pdf
stringlengths 49
199
⌀ | aff
stringlengths 1
1.36k
⌀ | year
stringclasses 19
values | technical_novelty_avg
float64 0
4
⌀ | video
stringlengths 21
47
⌀ | doi
stringlengths 31
63
⌀ | presentation_avg
float64 0
4
⌀ | proceeding
stringlengths 43
129
⌀ | presentation
stringclasses 796
values | sess
stringclasses 576
values | technical_novelty
stringclasses 700
values | arxiv
stringlengths 10
16
⌀ | author
stringlengths 1
1.96k
⌀ | site
stringlengths 37
191
⌀ | keywords
stringlengths 2
582
⌀ | oa
stringlengths 86
198
⌀ | empirical_novelty_avg
float64 0
4
⌀ | poster
stringlengths 57
95
⌀ | openreview
stringlengths 41
45
⌀ | conference
stringclasses 11
values | corr_rating_confidence
float64 -1
1
⌀ | corr_rating_correctness
float64 -1
1
⌀ | project
stringlengths 1
162
⌀ | track
stringclasses 3
values | rating_avg
float64 0
10
⌀ | rating
stringlengths 1
17
⌀ | correctness
stringclasses 809
values | slides
stringlengths 32
41
⌀ | title
stringlengths 2
192
⌀ | github
stringlengths 3
165
⌀ | authors
stringlengths 7
161
⌀ | correctness_avg
float64 0
5
⌀ | confidence_avg
float64 0
5
⌀ | status
stringclasses 22
values | confidence
stringlengths 1
17
⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Efficient Calculation of Polynomial Features on Sparse Matrices
| null | null | 0 | 2.333333 |
Reject
|
3;3;1
| null |
|
null | null |
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Structured prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 8 |
7;8;9
| null | null |
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Department of Computer Science, University of California, Irvine
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
4;8;8
| null | null |
Stick-Breaking Variational Autoencoders
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Computer Science and Engineering, Hong Kong University of Science and Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Loss-aware Binarization of Deep Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Technion - Israel Institute of Technology, Haifa, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep unsupervised learning through spatial contrasting
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Stanford University; DeepScale∗& UC Berkeley
|
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
| 6.333333 |
5;7;7
| null | null |
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
|
https://github.com/DeepScale/SqueezeNet
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Criteo Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Efficient iterative policy optimization
| null | null | 0 | 3 |
Reject
|
2;4;3
| null |
null |
University of Oxford and DeepMind; DeepMind
|
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 |
The Neural Noisy Channel
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Cambridge, UK; University of Cambridge, UK; Uber AI Labs, USA; DeepMind, UK; Google Brain, USA; UC Berkeley, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 7.25 |
7;7;7;8
| null | null |
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
| null | null | 0 | 4 |
Oral
|
4;5;4;3
| null |
null |
DeepMind, London, UK
|
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 |
Reinforcement Learning with Unsupervised Auxiliary Tasks
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null |
Department of Computer Science, Johns Hopkins University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Unsupervised Learning Using Generative Adversarial Training And Clustering
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization;Theory
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Demystifying ResNet
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Computer Vision Center, Universitat Aut `onoma de Barcelona, Bellaterra, Barcelona (Spain); Department of Computer Science, Universidade da Beira Interior, Covilh ˜a, Portugal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Understanding trained CNNs by indexing neuron selectivity
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Samsung Research America, Mountain View, CA 94043, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Simple Black-Box Adversarial Perturbations for Deep Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
McGill University; Université de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Reinforcement Learning;Structured prediction
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
4;8;8
| null | null |
An Actor-Critic Algorithm for Sequence Prediction
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
NVIDIA
|
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
| 7.333333 |
6;7;9
| null | null |
Pruning Convolutional Neural Networks for Resource Efficient Inference
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
CMLA, ENS Cachan, CNRS, Universit´e Paris-Saclay, 94235 Cachan, France; Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Understanding Trainable Sparse Coding with Matrix Factorization
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null |
Department of Computer Science and Information Systems, Birkbeck, University of London, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
nitbix
| null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Boosted Residual Networks
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Redwood Center, UC Berkeley
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Emergence of foveal image sampling from learning to attend in visual scenes
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Research School of Engineering, Australian National University; Commonwealth Scientific and Industrial Research Organisation
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Transfer Learning;Supervised Learning;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Distributed Transfer Learning for Deep Convolutional Neural Networks by Basic Probability Assignment
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
DeepMind, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://youtu.be/lNoaTyMZsWI
|
main
| 6.333333 |
5;7;7
| null | null |
Learning to Navigate in Complex Environments
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences
|
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
| 4 |
3;3;6
| null | null |
What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
University of Maryland, Baltimore County; U.S. Naval Research Laboratory, Code 5514
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Deep Convolutional Neural Network Design Patterns
|
https://github.com/iPhysicist/CNNDesignPatterns
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
D´epartement d’informatique, Universit ´e de Sherbrooke; Department of Computer Science, Dartmouth College; Amazon, Berlin, Germany
