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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Computer Science, ETH Zurich, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Emre Aksan, Otmar Hilliges
|
https://iclr.cc/virtual/2019/poster/1126
|
latent variables;variational inference;temporal convolutional networks;sequence modeling;auto-regressive modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
STCN: Stochastic Temporal Convolutional Networks
| null | null | 0 | 4 |
Poster
|
4;3;5
| 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
| 5 |
5;5;5
| null | null |
Quantization for Rapid Deployment of Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
contextual modulation;recurrent convolutional network;robust visual learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Contextual Recurrent Convolutional Model for Robust Visual Learning
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Canonical Correlation Analysis;implicit probabilistic model;cross-view structure output prediction
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Canonical Correlation Analysis with Implicit Distributions
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
video prediction;GANs;variational autoencoder
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Stochastic Adversarial Video Prediction
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
PROWLER.io, Cambridge, United Kingdom
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jordi Grau-Moya, Felix Leibfried, Peter Vrancx
|
https://iclr.cc/virtual/2019/poster/822
|
reinforcement learning;regularization;entropy;mutual information
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Soft Q-Learning with Mutual-Information Regularization
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Noisy Labels;Deep Learning;Meta Approach
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Department of Computer Science, Universidad de Chile & IMFD Chile
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jorge Pérez, Javier Marinković, Pablo Barceló
|
https://iclr.cc/virtual/2019/poster/707
|
Transformer;NeuralGPU;Turing completeness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
On the Turing Completeness of Modern Neural Network Architectures
| null | null | 0 | 2 |
Poster
|
2;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dataset denoising;supervised learning;implicit regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Better Generalization with On-the-fly Dataset Denoising
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
The Swiss AI Lab, IDSIA / USI / SUPSI, NNAISENSE; The Swiss AI Lab, IDSIA / USI / SUPSI
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Robert Csordas, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2019/poster/691
|
rnn;dnc;memory augmented neural networks;mann
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null |
Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Vincent Tjeng, Kai Xiao, Russ Tedrake
|
https://iclr.cc/virtual/2019/poster/817
|
verification;adversarial robustness;adversarial examples;deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Evaluating Robustness of Neural Networks with Mixed Integer Programming
| null | null | 0 | 3.666667 |
Poster
|
1;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;theory;non convex optimization;over-parameterization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Over-parameterization Improves Generalization in the XOR Detection Problem
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Illinois at Urbana-Champaign; Rice University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Konik Kothari, Sidharth Gupta, Maarten V de Hoop, Ivan Dokmanic
|
https://iclr.cc/virtual/2019/poster/704
|
imaging;inverse problems;subspace projections;random Delaunay triangulations;CNN;geophysics;regularization
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Random mesh projectors for inverse problems
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neuroevolution;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.027778 | 0 | null |
main
| 5.2 |
3;4;6;6;7
| null | null |
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
| null | null | 0 | 3.8 |
Reject
|
4;4;2;4;5
| null |
null |
University of Illinois at Urbana-Champaign; University of Science and Technology of China; Microsoft Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu
|
https://iclr.cc/virtual/2019/poster/1045
|
Dual Learning;Machine Learning;Neural Machine Translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Multi-Agent Dual Learning
| null | null | 0 | 3 |
Poster
|
3;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial attack;adversarial examples;audio processing;speech to text;deep learning;adversarial audio;black box;machine learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Targeted Adversarial Examples for Black Box Audio Systems
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Google Research, Mountain View, CA, USA; Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan; Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan and Electronic and Optoelectronic System Research Laboratories, ITRI, Hsinchu, Taiwan
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
|
https://iclr.cc/virtual/2019/poster/678
|
optimization;entropy;image recognition;natural language understanding;adversarial attacks;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Complement Objective Training
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language goals;task generalization;hindsight experience replays;language grounding
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
ACTRCE: Augmenting Experience via Teacher’s Advice
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Adversarial example;Transferability;Smoothed gradient
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Exploring and Enhancing the Transferability of Adversarial Examples
| null | null | 0 | 2.666667 |
Reject
|
2;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Generative Models;Variational Inference;Generative Adversarial Networks.
