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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu
|
https://iclr.cc/virtual/2019/poster/759
|
Natural Language Processing;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Representation Degeneration Problem in Training Natural Language Generation Models
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Department of EECS, UC Berkeley; Department of Statistics, UC Berkeley; Department of Statistics, EECS, UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chandan Singh, William Murdoch, Bin Yu
|
https://iclr.cc/virtual/2019/poster/681
|
interpretability;natural language processing;computer vision
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Hierarchical interpretations for neural network predictions
|
https://github.com/csinva/acd
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
forward model;adversarial learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
DEEP ADVERSARIAL FORWARD MODEL
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Generation;Computation and Language;Machine Learning;Generative Adversarial Networks;Sentence Embeddings
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
Generating Text through Adversarial Training using Skip-Thought Vectors
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph signal processing;graph alignment;manifold alignment;spectral graph wavelet transform;diffusion geometry;harmonic analysis
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Manifold Alignment via Feature Correspondence
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed;asynchronous;gradient staleness;nesterov;optimization;out-of-the-box;stochastic gradient descent;sgd;imagenet;distributed training;neural networks;deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Infinite-width networks;initialisation;kernel methods;reproducing kernel Hilbert spaces;Gaussian processes
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Infinitely Deep Infinite-Width 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 | null | null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
Heated-Up Softmax Embedding
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning;End-to-End Driving;Learning to drive;Autonomous Driving
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Learning to Drive by Observing the Best and Synthesizing the Worst
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research; Inria; Facebook AI Research⋆Inria
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou
|
https://iclr.cc/virtual/2019/poster/1005
|
dimensionality reduction;similarity search;indexing;differential entropy
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Spreading vectors for similarity search
|
https://github.com/facebookresearch/spreadingvectors
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic gradient descent;Deep neural networks;Entropy;Information theory;Markov chains;Hidden Markov process.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
2;4;6
| null | null |
On the Trajectory of Stochastic Gradient Descent in the Information Plane
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Genearative Adversarial Network;Variational Autoencoder;Wasserstein GAN;Autoregressive Model;Dynamic Texture;Video
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Linearizing Visual Processes with Deep Generative Models
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
Princeton University and Institute for Advanced Study; Harvard University; Institute for Advanced Study; Princeton University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu
|
https://iclr.cc/virtual/2019/poster/789
|
Deep Learning;Learning Theory;Non-Convex Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
Department of Cybernectics, Czech Technical University in Prague
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexander (Oleksandr) Shekhovtsov, Boris Flach
|
https://iclr.cc/virtual/2019/poster/1122
|
probabilistic neural network;uncertainty;dropout;bayesian;softmax;argmax;logsumexp
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
University of Pennsylvania, Philadelphia, PA, 19142
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ari S Benjamin, David Rolnick, Konrad P Kording
|
https://iclr.cc/virtual/2019/poster/837
|
function space;Hilbert space;empirical characterization;multitask learning;catastrophic forgetting;optimization;natural gradient
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Measuring and regularizing networks in function space
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised text generation;coarse-to-fine generator;multiple instance discriminator;GAN;DelibGAN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Auto Encoder;Importance Sampling;Discrete latent representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.333333 |
1;3;3
| null | null |
Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;graph neural network;open vocabulary;natural language processing;source code;abstract syntax tree;code completion;variable naming
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Facebook AI Research, Facebook Inc., Menlo Park, California 94025, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sho Yaida
|
https://iclr.cc/virtual/2019/poster/784
|
stochastic gradient descent;adaptive method;loss surface;Hessian
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Fluctuation-dissipation relations for stochastic gradient descent
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Learning;Federated Learning;Privacy;Security;Differential Privacy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Differentially Private Federated Learning: A Client Level Perspective
| 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
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-valued Inverse Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Facebook AI Research; Cornell University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli
|
https://iclr.cc/virtual/2019/poster/1043
|
Deep learning;sequence to sequence learning;convolutional neural networks;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Pay Less Attention with Lightweight and Dynamic Convolutions
|
http://github.com/pytorch/fairseq
| null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
architecture search;stochastic natural gradient;convolutional neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Probabilistic Model-Based Dynamic Architecture Search
