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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Extrapolation;Graphs;GNNs;SCM;Causality;Counterfactual Inference
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
On Single-environment Extrapolations in Graph Classification and Regression Tasks
| null | null | 0 | 3 |
Reject
|
5;2;2
| null |
null |
The University of Tokyo, RIKEN Center for AIP
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2620; None
| null | 0 | null | null | null | null | null |
Zeke Xie, Issei Sato, Masashi Sugiyama
|
https://iclr.cc/virtual/2021/poster/2620
|
deep learning dynamics;SGD;diffusion;flat minima;stochastic optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2620
|
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
| null | null | 0 | 3.5 |
Poster
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
There is no trade-off: enforcing fairness can improve accuracy
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT compression;neural architecture search;adaptive sizes;across tasks;knowledge distillation
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Task-Agnostic and Adaptive-Size BERT Compression
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2798; None
| null | 0 | null | null | null | null | null |
Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B Tenenbaum, Sanja Fidler, Antonio Torralba
|
https://iclr.cc/virtual/2021/poster/2798
|
social perception;human-AI collaboration;theory of mind;multi-agent platform;virtual environment
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2798
|
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration
| null | null | 0 | 3.75 |
Spotlight
|
4;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient DNN Training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.6 |
4;5;6;6;7
| null | null |
Accelerating DNN Training through Selective Localized Learning
| null | null | 0 | 4 |
Reject
|
4;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Machine Translation;Post-editing mechanism;Polish Mechanism;Proper Termination Policy
| null | 0 | null | null |
iclr
| -0.544331 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Rewriter-Evaluator Framework for Neural Machine Translation
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.288675 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
NNGeometry: Easy and Fast Fisher Information Matrices and Neural Tangent Kernels in PyTorch
| null | null | 0 | 3 |
Reject
|
3;4;2;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3063; None
| null | 0 | null | null | null | null | null |
Xin Yuan, Pedro Savarese, Michael Maire
|
https://iclr.cc/virtual/2021/poster/3063
|
deep learning;computer vision;network pruning;neural architecture search
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3063
|
Growing Efficient Deep Networks by Structured Continuous Sparsification
| null | null | 0 | 3.75 |
Oral
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;application;sim2real;scene graph;object detection;simulation in machine learning;transfer learning;synthetic data;driving simulation.
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
compositionality;Symbolic;Equivariance;question answering;Language Processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Symbol-Shift Equivariant Neural Networks
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null |
Instacart, San Francisco, CA 94107, USA; Walmart Labs, Sunnyvale, CA 94086, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2602; None
| null | 0 | null | null | null | null | null |
Da Xu, Chuanwei Ruan, evren korpeoglu, Sushant Kumar, kannan achan
|
https://iclr.cc/virtual/2021/poster/2602
|
Kernel Learning;Continuous-time System;Spectral Distribution;Random Feature;Reparameterization;Learning Theory
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2602
|
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
| null | null | 0 | 2.5 |
Poster
|
3;2;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual learning;task-free continual learning
| null | 0 | null | null |
iclr
| -0.845154 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Gradient Based Memory Editing for Task-Free Continual Learning
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Time Series
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES
| null | null | 0 | 4.25 |
Withdraw
|
5;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Offline Reinforcement Learning;Model-Based Reinforcement Learning;Off-policy Reinforcement Learning;uncertainty estimation
| null | 0 | null | null |
iclr
| -0.534522 | 0 | null |
main
| 4.2 |
3;4;4;5;5
| null | null |
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization
| null | null | 0 | 3.6 |
Reject
|
4;4;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph matching;generative graph model;latent structure learning
| null | 0 | null | null |
iclr
| -0.114109 | 0 | null |
main
| 5.8 |
4;4;6;7;8
| null | null |
Learning Latent Topology for Graph Matching
| null | null | 0 | 3 |
Reject
|
4;3;1;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;adversarial training;adversarial attacks
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Testing Robustness Against Unforeseen Adversaries
| null | null | 0 | 3.75 |
Reject
|
3;3;5;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3017; None
| null | 0 | null | null | null | null | null |
Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, Stephen Clark
|
https://iclr.cc/virtual/2021/poster/3017
|
language;cognition;fast-mapping;grounding;word-learning;memory;meta-learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
6;8;8;8
| null |
https://iclr.cc/virtual/2021/poster/3017
