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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
California Institute of Technology; Janelia Research Campus HHMI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Semi-Supervised Learning;Reinforcement Learning;Applications
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Recurrent Representations for Hierarchical Behavior Modeling
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
New York University; University of Montreal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Natural language processing;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Skip-graph: Learning graph embeddings with an encoder-decoder model
| null | null | 0 | 2.666667 |
Reject
|
4;1;3
| null |
null |
Facebook AI Research, New York; Facebook AI Research, Menlo Park
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Speech;Structured prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.5 |
6;7
| null | null |
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
| null | null | 0 | 4.5 |
Reject
|
4;5
| null |
null |
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Universitat Paderborn
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Hebrew University of Jerusalem
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Inductive Bias of Deep Convolutional Networks through Pooling Geometry
| null | null | 0 | 3.666667 |
Poster
|
3;5;3
| null |
null |
UMR CNRS NIA 7260, Aix Marseille Univ., Marseille, France; Teklia SAS, Paris, France; A2iA SAS, Paris, France; UMR CNRS LSIS 7296, AMU, Univ. Toulon, ENSAM, IUF, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Cortical-Inspired Open-Bigram Representation for Handwritten Word Recognition
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, 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 |
4;5;6
| null | null |
Inefficiency of stochastic gradient descent with larger mini-batches (and more learners)
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of Brain and Cognitive Sciences, MIT; Department of Electrical Engineering and Computer Science, MIT
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
A Compositional Object-Based Approach to Learning Physical Dynamics
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
Making Neural Programming Architectures Generalize via Recursion
| null | null | 0 | 4 |
Oral
|
3;4;5
| null |
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Speech;Computer vision;Deep learning;Multi-modal learning;Unsupervised Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning Word-Like Units from Joint Audio-Visual Analylsis
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Microsoft Research Asia
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Boosting Image Captioning with Attributes
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Department of Computer Science and Software Engineering, Laval University, Quebec, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
Google Deepmind; Google / University of Warsaw
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Learning Efficient Algorithms with Hierarchical Attentive Memory
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, P.R. China; School of Information, Renmin University of China, Beijing, P.R. China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Investigating Different Context Types and Representations for Learning Word Embeddings
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Department of Computer Science, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK; Google DeepMind, London, UK; CIFAR, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 |
https://youtube.com/playlist?list=PLXkuFIFnXUAPIrXKgtIpctv2NuSo7xw3k
|
main
| 4.666667 |
4;4;6
| null | null |
LipNet: End-to-End Sentence-level Lipreading
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Visual Geometry Group, University of Oxford
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Warped Convolutions: Efficient Invariance to Spatial Transformations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
U2IS, ENSTA ParisTech, Inria FLOWERS, Universit ´e Paris-Saclay
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Unsupervised Deep Learning of State Representation Using Robotic Priors
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Computer Science, University of Wisconsin – Milwaukee, Milwaukee, WI, USA; Department of Computational Neuroscience, The University of Massachusetts, Lowell, Lowell, MA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications;Optimization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning;Reinforcement Learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Unsupervised Perceptual Rewards for Imitation Learning
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null |
Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Bit-Pragmatic Deep Neural Network Computing
| null | null | 0 | 2.333333 |
Workshop
|
2;3;2
| null |
null |
Sentient Technologies, 1 California Street Suite 2300, San Francisco, CA 94111
|
2017
| 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 |
L-SR1: A Second Order Optimization Method for Deep Learning
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Department of Engineering Science, University of Oxford; Department of Engineering Science, University of Oxford and Alan Turing Institute
|
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 |
Trusting SVM for Piecewise Linear CNNs
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Computer Science, University of Toronto, Ontario, ON M5S 3G4, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Song From PI: A Musically Plausible Network for Pop Music Generation
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null |
Department of Computer Science, University of Chicago, Chicago, IL 60637, USA; Microsoft Research Cambridge, Cambridge, CB1 2FB, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Supervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
LAL/LRI, CNRS/Université Paris-Saclay; MINES ParisTech, PSL Research University, CGS-I3 UMR 9217
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Out-of-class novelty generation: an experimental foundation
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Department of Electrical and Computer Engineering, Duke University; Machine Learning Group, NEC Laboratories America
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Adaptive Feature Abstraction for Translating Video to Language
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null |
Carnegie Mellon University; Carnegie Mellon University and DeepMind; DeepMind; DeepMind and University of Oxford
|
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
| 5.333333 |
5;5;6
| null | null |
Reference-Aware Language Models
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, University College London
|
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 |
Frustratingly Short Attention Spans in Neural Language Modeling
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Chubu University; Denso IT Laboratory, Inc.
