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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Robotics Institute, Carnegie Mellon University; Adobe Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Senthil Purushwalkam, Abhinav Gupta, Danny Kaufman, Bryan Russell
|
https://iclr.cc/virtual/2019/poster/1100
|
intuitive physics;visual prediction;surface normal;restitution;bounces
| null | 0 | null | null |
iclr
| 0.866025 | 0 |
http://www.cs.cmu.edu/~spurushw/projects/bouncelearn.html
|
main
| 7 |
6;7;8
| null | null |
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
The University of Chicago; Google Inc.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard
|
https://iclr.cc/virtual/2019/poster/898
|
deep learning;mobile;transfer learning;multi-task learning;computer vision;small models;imagenet;inception;batch normalization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;manifold regularization;adversarial training
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
| null | null | 0 | 4 |
Withdraw
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;black-box attack;transferability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Evading Defenses to Transferable Adversarial Examples by Mitigating Attention Shift
| null | null | 0 | 3.333333 |
Withdraw
|
3;3;4
| null |
null |
Dynamical Neuroscience, Institute for Collaborative Biotechnologies, UC Santa Barbara, CA, USA; Psychological and Brain Sciences, Institute for Collaborative Biotechnologies, UC Santa Barbara, CA, USA; Electric and Computer Engineering, UC Santa Barbara, CA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Arturo Deza, Aditya Jonnalagadda, Miguel Eckstein
|
https://iclr.cc/virtual/2019/poster/749
|
Metamerism;foveation;perception;style transfer;psychophysics
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Towards Metamerism via Foveated Style Transfer
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Reading;Natural Language Processing;Neural Theorem Proving;Representation Learning;First Order Logic
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Scalable Neural Theorem Proving on Knowledge Bases and Natural Language
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational inference;approximate inference;generative models;gradient estimators
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Revisiting Reweighted Wake-Sleep
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
beam search;sequence models;search;sequence to sequence
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
(Unconstrained) Beam Search is Sensitive to Large Search Discrepancies
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
University of Tokyo; University of Toronto, Vector Institute; Nagoya Institute of Technology; Institute of Statistical Mathematics; Carnegie Mellon University; Kyoto University, RIKEN AIP, JST PRESTO, Institute of Statistical Mathematics
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Makoto Yamada, Yi Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
|
https://iclr.cc/virtual/2019/poster/717
|
Maximum Mean Discrepancy;Selective Inference;Feature Selection;GAN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
SIQI LIU, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
|
https://iclr.cc/virtual/2019/poster/928
|
Multi-agent learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Emergent Coordination Through Competition
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Direct Feedback Alignment;Convolutional Neural Network;DNN Training
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Efficient Convolutional Neural Network Training with Direct Feedback Alignment
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robust reinforcement learning;noisy reward;sample complexity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Reinforcement Learning with Perturbed Rewards
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;representation learning;state representation;disentangled representation;dataset;autonomous system;temporal multimodal data
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning State Representations in Complex Systems with Multimodal Data
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
MIT CSAIL
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Ilyas, Logan Engstrom, Aleksander Madry
|
https://iclr.cc/virtual/2019/poster/851
|
adversarial examples;gradient estimation;black-box attacks;model-based optimization;bandit optimization
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
|
https://git.io/fAjOJ
| null | 0 | 3.333333 |
Poster
|
3;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
grounding;policy gradient;language drift;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Countering Language Drift via Grounding
| 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;Normalization;Least squares;Gradient regression
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Normalization Gradients are Least-squares Residuals
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative modelling;latent variable modelling;variational autoencoders;variational inference;natural language processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Improving latent variable descriptiveness by modelling rather than ad-hoc factors
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
Ricoh Company, Ltd.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Fumihiro Sasaki
|
https://iclr.cc/virtual/2019/poster/1050
|
Imitation Learning;Continuous Control;Reinforcement Learning;Inverse Reinforcement Learning;Conditional Generative Adversarial Network
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Sample Efficient Imitation Learning for Continuous Control
| null | null | 0 | 4.75 |
Poster
|
5;5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Network;Hierarchical Neural Architecture;Structural Sparsity;Evolving Algorithm