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robust;adversarial;equivariance;rotations;GCNNs;CNNs;steerable;neural networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Robustness and Equivariance of Neural Networks
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Computer Science, ETH Zürich, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexandru Tifrea, Gary Bécigneul, Octavian Ganea
|
https://iclr.cc/virtual/2019/poster/787
|
word embeddings;hyperbolic spaces;poincare ball;hypernymy;analogy;similarity;gaussian embeddings
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Poincare Glove: Hyperbolic Word Embeddings
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
input method;language model;neural network;softmax
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Real-time Neural-based Input Method
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;scalable representations;combinatorial optimization;reinforcement learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Graph2Seq: Scalable Learning Dynamics for Graphs
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Auto-Encoders;Sparse Coding;Variational Inference
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Variational Sparse Coding
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
ETH Zürich; DeepMind; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly
|
https://iclr.cc/virtual/2019/poster/790
|
deep learning;reinforcement learning;curiosity;exploration;episodic memory
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 7.25 |
6;7;8;8
| null | null |
Episodic Curiosity through Reachability
|
https://github.com/google-research/episodic-curiosity
| null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep neural network;information theory;training dynamics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Restricted Boltzmann Machines;Transfer Learning;Compositional Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Combining Learned Representations for Combinatorial Optimization
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Department of Transdisciplinary Studies, Seoul National University, Seoul, Korea; Clova AI Research, NAVER Corp., Seongnam, Korea; Department of Mathematical Sciences, Seoul National University, Seoul, Korea
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
speech enhancement;deep learning;complex neural networks;phase estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Phase-Aware Speech Enhancement with Deep Complex U-Net
|
http://kkp15.github.io/DeepComplexUnet
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;circulant matrices;universal approximation
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
The Expressive Power of Deep Neural Networks with Circulant Matrices
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
DeepMind, London, United Kingdom; College of Computing, Georgia Institute of Technology, Atlanta, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yannick Schroecker, Mel Vecerik, Jon Scholz
|
https://iclr.cc/virtual/2019/poster/814
|
Imitation Learning;Generative Models;Deep Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Generative predecessor models for sample-efficient imitation learning
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Encoder;Graph Decoder;Graph2Seq;Graph Attention
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Graph2Seq: Graph to Sequence Learning with Attention-Based Neural 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 |
multicut graph decomposition;optimization by learning;pose estimation;clustering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Learning Graph Decomposition
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Department of Computer Science, University of Bristol, Bristol, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ivan Chelombiev, Conor Houghton, Cian O'Donnell
|
https://iclr.cc/virtual/2019/poster/977
|
deep neural networks;mutual information;information bottleneck;noise;L2 regularization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Adaptive Estimators Show Information Compression in Deep Neural 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 | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
NA
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
Language Technology Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Monash University, Clayton VIC 3800, Australia
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig
|
https://iclr.cc/virtual/2019/poster/1052
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Multilingual Neural Machine Translation With Soft Decoupled Encoding
|
https://github.com/cindyxinyiwang/SDE
| null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
source separation;non-adversarial training;source unmixing;iterative neural training;generative modeling
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Neural separation of observed and unobserved distributions
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| 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 |
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recurrent Neural Networks;MGU;LSTM;Generalization Bound;PAC-Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
On Generalization Bounds of a Family of Recurrent Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
metric learning;gradient equivalence;image retrieval
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
A Unified View of Deep Metric Learning via Gradient Analysis
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Eberhard Karls University of Tübingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wieland Brendel, Matthias Bethge
|
https://iclr.cc/virtual/2019/poster/667
|
interpretability;representation learning;bag of features;deep learning;object recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
| null | null | 0 | 4 |
Poster
|
4;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
| 4 |
3;4;5
| null | null |
In search of theoretically grounded pruning
| null | null | 0 | 3.333333 |
Withdraw
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recommender systems;reinforcement learning;predictive learning;self-supervised RL;model-based planning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Purchase as Reward : Session-based Recommendation by Imagination Reconstruction
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolution;unit sphere;3D object recognition
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Volumetric Convolution: Automatic Representation Learning in Unit Ball