|
Grounded Language Learning Fast and Slow
| null | null | 0 | 3.5 |
Spotlight
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Uncertainty estimation;Calibration;Label smoothing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
One Size Doesn't Fit All: Adaptive Label Smoothing
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2785; None
| null | 0 | null | null | null | null | null |
Rui Wang, Robin Walters, Rose Yu
|
https://iclr.cc/virtual/2021/poster/2785
|
deep sequence model;equivariant neural network;physics-guided deep learning;AI for earth science
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 5.25 |
4;4;6;7
| null |
https://iclr.cc/virtual/2021/poster/2785
|
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
| null | null | 0 | 2.75 |
Poster
|
4;2;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
minimax estimator;covariate shift;model shift
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Near-Optimal Linear Regression under Distribution Shift
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
Buffer Zone based Defense against Adversarial Examples in Image Classification
| null | null | 0 | 4 |
Withdraw
|
5;3;4;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3356; None
| null | 0 | null | null | null | null | null |
Siyuan Li, Lulu Zheng, Jianhao Wang, Chongjie Zhang
|
https://iclr.cc/virtual/2021/poster/3356
|
Hierarchical Reinforcement Learning;Representation Learning;Exploration
| null | 0 | null | null |
iclr
| -0.936586 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3356
|
Learning Subgoal Representations with Slow Dynamics
| null | null | 0 | 3.25 |
Poster
|
5;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
action recognition;video understanding;temporal modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Temporal Difference Networks for Action Recognition
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Compressed Sensing;Adaptive acquisition;object detection
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Adaptive Automotive Radar data Acquisition
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Interpretability;XAI;Variational Inference
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Variational saliency maps for explaining model's behavior
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed machine learning;SGD;decentralized algorithms;quantization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Decentralized SGD with Asynchronous, Local and Quantized Updates
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Clustering;Ensemble Learning;Representation Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Consensus Clustering with Unsupervised Representation Learning
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural architecture search (NAS);Parameter Sharing NAS;Predictor-based NAS
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
A Surgery of the Neural Architecture Evaluators
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null |
MIT BCS, CBMM, CSAIL; MIT; UC San Diego; Peking University; MIT-IBM Watson AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2981; None
| null | 0 | null | null | null | null | null |
Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B Tenenbaum, Chuang Gan
|
https://iclr.cc/virtual/2021/poster/2981
|
Soft Body;Differentiable Physics;Benchmark
| null | 0 | null | null |
iclr
| -0.662266 | 0 |
http://plasticinelab.csail.mit.edu
|
main
| 7.25 |
6;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2981
|
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
| null | null | 0 | 3.25 |
Spotlight
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial attacks;Binary images;Image Recognition;Check processing systems
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Adversarial Attacks on Binary Image Recognition Systems
| null | null | 0 | 2.75 |
Reject
|
3;3;2;3
| null |
null |
Informatics Institute, University of Amsterdam, The Netherlands; Transmute AI Research, The Netherlands; Indian Institute of Technology, ISM Dhanbad, India
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2768; None
| null | 0 | null | null | null | null | null |
Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, Deepak Gupta
|
https://iclr.cc/virtual/2021/poster/2768
|
Structured Pruning;Budget-Aware Pruning;Budget constraints;Sparsity Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2768
|
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
|
https://github.com/transmuteAI/ChipNet
| null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RBF network;OOD detection;overconfident neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
The Compact Support Neural Network
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fairness model evaluation;fair deep learning;adversarial fairness
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
| null | null | 0 | 3.75 |
Reject
|
5;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent systems;opponent modelling;reinforcement learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.5 |
3;6;6;7
| null | null |
Local Information Opponent Modelling Using Variational Autoencoders
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;learning-to-learn;step size tuning;optimization;generalization
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5 |
4;4;4;8
| null | null |
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
| null | null | 0 | 3.75 |
Reject
|
3;5;4;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3263; None
| null | 0 | null | null | null | null | null |
Ruozi Huang, Huang Hu, Wei Wu, Kei Sawada, Mi Zhang, Daxin Jiang
|
https://iclr.cc/virtual/2021/poster/3263