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning Internal Dense But External Sparse Structures of Deep Neural Network
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;open-domain question answering;semi-supervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Network;Geometric Operator;Image Classification;Theoretical Analysis
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Geometric Operator Convolutional Neural Network
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fairness
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Microsoft Research, Redmond, WA, USA; Microsoft Research, Cambridge, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Marc Brockschmidt, Miltiadis Allamanis, Alexander Gaunt, Oleksandr Polozov
|
https://iclr.cc/virtual/2019/poster/1000
|
Generative Model;Source Code;Graph Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Generative Code Modeling with Graphs
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model-based reinforcement learning;structured representation learning;robotics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
federated learning;communication efficient;variational dropout;sparse model
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Efficient Federated Learning via Variational Dropout
| null | null | 0 | 3.666667 |
Withdraw
|
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 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
| null | null | 0 | 2.333333 |
Reject
|
1;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;neural networks;nonlinearity;activation functions;exploding gradients;vanishing gradients;neural architecture search
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School; Department of Computer Science, University of California, Los Angeles
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alessandro Achille, Matteo Rovere, Stefano Soatto
|
https://iclr.cc/virtual/2019/poster/1140
|
Critical Period;Deep Learning;Information Theory;Artificial Neuroscience;Information Plasticity
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
Critical Learning Periods in Deep Networks
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed training;stochastic gradient descent;machine translation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Combining Global Sparse Gradients with Local Gradients
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null |
Sorbonne Universit ´e, CNRS UMR 7222 Institut des Syst `emes Intelligents et de Robotique, F-75005 Paris, France; Gleamer, 96bis Boulevard Raspail, 75006 Paris, France
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Aloïs Pourchot, Olivier Sigaud
|
https://iclr.cc/virtual/2019/poster/1031
|
evolution strategy;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
CEM-RL: Combining evolutionary and gradient-based methods for policy search
| null | null | 0 | 4 |
Poster
|
3;5;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
| 5.666667 |
5;6;6
| null | null |
LIT: Block-wise Intermediate Representation Training for Model Compression
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
mutual information;predictive coding;unsupervised learning;predictive learning;generalization bounds;MINE;DIM;contrastive predictive coding
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Formal Limitations on the Measurement of Mutual Information
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
University of Toronto, Uber ATG Toronto, Vector Institute; University of Illinois at Urbana-Champaign; University of Toronto, Vector Institute, Canadian Institute for Advanced Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel
|
https://iclr.cc/virtual/2019/poster/1099
|
Lanczos Network;Graph Neural Networks;Deep Graph Convolutional Networks;Deep Learning on Graph Structured Data;QM8 Quantum Chemistry Benchmark
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Floyd-Warshall;Reinforcement learning;goal conditioned value functions;multi-goal
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 2.666667 |
1;3;4
| null | null |
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Affiliation not provided
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visual attention;video action recognition;network interpretability
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Where and when to look? Spatial-temporal attention for action recognition in videos
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Counterfactual Regret Minimization;Imperfect Information game
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Double Neural Counterfactual Regret Minimization
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
federated learning;model poisoning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Analyzing Federated Learning through an Adversarial Lens
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
architecture search;Pareto optimality;multi-objective;optimization;cnn;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 |
https://goo.gl/1QZX6a
|
main
| 4.333333 |
4;4;5
| null | null |
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Atari;RL;Demonstrations
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Towards Consistent Performance on Atari using Expert Demonstrations
| null | null | 0 | 3.25 |
Reject
|
4;4;4;1
| null |
null |
University of T ¨ubingen; University of Bremen, Center for Industrial Mathematics; Vector Institute and University of Toronto
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
|
https://iclr.cc/virtual/2019/poster/900
|
Generalization;Adversarial Examples;Invariance;Information Theory;Invertible Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Excessive Invariance Causes Adversarial Vulnerability
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
zero-shot learning;speech recognition;acoustic modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Zero-shot Learning for Speech Recognition with Universal Phonetic Model
