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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Deep Learning for Detecting Robotic Grasps
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Barnes-Hut-SNE
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Clustering Learning for Robotic Vision
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Efficient Estimation of Word Representations in Vector Space
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Natural Gradient Revisited
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Local Component Analysis
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Zero-Shot Learning Through Cross-Modal Transfer
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learnable Pooling Regions for Image Classification
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Tree structured sparse coding on cubes
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Training Neural Networks with Stochastic Hessian-Free Optimization
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Indoor Semantic Segmentation using depth information
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Discrete Restricted Boltzmann Machines
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Saturating Auto-Encoder
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Deep Predictive Coding Networks
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Regularized Discriminant Embedding for Visual Descriptor Learning
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Audio Artist Identification by Deep Neural Network
| null | null | 0 | 0 |
Reject
| null | null |
null |
Twitter; University of Toronto
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Transfer Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Prototypical Networks for Few-shot Learning
| null | null | 0 | 4 |
Reject
|
5;3;4
| 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
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Machine Comprehension Using Match-LSTM and Answer Pointer
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
DeepMind, London
|
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 |
Learning in Implicit Generative Models
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
OpenAI, UC Berkeley, Department of Statistics; OpenAI, UC Berkeley, Departments of EECS and ICSI; OpenAI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Third Person Imitation Learning
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| 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
| 7 |
5;7;9
| null | null |
Improving Neural Language Models with a Continuous Cache
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null |
Computer Science Department, New York University; Facebook AI Research, New York; Mathematics Department, New York University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Department of Statistics, University of California, Los Angeles
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Cooperative Training of Descriptor and Generator Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
MILA, Université de Montréal; Neural Dynamics and Computation Lab, Stanford; New York University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Adversarially Learned Inference
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Graduate School of Engineering, The University of Tokyo; The University of Tokyo; Graduate School of Information Science and Technology, The University of Tokyo
|
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 |
Neural Machine Translation with Latent Semantic of Image and Text
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Indian Institute of Technology, Madras
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Criteo Research, Palo Alto, CA 94301, USA
|
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
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Efficient Vector Representation for Documents through Corruption
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
University College London; Shanghai Jiao Tong 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
| 5.333333 |
5;5;6
| null | null |
A Neural Stochastic Volatility Model
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, 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
| 4.666667 |
4;5;5
| null | null |
Here's My Point: Argumentation Mining with Pointer Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
OpenAI; School of Computer Science, Carnegie Mellon University; Department of Computer Science, University of Toronto
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
On the Quantitative Analysis of Decoder-Based Generative Models
|
https://github.com/tonywu95/eval_gen
| null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
University of Washington; Seoul National University; 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 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Query-Reduction Networks for Question Answering
|
https://seominjoon.github.io/qrn/
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
The University of Tokyo
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 2 |
1;2;3
| null | null |
Multiagent System for Layer Free Network
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Stanford University and Google Brain; Google Brain and Cornell University; Stanford University; Cornell University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
On the Expressive Power of Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
Department of Electrical and Computer Engineering, New York University; Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino; Computer Science Department, University of California, Los Angeles; Courant Institute of Mathematical Sciences, New York University; Microsoft Research New England, Cambridge
|
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 |
7;8;9
| null | null |
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Department of Computer Science, University of Miami, Coral Gables, FL 33124, USA
|
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 |
Revisiting Denoising Auto-Encoders
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
School of Electrical Engineering, Korea Advanced Institute of Science Technology, Republic of Korea
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Multi-modal learning;Structured prediction
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.5 |
5;6
| null | null |
Making Stochastic Neural Networks from Deterministic Ones
| null | null | 0 | 4.5 |
Reject
|
5;4
| null |
null |
Jagiellonian University, Cracow; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Harvard University, Cambridge, MA 02215, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 |
https://coxlab.github.io/prednet/
|
main
| 7.333333 |
6;8;8
| null | null |
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
|
https://github.com/coxlab/prednet
| null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Department of Computer Science, Peking University, Beijing, 100871, China; Department of Cognitive Science, John Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Transfer Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
| null | null | 0 | 4 |
Poster
|
5;4;3
| 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
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Words or Characters? Fine-grained Gating for Reading Comprehension
|
https://github.com/kimiyoung/fg-gating
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Bellevue, WA 98007, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Charged Point Normalization: An Efficient Solution to the Saddle Point Problem
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
Deepmind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
Combining policy gradient and Q-learning
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
University of Michigan, Ann Arbor, MI 48109 and Google Brain, Mountain View, CA 94043; University of Michigan, Ann Arbor, MI 48109
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Theory
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Towards Understanding the Invertibility of Convolutional Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science; OpenAI; International Computer Science Institute; 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;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
http://bit.ly/snn4hrl-videos
|
main
| 7.333333 |
7;7;8
| null | null |
Stochastic Neural Networks for Hierarchical Reinforcement Learning
|
https://github.com/florensacc/snn4hrl
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
DeepMind, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Applications
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Recurrent Environment Simulators
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
School of Informatics, University of Edinburgh, Edinburgh, UK and The Alan Turing Institute, London, UK; School of Informatics, University of Edinburgh, Edinburgh, UK; Microsoft Research, Microsoft, Redmond, WA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Continuous Semantic Representations of Symbolic Expressions
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null |
Facebook AI Research, New York; Dept. of Computer Science, Courant Institute, New York University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Semi-Supervised Learning;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Twitter, USA.; Université de Montréal, Canada.; IBM Watson Research Center, USA.