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Implicit Autoencoders
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Network;RNN;Sequence Modelling
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
SEQUENCE MODELLING WITH AUTO-ADDRESSING AND RECURRENT MEMORY INTEGRATING NETWORKS
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;training data selection;reinforcement learning;natural language processing
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING
| null | null | 0 | 3 |
Reject
|
2;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Structured Latent Space;Stable Training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine translation;dual learning
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 4.333333 |
2;5;6
| null | null |
Dual Learning: Theoretical Study and Algorithmic Extensions
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
University of Edinburgh
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Lucas Deecke, Iain Murray, Hakan Bilen
|
https://iclr.cc/virtual/2019/poster/833
|
Deep Learning;Expert Models;Normalization;Computer Vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Mode Normalization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tianxing He, James R Glass
|
https://iclr.cc/virtual/2019/poster/861
|
Deep Learning;Natural Language Processing;Adversarial Attacks;Dialogue Response Generation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Detecting Egregious Responses in Neural Sequence-to-sequence Models
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Natural Language Processing;Representation Learning;Document Embedding
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Unsupervised Document Representation using Partition Word-Vectors Averaging
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Explanation;Network interpretation;Contrastive explanation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
CDeepEx: Contrastive Deep Explanations
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NP-hardness;ReLU activation;Two hidden layer networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Complexity of Training ReLU Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNN optimization;Reduction on convolution calculation;dynamic convolution;surveillance video
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Networks;Representation;Information density;Transfer Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Measuring Density and Similarity of Task Relevant Information in Neural Representations
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;hierarchy;linear markov decision process;lmdl;subtask discovery;incremental
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Incremental Hierarchical Reinforcement Learning with Multitask LMDPs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Dibya Ghosh, Abhishek Gupta, Sergey Levine
|
https://iclr.cc/virtual/2019/poster/910
|
Representation Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Actionable Representations with Goal Conditioned Policies
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chongli Qin, Krishnamurthy Dvijotham, Brendan ODonoghue, Rudy R Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli
|
https://iclr.cc/virtual/2019/poster/878
|
Verification;Convex Optimization;Adversarial Robustness
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Verification of Non-Linear Specifications for Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
Department of Computer Science, Technical University of Munich
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Lukas Prantl, Boris Bonev, Nils Thuerey
|
https://iclr.cc/virtual/2019/poster/912
|
deformation learning;spatial transformer networks;fluid simulation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Generating Liquid Simulations with Deformation-aware Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Georgia Institute of Technology; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
|
https://iclr.cc/virtual/2019/poster/800
|
Dynamic Graphs;Representation Learning;Dynamic Processes;Temporal Point Process;Attention;Latent Representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
DyRep: Learning Representations over Dynamic Graphs
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;Markov decision processes;safety constraints;multi-objective optimization;geometric analysis
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Multi-Objective Value Iteration with Parameterized Threshold-Based Safety Constraints
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Noise engineered GAN;Latent space engineering;Mode matching;Unsupervised learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Unsupervised Conditional Generation using noise engineered mode matching GAN
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Carnegie Mellon University and Bosch Center for AI; Carnegie Mellon University; Intel Labs
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Shaojie Bai, Zico Kolter, Vladlen Koltun
|
https://iclr.cc/virtual/2019/poster/825
|
sequence modeling;language modeling;recurrent networks;convolutional networks;trellis networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Trellis Networks for Sequence Modeling
|
https://github.com/locuslab/trellisnet
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretabile machine learning;neural network;hierarchical clustering
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Interpreting Layered Neural Networks via Hierarchical Modular Representation
| 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;spatio-temporal dynamics;physical processes;differential equations;dynamical systems
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning Partially Observed PDE Dynamics with Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
rnns;hmms;latent variable models;language modelling;interpretability;sequence modelling
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Bridging HMMs and RNNs through Architectural Transformations
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dialogue generation;nlp applications;grounded text generation;contextual representation learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
I Know the Feeling: Learning to Converse with Empathy
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
activation pruning;weight pruning;computation cost reduction;efficient DNNs
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Integral Pruning on Activations and Weights for Efficient Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;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
| 5.666667 |
5;5;7
| null | null |
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Laboratory for Information & Decision Systems, Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Karren Yang, Caroline Uhler
|
https://iclr.cc/virtual/2019/poster/841
|
unbalanced optimal transport;generative adversarial networks;population modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model acceleration;mimic;knowledge distillation;channel pruning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 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 |
