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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Hierarchical reinforcement learning;temporal logic;skill composition | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | anomaly detection;one class support vector machine;adversarial learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Knowledge Discovery;Generative Modeling;Medical;Entity Pair | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 3.333333 | 2;4;4 | null | null | Generative Discovery of Relational Medical Entity Pairs | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Samuel Smith, Quoc V Le | https://iclr.cc/virtual/2018/poster/289 | generalization;stochastic gradient descent;stochastic differential equations;scaling rules;large batch training;bayes theorem;batch size | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.666667 | 3;7;7 | null | null | A Bayesian Perspective on Generalization and Stochastic Gradient Descent | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | Amsterdam Machine Learning Lab, University of Amsterdam, Amsterdam, 1098 XH, NL; Courant Institute of Mathematical Sciences, New York University, New York City, NY, 10010, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Victor Garcia Satorras, Joan Bruna | https://iclr.cc/virtual/2018/poster/43 | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Few-Shot Learning with Graph Neural Networks | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | convolutional neural networks;image classification;deep learning;feature representation;hilbert maps;reproducing kernel hilbert space | null | 0 | null | null | iclr | -1 | 0 | null | main | 5 | 4;5;6 | null | null | Continuous Convolutional Neural Networks for Image Classification | null | null | 0 | 3 | Reject | 4;3;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | convolutional neural networks;loss surface;expressivity;critical point;global minima;linear separability | null | 0 | null | null | iclr | -0.6742 | 0 | null | main | 5.5 | 4;5;6;7 | null | null | The loss surface and expressivity of deep convolutional neural networks | null | null | 0 | 2.75 | Workshop | 4;2;3;2 | null |
null | RIKEN AIP, Tokyo, Japan; RIKEN AIP, Tokyo, Japan; The University of Tokyo, Tokyo, Japan; The University of Aveiro, Aveiro, Portugal | 2018 | 0 | null | null | 0 | null | null | null | null | null | Voot Tangkaratt, , Masashi Sugiyama | https://iclr.cc/virtual/2018/poster/186 | Reinforcement learning;actor-critic;continuous control | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Guide Actor-Critic for Continuous Control | null | null | 0 | 3.333333 | Poster | 4;2;4 | null |
null | Massachusetts Institute of Technology | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chulhee Yun, Suvrit Sra, Ali Jadbabaie | https://iclr.cc/virtual/2018/poster/90 | deep linear neural networks;global optimality;deep learning | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 6.666667 | 5;7;8 | null | null | Global Optimality Conditions for Deep Neural Networks | null | null | 0 | 4.666667 | Poster | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | uncertainty estimation;deep learning;Bayesian learning;batch normalization | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Bayesian Uncertainty Estimation for Batch Normalized Deep Networks | null | null | 0 | 3.666667 | Reject | 3;4;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 | 4.666667 | 4;5;5 | null | null | Unseen Class Discovery in Open-world Classification | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | University of California, Davis, CA 95616, USA; Microsoft Research, Redmond, WA 98052, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ke Wang, Rishabh Singh, Zhendong Su | https://iclr.cc/virtual/2018/poster/69 | Program Embedding;Program Semantics;Dynamic Traces | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Dynamic Neural Program Embeddings for Program Repair | null | null | 0 | 3 | Poster | 2;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | semi-supervised learning;image recognition;sequence tagging;dependency parsing | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 2;5;7 | null | null | Cross-View Training for Semi-Supervised Learning | null | null | 0 | 4 | Workshop | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | unsupervised learning;clustering;deep learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Neural Clustering By Predicting And Copying Noise | https://github.com/neuralclusteringNAT/paper-resources/tree/master/tweet-clustering | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Reinforcement Learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | category representations;psychology;cognitive science;deep neural networks | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Capturing Human Category Representations by Sampling in Deep Feature Spaces | null | null | 0 | 4.333333 | Workshop | 4;5;4 | null |
null | Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA; Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5.333333 | 4;6;6 | null | null | Accelerating Neural Architecture Search using Performance Prediction | null | null | 0 | 4 | Workshop | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Model parallelism;Learning theory | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | Continuous Propagation: Layer-Parallel Training | null | null | 0 | 3.666667 | Withdraw | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement Learning;TD Learning;DQN | null | 0 | null | null | iclr | 0 | 0 | null | main | 3 | 2;3;4 | null | null | TD Learning with Constrained Gradients | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | attention;sensor-selection;multi-sensor;natural noise | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 3;4;7 | null | null | Sensor Transformation Attention Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep RL;Thompson Sampling;Posterior update | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Efficient Exploration through Bayesian Deep Q-Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | asymmetric structure;RNN-CNN;fast;unsupervised;representation;sentence | null | 0 | null | null | iclr | -0.970725 | 0 | null | main | 5.333333 | 3;6;7 | null | null | Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | University of Massachusetts, Amherst, MA, USA; University of Alberta, Edmonton, AB, Canada; IBM Research, Yorktown Heights, NY, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell | https://iclr.cc/virtual/2018/poster/201 | reinforcement learning;options;successor representation;proto-value functions;Atari;Arcade Learning Environment | null | 0 | null | null | iclr | 0.981981 | 0 | null | main | 7.333333 | 6;7;9 | null | null | Eigenoption Discovery through the Deep Successor Representation | null | null | 0 | 4 | Poster | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Long-tail datasets;Imbalanced datasets | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Bayesian Embeddings for Long-Tailed Datasets | null | null | 0 | 4 | Withdraw | 4;4;4 | null |
null | Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Emilio Parisotto, Ruslan Salakhutdinov | https://iclr.cc/virtual/2018/poster/196 | deep reinforcement learning;deep learning;memory | null | 0 | null | null | iclr | 0.188982 | 0 | null | main | 7.333333 | 6;7;9 | null | null | Neural Map: Structured Memory for Deep Reinforcement Learning | null | null | 0 | 4.666667 | Poster | 5;4;5 | null |
null | Boston University | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | domain adaptation;adversarial networks;statistical distance;duality | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Stable Distribution Alignment Using the Dual of the Adversarial Distance | null | null | 0 | 3.666667 | Workshop | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Convolutional Neural Networks;CNN;CP Decomposition;Low Rank Approximation | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Accelerating Convolutional Neural Networks using Iterative Two-Pass Decomposition | null | null | 0 | 4.333333 | Withdraw | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep reinforcement learning | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Regret Minimization for Partially Observable Deep Reinforcement Learning | null | null | 0 | 4.333333 | Workshop | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | predictive distribution estimation;probabilistic RNN;uncertainty in time series prediction | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learning temporal evolution of probability distribution with Recurrent Neural Network | null | null | 0 | 3.333333 | Reject | 4;2;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Non-convex optimization;Two-layer Neural Network;global optimality;first-order optimality | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6 | 4;7;7 | null | null | Theoretical properties of the global optimizer of two-layer Neural Network | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | Department of Electrical and Computer Engineering, University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Pouya Samangouei, Maya Kabkab, Rama Chellappa | https://iclr.cc/virtual/2018/poster/113 | null | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;6;8 | null | null | Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | Borealis AI, Canada | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yanshuai Cao, Gavin Weiguang Ding, Yik Chau Lui, Ruitong Huang | https://iclr.cc/virtual/2018/poster/24 | null | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Improving GAN Training via Binarized Representation Entropy (BRE) Regularization | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Learning to learn;meta-learning;reinforcement learning;optimization | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learning to Optimize Neural Nets | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | representation learning;natural language generation;discrete structure modeling;adversarial training;unaligned text style-transfer | null | 0 | null | null | iclr | -0.80829 | 0 | null | main | 5.75 | 3;5;6;9 | null | null | Adversarially Regularized Autoencoders | null | null | 0 | 3.5 | Workshop | 4;4;3;3 | null |
null | Facebook AI Research; Facebook AI Research, Tel Aviv University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yedid Hoshen, Lior Wolf | https://iclr.cc/virtual/2018/poster/226 | unsupervised mapping;cross domain mapping | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Identifying Analogies Across Domains | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Verification;SMT solver;Mixed Integer Programming;Neural Networks | null | 0 | null | null | iclr | -0.981981 | 0 | null | main | 4.666667 | 3;5;6 | null | null | Piecewise Linear Neural Networks verification: A comparative study | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | autoencoders;sequence models;discrete representations | null | 0 | null | null | iclr | -0.720577 | 0 | null | main | 5 | 4;5;6 | null | null | Discrete Autoencoders for Sequence Models | null | null | 0 | 3.333333 | Reject | 4;5;1 | null |
