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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Characterizing Attacks on Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learned representation;statistical characteristics;information theoretical characteristics;deep network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
On the Statistical and Information Theoretical Characteristics of DNN Representations
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Department of Electrical and Computer Engineering, Duke University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin
|
https://iclr.cc/virtual/2019/poster/741
|
generalized reparameterization gradient;variance reduction;non-reparameterizable;discrete random variable;GO gradient;general and one-sample gradient;expectation-based objective;variable nabla;statistical back-propagation;hierarchical;graphical model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
GO Gradient for Expectation-Based Objectives
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Montreal Institute for Learning Algorithms (MILA), Canada; CIFAR Senior Fellow; Montreal Institute for Learning Algorithms (MILA), Canada
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
|
https://iclr.cc/virtual/2019/poster/683
|
LSTM;Optimization;Long term dependencies;Back-propagation through time
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
h-detach: Modifying the LSTM Gradient Towards Better Optimization
|
https://github.com/bhargav104/h-detach
| null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Watermarking;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Fatty and Skinny: A Joint Training Method of Watermark Encoder and Decoder
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Department of Applied Physics, Stanford University and Google Brain; Department of Psychology, Stanford University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Lampinen, Surya Ganguli
|
https://iclr.cc/virtual/2019/poster/798
|
Generalization;Theory;Transfer;Multi-task;Linear
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
An analytic theory of generalization dynamics and transfer learning in deep linear networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative models;Input manipulation;Unsupervised feature learning;Variations
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Variation Network: Learning High-level Attributes for Controlled Input Manipulation
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;Deep Q-networks;actor-critic algorithm;ODE approximation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
belief states;representation learning;contrastive predictive coding;reinforcement learning;predictive state representations;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Neural Predictive Belief Representations
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Probability distillation;Autoregressive models;normalizing flows;wavenet;pixelcnn
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
On Difficulties of Probability Distillation
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image Composition;GAN;Conditional Image generation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Compositional GAN: Learning Conditional Image Composition
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
imitation;from pixels;adversarial
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Visual Imitation with a Minimal Adversary
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;computational biology;generating;generation;extrapolation;out-of-sample;neural network inference
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Out-of-Sample Extrapolation with Neuron Editing
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
SenseTime Research; The Chinese University of Hong Kong; The Chinese University of Hong Kong, The University of Hong Kong
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ping Luo, jiamin ren, zhanglin peng, Ruimao Zhang, Jingyu Li
|
https://iclr.cc/virtual/2019/poster/1116
|
normalization;deep learning;CNN;computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Differentiable Learning-to-Normalize via Switchable Normalization
|
https://github.com/switchablenorms/
| null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Information Bottleneck;Blackwell Sufficiency;Le Cam Deficiency;Information Channel
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
The Variational Deficiency Bottleneck
| null | null | 0 | 3 |
Reject
|
5;2;2
| null |
null |
Gatsby Unit, University College London; Department of Computer Science, ETH Zürich
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
|
https://iclr.cc/virtual/2019/poster/729
|
deep learning;self-organizing map;variational autoencoder;representation learning;time series;machine learning;interpretability
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;6;9
| null | null |
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3D Reconstruction;3D Scene Understanding;Relative Prediction
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
3D-RelNet: Joint Object and Relational Network for 3D Prediction
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Massachusetts Institute of Technology; Google Inc.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wei-Ning Hsu, Yu Zhang, Ron Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang
|
https://iclr.cc/virtual/2019/poster/754
|
speech synthesis;representation learning;deep generative model;sequence-to-sequence model
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Hierarchical Generative Modeling for Controllable Speech Synthesis
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Language Model Pre-training for Hierarchical Document Representations
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Unknown Affiliation
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;model compression;optimization;ADMM;weight pruning
| null | 0 | null | null |
iclr
| -0.5 | 0 |
bit.ly/2zxdlss
|
main
| 4.666667 |
4;5;5
| null | null |
Progressive Weight Pruning Of Deep Neural Networks Using ADMM
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;attentional mechanisms;neural machine translation;image captioning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Area Attention
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Language Technologies Institute, Carnegie Mellon University; Machine Learning Department, Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Ali Bagherzade, Louis-Philippe Morency, Ruslan Salakhutdinov
|
https://iclr.cc/virtual/2019/poster/925
|
multimodal learning;representation learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Factorized Multimodal Representations
| null | null | 0 | 2.666667 |
Poster
|
3;2;3
| null |
null |
University of Southern California
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Youngwoon Lee, Shao-Hua Sun, Sriram Somasundaram, Edward S Hu, Joseph Lim
|
https://iclr.cc/virtual/2019/poster/792
|
reinforcement learning;hierarchical reinforcement learning;continuous control;modular framework
| null | 0 | null | null |
iclr
| 0 | 0 |
https://youngwoon.github.io/transition
|
main
| 7.666667 |
7;7;9
| null | null |
Composing Complex Skills by Learning Transition Policies
|
https://github.com/youngwoon/transition
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
likelihood-free inference;implicit probabilistic models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Implicit Maximum Likelihood Estimation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;General Value Functions;Policy Gradient;Hierarchical Reinforcement Learning;Successor Features