|
Multimodal Learning;Computer Vision;Sequence Modeling;Generative Models
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3263
|
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
| null | null | 0 | 3.75 |
Poster
|
3;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse Coding;Deep Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
A Simple Sparse Denoising Layer for Robust Deep Learning
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2841; None
| null | 0 | null | null | null | null | null |
Samyadeep Basu, Phil Pope, Soheil Feizi
|
https://iclr.cc/virtual/2021/poster/2841
|
Influence Functions;Interpretability
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2841
|
Influence Functions in Deep Learning Are Fragile
| null | null | 0 | 3.75 |
Poster
|
3;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolutional Networks;Over-smoothing;Topology Optimization;Stochastic Block Model;Variational EM
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;6;8
| null | null |
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recommendation System;Large-scale Recommendation;User Behavior Modeling;Long-range sequences
| null | 0 | null | null |
iclr
| -0.666667 | 0 | null |
main
| 5.5 |
3;6;6;7
| null | null |
Learning Two-Time-Scale Representations For Large Scale Recommendations
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SG-MCMC;Non-uniform weight
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Search;partially observable games;multi-agent learning;Hanabi
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5;5
| null | null |
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
| null | null | 0 | 3 |
Reject
|
4;4;3;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
text generation;knowledge graph
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 2.25 |
2;2;2;3
| null | null |
KETG: A Knowledge Enhanced Text Generation Framework
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Super Resolution;Cross Modality;Misalignment;Deep Learning;CNN;Unsupervised;Optimization
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Single Pair Cross-Modality Super Resolution
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
contagion dynamics;theory of graph neural networks;epidemic modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;3;5;7
| null | null |
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null |
Google Research, Brain team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3294; None
| null | 0 | null | null | null | null | null |
Irwan Bello
|
https://iclr.cc/virtual/2021/poster/3294
|
deep learning;neural networks;attention;transformer;vision;image classification
| null | 0 | null | null |
iclr
| 0.612372 | 0 | null |
main
| 6.4 |
6;6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3294
|
LambdaNetworks: Modeling long-range Interactions without Attention
| null | null | 0 | 3.4 |
Spotlight
|
3;3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data augmentation;stochastic optimization;scheduling;convex optimization;overparametrization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Data augmentation as stochastic optimization
| null | null | 0 | 3 |
Reject
|
4;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.6 |
3;3;5;6;6
| null | null |
Class2Simi: A New Perspective on Learning with Label Noise
| null | null | 0 | 4 |
Reject
|
4;4;4;4;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3079; None
| null | 0 | null | null | null | null | null |
Xingyu Cai, Jiaji Huang, Yuchen Bian, Kenneth Church
|
https://iclr.cc/virtual/2021/poster/3079
|
Contextual embedding space;Isotropy;Clusters;Manifolds
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3079
|
Isotropy in the Contextual Embedding Space: Clusters and Manifolds
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Information Extraction;Natural Language Processing;Long-tailed Classification;Causal Inference
| null | 0 | null | null |
iclr
| -0.478091 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Counterfactual Thinking for Long-tailed Information Extraction
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GRAPH NEURAL NETWORKS;LABEL SELECTION BIAS
| null | 0 | null | null |
iclr
| 0.440225 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Debiased Graph Neural Networks with Agnostic Label Selection Bias
| null | null | 0 | 3.5 |
Reject
|
2;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hidden markov models;recurrent neural networks;disease progression
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Hidden Markov models are recurrent neural networks: A disease progression modeling application
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
collaborative filtering;matrix completion;inductive learning;relation learning;recommender systems
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Inductive Collaborative Filtering via Relation Graph Learning
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty quantification;coverage;dataset shift
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null |
Department of CS, UC San Diego; IBM Research; MIT-IBM Watson AI Lab, IBM Research; RPI, MIT-IBM Watson AI Lab; Department of CS, RPI; Cold Spring Harbor Laboratory
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3085; None
| null | 0 | null | null | null | null | null |
Yuchen Liang, Chaitanya Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J Zaki, Dmitry Krotov
|
https://iclr.cc/virtual/2021/poster/3085
|
neurobiology;neuroscience;fruit fly;locality sensitive hashing;word embedding;sparse representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3085
|
Can a Fruit Fly Learn Word Embeddings?