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Recurrent Mixture Density Network for Spatiotemporal Visual Attention
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Google Brain; Georgia Tech
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning to Remember Rare Events
| null | null | 0 | 4 |
Poster
|
5;4;3
| 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;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Semi-Supervised Classification with Graph Convolutional Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
4;7;7
| null | null | null | null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Salesforce Research; The University of Tokyo
|
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
| 4.666667 |
3;5;6
| null | null |
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
´Ecole Polytechnique de Montr´eal; Lawrence Berkeley National Lab, Berkeley, CA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-Supervised Learning;Applications;Computer vision
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
FastText.zip: Compressing text classification models
|
https://github.com/facebookresearch/fastText
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Cambridge; Google Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-Supervised Learning;Natural language processing;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Neural Graph Machines: Learning Neural Networks Using Graphs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France
|
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 |
3;5;6
| null | null |
Multi-view Generative Adversarial Networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
DYNI, LSIS, Machine Learning and Bioacoustics team, AMU, University of Toulon, ENSAM, CNRS, 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 |
Unsupervised Learning;Applications;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null |
National University of Singapore; University of California, Berkeley; Google Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Unsupervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Adversarial examples for generative models
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Facebook AI Research; Facebook AI Research, WILLOW project team, Inria / ENS / CNRS
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Revisiting Classifier Two-Sample Tests
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Department of Computer Science, Shahid Bahonar University, Kerman, Iran
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Administratr
| null |
Deep learning;Supervised Learning;Optimization;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
New Learning Approach By Genetic Algorithm In A Convolutional Neural Network For Pattern Recognition
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Microsoft Research, Redmond, WA, USA; University of Michigan, Ann Arbor, MI, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.722806 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Communicating Hierarchical Neural Controllers for Learning Zero-shot Task Generalization
| null | null | 0 | 3 |
Reject
|
4;3;5;0
| null |
null |
Department of Statistics, UC Berkeley, Berkeley, CA 94709, USA; Facebook AI Research, New York City, NY, 10003, 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
| 7 |
7;7;7
| null | null |
Automatic Rule Extraction from Long Short Term Memory Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Department of Computing, Imperial College London, London, UK; Department of Bioengineering, Imperial College London, London, UK
|
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
| 5.333333 |
4;4;8
| null | null |
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Bosch Center for Artificial Intelligence, Robert Bosch GmbH
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
On Detecting Adversarial Perturbations
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Montreal Institute for Learning Algorithms, Montreal, Quebec, Canada; University of Montreal, Faculty of Medicine, Montreal Heart Institute, Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Quebec, Canada; University of Montreal, Faculty of Medicine, Montreal, Quebec, Canada; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Diet Networks: Thin Parameters for Fat Genomics
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Facebook AI Research, Tel-Aviv, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Unsupervised Cross-Domain Image Generation
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
University of Adelaide; Queensland University of Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Optimization;Structured prediction
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Non-linear Dimensionality Regularizer for Solving Inverse Problems
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
University of Massachusetts Boston, Boston, MA 02125; University of Massachusetts Amherst, Amherst, MA 01003
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Filling in the details: Perceiving from low fidelity visual input
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
AIFounded Inc., Toronto, ON; University of Guelph, Guelph, ON
|
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 |
Generative Adversarial Parallelization
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
The School of Computer Science, Tel Aviv University, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Theory
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
The loss surface of residual networks: Ensembles and the role of batch normalization
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Data Analysis Systems, Software Competence Center Hagenberg, Austria; Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Austria
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer Learning;Deep learning;Computer vision
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
|
https://github.com/wzell/cmd
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Machine Learning Group, Technische Universität Berlin, Berlin, Germany
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Unsupervised Learning;Supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Learning similarity preserving representations with neural similarity and context encoders
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Vision and Security Technology (VAST) Lab, University of Colorado, Colorado Springs, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Exploring LOTS in Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA; Department of Computer Science, University of Southern California, Los Angeles, CA 90020, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
