BASED CONTEXT | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | Stanford University; Intel Labs | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ozan Sener, Silvio Savarese | https://iclr.cc/virtual/2018/poster/194 | Active Learning;Convolutional Neural Networks;Core-Set Selection | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Active Learning for Convolutional Neural Networks: A Core-Set Approach | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Multitask learning;computer vision;multitask loss function | null | 0 | null | null | iclr | -1 | 0 | null | main | 4.666667 | 4;4;6 | null | null | GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks | null | null | 0 | 3.333333 | Reject | 4;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | softmax;optimization;implicit sgd | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Unbiased scalable softmax optimization | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement learning;Q-learning;ensemble method;upper confidence bound | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6 | 5;6;7 | null | null | UCB EXPLORATION VIA Q-ENSEMBLES | null | null | 0 | 4 | Reject | 3;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | convolution neural networks;attention;music information retrieval | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Learning Audio Features for Singer Identification and Embedding | null | null | 0 | 0 | Withdraw | null | null |
null | Centre for Artificial Intelligence, School of Software, University of Technology Sydney; Paul G. Allen School of Computer Science & Engineering, University of Washington | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang | https://iclr.cc/virtual/2018/poster/234 | deep learning;attention mechanism;sequence modeling;natural language processing;sentence embedding | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 6;6;9 | null | null | Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | University of Oxford, United Kingdom | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tim Rocktäschel | https://iclr.cc/virtual/2018/poster/198 | reinforcement learning;deep learning;planning | null | 0 | null | null | iclr | 0.27735 | 0 | null | main | 5.666667 | 4;5;8 | null | null | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning | null | null | 0 | 4.333333 | Poster | 5;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reading comprehension;question answering | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension | null | null | 0 | 3.666667 | Workshop | 4;3;4 | null |
null | IBM Research AI, Yorktown Heights, NY | 2018 | 0 | null | null | 0 | null | null | null | null | null | Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan | https://iclr.cc/virtual/2018/poster/82 | disentangled representations;variational inference | null | 0 | null | null | iclr | -1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Variational Inference of Disentangled Latent Concepts from Unlabeled Observations | null | null | 0 | 4.333333 | Poster | 5;4;4 | null |
null | N/A | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;pretrained;deep learning;perception;algorithmic | null | 0 | null | null | iclr | 0 | 0 | null | main | 3 | 2;3;4 | null | null | Sequential Coordination of Deep Models for Learning Visual Arithmetic | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | natural gradient;generalization;optimization;function space;Hilbert | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Improving generalization by regularizing in $L^2$ function space | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | Computer Science, University of Texas at Austin, Austin, TX, 78712; Google, Kirkland, WA, 98033; Microsoft, Redmond, WA, 98052; Computer Science, UESTC, Chengdu, China; Computer Science, UIUC, Urbana, IL 61801; Computer science, University of Texas at Austin, Austin, TX, 78712 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu | https://iclr.cc/virtual/2018/poster/106 | reinforcement learning;control variates;sample efficiency;variance reduction | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Action-dependent Control Variates for Policy Optimization via Stein Identity | null | null | 0 | 3.333333 | Poster | 4;3;3 | null |
null | Salesforce Research, Palo Alto, CA 94301, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Caiming Xiong, richard socher, Victor Zhong | https://iclr.cc/virtual/2018/poster/258 | question answering;deep learning;natural language processing;reinforcement learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7 | 6;7;8 | null | null | DCN+: Mixed Objective And Deep Residual Coattention for Question Answering | null | null | 0 | 3.333333 | Poster | 4;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning theory;infinite neural networks;topology | null | 0 | null | null | iclr | -0.995871 | 0 | null | main | 4.666667 | 3;4;7 | null | null | Deep Function Machines: Generalized Neural Networks for Topological Layer Expression | null | null | 0 | 2.666667 | Reject | 4;3;1 | null |
