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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
blind source separation;log-linear model;energy-based model;information geometry
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null |
Google Research, New York, NY
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2683; None
| null | 0 | null | null | null | null | null |
Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar
|
https://iclr.cc/virtual/2021/poster/2683
|
overparameterisation;worst-case generalisation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2683
|
Overparameterisation and worst-case generalisation: friend or foe?
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Network Pruning;Unstructured Pruning
| null | 0 | null | null |
iclr
| 0.845154 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Revisiting Loss Modelling for Unstructured Pruning
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null |
IBM Research, TJ Watson Research Center, NY; Rensselaer Polytechnique Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2580; None
| null | 0 | null | null | null | null | null |
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R Varshney
|
https://iclr.cc/virtual/2021/poster/2580
|
invariant risk minimization;IRM
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2580
|
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
| null | null | 0 | 3.25 |
Poster
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty quantification;calibration;temperature scaling
| null | 0 | null | null |
iclr
| -0.392232 | 0 | null |
main
| 4.5 |
2;4;5;7
| null | null |
Improved Uncertainty Post-Calibration via Rank Preserving Transforms
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Temporal Modeling;Object-Centric Representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Temporal and Object Quantification Nets
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; Gaoling School of Artificial Intelligence, Renmin University of China; Beijing Key Laboratory of Big Data Management and Analysis Methods
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2922; None
| null | 0 | null | null | null | null | null |
Yong Liu, Jiankun Liu, Shuqiang Wang
|
https://iclr.cc/virtual/2021/poster/2922
|
Risk bound;statistical learning theory;kernel methods
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null |
https://iclr.cc/virtual/2021/poster/2922
|
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameter optimization;machine learning;neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.5 |
2;2;3;3
| null | null |
Multi-Task Multicriteria Hyperparameter Optimization
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fast training;BERT;transformer;weight sharing;deep learning
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Speeding up Deep Learning Training by Sharing Weights and Then Unsharing
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2947; None
| null | 0 | null | null | null | null | null |
Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang
|
https://iclr.cc/virtual/2021/poster/2947
|
pre-training;self-training theory;robustness;out-of-distribution;unlabeled data;auxiliary information;multi-task learning theory;distribution shift
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2947
|
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data-driving modeling;neural PDE;hyperbolic dynamic system
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
| null | null | 0 | 2.666667 |
Withdraw
|
2;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Tensor;Principal Component Analysis;Tensor decomposition;trace invariant
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
A new framework for tensor PCA based on trace invariants
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;optimizers;benchmark
| null | 0 | null | null |
iclr
| -0.070535 | 0 | null |
main
| 5.75 |
4;4;6;9
| null | null |
Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers
|
https://github.com/AnonSubmitter3/Submission543
| null | 0 | 4.75 |
Reject
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pruning;Residual Networks;Structured Method;Energy Distance
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Model-Free Energy Distance for Pruning DNNs
|
https://github.com/suuyawu/PEDmodelcompression
| null | 0 | 4 |
Withdraw
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Improving Local Effectiveness for Global Robustness Training
| null | null | 0 | 4.75 |
Reject
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
offline;Reinforcement Learning;off-policy;control
| null | 0 | null | null |
iclr
| -0.648886 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Hellinger Distance Constrained Regression
| null | null | 0 | 3.75 |
Reject
|
4;5;4;2
| null |
null |
Department of Computer Science, ETH Zurich; Center for Human-Compatible AI, UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2954; None
| null | 0 | null | null | null | null | null |
David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan
|
https://iclr.cc/virtual/2021/poster/2954
|
imitation learning;reward learning;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2954
|
Learning What To Do by Simulating the Past
| null | null | 0 | 3 |
Poster
|
4;2;2;4
| null |
null |
Language Technologies Institute, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2701; None
| null | 0 | null | null | null | null | null |
Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth, Alan Black, Yulia Tsvetkov
|
https://iclr.cc/virtual/2021/poster/2701
|
negotiation;dialogue;graph neural networks;interpretability;structure
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2701
|
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
|
https://github.com/rishabhjoshi/DialoGraph_ICLR21
| null | 0 | 4 |
Poster
|
3;4;4;5
| null |
null |
Video & Image Sense Lab, University of Amsterdam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3154; None
| null | 0 | null | null | null | null | null |
Jiaojiao Zhao, Cees G Snoek
|
https://iclr.cc/virtual/2021/poster/3154
|
bidirectional;pooling
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3154
|
LiftPool: Bidirectional ConvNet Pooling
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null |
Amazon Web Services; Amazon Web Services, USC Information Sciences Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3309; None
| null | 0 | null | null | null | null | null |
Hrayr Harutyunyan, Alessandro Achille, Giovanni Paolini, Orchid Majumder, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto
|
https://iclr.cc/virtual/2021/poster/3309
|
sample information;information theory;stability theory;ntk;dataset summarization
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3309
|
Estimating informativeness of samples with Smooth Unique Information
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning reliability;Reinforcement learning stability;Drift detection;Degradation test;Bootstrapping
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null |
ByteDance AI Lab, Shanghai, China; Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3352; None