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Knowledge Representation for Reinforcement Learning using General Value Functions
| null | null | 0 | 3.333333 |
Withdraw
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image translation;domain adaptation;saliency detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
LIT AI Lab, Johannes Kepler University Linz; Institute for Machine Learning, Johannes Kepler University Linz; Department of Medical Chemistry, Center for Pathobiochemistry and Genetics, Medical University of Vienna
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer
|
https://iclr.cc/virtual/2019/poster/1108
|
Convolutional Neural Networks;High-resolution images;Multiple-Instance Learning;Microscopy Imaging;Protein Localization
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Human-level Protein Localization with Convolutional Neural Networks
|
https://github.com/ml-jku/gapnet-pl
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
The Robotics Institute, Carnegie Mellon University and Facebook AI Research; The Robotics Institute, Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wenxuan Zhou, Lerrel Pinto, Abhinav Gupta
|
https://iclr.cc/virtual/2019/poster/915
|
Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Environment Probing Interaction Policies
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language modeling;variational inference;dynamic model;temporal data;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Modeling Evolution of Language Through Time with Neural Networks
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null |
University of California San Diego; Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick
|
https://iclr.cc/virtual/2019/poster/640
|
variational autoencoders;posterior collapse;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
|
https://github.com/jxhe/vae-lagging-encoder
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Mathematics, University of California, Los Angeles, 520 Portola Plaza, Los Angeles, CA 90095, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Robert Hannah, Fei Feng, Wotao Yin
|
https://iclr.cc/virtual/2019/poster/1125
|
asynchronous;optimization;parallel;accelerated;complexity
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
A2BCD: Asynchronous Acceleration with Optimal Complexity
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
non-convex optimization;generative adversarial network;primal dual algorithm
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Understand the dynamics of GANs via Primal-Dual Optimization
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;generalization;benchmark
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Assessing Generalization in Deep Reinforcement Learning
| null | null | 0 | 3.333333 |
Reject
|
5;2;3
| null |
null |
Massachusetts Institute of Technology; Massachusetts Institute of Technology, Google Research; Princeton University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William Freeman, Joshua B Tenenbaum, Jiajun Wu
|
https://iclr.cc/virtual/2019/poster/639
|
Program Synthesis;3D Shape Modeling;Self-supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 |
http://shape2prog.csail.mit.edu
|
main
| 6.666667 |
6;7;7
| null | null |
Learning to Infer and Execute 3D Shape Programs
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NA
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 3.333333 |
1;4;5
| null | null |
NA
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;generative models;few shot learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Learning with Little Data: Evaluation of Deep Learning Algorithms
| null | null | 0 | 4 |
Withdraw
|
5;3;4
| null |
null |
Department of Mathematics and College of Computer and Information Science, Northeastern University; Department of Electrical and Computer Engineering, Rice University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Reinhard Heckel, Paul Hand
|
https://iclr.cc/virtual/2019/poster/973
|
natural image model;image prior;under-determined neural networks;untrained network;non-convolutional network;denoising;inverse problem
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial examples;Robustness
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial Neural Network;Convolution Neural Network;Long Short-Term Memory;Activation Function;Neuromodulation
| null | 0 | null | null |
iclr
| -0.133631 | 0 | null |
main
| 4.4 |
4;4;4;4;6
| null | null |
Context Dependent Modulation of Activation Function
| null | null | 0 | 4.2 |
Reject
|
5;4;3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;TD Learning;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
HR-TD: A Regularized TD Method to Avoid Over-Generalization
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;abstractive summarization;reviews;text generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;5;9
| null | null |
Unsupervised Neural Multi-Document Abstractive Summarization of Reviews
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised disentangled representation learning;GAN;Information Bottleneck;Variational Inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
SenseTime
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin
|
https://iclr.cc/virtual/2019/poster/700
|
Neural Architecture Search
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
SNAS: stochastic neural architecture search
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;architecture search;evolution strategy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Neuron Hierarchical Networks
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;generative modeling;unsupervised learning;maximum likelihood;adversarial learning;gan;vae
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Coverage and Quality Driven Training of Generative Image Models
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Machine Translation;Natural Language Processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Contextualized Role Interaction for Neural Machine Translation
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Network;Semi-Supervised Learning;Adversarial Training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Difference-Seeking Generative Adversarial Network
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
confounder;causal inference;reinforcement learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Deconfounding Reinforcement Learning in Observational Settings
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Curriculum Learning;Transfer Learning;Self-Paced Learning;Image Recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
In Your Pace: Learning the Right Example at the Right Time
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Hierarchical Reinforcement Learning;Model-based Reinforcement Learning;Exploration
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning Abstract Models for Long-Horizon Exploration
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;energy models;conditional random fields;label correlation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
CGNF: Conditional Graph Neural Fields
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Logically-Constrained Neural Fitted Q-iteration
| null | null | 0 | 3.666667 |
Withdraw
|
5;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data diversification;domain adaptation;transfer learning;stacked generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Stacking for Transfer Learning
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Section on Functional Imaging Methods, National Institute of Mental Health; Section on Learning and Plasticity, National Institute of Mental Health