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Serial Crystallography;Deep Learning;Image Classification
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
IDSIA, USI, SUPSI, Lugano, Switzerland; IFES, UFES, Serra, Brazil; USI, Lugano, Switzerland; IDSIA, USI, SUPSI, NNAISENSE, Lugano, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2019/poster/891
|
reinforcement learning;policy gradients;multi-goal reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Hindsight policy gradients
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
College of Information Science and Electronic Engineering, Zhejiang University; Department of Computer Science, University of Southern California; Center for Data Science, Beijing Institute of Big Data Research, Peking University; MOE Key Lab of Computational Linguistics, School of EECS, Peking University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu Sun
|
https://iclr.cc/virtual/2019/poster/974
|
Optimization;SGD;Adam;Generalization
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
|
https://github.com/Luolc/AdaBound
| null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary Functional Search;Large-scale Search;Approximate Nearest Neighbor Search
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.5 |
4;5
| null | null |
Fast Binary Functional Search on Graph
| null | null | 0 | 4.5 |
Reject
|
4;5
| null |
null |
University of Freiburg, Freiburg, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ilya Loshchilov, Frank Hutter
|
https://iclr.cc/virtual/2019/poster/935
|
optimization;regularization;weight decay;Adam
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Decoupled Weight Decay Regularization
|
https://github.com/loshchil/AdamW-and-SGDW
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore; Singapore University of Technology and Design, 8 Somapah Road, Singapore; Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras
|
https://iclr.cc/virtual/2019/poster/840
|
Mirror descent;extra-gradient;generative adversarial networks;saddle-point problems
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
| null | null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bilingual lexicon induction;semi-supervised methods;embeddings
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
BLISS in Non-Isometric Embedding Spaces
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Hong Kong University of Science and Technology, Clova AI Research, NAVER; Clova AI Research, NAVER; New York University, CIFAR Azrieli Global Scholar
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim
|
https://iclr.cc/virtual/2019/poster/743
|
dialogue;GAN;VAE;WAE;chatbot
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Parameter Recovery;Non-convex optimization;high threshold activation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Recovering the Lowest Layer of Deep Networks with High Threshold Activations
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Super-Resolution;Resource-Efficiency
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Facebook AI Research; Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
John Wieting, Douwe Kiela
|
https://iclr.cc/virtual/2019/poster/1087
|
sentence embeddings
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
No Training Required: Exploring Random Encoders for Sentence Classification
|
https://github.com/facebookresearch/randsent
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dimensionality Reduction;Visualization;Triplets;t-SNE;LargeVis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
A More Globally Accurate Dimensionality Reduction Method Using Triplets
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Department of Computer Science, University of Toronto; Vector Institute; Department of Computer Science, University of Toronto; Vector Institute; NVIDIA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
|
https://iclr.cc/virtual/2019/poster/919
|
Reinforcement learning;graph neural networks;robotics;deep learning;transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Neural Graph Evolution: Towards Efficient Automatic Robot Design
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Long short term memory;Recurrent neural network;Dynamical systems;Difference equation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
DecayNet: A Study on the Cell States of Long Short Term Memories
| 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
| 0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
A Self-Supervised Method for Mapping Human Instructions to Robot Policies
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Gaussian Process Model;Recurrent Model;State-Space Model;Nonlinear system identification;Dynamical modeling
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null |
Department of Computer Science & Technology, Institute for Artificial Intelligence, State Key Lab for Intell. Tech. & Sys., BNRist Center, THBI Lab, Tsinghua University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang
|
https://iclr.cc/virtual/2019/poster/1119
|
Bayesian neural networks;uncertainty estimation;variational inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Function Space Particle Optimization for Bayesian Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
University of California, Los Angeles, USA; Northeastern University, USA; MIT-IBM Watson AI Lab, IBM Research, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin
|
https://iclr.cc/virtual/2019/poster/859
| null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
|
https://github.com/KaidiXu/StrAttack
| null | 0 | 2.333333 |
Poster
|
2;3;2
| null |
null |
Stanford University; Lawrence Berkeley Nat’l Lab; Technical University of Munich; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chiyu Jiang, Jingwei Huang, Karthik Kashinath, Mr Prabhat, Philip Marcus, Matthias Niessner
|
https://iclr.cc/virtual/2019/poster/708
|
Spherical CNN;unstructured grid;panoramic;semantic segmentation;parameter efficiency