|
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
| 4.666667 |
4;5;5
| null | null |
Hierarchical Memory Networks
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Dept. of Computer Science, University of Maryland, College Park, MD; Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, 76100 Israel
|
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
| 6 |
5;6;7
| null | null |
Efficient Representation of Low-Dimensional Manifolds using Deep Networks
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
Departments of Statistics and Computer Science, University of Chicago, Chicago, IL 60637, USA; Department of Statistics, University of Chicago, Chicago, IL 60637, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
3;3;6
| null | null |
Dynamic Partition Models
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Amsterdam, Canadian Institute of Advanced Research; University of Amsterdam; University of Amsterdam, Vrije Universiteit Brussel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
NVIDIA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Temporal Ensembling for Semi-Supervised Learning
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
Massachusetts Institute of Technology, USA; Google Brain, USA; University of Cambridge, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Structured prediction;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Tuning Recurrent Neural Networks with Reinforcement Learning
| null | null | 0 | 4.333333 |
Workshop
|
5;5;3
| null |
null |
Department of Computer Science and Operations Research, Universite de Montreal; School of Computer Science, McGill University; College of Information Sciences & Technology, Penn State University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Structured prediction;Natural language processing
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Multi-modal Variational Encoder-Decoders
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
A Convolutional Encoder Model for Neural Machine Translation
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
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;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning to Generate Samples from Noise through Infusion Training
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null |
Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA; Department of Computer Science, New York University, New York, NY 10012, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Recurrent Neural Networks for Multivariate Time Series with Missing Values
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
IBM Watson; Montreal Institute for Learning Algorithms (MILA), Université de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
IBM Watson Group, T. J. Watson Research Center, IBM, Yorktown Heights, NY 10598, USA; Department of EECS, Oregon State University, Corvallis, OR 97331, USA
|
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 |
Group Sparse CNNs for Question Sentence Classification with Answer Sets
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
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
| 6.333333 |
5;7;7
| null | null |
Nonparametric Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
CIFAR Fellow; CentraleSupélec; Computer Vision Center & Universitat Autonoma de Barcelona; Politecnico di Milano; Department of Computer Science and Engineering, IIT Kanpur; Montreal Institute for Learning Algorithms, Université de Montréal
|
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
| 6.666667 |
6;7;7
| null | null |
PixelVAE: A Latent Variable Model for Natural Images
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Google Brain, Mountain View, CA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
A Learned Representation For Artistic Style
| null | null | 0 | 4.333333 |
Poster
|
3;5;5
| null |
null |
University at Buffalo, Buffalo, NY 14260; Microsoft Research, Beijing, China, 100080
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Modularized Morphing of Neural Networks
| null | null | 0 | 4.5 |
Workshop
|
4;5;4;5
| null |
null |
MIT
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Learning Approximate Distribution-Sensitive Data Structures
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Cornell University; Tsinghua University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Snapshot Ensembles: Train 1, Get M for Free
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Rethinking Numerical Representations for Deep Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
5;2;3
| null |
null |
Department of Computer Science, University of California, Los Angeles
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Information Dropout: learning optimal representations through noise
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Neurobiology, Weizmann Institute of Science, Rehovot, PA 7610001, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Transfer Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Human perception in computer vision
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
IBM Haifa Research Lab, Haifa, Israel; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Bar-Ilan University, Ramat-Gan, Israel
|
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
| 8 |
8;8;8
| null | null |
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
Centre for Integrative Neuroscience, University of Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany; Graduate School of Neural Information Processing, University of Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany; Graduate School of Neural Information Processing, University of Tübingen, Germany
|
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
| 7.666667 |
7;8;8
| null | null |
What does it take to generate natural textures?