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
University of Illinois at Urbana-Champaign; Brandeis University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3241; None
| null | 0 | null | null | null | null | null |
Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu
|
https://iclr.cc/virtual/2021/poster/3241
|
algorithmic fairness;graph-structured data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3241
|
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Monotonic neural network: combining deep learning with domain knowledge for chiller plants energy optimization
| null | null | 0 | 4.25 |
Reject
|
4;4;5;4
| null |
null |
AI Lab, Institute for Data Science and Analytics, Bocconi University, 20136 Milano, Italy; Dept. Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy; AI Lab, Institute for Data Science and Analytics, Bocconi University, 20136 Milano, Italy
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2728; None
| null | 0 | null | null | null | null | null |
Fabrizio Pittorino, Carlo Lucibello, Christoph Feinauer, Gabriele Perugini, Carlo Baldassi, Elizaveta Demyanenko, Riccardo Zecchina
|
https://iclr.cc/virtual/2021/poster/2728
|
flat minima;entropic algorithms;statistical physics;belief-propagation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2728
|
Entropic gradient descent algorithms and wide flat minima
| null | null | 0 | 4 |
Poster
|
4;5;4;3
| null |
null |
University of Cambridge, UK, University of California, Los Angeles, USA, The Alan Turing Institute, UK; University of Oxford, UK, The Alan Turing Institute, UK; University of Cambridge, UK; University of Cambridge, UK, Cambridge University Hospitals NHS Foundation Trust, UK; Google Cloud AI, Sunnyvale, USA, University of California, Los Angeles, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2791; None
| null | 0 | null | null | null | null | null |
Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
|
https://iclr.cc/virtual/2021/poster/2791
|
reproducibility;healthcare;medical time series;pipeline toolkit;software
| null | 0 | null | null |
iclr
| 0.581675 | 0 | null |
main
| 5.75 |
4;5;6;8
| null |
https://iclr.cc/virtual/2021/poster/2791
|
Clairvoyance: A Pipeline Toolkit for Medical Time Series
|
https://github.com/vanderschaarlab/clairvoyance
| null | 0 | 3.75 |
Poster
|
4;2;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial example;Feature contributive regions;Local attack
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
An Adversarial Attack via Feature Contributive Regions
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null |
Department of Computer Science, University of North Carolina at Chapel Hill
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3214; None
| null | 0 | null | null | null | null | null |
Ahsan Mahmood, Junier Oliva, Martin A Styner
|
https://iclr.cc/virtual/2021/poster/3214
|
out-of-distribution detection;score matching;deep learning;outlier detection
| null | 0 | null | null |
iclr
| 0.440225 | 0 | null |
main
| 6.25 |
5;5;6;9
| null |
https://iclr.cc/virtual/2021/poster/3214
|
Multiscale Score Matching for Out-of-Distribution Detection
|
https://github.com/ahsanMah/msma
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
normalized gradient;large batch training size
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Stochastic Normalized Gradient Descent with Momentum for Large Batch Training
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null |
DeepMind; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2735; None
| null | 0 | null | null | null | null | null |
Hyung Won Chung, Thibault Fevry, Henry Tsai, Melvin Johnson, Sebastian Ruder
|
https://iclr.cc/virtual/2021/poster/2735
|
natural language processing;transfer learning;efficiency;pre-training
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2735
|
Rethinking Embedding Coupling in Pre-trained Language Models
| null | null | 0 | 4 |
Poster
|
5;3;3;5
| null |
null |
Max Planck Institute for Intelligent Systems, Tübingen, Germany; Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Computer Science, ETH Zurich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3331; None
| null | 0 | null | null | null | null | null |
Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
|
https://iclr.cc/virtual/2021/poster/3331
|
self-supervision;autonomous learning;object-centric representations;visual reinforcement learning
| null | 0 | null | null |
iclr
| -0.29277 | 0 | null |
main
| 7.25 |
5;7;8;9
| null |
https://iclr.cc/virtual/2021/poster/3331
|
Self-supervised Visual Reinforcement Learning with Object-centric Representations
|
https://martius-lab.github.io/SMORL
| null | 0 | 4.5 |
Spotlight
|
5;5;3;5
| null |
null |
Department of Astrophysical Sciences, Princeton University; Department of Computer Science, Princeton University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2684; None
| null | 0 | null | null | null | null | null |
Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P Adams
|
https://iclr.cc/virtual/2021/poster/2684
|
automatic differentiation;autodiff;backprop;deep learning;pdes;stochastic optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2684
