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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Montreal Institute for Learning Algorithms (MILA), D´epartement d’Informatique et de Recherche Op ´erationnelle, Universit ´e de Montr ´eal, Montr ´eal, Qu ´ebec, Canada, Associate Fellow, Canadian Institute For Advanced Research (CIFAR); Montreal Institute for Learning Algorithms (MILA), D´epartement d’Informatique et de Recherche Op ´erationnelle, Universit ´e de Montr ´eal, Montr ´eal, Qu ´ebec, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Recurrent Normalization Propagation
| null | null | 0 | 3.666667 |
Workshop
|
4;4;3
| null |
null |
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
2;3;7
| null | null |
Sample Importance in Training Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Games
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Microsoft Research, Redmond, WA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared Memory
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Technion - Israel Institute of Technology, Haifa, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;6
| null | null |
Semi-supervised deep learning by metric embedding
| null | null | 0 | 4 |
Workshop
|
4;4
| null |
null |
École Polytechnique de Montréal, Canada.; Goldsmiths College, University of London. Department of Computing.
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 9 |
9;9;9
| null | null |
Neural Architecture Search with Reinforcement Learning
| null | null | 0 | 4.333333 |
Oral
|
5;4;4
| null |
null |
Google Brain; MILA, Universit ´e de Montr ´eal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Counterpoint by Convolution
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
NVIDIA, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
|
https://github.com/NVlabs/GA3C
| null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null |
MILA - Université de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Recurrent Batch Normalization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Improving Stochastic Gradient Descent with Feedback
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
OpenAI; Preferred Networks, Inc., ATR Cognitive Mechanisms Laboratories, Kyoto University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Adversarial Training Methods for Semi-Supervised Text Classification
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
OpenAI, International Computer Science Institute; UC Berkeley, Department of Electrical Engineering and Computer Sciences
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 |
http://rll.berkeley.edu/visual_servoing
|
main
| 7.333333 |
7;7;8
| null | null |
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Robotics and Biology Laboratory, Technische Universität Berlin, Germany; Robotics and Mechatronics Center, German Aerospace Center (DLR), Wessling, Germany; Ecole Nationale Supérieure de Techniques Avancées (ENSTA-ParisTech), Paris, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Unsupervised Learning of State Representations for Multiple Tasks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Stanford University, Stanford, CA, USA; Salesforce Research, Palo Alto, CA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Short and Deep: Sketching and Neural Networks
| null | null | 0 | 2.666667 |
Workshop
|
2;4;2
| null |
null |
Department of Electrical and Computer Engineering, McGill University, Montréal, Québec, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications;Optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Department of Computer Science, University of the British Columbia, Vancouver, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Department of Computer Science, University College London
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Python Code Suggestion with a Sparse Pointer Network
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of California Berkeley; DeepMind, Canadian Institute for Advanced Research; DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null | null |
Learning to Perform Physics Experiments via Deep Reinforcement Learning
| null | null | 0 | 3.25 |
Poster
|
3;4;3;3
| null |
null |
Department of Information and Computer Science, Keio University, Hiyoshi 3-14-1, Kohokuku, Yokohama City, Kanagawa, Japan
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Fuzzy paraphrases in learning word representations with a lexicon
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
NA VER LABS Corp. & NA VER Corp., Gyeonggi-do 13561, Republic of Korea; School of Computer Science and Engineering & Interdisciplinary Program in Cognitive Science, Seoul National University & Surromind Robotics, Seoul 08826, Republic of Korea; School of Computer Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea; Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Republic of Korea; School of Computing, KAIST, Daejeon 34141, Republic of Korea
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Multi-modal learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Hadamard Product for Low-rank Bilinear Pooling
| null | null | 0 | 3.666667 |
Poster
|
3;5;3
| null |
null |
Microsoft Research, Redmond, WA 98052, USA; Department of Engineering Science - University of Oxford; Department of Engineering Science - University of Oxford & Alan Turing Institute
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning to superoptimize programs
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Microsoft Research; Department of Computer Science and Engineering, University of California, San Diego
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Applications
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Combating Deep Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null |
IBM Watson, Yorktown Heights, NY, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Department of Information Engineering, The Chinese University of Hong Kong; Noah’s Ark Lab, Huawei Technologies Co. Ltd.