null | Department of Engineering, University of Cambridge | 2018 | 0 | null | null | 0 | null | null | null | null | null | Viet Cuong Nguyen, Yingzhen Li, Thang Bui, Richard E Turner | https://iclr.cc/virtual/2018/poster/199 | continual learning;online variational inference | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Variational Continual Learning | null | null | 0 | 3 | Poster | 2;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Semi-supervised Learning;Generative And Adversary Framework;One-class classification;Outlier detection | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Semi-supervised Outlier Detection using Generative And Adversary Framework | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | Baidu Inc., Beijing, China; National Engineering Laboratory of Deep Learning Technology and Application, China; Baidu Inc., Beijing, China | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chao Qiao, Bo Huang, Guocheng Niu, daren li, daxiang dong, wei he, Dianhai Yu, hua wu | https://iclr.cc/virtual/2018/poster/325 | region embedding;local context unit;text classification | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | A New Method of Region Embedding for Text Classification | null | null | 0 | 4 | Poster | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Joint embeddings;Hard Negatives;Visual-semantic embeddings;Cross-modal retrieval;Ranking | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4 | null | null | VSE++: Improving Visual-Semantic Embeddings with Hard Negatives | null | null | 0 | 4 | Withdraw | 4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Program Synthesis;Semantic Parsing;WikiTable;SQL;Pointer Network | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 3;4;7 | null | null | Pointing Out SQL Queries From Text | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | IBM Research AI, Yorktown Heights, NY 10598; Massachusetts Institute of Technology, Cambridge, MA 02139; Tencent AI Lab, Bellevue, WA 98004; University of California, Davis, CA 95616 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel | https://iclr.cc/virtual/2018/poster/97 | robustness;adversarial machine learning;neural network;extreme value theory;adversarial example;adversarial perturbation | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach | null | null | 0 | 2.333333 | Poster | 1;3;3 | null |
null | Google AI; University of Illinois at Urbana-Champaign; Georgia Institute of Technology; Georgia Institute of Technology, Ant Financial Services Group | 2018 | 0 | null | null | 0 | null | null | null | null | null | Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song | https://iclr.cc/virtual/2018/poster/23 | reinforcement learning;actor-critic algorithm;Lagrangian duality | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Boosting the Actor with Dual Critic | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | label embedding;deep learning;label representation;computer vision;natural language processing | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks | null | null | 0 | 4 | Reject | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Continual Learning;Catastrophic Forgetting;Sequential Multitask Learning;Deep Generative Models;Dual Memory Networks;Deep Learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Deep Generative Dual Memory Network for Continual Learning | null | null | 0 | 3.333333 | Reject | 4;4;2 | null |
null | The University of Texas at Austin and Sentient Technologies, Inc. | 2018 | 0 | null | null | 0 | null | null | null | null | null | Elliot Meyerson, Risto Miikkulainen | https://iclr.cc/virtual/2018/poster/49 | multitask learning;deep learning;modularity | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Memory Networks;Combinatorial Optimization;Binary LP | null | 0 | null | null | iclr | -0.944911 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Long Term Memory Network for Combinatorial Optimization Problems | null | null | 0 | 2.333333 | Reject | 4;2;1 | null |
null | Facebook AI Research, New York, NY | 2018 | 0 | null | null | 0 | null | null | null | null | null | Alex Peysakhovich, Adam Lerer | https://iclr.cc/virtual/2018/poster/335 | deep reinforcement learning;cooperation;social dilemma;multi-agent systems | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Consequentialist conditional cooperation in social dilemmas with imperfect information | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Neural networks;Training;Convergence | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | FastNorm: Improving Numerical Stability of Deep Network Training with Efficient Normalization | null | null | 0 | 3.333333 | Withdraw | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement learning;deep learning;autonomous control | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4 | 3;4;5 | null | null | Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control | null | null | 0 | 4 | Reject | 4;5;3 | null |