| null | null | 0 | 3.333333 |
Reject
|
3;5;2
| null |
null |
DeepMind, London, England; Technion Israel Institute of Technology, Haifa, Israel
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chen Tessler, Daniel J Mankowitz, Shie Mannor
|
https://iclr.cc/virtual/2019/poster/756
|
reinforcement learning;markov decision process;constrained markov decision process;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Reward Constrained Policy Optimization
| null | null | 0 | 2.666667 |
Poster
|
2;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Optimizer;Adam;Gradient Descent
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Accelerating first order optimization algorithms
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NA
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
NA
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neuroevolution;Reinforcement Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Facebook AI Research; School of Informatics, University of Edinburgh; Ruhr University Bochum; Jagiellonian University; Mila / University of Montreal
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey
|
https://iclr.cc/virtual/2019/poster/875
|
optimization;generalization;theory of deep learning;SGD;hessian
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Computer Vision;Deep Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Unsupervised Video-to-Video Translation
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Facebook AI Research; Georgia Institute of Technology; CIFAR Senior Fellow; Mila, Universit é de Montréal; Polytechnique Montréal
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra
|
https://iclr.cc/virtual/2019/poster/1075
|
model-based reinforcement learning;variation inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Modeling the Long Term Future in Model-Based Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;protein interface prediction;deep learning;structural biology
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Transferrable End-to-End Learning for Protein Interface Prediction
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;adversarial training;autoencoders;hidden state
| null | 0 | null | null |
iclr
| -0.161165 | 0 | null |
main
| 6 |
4;5;6;9
| null | null |
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
N/A
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
N/A
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;Adversarial examples;Navigation;Evaluation;Analysis
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null |
Oregon State University; Université Laval
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Networks;Adversarial Instances;Out-distribution Samples;Rejection Option;Over-generalization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial attacks;action recognition;video classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
structured prediction energy networks;indirect supervision;search-guided training;reward functions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Invertible Mappings;Bijectives;Dimensionality reduction;Autoencoder
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
PIE: Pseudo-Invertible Encoder
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Peking University; University of Southern California
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;robust machine learning;cnn structure;metric;deep feature representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
RANDOM MASK: Towards Robust Convolutional Neural Networks
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial user model;Recommendation system;combinatorial recommendation policy;model-based reinforcement learning;deep Q-networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Neural Model-Based Reinforcement Learning for Recommendation
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Relational Reasoning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Graph Neural Networks with Generated Parameters for Relation Extraction
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Qualcomm AI Research, San Diego; Department of Mathematics, University of California, Irvine; Department of Mathematics, University of California, Los Angeles
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Penghang Yin, Jiancheng Lyu, shuai zhang, Stanley J Osher, YINGYONG QI, Jack Xin
|
https://iclr.cc/virtual/2019/poster/671
|
straight-through estimator;quantized activation;binary neuron
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
vae;unsupervised learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Non-Synergistic Variational Autoencoders
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.246183 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
| null | null | 0 | 3.25 |
Reject
|
4;2;4;3
| null |
null |
Preferred Networks
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Shoichiro Yamaguchi, Masanori Koyama
|
https://iclr.cc/virtual/2019/poster/842
|
Generative Adversarial Networks;regularization;optimal transport;functional gradient;convex analysis
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null | null |
DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
| null | null | 0 | 2.5 |
Poster
|
4;1;4;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Learning;Embeddings;Training Time;Optimization;Autoencoders
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Faster Training by Selecting Samples Using Embeddings
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional networks;signal processing;shift;translation;invariance;equivariance
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Making Convolutional Networks Shift-Invariant Again
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Curiosity-Driven;Experience Prioritization;Hindsight Experience;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Curiosity-Driven Experience Prioritization via Density Estimation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
non-convex optimization;denoising;generative neural network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language modeling;regularization;LSTM
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Improved Language Modeling by Decoding the Past
| 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
| -1 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Metric-Optimized Example Weights