|
2017
| 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 |
Collaborative Deep Embedding via Dual Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Information Science and Technology, The University of Tokyo
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Development of JavaScript-based deep learning platform and application to distributed training
| null | null | 0 | 3 |
Workshop
|
2;4;3
| null |
null |
´Ecole polytechnique, Palaiseau, France; Facebook AI Research, Menlo Park, CA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Iterative Refinement for Machine Translation
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null |
Electrical Engineering Department, The Technion - Israel Institute of Technology, Haifa 32000, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Put All Your Authors Here, Separated by Commas
| null |
Reinforcement Learning;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
IBM Watson Research, Yorktown Heights, NY; Computer Science, UMass Amherst; Computer Science & Engineering, University of Michigan
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning to Query, Reason, and Answer Questions On Ambiguous Texts
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Microsoft Research, USA and Carnegie Mellon University, USA; Microsoft Research, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Structured prediction
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Neuro-Symbolic Program Synthesis
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
MILA, Universit ´e de Montr ´eal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Generalizable Features From Unsupervised Learning
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null |
Department of Computer Science, University of Illinois at Urbana-Champaign; Zhejiang University; Department of Computer Science, University of Illinois at Urbana-Champaign; Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Optimization;Games
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
4;9;9
| null | null |
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Samsung US R&D Center, San Diego, CA 92121, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Towards the Limit of Network Quantization
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Predicting Medications from Diagnostic Codes with Recurrent Neural Networks
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Department of Electrical Engineering, The Technion - Israel Institute of Technology, Haifa 32000, Israel; Walmart Labs, Sunnyvale, California
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-modal learning;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Engineering Science, University of Oxford, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Layer Recurrent Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Intel Labs
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Learning to Act by Predicting the Future
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null |
IBM Watson, V Parku 4, 140 00 Prague, Czech Republic
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Semi-Supervised Learning;Deep learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Finding a Jack-of-All-Trades: An Examination of Semi-supervised Learning in Reading Comprehension
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Electrical and Computing Engineering, University of Pittsburgh; Intel Labs; Department of Electrical and Computer Engineering, Duke University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
University of Amsterdam, Canadian Institute for Advanced Research; University of Amsterdam
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Steerable CNNs
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Aylien Ltd., Dublin, Ireland; Insight Centre for Data Analytics, National University of Ireland, Galway
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Transfer Learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Knowledge Adaptation: Teaching to Adapt
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Facebook AI Research; Facebook AI Research, Courant Institute, New York University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Tracking the World State with Recurrent Entity Networks
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Conditional Image Synthesis With Auxiliary Classifier GANs
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Twitter, Cambridge, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
Optimization as a Model for Few-Shot Learning
| null | null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Neural Combinatorial Optimization with Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of California, Berkeley
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Neural Code Completion
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Deep Learning Technology Center, Microsoft Research; Columbia University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
ETH Zürich, Zürich, Switzerland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning;Games;Transfer Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Multi-task learning with deep model based reinforcement learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Salesforce Research, Palo Alto, CA 94301, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Dynamic Coattention Networks For Question Answering
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Carnegie Mellon University, Pittsburgh, PA, USA; Microsoft Research, Cambridge, UK; DigitalGenius Ltd., London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Batch Policy Gradient Methods for Improving Neural Conversation Models
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Department of Information and Computing Sciences, Utrecht University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Incremental Sequence Learning
|
https://edwin-de-jong.github.io/