The Power of Sparsity in Convolutional Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Google; Microsoft Research; University of Texas at Arlington
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Multi-modal learning;Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Memory-augmented Attention Modelling for Videos
| null | null | 0 | 3 |
Reject
|
0;5;4
| null |
null |
Department of Electrical and Computer Engineering, University of Florida, Gainesvillle, FL 32611, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Structured prediction;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Perception Updating Networks: On architectural constraints for interpretable video generative models
| null | null | 0 | 3.666667 |
Workshop
|
4;4;3
| null |
null |
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Theory;Deep learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
| null | null | 0 | 2.333333 |
Poster
|
2;3;2
| null |
null |
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213; Baidu Research, Sunnyvale, CA 94089, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
An Analysis of Feature Regularization for Low-shot Learning
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Georgia 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 | 0 | null |
main
| 6 |
6;6;6
| null | null |
GRAM: Graph-based Attention Model for Healthcare Representation Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Queen Mary, University of London
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
University of Toronto and Google DeepMind; University of Toronto
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.5 |
6;7
| null | null |
Distributed Second-Order Optimization using Kronecker-Factored Approximations
| null | null | 0 | 3.5 |
Poster
|
3;4
| null |
null |
The Swiss AI Lab IDSIA (USI-SUPSI); The Swiss AI Lab IDSIA (USI-SUPSI) & NNAISENSE, Lugano, Switzerland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Highway and Residual Networks learn Unrolled Iterative Estimation
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Coordinated Science Laboratory and Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
|
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 |
Why Deep Neural Networks for Function Approximation?
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
University of Freiburg, Freiburg, Germany
|
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 |
SGDR: Stochastic Gradient Descent with Warm Restarts
|
https://github.com/loshchil/SGDR
| null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
Hitachi, Ltd
|
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 | 0 | null |
main
| 2.666667 |
2;2;4
| null | null |
PREDICTION OF POTENTIAL HUMAN INTENTION USING SUPERVISED COMPETITIVE LEARNING
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Signal Processing, Tampere University of Technology, Tampere, Finland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Optimization;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Taming the waves: sine as activation function in deep neural networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Universit´e Paris-Est, ´Ecole des Ponts ParisTech, Paris, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
|
https://github.com/szagoruyko/attention-transfer
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Basis Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Applications
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
| null | null | 0 | 3.333333 |
Workshop
|
3;3;4
| null |
null |
DeepMind; DeepMind and 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
| 7 |
6;7;8
| null | null |
Learning to Compose Words into Sentences with Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Computer Science and Engineering Department, Lehigh University, Bethlehem, PA 18015, USA; Samsung Research America (SRA)
|
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 |
DeepRebirth: A General Approach for Accelerating Deep Neural Network Execution on Mobile Devices
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Montreal, CIFAR Senior Fellow; University of Montreal, CIFAR Fellow; University of Montreal; IIT Kanpur; SSNCE
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Speech;Deep learning;Unsupervised Learning;Applications
| null | 0 | null | null |
iclr
| 0.5 | 0 |
https://soundcloud.com/samplernn/sets
|
main
| 8.333333 |
8;8;9
| null | null |
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
|
https://github.com/soroushmehr/sampleRNN_ICLR2017
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Management Information Systems, University of Arizona; Department of Computer Science, UNC Charlotte; Machine Learning Group, NEC Laboratories America
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Natural language processing;Applications
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Context-aware Attention Network for Interactive Question Answering
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
North Carolina State University; Microsoft Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Parallel Stochastic Gradient Descent with Sound Combiners
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Google DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Computer Science, Dartmouth College
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Two Methods for Wild Variational Inference
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
McGill University; University of Michigan; Indian Institute of Technology Madras
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Transfer Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Microsoft Research Asia; Nankai University
|
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
| 4 |
3;4;5
| null | null |
An Actor-critic Algorithm for Learning Rate Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Massachusetts Institute of Technology; University of California, Berkeley; Google Brain; Google DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 9.666667 |
9;10;10
| null | null |
Understanding deep learning requires rethinking generalization
| null | null | 0 | 3.666667 |
Oral
|
3;4;4
| null |
null |
Courant Institute of Mathematical Sciences and Centre for Data Science, New York University, New York, NY 10012, USA; Lunit Inc., Seoul, South Korea
|
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
| 3 |
2;3;4
| null | null |
Semantic Noise Modeling for Better Representation Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Electronics and Information Systems (ELIS), Ghent University – iMinds, IDLab; Former member, currently unaffiliated