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; Institute for Anatomy and Cell Biology, Heidelberg University, Germany; Institute of Physiology and Pathophysiology, Heidelberg University, Germany; Dept. Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Elke Kirschbaum, Manuel Haussmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A Hamprecht
|
https://iclr.cc/virtual/2019/poster/763
|
VAE;unsupervised learning;neuronal assemblies;calcium imaging analysis
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
5;8;8
| null | null |
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Over-parameterization;A priori estimates;Path norm;Neural networks;Generalization error;Approximation error
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
A Priori Estimates of the Generalization Error for Two-layer Neural Networks
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Networks;Program Synthesis;Source Code Modeling
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Neural Networks for Modeling Source Code Edits
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model-Based Reinforcement Learning;Intuitive Physics
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Physics Priors for Deep Reinforcement Learing
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Salesforce Research, Palo Alto, 94301
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hao Liu, Alexander Trott, richard socher, Caiming Xiong
|
https://iclr.cc/virtual/2019/poster/1091
|
reinforcement learning;sparse reward;goal-based learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Competitive experience replay
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
AlexNet;neural networks;selectivity;localist;distributed;represenataion;precision;measures of selectivity;object detectors;single directions;network analysis
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA 94158; INRIA-CNRS-UPSud-UPSaclay, TAU, U. Paris-Sud, 91405 Orsay
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alice Schoenauer Sebag, Louise E Heinrich, Marc Schoenauer, Michele Sebag, Lani Wu, Steven Altschuler
|
https://iclr.cc/virtual/2019/poster/952
|
multi-domain learning;domain adaptation;adversarial learning;H-divergence;deep representation learning;high-content microscopy
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Multi-Domain Adversarial Learning
|
https://github.com/AltschulerWu-Lab/MuLANN
| null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
8-bit low precision inference;convolutional neural networks;statistical accuracy;8-bit Winograd convolution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph;hierarchical clustering;dendrogram;quality metric;reconstruction;entropy
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Graph Representations by Dendrograms
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
nmt;translate;dynamics;rnn
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Hallucinations in Neural Machine Translation
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Karlsruhe Inst. of Technology (KIT); UC Berkeley, KIT; UC Berkeley, Covariant.ai; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
|
https://iclr.cc/virtual/2019/poster/788
|
Meta-Reinforcement Learning;Meta-Learning;Reinforcement-Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
ProMP: Proximal Meta-Policy Search
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Babylon Health
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Vitalii Zhelezniak, Aleksandar D Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Hammerla
|
https://iclr.cc/virtual/2019/poster/705
|
word vectors;sentence representations;distributed representations;fuzzy sets;bag-of-words;unsupervised learning;word vector compositionality;max-pooling;Jaccard index
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
5;8;8
| null | null |
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Automatic Operation Batching;Dynamic Computation Graphs
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
An Automatic Operation Batching Strategy for the Backward Propagation of Neural Networks Having Dynamic Computation Graphs
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
material property prediction;neural network;material structure representation;chemistry
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.333333 |
1;3;3
| null | null |
Pixel Chem: A Representation for Predicting Material Properties with Neural Network
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Statistical Relational Learning;Sentence Embedding;Composition functions;Natural Language Inference;InferSent;SentEval;ComplEx
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
relation representations;natural language processing;theoretical analysis;knowledge graphs
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding
| null | null | 0 | 4.333333 |
Reject
|
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
| 4.666667 |
4;5;5
| null | null |
Online Bellman Residue Minimization via Saddle Point Optimization
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Sequence Modeling;Text Generation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
CoT: Cooperative Training for Generative Modeling of Discrete Data
| null | null | 0 | 2.666667 |
Reject
|
4;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Goal-oriented Dialogue Systems;Graph Convolutional Networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visualization;embeddings;representations;t-sne;natural;language;processing;machine;learning;algebra
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;learning theory;convergence analysis;batch normalization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Diminishing Batch Normalization
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Exploration;curiosity;manipulation
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://doubleblindICLR.github.io/robot-exploration/
|
main
| 3.666667 |
3;3;5
| null | null |
Beyond Games: Bringing Exploration to Robots in Real-world
| null | null | 0 | 4 |
Reject
|
4;5;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
| 4.333333 |
4;4;5
| null | null |
N/A
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
knowledge distillation;deep learning;few-shot learning;adversarial attack
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Dataset Distillation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Nets;Cross-Domain Learning;Materials Science;Higher-order Complexity
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
| null | null | 0 | 2.666667 |
Withdraw
|
4;2;2
| null |
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