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Ternary Weight Decomposition and Binary Activation Encoding for Fast and Compact Neural Network
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Computer Vision Center - Univ. Autònoma de Barcelona (UAB), 08193 Bellaterra, Catalonia Spain; Visual Tagging Services, Campus UAB, 08193 Bellaterra, Catalonia Spain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Regularizing CNNs with Locally Constrained Decorrelations
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
National University of Singapore; Qihoo 360 AI Institute
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Training Group Orthogonal Neural Networks with Privileged Information
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Vanderbilt University; University of Michigan; Shanghai Jiao Tong University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Evaluation of Defensive Methods for DNNs against Multiple Adversarial Evasion Models
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Georgia Institute of Technology; California Institute of Technology; Sutter Health
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Neural Causal Regularization under the Independence of Mechanisms Assumption
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Department of Computer Science and Engineering, York University, Toronto, CA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Higher Order Recurrent Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Informatics Forum, University of Edinburgh, 10, Crichton St, Edinburgh, EH89AB, UK; Informatics Forum, University of Edinburgh, 10, Crichton St, Edinburgh, EH89AB, UK; Additional affiliation: Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Applications;Optimization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Autoencoding Variational Inference For Topic Models
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
Google, New York, NY, [email protected]; Carnegie Mellon University, Pittsburgh, PA, [email protected]; Google, New York, NY, [email protected]; Google, New York, NY, [email protected]; Google, New York, NY, [email protected]
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Multi-modal learning;Structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
DRAGNN: A Transition-Based Framework for Dynamically Connected Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Google Brain, Google Inc., Mountain View, CA 94043, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Capacity and Trainability in Recurrent Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Stanford University; Google DeepMind; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
Unrolled Generative Adversarial Networks
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null |
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw; Weldon School of Biomedical Engineering, Purdue University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
An Analysis of Deep Neural Network Models for Practical Applications
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Harvard University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Lie-Access Neural Turing Machines
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null |
MetaMind - A Salesforce Company, 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
| 7.666667 |
7;8;8
| null | null |
Pointer Sentinel Mixture Models
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
College of Information and Computer Sciences, University of Massachusetts, Amherst, Amherst, MA 01060, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Games
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Generative Multi-Adversarial Networks
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Baidu Research; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Speech;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.5 |
6;7
| null | null |
Exploring Sparsity in Recurrent Neural Networks
| null | null | 0 | 3.5 |
Poster
|
4;3
| null |
null |
Department of Engineering, University of Cambridge; Also affiliated with Max-Planck Institute for Intelligent Systems, Tübingen, Germany. Work done while author was an intern at Microsoft Research.; Microsoft Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Applications;Structured prediction
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
DeepCoder: Learning to Write Programs
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null |
Department of Computer Science, Technion-Israel Institute of Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| 0.142857 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Learning Invariant Representations Of Planar Curves
| null | null | 0 | 3.333333 |
Poster
|
2;5;3
| null |
null |
Department of Kinesiology, University of Waterloo, Ontario, Canada; Noah’s Ark Laboratory, Huawei Technologies, Hong Kong, China; David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Transfer Learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Online Bayesian Transfer Learning for Sequential Data Modeling
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Department of Computer Science and Technology, Ocean University of China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Marginal Deep Architectures: Deep learning for Small and Middle Scale Applications
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science; OpenAI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Variational Lossy Autoencoder
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Robotics and Biology Lab, Technische Universit ¨at Berlin, Berlin, Germany
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Rico Jonschkowski and Oliver Brock
| null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
End-to-End Learnable Histogram Filters
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Universidad de Buenos Aires; University of California, Berkeley
|
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 |
3;4;5
| null | null |
Exploring the Application of Deep Learning for Supervised Learning Problems
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