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Twitter, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Lossy Image Compression with Compressive Autoencoders
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Department of Electronic Engineering, Tsinghua University, Beijing, China; Cognitive Computing Laboratory, Intel Labs China, Beijing, China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Chess Game Concepts Emerge under Weak Supervision: A Case Study of Tic-tac-toe
| null | null | 0 | 3.333333 |
Reject
|
2;3;5
| null |
null |
MILA, Université de Montréal; École Polytechnique de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
|
http://github.com/teganmaharaj/zoneout
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
NEC Labs America; University of Maryland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null | null |
Pruning Filters for Efficient ConvNets
| null | null | 0 | 4.5 |
Poster
|
5;5;4;4
| null |
null |
School of Mathematical Sciences, Peking University, Beijing 100871, China; Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Character-Level Neural Machine Translation By Learning Morphology
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Stanford University, Palo Alto, California; Salesforce Research, Palo Alto, California
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, University of Chicago, Chicago, IL 60637, USA; Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Multi-view Recurrent Neural Acoustic Word Embeddings
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Center for Neural Science and Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA; Center for Neural Science, New York University, New York, NY 10003, USA; Image Processing Laboratory, Universitat de Val `encia, 46980 Paterna, Spain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 8.25 |
8;8;8;9
| null | null |
End-to-end Optimized Image Compression
| null | null | 0 | 3.75 |
Oral
|
3;4;4;4
| null |
null |
Google Inc., Mountain View, CA 94043, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Gradients of Counterfactuals
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Toronto, Toronto ON, CANADA and Canadian Institute for Advanced Research (CIFAR); University of Toronto, Toronto ON, CANADA; Baylor College of Medicine, Houston TX, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Variational Recurrent Adversarial Deep Domain Adaptation
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Stanford University; University of Cambridge, MPI Tübingen; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Semi-Supervised Learning;Optimization;Structured prediction
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Categorical Reparameterization with Gumbel-Softmax
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Facebook AI Research, New York, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Learning through Dialogue Interactions by Asking Questions
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
University of Michigan, Ann Arbor, USA and Google Brain, Mountain View, CA 94043; POSTECH, Pohang, Korea; University of Michigan, Ann Arbor, USA; Beihang University, Beijing, China; Adobe Research, San Jose, CA 95110
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Decomposing Motion and Content for Natural Video Sequence Prediction
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Computer Science and Engineering, University of Washington; Department of Statistics, University of Washington; Department of Computer Science and Engineering, Department of Statistics, University of Washington
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications
| null | 0 | null | null |
iclr
| 0 | 0 |
http://homes.cs.washington.edu/~thickstn/musicnet.html
|
main
| 6.666667 |
6;6;8
| null | null |
Learning Features of Music From Scratch
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Pacific Northwest National Laboratory
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Leveraging Asynchronicity in Gradient Descent for Scalable Deep Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
School of Engineering and Applied Sciences, Harvard University; Language Technologies Institute, Carnegie Mellon University; Machine Learning Department, Carnegie Mellon University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Dropout with Expectation-linear Regularization
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Telefónica Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Compact Embedding of Binary-coded Inputs and Outputs using Bloom Filters
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Institute of Numerical Mathematics, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia; Moscow Institute of Physics and Technology, Moscow, Russia; National Research University Higher School of Economics, Moscow, Russia; Institute of Numerical Mathematics, Moscow, Russia
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning;Optimization
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Exponential Machines
| null | null | 0 | 3.75 |
Workshop
|
4;4;4;3
| null |
null |
School of Informatics, The University of Edinburgh
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Low-rank passthrough neural networks
| null | null | 0 | 2.666667 |
Reject
|
4;0;4
| null |
null |
College of Information and Computer Sciences, University of Massachusetts Amherst; OpenAI; Google Brain; University of Toronto
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Adding Gradient Noise Improves Learning for Very Deep Networks
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Efficient Softmax Approximation for GPUs
|
https://github.com/facebookresearch/adaptive-softmax
| null | 0 | 4 |
Workshop
|
5;4;3
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
HyperNetworks
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