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Charles Zheng, Francisco Pereira, Chris I Baker, Martin N Hebart
|
https://iclr.cc/virtual/2019/poster/712
|
category representation;sparse coding;representation learning;interpretable representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Revealing interpretable object representations from human behavior
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;lipschitz neural networks;generalization;universal approximation;adversarial examples;generative models;optimal transport;adversarial robustness
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Sorting out Lipschitz function approximation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Dual Skew Divergence Loss for Neural Machine Translation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Google Brain, Mountain View, CA, USA; University of British Columbia, Vancouver, BC, Canada
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi
|
https://iclr.cc/virtual/2019/poster/696
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
efficient machine learning，binary neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5
| null | null |
A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks
| null | null | 0 | 4 |
Withdraw
|
4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural-symbolic models;visual question answering;reasoning;interpretability;graphical models;variational inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Networks;Boolean satisfiability problem;Satisfiability modulo theories
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;Sparse Optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Single Shot Neural Architecture Search Via Direct Sparse Optimization
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causal inference;CATE estimation;ITE;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Estimating Heterogeneous Treatment Effects Using Neural Networks With The Y-Learner
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
‡Salesforce Research; †The Hong Kong University of Science and Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chien-Sheng Wu, richard socher, Caiming Xiong
|
https://iclr.cc/virtual/2019/poster/690
|
pointer networks;memory networks;task-oriented dialogue systems;natural language processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7 |
5;8;8
| null | null |
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
| null | null | 0 | 2.333333 |
Poster
|
3;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph convolution;hierarchical models;neural networks;multigraph;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Spectral Convolutional Networks on Hierarchical Multigraphs
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continuous learning;catastrophic forgetting;architecture learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Continual Learning via Explicit Structure Learning
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;conditional GAN
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
From Adversarial Training to Generative Adversarial Networks
|
https://github.com/anonymous
| null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null |
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea; AItrics, Seoul, Korea; Pohang University of Science and Technology (POSTECH), Pohang, Korea
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sangwoo Mo, Minsu Cho, Jinwoo Shin
|
https://iclr.cc/virtual/2019/poster/742
|
Image-to-Image Translation;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
InstaGAN: Instance-aware Image-to-Image Translation
|
https://github.com/sangwoomo/instagan
| null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null |
KAUST; Intel Labs
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl
|
https://iclr.cc/virtual/2019/poster/978
|
deep networks;optimization
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Deep Layers as Stochastic Solvers
| null | null | 0 | 3.333333 |
Poster
|
5;4;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sparse recovery
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Accelerated Sparse Recovery Under Structured Measurements
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Computer Science, Brown University, Providence, RI, USA; College of Computer and Information Science, Northeastern University, Boston, MA, USA; Department of Computer Science, Boston University, Boston, MA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Levy, George D Konidaris, Robert Platt, Kate Saenko
|
https://iclr.cc/virtual/2019/poster/913
|
Hierarchical Reinforcement Learning;Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Multi-Level Hierarchies with Hindsight
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data selection;deep learning;uncertainty sampling
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Select Via Proxy: Efficient Data Selection For Training Deep Networks
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Machine Translation;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Machine Translation With Weakly Paired Bilingual Documents
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
traffic flow forecasting;spatiotemporal dependencies;deep learning;intelligent transportation system
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Layerwise Recurrent Autoencoder for General Real-world Traffic Flow Forecasting
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2014
| 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 return of AdaBoost.MH: multi-class Hamming trees
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Adaptive Feature Ranking for Unsupervised Transfer Learning
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Type-Driven Tensor-Based Meaning Representations
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Auto-Encoding Variational Bayes
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Fast Training of Convolutional Networks through FFTs
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Can recursive neural tensor networks learn logical reasoning?
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 by Convex Combination of Semantic Embeddings
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Transformations for Classification Forests
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 generative models with visual attention
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Belief Networks for Image Denoising
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Visual Coding: From Retina To V2
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Semistochastic Quadratic Bound Methods
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Spectral Networks and Locally Connected Networks on Graphs
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Continuous Learning: Engineering Super Features With Feature Algebras
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 and Wide Multiscale Recursive Networks for Robust Image Labeling
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Reference Distance Estimator
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
How to Construct Deep Recurrent Neural Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Unit Tests for Stochastic Optimization
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Image Representation Learning Using Graph Regularized Auto-Encoders
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
A Simple Model for Learning Multilingual Compositional Semantics
| null | null | 0 | 0 |
Poster
| null | null |
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