School of Computer Science, Carnegie Mellon University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
|
https://github.com/kimiyoung/transfer
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Engineering Faculty, Bar-Ilan University, Ramat-Gan 52900, Israel
|
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
| 5.666667 |
5;5;7
| null | null |
Training deep neural-networks using a noise adaptation layer
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null |
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Inverse Problems in Computer Vision using Adversarial Imagination Priors
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
ESAT-PSI, KU Leuven; Department of Computer Science, University of Amsterdam
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Dynamic Steerable Frame Networks
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
DYNI, LSIS, Machine Learning & Bioacoustics team, AMU, University of Toulon, ENSAM, CNRS, La Garde, France; DYNI, LSIS, Machine Learning & Bioacoustics team, AMU, University of Toulon, ENSAM, CNRS, IUF, La Garde, France; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Supervised Learning;Deep learning;Speech
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Fast Chirplet Transform to Enhance CNN Machine Listening - Validation on Animal calls and Speech
| null | null | 0 | 4 |
Workshop
|
4;5;3
| null |
null |
Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
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
| 5.333333 |
5;5;6
| null | null |
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Nanyang Technological University, Singapore; SAP Innovation Center, Singapore; SAP, Singapore
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Character-aware Attention Residual Network for Sentence Representation
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
University of Illinois at Urbana-Champaign; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Semi-Supervised Learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Unsupervised Pretraining for Sequence to Sequence Learning
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
TTI-Chicago, Chicago, IL 60637, USA; University of Michigan, Ann Arbor, MI 48109, USA
|
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;5;7
| null | null |
Deep Variational Canonical Correlation Analysis
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain, Oxford University; Google Brain, Wroclaw University; Google Inc.
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Intelligible Language Modeling with Input Switched Affine Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Stanford University; Computer Science Department, Stanford University
|
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
| 6.666667 |
6;6;8
| null | null |
Data Noising as Smoothing in Neural Network Language Models
| null | null | 0 | 2.666667 |
Poster
|
0;4;4
| null |
null |
Stanford University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
Deep Information Propagation
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null |
Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea; Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Near-Data Processing for Machine Learning
| null | null | 0 | 2.666667 |
Reject
|
4;2;2
| null |
null |
Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Rule Mining in Feature Space
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Statistics, Columbia University, New York, NY 10027, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Maximum Entropy Flow Networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Sorbonne Universit ´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Options Discovery with Budgeted Reinforcement Learning
|
https://github.com/aureliale/BONN-model
| null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null |
Harvard University; University of Cambridge; Siemens AG and Technical University of Munich; Siemens AG
|
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
| 6.666667 |
6;7;7
| null | null |
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
3National Research University Higher School of Economics (HSE); 1Skolkovo Institute of Science and Technology, 2The Institute for Information Transmission Problems RAS (Kharkevich Institute), 3National Research University Higher School of Economics (HSE)
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning;Applications;Supervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Generative Adversarial Networks for Image Steganography
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Computer Science Division, University of California, Berkeley
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Modular Multitask Reinforcement Learning with Policy Sketches
| null | null | 0 | 4.666667 |
Workshop
|
4;5;5
| null |
null |
Department of Computer Science, Dartmouth College
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
| null | null | 0 | 3.333333 |
Workshop
|
4;3;3
| null |
null |
Intel Corporation, Santa Clara, CA 95054; Intel Corporation, Bangalore, India; Northwestern University, Evanston, IL 60208
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 8 |
6;8;10
| null | null |
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
| null | null | 0 | 3.333333 |
Oral
|
4;3;3
| null |
null |
Princeton University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Nonparametrically Learning Activation Functions in Deep Neural Nets
| null | null | 0 | 4.333333 |
Workshop
|
4;4;5
| null |
null |
Salesforce Research, Palo Alto, California
|
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
| 6.666667 |
6;7;7
| null | null |
Quasi-Recurrent Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Computer Science, Harvey Mudd College, 301 Platt Boulevard
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning;Structured prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 8.333333 |
7;9;9
| null | null |
Learning Graphical State Transitions
| null | null | 0 | 2.666667 |
Oral
|
2;3;3
| null |
null |
Allen Institute for Artificial Intelligence; University of Washington; University of Washington, Allen Institute for Artificial Intelligence