|
Randomized Automatic Differentiation
| null | null | 0 | 4 |
Oral
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain generalization;adversarial splitting;meta-learning;image recognition
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Domain-Free Adversarial Splitting for Domain Generalization
| null | null | 0 | 3.25 |
Reject
|
4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Suppressing Texture;Transfer learning;Self-Supervised Learning
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Learning Visual Representations for Transfer Learning by Suppressing Texture
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generalization;deep learning
| null | 0 | null | null |
iclr
| 0.942809 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
Making Coherence Out of Nothing At All: Measuring Evolution of Gradient Alignment
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolutional Networks;Sampling;Distributed Training
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks
| null | null | 0 | 4 |
Reject
|
5;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;data augmentation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Mixup Training as the Complexity Reduction
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null |
Max Planck Institute for Biological Cybernetics and University of Tübingen; Gatsby Computational Neuroscience Unit, University College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2956; None
| null | 0 | null | null | null | null | null |
Sanjeevan Ahilan, Peter Dayan
|
https://iclr.cc/virtual/2021/poster/2956
|
multi-agent reinforcement learning;experience replay;communication;relabelling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2956
|
Correcting experience replay for multi-agent communication
| null | null | 0 | 3 |
Spotlight
|
2;4;3;3
| null |
null |
Affiliation of Another Author; Affiliation of the Author
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
vehicle routing problem;reinforcement learning;optimization
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
|
https://github.com/RBG4VRPs/Rewriting-By-Generating
| null | 0 | 4.5 |
Reject
|
5;3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial machine learning;ensemble;mahalanobis distance
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Ensemble-based Adversarial Defense Using Diversified Distance Mapping
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
out-of-distribution;causality;latent variable model;generative model;variational auto-encoder;domain adaptation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Causal Semantic Representation for Out-of-Distribution Prediction
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Alibaba DAMO Academy, Hangzhou, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; University of Surrey, Guildford, Surrey, UK; The University of Hong Kong, Hong Kong
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2770; None
| null | 0 | null | null | null | null | null |
Manli Zhang, Jianhong Zhang, Zhiwu Lu, Tao Xiang, Mingyu Ding, Songfang Huang
|
https://iclr.cc/virtual/2021/poster/2770
|
few-shot learning;self-supervised learning;episode-level pretext task
| null | 0 | null | null |
iclr
| -0.342997 | 0 | null |
main
| 6.2 |
5;5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2770
|
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning
| null | null | 0 | 3.8 |
Poster
|
4;4;4;3;4
| null |
null |
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Hong Kong SAR; Department of Computer Science, Fudan University, Shanghai, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2886; None
| null | 0 | null | null | null | null | null |
Yuanyuan Yuan, Shuai Wang, Junping Zhang
|
https://iclr.cc/virtual/2021/poster/2886
|
side channel analysis
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2886
|
Private Image Reconstruction from System Side Channels Using Generative Models
| null | null | 0 | 3.25 |
Poster
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Monotonic alignments;sequence-to-sequence model;aligned attention;streaming speech recognition;long-form speech recognition
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Learning Monotonic Alignments with Source-Aware GMM Attention
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Affordance Learning;Imagination;Generative Models;Activation Maximisation
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
| null | null | 0 | 4 |
Reject
|
5;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;approximation theory;robust machine learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
A law of robustness for two-layers neural networks
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural architecture search;differentiable architecture search;topology search
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
DOTS: Decoupling Operation and Topology in Differentiable Architecture Search
| null | null | 0 | 4.75 |
Withdraw
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Generalizing Graph Convolutional Networks via Heat Kernel
| null | null | 0 | 4.5 |
Reject