Temporal Gaussian Mixture Layer for Videos
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null |
Department of Statistics, University of California, Irvine; Department of Computer Science, University of California, Irvine
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Stephen McAleer, Forest Agostinelli, Alexander K Shmakov, Pierre Baldi
|
https://iclr.cc/virtual/2019/poster/1094
|
reinforcement learning;Rubik's Cube;approximate policy iteration;deep learning;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Solving the Rubik's Cube with Approximate Policy Iteration
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient inference;Hardware-efficient model architectures;Quantization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
NICE: noise injection and clamping estimation for neural network quantization
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
MIT Computer Science and Artificial Intelligence Laboratory
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh
|
https://iclr.cc/virtual/2019/poster/1025
|
reinforcement learning;policy gradient;input-driven environments;variance reduction;baseline
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Variance Reduction for Reinforcement Learning in Input-Driven Environments
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Latent space;Generative adversarial network;variational autoencoder;conditioned generation
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Learning Latent Semantic Representation from Pre-defined Generative Model
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Generative Models;Normalizing Flows;RealNVP;Density Estimation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
The Alan Turing Institute, London, UK; Amazon, Cambridge, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sebastian Flennerhag, Pablo Moreno, Neil D Lawrence, Andreas Damianou
|
https://iclr.cc/virtual/2019/poster/771
|
meta-learning;transfer learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
6;8;8
| null | null |
Transferring Knowledge across Learning Processes
| null | null | 0 | 3.666667 |
Oral
|
4;3;4
| null |
null |
Courant Institute, New York University; Facebook AI Research; Courant Institute, New York University; Courant Institute, New York University; Microsoft Research, NYC
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mikael Henaff, Alfredo Canziani, Yann LeCun
|
https://iclr.cc/virtual/2019/poster/1121
|
model-based reinforcement learning;stochastic video prediction;autonomous driving
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| 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 |
3;5;6
| null | null |
$A^*$ sampling with probability matching
| null | null | 0 | 4 |
Reject
|
5;5;2
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Strategic Exploration;Model Based Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;5
| null | null |
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regularization;generalization;image classification;latent space;feature learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
MixFeat: Mix Feature in Latent Space Learns Discriminative Space
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image outlier;CNN;Deep Neural Forest
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Outlier Detection from Image Data
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;adversarial attack;defence;detection;Bayes' rule
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 5.5 |
4;4;6;8
| null | null |
Are Generative Classifiers More Robust to Adversarial Attacks?
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
consciousness;conscious inference;object detection;object pose estimation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Conscious Inference for Object Detection
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua Tenenbaum, William Freeman, Antonio Torralba
|
https://iclr.cc/virtual/2019/poster/1089
|
GANs;representation;interpretability;causality
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimal margin distribution;Deep neural network;Generalization bound
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Optimal margin Distribution Network
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Imitation Learning;Sequential Information
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
University of Melbourne; RMIT University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wei Wang, Yuan Sun, Saman Halgamuge
|
https://iclr.cc/virtual/2019/poster/676
|
generative adversarial nets;loss function;maximum mean discrepancy;image generation;unsupervised learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Improving MMD-GAN Training with Repulsive Loss Function
|
https://github.com/richardwth/MMD-GAN
| null | 0 | 4 |
Poster
|
2;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representation learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Department of Computer Science, Carnegie Mellon University & Bosch Center for AI, Pittsburgh, PA; Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Vaishnavh Nagarajan, Zico Kolter
|
https://iclr.cc/virtual/2019/poster/954
|
generalization;PAC-Bayes;SGD;learning theory;implicit regularization
| null | 0 | null | null |
iclr
| 0.102598 | 0 | null |
main
| 6.75 |
5;7;7;8
| null | null |
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
| null | null | 0 | 3.5 |
Poster
|
4;3;2;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial examples;Image denoising
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null | null |
2019
| 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 |
Rating Continuous Actions in Spatial Multi-Agent Problems
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
University of California, Berkeley; Google Deepmind; IIT Kanpur; Mila, University of Montreal; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio
|
https://iclr.cc/virtual/2019/poster/1033
|
Model free RL;Variational Inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Recall Traces: Backtracking Models for Efficient Reinforcement Learning
| null | null | 0 | 2.666667 |
Poster
|
3;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learned compression;generative adversarial networks;extreme compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Generative Adversarial Networks for Extreme Learned Image Compression
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
gradient method;max-margin;ReLU model
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;meta learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Meta Learning with Fast/Slow Learners
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
University of California, Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
John Miller, Moritz Hardt
|
https://iclr.cc/virtual/2019/poster/658
|
stability;gradient descent;non-convex optimization;recurrent neural networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Stable Recurrent Models
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
low-resource deep neural networks;quantized weights;weight-clustering;resource efficient neural networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;4;7