; University of Montreal, CIFAR Senior Fellow; University of Montreal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
The Variational Walkback Algorithm
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Seoul National University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;5;8
| null | null |
LSTM-Based System-Call Language Modeling and Ensemble Method for Host-Based Intrusion Detection
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Janelia Research Campus, HHMI, AIFounded Inc.; AIFounded Inc.; University of Toronto
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
An Empirical Analysis of Deep Network Loss Surfaces
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science and Center for Sensorimotor Neural Engineering, University of Washington, Seattle, WA 98105, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Computer vision;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Transformational Sparse Coding
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
NYU; UC Berkeley; Google; UCLA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
University of Massachusetts Amherst; OpenAI; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning a Natural Language Interface with Neural Programmer
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Maluuba Research, Montréal, Québec, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 |
datasets.maluuba.com/NewsQA
|
main
| 6 |
6;6;6
| null | null |
NEWSQA: A MACHINE COMPREHENSION DATASET
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, United States
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning to Optimize
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Carnegie Mellon University; Oracle Labs
|
2017
| 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 a Static Analyzer: A Case Study on a Toy Language
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Binghamton University, Vestal, NY 13902, USA; IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Generative Adversarial Networks as Variational Training of Energy Based Models
|
https://github.com/Shuangfei/vgan
| null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null |
ICSI and Department of Statistics, University of California at Berkeley, Berkeley, CA, 94704; Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China, 100044
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Multi-label learning with semantic embeddings
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
School of Computer and Communication Sciences, Ecole polytechnique federale de Lausanne, Lausanne, Switzerland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
TreNet: Hybrid Neural Networks for Learning the Local Trend in Time Series
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Structured prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Submodular Sum-product Networks for Scene Understanding
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Department of Computer Science and Operations Research, Universit ´e de Montr ´eal, Montreal, QC. H3C 3J7
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Understanding intermediate layers using linear classifier probes
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Berkeley AI Research (BAIR), University of California, Berkeley; Berkeley AI Research (BAIR), University of California, Berkeley; OpenAI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Generalizing Skills with Semi-Supervised Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
D-Wave Systems, Burnaby, BC V5G-4M9, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
Discrete Variational Autoencoders
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null |
D´epartement Informatique, Ecole Normale Sup ´erieure, Paris, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
A hybrid network: Scattering and Convnet
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
NTT Resonant Inc.
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
CAN AI GENERATE LOVE ADVICE?: TOWARD NEURAL ANSWER GENERATION FOR NON-FACTOID QUESTIONS
| null | null | 0 | 2.666667 |
Reject
|
0;4;4
| null |
null |
Google Brain, Department of Computing Science, University of Alberta; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Improving Policy Gradient by Exploring Under-appreciated Rewards
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Unsupervised Learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Generative Paragraph Vector
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Criteo Research, Paris, 32 Blanche, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
CONTENT2VEC: SPECIALIZING JOINT REPRESENTATIONS OF PRODUCT IMAGES AND TEXT FOR THE TASK OF PRODUCT RECOMMENDATION
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
IST Austria
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Georg Martius and Christoph H. Lampert
| null |
Supervised Learning;Deep learning;Structured prediction
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Extrapolation and learning equations
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
Toyota Technological Institute at Chicago
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Emergent Predication Structure in Vector Representations of Neural Readers
|
https://github.com/sohuren
| null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Department of Bioengineering, Imperial College London, London SW7 2BP, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Improving Sampling from Generative Autoencoders with Markov Chains
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Pacific Northwest National Laboratory, Seattle, WA; Eastern Washington University, Cheney, Washington; Pacific Northwest National Laboratory, Richland, WA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Transfer Learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
| null | null | 0 | 3.666667 |
Reject
|
4;2;5
| null |
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