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Long-tailed Learning;GAN;Universal Adversarial Perturbations
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Class Balancing GAN with a Classifier in the Loop
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
School of Electrical Engineering and Robotics, Queensland University of Technology, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Australia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2815; None
| null | 0 | null | null | null | null | null |
Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh
|
https://iclr.cc/virtual/2021/poster/2815
|
semi-supervised learning;keypoint localization;limited data;unsupervised loss
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2815
|
Semi-supervised Keypoint Localization
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2812; None
| null | 0 | null | null | null | null | null |
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai
|
https://iclr.cc/virtual/2021/poster/2812
|
Program Synthesis
| null | 0 | null | null |
iclr
| 0.572078 | 0 | null |
main
| 7 |
5;6;8;9
| null |
https://iclr.cc/virtual/2021/poster/2812
|
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
| null | null | 0 | 3.75 |
Spotlight
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Federated Learning's Blessing: FedAvg has Linear Speedup
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
program synthesis
| null | 0 | null | null |
iclr
| 0.258199 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Optimal Neural Program Synthesis from Multimodal Specifications
| null | null | 0 | 4.25 |
Reject
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
singing voice synthesis;high-fidelity;adversarial training;high sampling rate
| null | 0 | null | null |
iclr
| 0.555556 | 0 |
https://hifisinger.github.io
|
main
| 4.25 |
3;3;5;6
| null | null |
HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
|
https://github.com/hifisinger
| null | 0 | 4.75 |
Withdraw
|
5;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.612372 | 0 | null |
main
| 4.6 |
4;4;5;5;5
| null | null |
Adaptive Learning Rates for Multi-Agent Reinforcement Learning
| null | null | 0 | 2.6 |
Reject
|
2;2;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Dual Graph Complementary Network
| null | null | 0 | 4.75 |
Reject
|
4;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;large scale learning;neural networks;sgd
| null | 0 | null | null |
iclr
| 0.132453 | 0 |
Not provided
|
main
| 5.25 |
5;5;5;6
| null | null |
Demon: Momentum Decay for Improved Neural Network Training
|
Not provided
| null | 0 | 3.75 |
Withdraw
|
2;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Explicit Connection Distillation
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient Transformer;Question Answering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Block Skim Transformer for Efficient Question Answering
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Full-structure Noise;Hierarchical Bayesian Regression Models;Sparse Bayesian Learning;Unsupervised Learning;Brain Source Imaging;Covariance Estimation.
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
| null | null | 0 | 3.5 |
Reject
|
3;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
early phase of training;implicit regularization;SGD;learning rate;batch size;Hessian;Fisher Information Matrix;curvature;gradient norm
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null |
University of Freiburg, Bosch Center for Artificial Intelligence; Bosch Center for Artificial Intelligence; Department of Computer Science, TU Chemnitz
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3235; None
| null | 0 | null | null | null | null | null |
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer, Jan Hendrik Metzen
|
https://iclr.cc/virtual/2021/poster/3235
|
neural network robustness;shape bias;corruptions;distribution shift
| null | 0 | null | null |
iclr
| 0.749269 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3235
|
Does enhanced shape bias improve neural network robustness to common corruptions?
| null | null | 0 | 3.75 |
Poster
|
4;2;4;5
| null |
null |
Stanford University; MIT CSAIL; NVIDIA, University of Toronto, Vector Institute; NVIDIA, University of Waterloo
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3222; None
| null | 0 | null | null | null | null | null |
Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler
|
https://iclr.cc/virtual/2021/poster/3222
|
Differentiable rendering;inverse graphics;GANs
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3222
|
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
| null | null | 0 | 3.333333 |
Oral
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;contrastive learning;contrastive instance discrimination;negatives;understanding self-supervised learning;ssl
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.25 |
2;5;5;5
| null | null |
Are all negatives created equal in contrastive instance discrimination?
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Do Transformers Understand Polynomial Simplification?
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
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