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
A Differentiable Physics Engine for Deep Learning in Robotics
| null | null | 0 | 3.333333 |
Workshop
|
4;2;4
| null |
null |
Microsoft Research; School of Computer Science and Technology, University of Science and Technology of China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning;Optimization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Neural Data Filter for Bootstrapping Stochastic Gradient Descent
| null | null | 0 | 3 |
Workshop
|
5;4;0
| null |
null |
Princeton University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Unsupervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
A Simple but Tough-to-Beat Baseline for Sentence Embeddings
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
UC Berkeley, Department of Mathematics; OpenAI; UC Berkeley, Department of Electrical Engineering and Computer Sciences; Ghent University – imec, Department of Information Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Games
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Massachusetts Institute of Technology; Carnegie Mellon University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Speech;Applications;Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Latent Sequence Decompositions
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
UC Berkeley; UCLA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Paleo: A Performance Model for Deep Neural Networks
|
https://github.com/TalwalkarLab/paleo
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Maluuba Research, Montréal, QC, Canada
|
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;4;6
| null | null |
Towards Information-Seeking Agents
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Indian Institute of Technology Madras; University of California Berkeley; University of Washington Seattle; NITK Surathkal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Applications
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
| null | null | 0 | 2.666667 |
Poster
|
4;0;4
| null |
null |
Department of Computer Science, The University of Texas at Austin, 2317 Speedway, Stop D9500 Austin, TX 78712, USA; Microsoft Research, Vigyan, 9 Lavelle Road, Bengaluru 560001, India
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
On Robust Concepts and Small Neural Nets
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null |
Department of Computer Science, Brigham Young University, Provo, UT 84602, 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
| 4.333333 |
4;4;5
| null | null |
Improving Invariance and Equivariance Properties of Convolutional Neural Networks
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
LIMSI-CNRS / Orsay, France
|
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
| 3 |
2;3;4
| null | null |
Opening the vocabulary of neural language models with character-level word representations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
EECS, University of California, Merced
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep learning
| null | 0 | null | null |
iclr
| 0.870388 | 0 |
http://eecs.ucmerced.edu
|
main
| 5.25 |
4;5;6;6
| null | null |
ParMAC: distributed optimisation of nested functions, with application to binary autoencoders
| null | null | 0 | 3.5 |
Reject
|
2;4;4;4
| null |
null |
DeepMind, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Supervised Learning;Optimization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Local minima in training of deep networks
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
School of Design, Victoria University of Wellington, Wellington, New Zealand
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning;Computer vision
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Sampling Generative Networks
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
University of California, Berkeley; Shanghai Jiao Tong University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Delving into Transferable Adversarial Examples and Black-box Attacks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Department of Computer Science, University of Virginia, Charlottesville, VA 22901, 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
| 4.333333 |
3;5;5
| null | null |
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null |
COPPE/PESC, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Poli, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Optimization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning Identity Mappings with Residual Gates
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
MILA, Université de Montréal; Language Technologies Institute, Carnegie Mellon University; Maluuba Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Calibrating Energy-based Generative Adversarial Networks
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
University of Edinburgh, UK; UC Irvine, USA; Ecole Polytechnique de Montreal, CA; Microsoft Research, USA; University of Washington, USA; University of Alberta, CA
|
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
| 7 |
7;7
| null | null |
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
| null | null | 0 | 3.5 |
Poster
|
3;4
| null |
null |
Computer Science Department, Cornell University; Computer Science Department, Huazhong University of Science and Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Applications
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Deep Neural Networks and the Tree of Life
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
babylon health, London, UK; University of Cambridge, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Transfer Learning;Applications
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
| null | null | 0 | 4.333333 |
Poster
|
3;5;5
| 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
| 6 |
5;6;7
| null | null |
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
|
github.com/hendrycks/init
| null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Institute of Physiology (iDN), Medical University of Graz, Austria; Department of Computer Science, University of Bari, Italy
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Encoding and Decoding Representations with Sum- and Max-Product Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
New York University; Adobe Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Inference and Introspection in Deep Generative Models of Sparse Data
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
Politecnico di Milano; University of Oxford; University of Montreal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Mollifying Networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.