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Spherical CNNs on Unstructured Grids
| null | null | 0 | 3.666667 |
Poster
|
3;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Deep Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
TequilaGAN: How To Easily Identify GAN Samples
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;transfer learning;multitask learning
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
| 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
| 4.333333 |
4;4;5
| null | null |
Successor Uncertainties: exploration and uncertainty in temporal difference learning
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
large-scale structure prediction;likelihood approximation;deep class embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Large-scale classification of structured objects using a CRF with deep class embedding
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bandit learning;online learning;contextual bandits;neural network learning in online settings
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Neural Network Bandit Learning by Last Layer Marginalization
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Computer Vision Lab, ETH Zurich, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc Van Gool
|
https://iclr.cc/virtual/2019/poster/1114
|
generative models;optimal transport;distribution preserving operations
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
Department of Computer Science, Technische Universität Darmstadt, Germany; Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Computer Science, Technische Universität Darmstadt, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Michael Lutter, Christian Ritter, Jan Peters
|
https://iclr.cc/virtual/2019/poster/916
|
Deep Model Learning;Robot Control
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;drug design;deep learning;molecule optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Mol-CycleGAN - a generative model for molecular optimization
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;gans
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;3;6
| null | null |
microGAN: Promoting Variety through Microbatch Discrimination
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign; IBM T.J. Watson Research Center
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan
|
https://iclr.cc/virtual/2019/poster/664
|
reduced precision floating-point;partial sum accumulation bit-width;deep learning;training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep 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 |
sentence representation;unsupervised learning;LSTM
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Unsupervised Learning of Sentence Representations Using Sequence Consistency
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Engineering, University of Cambridge; department of Engineering, University of Cambridge
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
|
https://iclr.cc/virtual/2019/poster/751
|
Gaussian process;CNN;ResNet;Bayesian
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Deep Convolutional Networks as shallow Gaussian Processes
|
https://github.com/convnets-as-gps/convnets-as-gps
| null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
NEC Labs America; UC San Diego; University of Amsterdam
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker
|
https://iclr.cc/virtual/2019/poster/694
|
domain adaptation;distance metric learning;face recognition
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
5;8;8
| null | null |
Unsupervised Domain Adaptation for Distance Metric Learning
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Computer Science, Hochschule Fulda, Fulda 36037, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Benedikt Pfülb, Alexander RT Gepperth
|
https://iclr.cc/virtual/2019/poster/656
|
incremental learning;deep neural networks;catatrophic forgetting;sequential learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
A comprehensive, application-oriented study of catastrophic forgetting in DNNs
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Examples;Adversarial Training;FGSM;IFGSM;Robustness
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
EFFICIENT TWO-STEP ADVERSARIAL DEFENSE FOR DEEP NEURAL NETWORKS
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
MSR Montreal; MILA, UdeM, IV ADO, CIFAR; MRN, UNM; MSR Montreal, MILA, UdeM, IV ADO; MILA, UdeM; U Toronto
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
R Devon Hjelm, Alex Fedorov, Samuel Lavoie, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio
|
https://iclr.cc/virtual/2019/poster/649
|
representation learning;unsupervised learning;deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
Learning deep representations by mutual information estimation and maximization
| null | null | 0 | 4 |
Oral
|
4;5;3
| null |
null |
University of Massachusetts Amherst; IIT Bombay
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Shiv Shankar, Sunita Sarawagi
|
https://iclr.cc/virtual/2019/poster/858
|
posterior inference;attention;seq2seq learning;translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Posterior Attention Models for Sequence to Sequence Learning
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RGCN;attention;graph convolutional networks;semi-supervised learning;graph classification;molecules
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Relational Graph Attention Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Facebook AI Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mike Lewis, Angela Fan
|
https://iclr.cc/virtual/2019/poster/1020
|
Question answering;question generation;reasoning;squad;clevr
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Generative Question Answering: Learning to Answer the Whole Question
| 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