| null | null |
N-Ary Quantization for CNN Model Compression and Inference Acceleration
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantization;network capacity;hardware implementation;network compression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Capacity of Deep Neural Networks under Parameter Quantization
| null | null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
VECTORIZATION METHODS IN RECOMMENDER SYSTEM
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Capsule networks;generalization;scalability;adversarial robustness
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Generalized Capsule Networks with Trainable Routing Procedure
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph prediction;graph structure learning;graph neural network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Graph Learning Network: A Structure Learning Algorithm
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;multi-agent;hierarchical;noisy observation;partial observability;deep learning
| null | 0 | null | null |
iclr
| -0.960769 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null |
UCLA, Los Angeles, CA 90095; MIT, Cambridge, MA 02139; UT Austin, Austin, TX 78712
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Huan Zhang, Hongge Chen, Zhao Song, Duane S Boning, Inderjit Dhillon, Cho-Jui Hsieh
|
https://iclr.cc/virtual/2019/poster/730
|
Adversarial Examples;Adversarial Training;Blind-Spot Attack
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
The Limitations of Adversarial Training and the Blind-Spot Attack
|
https://github.com/MadryLab/mnist_challenge
| null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
|
https://iclr.cc/virtual/2019/poster/957
|
local optimality;second-order stationary point;escaping saddle points;nondifferentiability;ReLU;empirical risk
| null | 0 | null | null |
iclr
| -0.594089 | 0 | null |
main
| 5.75 |
3;6;6;8
| null | null |
Efficiently testing local optimality and escaping saddles for ReLU networks
| null | null | 0 | 3 |
Poster
|
4;3;2;3
| null |
null |
Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Han Cai, Ligeng Zhu, Song Han
|
https://iclr.cc/virtual/2019/poster/1029
|
Neural Architecture Search;Efficient Neural Networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
|
https://github.com/MIT-HAN-LAB/ProxylessNAS
| null | 0 | 2.666667 |
Poster
|
2;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Statistics;Sensitivity;Exploding Gradient;Convolutional Neural Networks;Residual Neural Networks;Batch Normalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Statistical Characterization of Deep Neural Networks and their Sensitivity
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
RIKEN AIP, Tokyo, Japan; RIKEN AIP, Tokyo, Japan; University of Tokyo, Tokyo, Japan; University of Tokyo, Tokyo, Japan; RIKEN AIP, Tokyo, Japan
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
|
https://iclr.cc/virtual/2019/poster/1109
|
Hierarchical reinforcement learning;Representation learning;Continuous control
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distance Kernel;Embeddings;Random Features;Structured Inputs
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
D2KE: From Distance to Kernel and Embedding via Random Features For Structured Inputs
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Text Embeddings;Document Ranking;Improving Retrieval;Question-Answering;Learning to Rank
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Text Embeddings for Retrieval from a Large Knowledge Base
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Task-GAN: Improving Generative Adversarial Network for Image Restoration
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Task-GAN for Improved GAN based Image Restoration
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised representation learning;sense embedding;word sense disambiguation;human evaluation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Representation Learning;Dynamic Graphs;Attention;Self-Attention;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Dynamic Graph Representation Learning via Self-Attention Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;exploration;representation learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
EMI: Exploration with Mutual Information Maximizing State and Action Embeddings
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Google Brain Amsterdam; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jacob Menick, Nal Kalchbrenner
|
https://iclr.cc/virtual/2019/poster/1064
| null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 8.666667 |
7;9;10
| null | null |
GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING
| null | null | 0 | 3.666667 |
Oral
|
3;3;5
| null |
null |
Department of Electrical Engineering, Stanford University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Farzan Farnia, Jesse Zhang, David Tse
|
https://iclr.cc/virtual/2019/poster/958
|
Adversarial attacks;adversarial training;spectral normalization;generalization guarantee
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Generalizable Adversarial Training via Spectral Normalization
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
University of Montreal, Montreal, Canada; Northwestern University, Evanston, IL, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ali Farshchian, Juan Álvaro Gallego, Joseph Paul Cohen, Yoshua Bengio, Lee E Miller, Sara A Solla
|
https://iclr.cc/virtual/2019/poster/686
|
Brain-Machine Interfaces;Domain Adaptation;Adversarial Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
University of California, Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Anusha Nagabandi, Chelsea Finn, Sergey Levine
|
https://iclr.cc/virtual/2019/poster/1078
|
meta-learning;model-based;reinforcement learning;online learning;adaptation
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/berkeley.edu/onlineviameta
|
main
| 7 |
7;7;7
| null | null |
Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Training;Gradient Regularization;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Adversarially Robust Training through Structured Gradient Regularization
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Chicago; Oregon State University; University of California, Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Dan Hendrycks and Mantas Mazeika and Thomas Dietterich
|
https://iclr.cc/virtual/2019/poster/772
|
confidence;uncertainty;anomaly;robustness
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Deep Anomaly Detection with Outlier Exposure
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
University of Michigan and Google Brain; University of Michigan; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee
|
https://iclr.cc/virtual/2019/poster/695
|
Reinforcement Learning;Exploration;Contingency-Awareness
| null | 0 | null | null |
iclr
| 0 | 0 |
https://coex-rl.github.io/
|
main
| 6.666667 |
6;7;7
| null | null |
Contingency-Aware Exploration in Reinforcement Learning
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
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