null | Department of Computer Science, University of Toronto and FOR.ai; FOR.ai; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Aidan Gomez, Sicong(Sheldon) Huang, Ivan Zhang, Bryan Li, Muhammad Osama, Lukasz Kaiser | https://iclr.cc/virtual/2018/poster/277 | null | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Unsupervised Cipher Cracking Using Discrete GANs | github.com/for-ai/ciphergan | null | 0 | 3 | Poster | 1;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Multi-agent Reinforcement Learning;Communication;Reward Distribution;Trusted Third Party;Auction Theory | null | 0 | null | null | iclr | -0.960769 | 0 | null | main | 5.333333 | 3;6;7 | null | null | Neuron as an Agent | null | null | 0 | 4 | Workshop | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Space-by-time non-negative matrix factorization;dimensionality reduction;baseline correction;neuronal decoding;mutual information | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 4;6;6 | null | null | Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Approximate Inference;Amortization;Posterior Approximations;Variational Autoencoder | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Inference Suboptimality in Variational Autoencoders | null | null | 0 | 4.666667 | Workshop | 4;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep reinforcement learning;navigation;path-planning;mapping | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.333333 | 3;3;7 | null | null | Do Deep Reinforcement Learning Algorithms really Learn to Navigate? | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | UC Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Fred Shentu, Evan Shelhamer, Jitendra Malik, Alexei Efros, Trevor Darrell | https://iclr.cc/virtual/2018/poster/51 | imitation;zero-shot;self-supervised;robotics;skills;navigation;manipulation;vizdoom;reinforcement | null | 0 | null | null | iclr | -0.866025 | 0 | https://pathak22.github.io/zeroshot-imitation/ | main | 7.666667 | 7;8;8 | null | null | Zero-Shot Visual Imitation | https://github.com/pathak22/zeroshot-imitation | null | 0 | 4 | Oral | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adaptive learning rates;analytical continuation;fully connected networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 3 | 3;3;3 | null | null | Per-Weight Class-Based Learning Rates via Analytical Continuation | null | null | 0 | 3.333333 | Withdraw | 4;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | structured attention;neural machine translation;grammar induction | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 4.666667 | 3;5;6 | null | null | Inducing Grammars with and for Neural Machine Translation | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;invariance;data set;evaluation | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.333333 | 3;3;4 | null | null | On the Construction and Evaluation of Color Invariant Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Shifting Mean Activation Towards Zero with Bipolar Activation Functions | null | null | 0 | 4 | Workshop | 4;5;3 | null |
null | Paper under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Iterative temporal differencing;feedback alignment;spike-time dependent plasticity;vanilla backpropagation;deep learning | null | 0 | null | null | iclr | -1 | 0 | null | main | 2.333333 | 2;2;3 | null | null | Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | Computer Science Division, University of California, Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | Warren He, Bo Li, Dawn Song | https://iclr.cc/virtual/2018/poster/57 | adversarial machine learning;supervised representation learning;decision regions;decision boundaries | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Decision Boundary Analysis of Adversarial Examples | null | null | 0 | 2.666667 | Poster | 3;3;2 | null |
null | Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong | 2018 | 0 | null | null | 0 | null | null | null | null | null | LU HOU, James Kwok | https://iclr.cc/virtual/2018/poster/203 | deep learning;network quantization | null | 0 | null | null | iclr | -1 | 0 | null | main | 6.666667 | 6;6;8 | null | null | Loss-aware Weight Quantization of Deep Networks | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | University of British Columbia; University of Oxford | 2018 | 0 | null | null | 0 | null | null | null | null | null | Atilim Gunes Baydin, Robert Cornish, David Martínez, Mark Schmidt, Frank Wood | https://iclr.cc/virtual/2018/poster/14 | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Online Learning Rate Adaptation with Hypergradient Descent | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | GAN;wasserstein distance;discrete probability distribution | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Discrete Wasserstein Generative Adversarial Networks (DWGAN) | null | null | 0 | 0 | Withdraw | null | null |
null | Facebook AI Research; Computer Science Department, Duke University; Data Sciences and Operations Department, University of Southern California | 2018 | 0 | null | null | 0 | null | null | null | null | null | Rong Ge, Jason Lee, Tengyu Ma | https://iclr.cc/virtual/2018/poster/119 | theory;non-convex optimization;loss surface | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 6;7;9 | null | null | Learning One-hidden-layer Neural Networks with Landscape Design | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | School of Informatics, University of Edinburgh, UK and Alan Turing Institute, London, UK; School of Informatics, University of Edinburgh, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Cian Eastwood, Chris Williams | https://iclr.cc/virtual/2018/poster/55 | null | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | A Framework for the Quantitative Evaluation of Disentangled Representations | null | null | 0 | 4.666667 | Poster | 4;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | gaussian process neuron activation function stochastic transfer function learning variational bayes probabilistic | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Gaussian Process Neurons | null | null | 0 | 3.666667 | Reject | 5;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | multimodal;knowledge base;relational modeling;embedding;link prediction;neural network encoders | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Embedding Multimodal Relational Data | null | null | 0 | 4.333333 | Withdraw | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | DNN Compression;Weigh-sharing;Model Compression | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.333333 | 3;3;4 | null | null | DNN Model Compression Under Accuracy Constraints | null | null | 0 | 3.666667 | Reject | 3;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | query completion;realtime;error correction;recurrent network;beam search | null | 0 | null | null | iclr | -0.866025 | 0 | http://www.deepquerycompletion.com | main | 5 | 4;5;6 | null | null | Realtime query completion via deep language models | null | null | 0 | 3.666667 | Reject | 5;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neuro-symbolic reasoning;information extraction;learn to search | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES | null | null | 0 | 3.666667 | Workshop | 4;3;4 | null |
null | Sorbonne Universités, UMR 7606, LIP6, F-75005 Paris, France | 2018 | 0 | null | null | 0 | null | null | null | null | null | Emmanuel d Bezenac, Arthur Pajot, patrick Gallinari | https://iclr.cc/virtual/2018/poster/40 | deep learning;physical processes;forecasting;spatio-temporal | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge | null | null | 0 | 2.666667 | Poster | 2;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Affect lexicon;word embeddings;Word2Vec;GloVe;WordNet;joint learning;sentiment analysis;word similarity;outlier detection;affect prediction | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Towards Building Affect sensitive Word Distributions | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;quantized deep neural network;activation quantization | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | PACT: Parameterized Clipping Activation for Quantized Neural Networks | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Attention Model;Machine Comprehension;Question Answering | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;5;8 | null | null | Phase Conductor on Multi-layered Attentions for Machine Comprehension | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | Karlsruhe Institute of Technology (KIT); University of California, Berkeley; OpenAI | 2018 | 0 | null | null | 0 | null | null | null | null | null | Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz | https://iclr.cc/virtual/2018/poster/228 | reinforcement learning;exploration;parameter noise | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Parameter Space Noise for Exploration | null | null | 0 | 4.333333 | Poster | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;homomorphic encryption;hybrid homomorphic encryption;privacy preserving;representation learning;neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Deep Learning Inferences with Hybrid Homomorphic Encryption | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | Department of Computer Engineering, Ulsan National Institute of Science and Technology, 50 UNIST, Ulsan 44919, Republic of Korea | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Information Bottleneck;Deep Neural Networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Parametric Information Bottleneck to Optimize Stochastic Neural Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Derivative Calculations;Optimization Algorithms | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.666667 | 2;4;5 | null | null | Understanding and Exploiting the Low-Rank Structure of Deep Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;external memory;deep learning;policy gradient;online learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Integrating Episodic Memory into a Reinforcement Learning Agent Using Reservoir Sampling | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | University of Oxford; DeepMind | 2018 | 0 | null | null | 0 | null | null | null | null | null | Gábor Melis, Chris Dyer, Phil Blunsom | https://iclr.cc/virtual/2018/poster/214 | rnn;language modelling | null | 0 | null | null | iclr | -0.785714 | 0 | null | main | 6.666667 | 5;7;8 | null | null | On the State of the Art of Evaluation in Neural Language Models | null | null | 0 | 3.333333 | Poster | 5;2;3 | null |
null | Department of Civil and Environmental Engineering, University of Southern California; Department of Computer Science, University of Southern California | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu | https://iclr.cc/virtual/2018/poster/327 | spatiotemporal data;graph convolutional network;data quality | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 6;6;8 | null | null | Automatically Inferring Data Quality for Spatiotemporal Forecasting | 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;data augmentation;regularization | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Data augmentation instead of explicit regularization | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Multi-instance learning;Medical Time Series;Concept Annotation | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;3;6 | null | null | Relational Multi-Instance Learning for Concept Annotation from Medical Time Series | null | null | 0 | 4 | Reject | 3;5;4 | null |
null | Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Engineering Science, University of Oxford, Oxford, UK; Alan Turing Institute, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jinsung Yoon, James Jordan, Mihaela v Schaar | https://iclr.cc/virtual/2018/poster/153 | Individualized Treatment Effects;Counterfactual Estimation;Generative Adversarial Nets | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets | null | null | 0 | 3.333333 | Poster | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | nearest neighbor;reinforcement learning;policy;continuous control | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Simple Nearest Neighbor Policy Method for Continuous Control Tasks | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | William Fedus, Ian Goodfellow, Andrew Dai | https://iclr.cc/virtual/2018/poster/10 | Deep learning;GAN | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | MaskGAN: Better Text Generation via Filling in the _______ | null | null | 0 | 4 | Poster | 3;5;4 | null |
null | Department of Computer Science, University of Southern California | 2018 | 0 | null | null | 0 | null | null | null | null | null | Michael Tsang, Dehua Cheng, Yan Liu | https://iclr.cc/virtual/2018/poster/285 | statistical interaction detection;multilayer perceptron;generalized additive model | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Detecting Statistical Interactions from Neural Network Weights | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | Computer Science, UIUC, Urbana, IL 61801 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tanmay Gangwani, Jian Peng | https://iclr.cc/virtual/2018/poster/160 | Genetic algorithms;deep reinforcement learning;imitation learning | null | 0 | null | null | iclr | 0.802955 | 0 | null | main | 5.666667 | 3;6;8 | null | null | Policy Optimization by Genetic Distillation | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neural architecture search | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Faster Discovery of Neural Architectures by Searching for Paths in a Large Model | null | null | 0 | 2.333333 | Workshop | 3;2;2 | null |
null | DeepMind; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew Dai, Shakir Mohamed, Ian Goodfellow | https://iclr.cc/virtual/2018/poster/180 | Deep learning;GAN | null | 0 | null | null | iclr | 0.240192 | 0 | null | main | 6.333333 | 4;7;8 | null | null | Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step | null | null | 0 | 4 | Poster | 4;3;5 | null |
null | Georgia Tech Research Institute, Atlanta, GA 30318, USA; Georgia Institute of Technology, Atlanta, GA 30332, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira | https://iclr.cc/virtual/2018/poster/333 | transfer learning;similarity prediction;clustering;domain adaptation;unsupervised learning;computer vision;deep learning;constrained clustering | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7 | 5;7;9 | null | null | Learning to cluster in order to transfer across domains and tasks | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | Preferred Networks, Inc.; Ritsumeikan University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Takeru Miyato, Masanori Koyama | https://iclr.cc/virtual/2018/poster/217 | Generative Adversarial Networks;GANs;conditional GANs;Generative models;Projection | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | cGANs with Projection Discriminator | https://github.com/pfnet-research/sngan_projection | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | University of Toronto, Vector Institute; General Motors Advanced Technical Center - Israel, Department of Electrical Engineering, Technion; General Motors Advanced Technical Center - Israel | 2018 | 0 | null | null | 0 | null | null | null | null | null | Oran Shayer, Dan Levi, Ethan Fetaya | https://iclr.cc/virtual/2018/poster/314 | deep learning;discrete weight network | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Learning Discrete Weights Using the Local Reparameterization Trick | null | null | 0 | 3.333333 | Poster | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | LSTM;RNN;rotation matrix;long-term memory;natural language processing | null | 0 | null | null | iclr | -1 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Modifying memories in a Recurrent Neural Network Unit | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | multiscale models;hidden Markov model;covariance prediction | null | 0 | null | null | iclr | 1 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Multiscale Hidden Markov Models For Covariance Prediction | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | distribution regression;supervised learning;regression analysis | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 5;7;7 | null | null | Distribution Regression Network | null | null | 0 | 3.333333 | Reject | 4;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Language AI Learning Reinforcement Deep | null | 0 | null | null | iclr | -0.327327 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Understanding Grounded Language Learning Agents | null | null | 0 | 4 | Reject | 5;3;4 | null |