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Countdown Regression: Sharp and Calibrated Survival Predictions
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;task transfer learning;H-score;transferability
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
An Information-Theoretic Metric of Transferability for Task Transfer Learning
| null | null | 0 | 3.333333 |
Reject
|
3;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
| 5 |
4;5;6
| null | null |
SupportNet: solving catastrophic forgetting in class incremental learning with support data
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Prajit Ramachandran, Quoc V Le
|
https://iclr.cc/virtual/2019/poster/1002
|
conditional computation;routing models;depth
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Diversity and Depth in Per-Example Routing Models
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;generalization;selectivity;neuroscience
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Causal importance of orientation selectivity for generalization in image recognition
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null |
Facebook AI Research; KU Leuven, ESAT-PSI, Belgium
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars
|
https://iclr.cc/virtual/2019/poster/917
|
Lifelong learning;Continual Learning;Sequential learning;Regularization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Selfless Sequential Learning
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial attacks;calibration;probability;adversarial defence
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Calibration of neural network logit vectors to combat adversarial attacks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational inference;information bottleneck;bayesian deep learning;latent variable models;amortized variational inference;uncertainty;learning non-linearities
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Predictive Uncertainty through Quantization
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
action-conditioned dynamics learning;deep learning;generalization;interpretability;sample efficiency
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Object-Oriented Model Learning through Multi-Level Abstraction
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
University of California, Los Angeles; Facebook AI Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tianmin Shu, Yuandong Tian
|
https://iclr.cc/virtual/2019/poster/1037
|
Multi-agent Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
|
https://github.com/facebookresearch/M3RL
| null | 0 | 2.666667 |
Poster
|
1;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial learning;structured prediction;energy networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Learning Discriminators as Energy Networks in Adversarial Learning
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Laplacian Smoothing;Nonconvex Optimization;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
Not provided
|
main
| 5.666667 |
5;6;6
| null | null |
Laplacian Smoothing Gradient Descent
|
Not provided
| null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Networks;Explainability;Knowledge Extraction
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
| null | null | 0 | 3.5 |
Reject
|
4;5;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Learning;Few-Shot Learning;Domain Adaptation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memorization in Deep Learning;Convolutional Autoencoders
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Downsampling leads to Image Memorization in Convolutional Autoencoders
| null | null | 0 | 2.333333 |
Reject
|
3;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;generative models;replay;distillation;variational autoencoder
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Three continual learning scenarios and a case for generative replay
| 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;software;framework;reproducibility
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Dopamine: A Research Framework for Deep Reinforcement Learning
| null | null | 0 | 3 |
Reject
|
3;2;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 |
Cross-Task Knowledge Transfer for Visually-Grounded Navigation
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Skolkovo Institute of Science and Technology, Samsung-HSE Laboratory, National Research University Higher School of Economics; Samsung AI Center Moscow; University of Amsterdam, Canadian Institute for Advanced Research; Skolkovo Institute of Science and Technology, National Research University Higher School of Economics; Samsung AI Center Moscow, Samsung-HSE Laboratory, National Research University Higher School of Economics
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling
|
https://iclr.cc/virtual/2019/poster/889
|
deep learning;variational inference;prior distributions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
The Deep Weight Prior
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Bosch Center for Artificial Intelligence and University of Freiburg; University of Freiburg; Bosch Center for Artificial Intelligence
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
|
https://iclr.cc/virtual/2019/poster/1090
|
Neural Architecture Search;AutoML;AutoDL;Deep Learning;Evolutionary Algorithms;Multi-Objective Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
MILA, Université de Montréal, Québec, Canada; LIA, Université d’Avignon, France
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Titouan Parcollet, Mirco Ravanellu, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio
|
https://iclr.cc/virtual/2019/poster/714
|
Quaternion recurrent neural networks;quaternion numbers;recurrent neural networks;speech recognition
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Quaternion Recurrent Neural Networks
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
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