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India; Student at R V College of Engineering, Bengaluru; Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India
|
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
| 4 |
3;4;5
| null | null |
Attentive Recurrent Comparators
|
https://github.com/pranv/ARC
| null | 0 | 4 |
Reject
|
5;5;2
| null |
null |
The Hebrew University of Jerusalem
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Tensorial Mixture Models
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Microsoft Research, Redmond, WA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Natural language processing
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
ReasoNet: Learning to Stop Reading in Machine Comprehension
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
OpenAI; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Adversarial Machine Learning at Scale
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
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 |
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
OpenAI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
|
https://github.com/openai/pixel-cnn
| null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Computer Science Division, University of California, Berkeley; Department of Computer Science, University of Texas, Austin
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Adversarial Feature Learning
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Institute of Computer Science and Technology, Peking University; TuSimple; SenseTime
|
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 |
Revisiting Batch Normalization For Practical Domain Adaptation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Handong Global University, Samsung Advanced Institute of Technology; Université de Montréal; Université de Montréal, CIFAR Senior Fellow
|
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 |
6;6;6
| null | null |
A Neural Knowledge Language Model
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Max Planck Institute for Intelligent Systems, Max Planck Institute for Biological Cybernetics, Graduate Training Center of Neuroscience, Tuebingen, Germany; Microsoft Research, Cambridge, UK
|
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 |
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Physics, University of California at Berkeley, Berkeley, CA 94720, USA; Courant Institute of Mathematical Sciences, New York University, New York, NY 10011, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning
| null | 0 | null | null |
iclr
| -0.987829 | 0 | null |
main
| 5.666667 |
2;7;8
| null | null |
Topology and Geometry of Half-Rectified Network Optimization
| null | null | 0 | 3.666667 |
Poster
|
5;3;3
| null |
null |
University of Chicago; Toyota Technological Institute at Chicago
|
2017
| 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 |
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
|
github.com/hendrycks/GELUs
| null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
DeepMind, London, United Kingdom
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Discovering objects and their relations from entangled scene representations
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null |
Stanford University; Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Epitomic Variational Autoencoders
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Universit ´e Paris-Dauphine, PSL Research University, CNRS, LAMSADE, 75016 PARIS, FRANCE
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Games;Supervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0.968496 | 0 | null |
main
| 4.25 |
3;3;4;7
| null | null |
Improved Architectures for Computer Go
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
DeepMind; UC Berkeley & DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.75 |
7;8;8;8
| null | null |
Metacontrol for Adaptive Imagination-Based Optimization
| null | null | 0 | 3 |
Poster
|
3;3;3;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
| -0.904534 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Vocabulary Selection Strategies for Neural Machine Translation
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null |
Department of Electrical Engineering and Computer Science, UC Berkeley
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
| null | null | 0 | 3.333333 |
Workshop
|
4;3;3
| null |
null |
Dimensional Mechanics, Bellevue, WA 98007, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Fachbereich Mathematik und Informatik, Freie Universit ¨at Berlin, Berlin, Germany
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Leon Sixt, Benjamin Wild, & Tim Landgraf
| null |
Unsupervised Learning;Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
RenderGAN: Generating Realistic Labeled Data
| null | null | 0 | 3.666667 |
Workshop
|
3;4;4
| null |
null |
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4 |
3;3;6
| null | null |
Convolutional Neural Networks Generalization Utilizing the Data Graph Structure
| null | null | 0 | 2 |
Reject
|
0;3;3
| null |
null |
DeepMind, London
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Supervised Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| -0.737043 | 0 | null |
main
| 6.333333 |
4;6;9
| null | null |
The Predictron: End-To-End Learning and Planning
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
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