null | Stanford University; DeepMind | 2018 | 0 | null | null | 0 | null | null | null | null | null | Rui Shu, Hung H Bui, Hirokazu Narui, Stefano Ermon | https://iclr.cc/virtual/2018/poster/26 | domain adaptation;unsupervised learning;semi-supervised learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | A DIRT-T Approach to Unsupervised Domain Adaptation | https://github.com/RuiShu/dirt-t | null | 0 | 3.333333 | Poster | 2;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | information theory;generative models;latent variable models;variational autoencoders | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;5;7 | null | null | An information-theoretic analysis of deep latent-variable models | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | Amazon Web Services; University of Illinois at Urbana-Champaign | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ashish Khetan, Zachary Lipton, anima anandkumar | https://iclr.cc/virtual/2018/poster/158 | crowdsourcing;noisy annotations;deep leaerning | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Learning From Noisy Singly-labeled Data | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;experimental analysis;hidden neurons | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Discovering the mechanics of hidden neurons | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | Department of EECS, UC Berkeley; OpenAI; Institute for Transportation Studies, UC Berkeley; Department of CSE, University of Washington | 2018 | 0 | null | null | 0 | null | null | null | null | null | Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham M Kakade, Igor Mordatch, Pieter Abbeel | https://iclr.cc/virtual/2018/poster/115 | reinforcement learning;policy gradient;variance reduction;baseline;control variates | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7 | 6;7;8 | null | null | Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines | null | null | 0 | 3.666667 | Oral | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | active inference;predictive coding;motor control | null | 0 | null | null | iclr | -0.944911 | 0 | null | main | 3.666667 | 3;3;5 | null | null | Toward predictive machine learning for active vision | null | null | 0 | 3.666667 | Reject | 5;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | medical diagnosis;medical imaging;multi-label classification | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Learning to diagnose from scratch by exploiting dependencies among labels | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | UC Irvine; Amazon AI, Imperial College London; Amazon AI, Caltech; Amazon AI; Amazon AI, UT Austin; Amazon AI, CMU | 2018 | 0 | null | null | 0 | null | null | null | null | null | Guneet Dhillon, Kamyar Azizzadenesheli, Zachary Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, anima anandkumar | https://iclr.cc/virtual/2018/poster/71 | null | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Stochastic Activation Pruning for Robust Adversarial Defense | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.666667 | 3;3;5 | null | null | Interpreting Deep Classification Models With Bayesian Inference | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | New York University; New York University, Facebook AI Research; Facebook AI Research | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jason Lee, Kyunghyun Cho, Jason Weston, Douwe Kiela | https://iclr.cc/virtual/2018/poster/219 | null | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 6.666667 | 5;7;8 | null | null | Emergent Translation in Multi-Agent Communication | null | null | 0 | 4.333333 | Poster | 5;3;5 | null |
null | University of California, Berkeley; OpenAI | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | machine teaching;interpretability;communication;cognitive science | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 4;8;8 | null | null | Interpretable and Pedagogical Examples | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Adversarial Attack;Interpretability;Saliency Map;Influence Function;Robustness;Machine Learning;Deep Learning;Neural Network | null | 0 | null | null | iclr | -0.654654 | 0 | null | main | 5 | 4;5;6 | null | null | INTERPRETATION OF NEURAL NETWORK IS FRAGILE | null | null | 0 | 3.666667 | Reject | 4;5;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | computer vision;scene understanding;text processing | null | 0 | null | null | iclr | -1 | 0 | null | main | 4 | 2;5;5 | null | null | pix2code: Generating Code from a Graphical User Interface Screenshot | https://github.com/Anonymous | null | 0 | 4.333333 | Withdraw | 5;4;4 | null |
null | Department of Computer Science, University of Texas at Austin; Department of Computer Science, Cornell University; Princeton University and Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Elad Hazan, Adam Klivans, Yang Yuan | https://iclr.cc/virtual/2018/poster/280 | Hyperparameter Optimization;Fourier Analysis;Decision Tree;Compressed Sensing | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7 | 6;6;9 | null | null | Hyperparameter optimization: a spectral approach | null | null | 0 | 4 | Poster | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Generative models;Evaluation of generative models;Data Augmentation | null | 0 | null | null | iclr | -1 | 0 | null | main | 3.666667 | 3;3;5 | null | null | Evaluation of generative networks through their data augmentation capacity | null | null | 0 | 4.333333 | Reject | 5;5;3 | null |