|
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
| 7.666667 |
7;8;8
| null | null |
Bidirectional Attention Flow for Machine Comprehension
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null |
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Theory;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Representing inferential uncertainty in deep neural networks through sampling
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research; Georgia Institute of Technology; Virginia Tech
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
| null | null | 0 | 2.333333 |
Poster
|
0;3;4
| null |
null |
Reasoning and Learning Lab, School of Computer Science, McGill University; Montreal Institute for Learning Algorithms, Université de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Towards an automatic Turing test: Learning to evaluate dialogue responses
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null |
College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China, 230009; Systems and Information Sciences lab, LSIS CNRS & Univ. of Sud-Toulon Var - La Garde, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.75 |
2;3;3;3
| null | null |
Pedestrian Detection Based On Fast R-CNN and Batch Normalization
| null | null | 0 | 5 |
Reject
|
5;5;5;5
| null |
null |
Sorbonne Universities, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, Institut VEDECOM, 77 rue des chantiers, 78000, Versailles; Sorbonne Universities, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu 75005 Paris, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Modelling Relational Time Series using Gaussian Embeddings
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Dataset Augmentation in Feature Space
| null | null | 0 | 4.666667 |
Workshop
|
5;5;4
| null |
null |
School of Informatics, University of Edinburgh, Edinburgh, UK
|
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 |
6;8;8
| null | null |
Towards a Neural Statistician
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null |
Google Brain, London, UK; Google Brain; Google Research
|
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 |
5;7;7
| null | null |
Generating Long and Diverse Responses with Neural Conversation Models
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Structured prediction;Computer vision;Supervised Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Deep Learning with Sets and Point Clouds
| null | null | 0 | 3 |
Workshop
|
4;4;1
| null |
null |
National Laboratory of Pattern Recognition, Chinese Academy of Sciences; Georgia Institute of Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Recurrent Hidden Semi-Markov Model
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
University of Amsterdam; University of Amsterdam, Canadian Institute for Advanced Research (CIFAR)
|
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 |
7;7;7
| null | null |
Soft Weight-Sharing for Neural Network Compression
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Department of Computer Science, University of Illinois at Urbana-Champaign
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
A Simple yet Effective Method to Prune Dense Layers of Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Computing, The Hong Kong Polytechnic University, Hong Kong; Montreal Institute for Learning Algorithms, Université de Montréal, Montréal, QC H3T 1J4, Canada; David R. Cheriton School of Computer Science, University Of Waterloo, Waterloo, ON N2L 3G1, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Mode Regularized Generative Adversarial Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
MILA Lab, University of Montreal, Canada; Center for Processing Speech and Images, KU Leuven, Belgium; INRIA Galen, University of Paris-Saclay, France; INRIA Parietal, Saclay, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning to Discover Sparse Graphical Models
| null | null | 0 | 2.666667 |
Workshop
|
2;3;3
| null |
null |
University of Toronto; Tsinghua University
|
2017
| 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 |
Efficient Summarization with Read-Again and Copy Mechanism
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Not provided
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| -0.5 | 0 |
Not provided
|
main
| 4.333333 |
4;4;5
| null | null |
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
|
Not provided
| null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Microsoft Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.666667 |
2;4;8
| null | null |
Lifelong Perceptual Programming By Example
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null |
Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, California, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning;Games;Theory
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Recursive Regression with Neural Networks: Approximating the HJI PDE Solution
| null | null | 0 | 3 |
Workshop
|
5;1;3
| null |
null |
School of Information Systems, Singapore Management 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 |
A Compare-Aggregate Model for Matching Text Sequences
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null |
Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA; School of Informatics and Computing, Indiana University, Bloomington, IN, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
NVIDIA Research, Austin, TX 78717, USA; Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Training Long Short-Term Memory With Sparsified Stochastic Gradient Descent
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
School of Computer Science and Communication, KTH, Stockholm, Sweden
|
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 |
The Preimage of Rectifier Network Activities
| null | null | 0 | 3 |
Reject
|
4;0;5
| null |
null |
Vicarious, San Francisco, CA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Hierarchical compositional feature learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
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