| null | 0 | null | null | null | null | null |
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, Lei Li
|
https://iclr.cc/virtual/2021/poster/3352
|
drug discovery;molecular graph generation;MCMC sampling
| null | 0 | null | null |
iclr
| -0.169031 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3352
|
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
|
https://github.com/yutxie/mars
| null | 0 | 4.5 |
Spotlight
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;imaga animation;generative modeling
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Motion Representations for Articulated Animation
| null | null | 0 | 3.25 |
Withdraw
|
3;3;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Joint Learning;Meta Learning;Multi-task Learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fine-Grained Visual Classification;long-tailed distribution;confusion energy
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Natural World Distribution via Adaptive Confusion Energy Regularization
| null | null | 0 | 4.25 |
Reject
|
3;5;4;5
| null |
null |
University of Bath; KAIST; Laiye Network Technology Co. Ltd.; Imperial College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2720; None
| null | 0 | null | null | null | null | null |
Jianhong Wang, Yuan Zhang, Tae-Kyun Kim, Yunjie Gu
|
https://iclr.cc/virtual/2021/poster/2720
|
Task-oriented Dialogue System;Natural Language Processing;Hierarchical Reinforcement Learning;Policy Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2720
|
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Understanding Neural Networks;Representation Metrics;Adversarial Machine Learning;Adversarial Attacks;Adversarial Defences
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
| null | null | 0 | 3.5 |
Withdraw
|
4;3;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
| 2.75 |
2;3;3;3
| null | null |
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning
| null | null | 0 | 4 |
Withdraw
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;learning to learn
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-task learning;variational Bayesian inference;Gumbel-softmax priors
| null | 0 | null | null |
iclr
| 0.207514 | 0 | null |
main
| 6.75 |
5;7;7;8
| null | null |
Variational Multi-Task Learning
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
speaker localisation;microphone array;U-Net
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Dynamically locating multiple speakers based on the time-frequency domain
| null | null | 0 | 4.5 |
Withdraw
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Text-to-Image;Text-to-Layout;Layout generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
LayoutTransformer: Relation-Aware Scene Layout Generation
| null | null | 0 | 4.5 |
Withdraw
|
4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explainability;constraints;uniform sampling
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Constraint-Driven Explanations of Black-Box ML Models
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
DeepMind; Google Brain; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3066; None
| null | 0 | null | null | null | null | null |
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, ziyu wang, Alexander Novikov, Sherry Yang, Michael Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Thomas Paine
|
https://iclr.cc/virtual/2021/poster/3066
|
reinforcement learning;off-policy evaluation;benchmarks
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3066
|
Benchmarks for Deep Off-Policy Evaluation
|
https://github.com/google-research/deep_ope
| null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null |
Facebook AI Research; University of Toronto; Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2743; None
| null | 0 | null | null | null | null | null |
Tian Qi Chen, Brandon Amos, Maximilian Nickel
|
https://iclr.cc/virtual/2021/poster/2743
|
differential equations;implicit differentiation;point processes
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2743
|
Learning Neural Event Functions for Ordinary Differential Equations
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
out-of-distribution;novel class detection;domain shift;concept drift
| null | 0 | null | null |
iclr
| 0.920575 | 0 | null |
main
| 4.75 |
3;4;4;8
| null | null |
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sobolev Training;Partial Differential Equations;Neural Networks;Convergence
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Sobolev Training for the Neural Network Solutions of PDEs
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;generalization
| null | 0 | null | null |
iclr
| 0.13484 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Monotonic Robust Policy Optimization with Model Discrepancy
| null | null | 0 | 3.25 |
Reject
|
3;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
style transfer;multi-view vision
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Multi-view Arbitrary Style Transfer
| 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 |
performance predictor;neural architecture search
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Weak NAS Predictor Is All You Need
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
evaluation methods;image completion;image inpainting;evaluation;generative adversarial model;GAN;autoregressive generative model
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
HYPE-C: Evaluating Image Completion Models Through Standardized Crowdsourcing
| null | null | 0 | 4.5 |
Withdraw
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Moitreya Chatterjee
| null |
BLANK
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4 |
3;3;3;7
| null | null |
BLANK
| 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 |
Adversarial robustness;min-max;distributed learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Conservatoire National des Arts et Métiers, CEDRIC, Paris, France; Sorbonne Université, CNRS, LIP6, Paris, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3126; None
| null | 0 | null | null | null | null | null |
Yuan Yin, Vincent Le Guen, Jérémie DONA, Emmanuel d Bezenac, Ibrahim Ayed, Nicolas THOME, patrick gallinari
|
https://iclr.cc/virtual/2021/poster/3126
|
spatio-temporal forecasting;deep learning;physics;differential equations;hybrid systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null |
https://iclr.cc/virtual/2021/poster/3126
|
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
| null | null | 0 | 3.333333 |
Oral
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;medical imaging;transfer learning;chest X-rays;deep learning
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 4 |
2;3;5;6
| null | null |
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models
| null | null | 0 | 4 |
Withdraw
|
5;4;4;3
| null |
null |
DeepMind; University of Toronto; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2845; None
| null | 0 | null | null | null | null | null |
Michael Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, ziyu wang, Mohammad Norouzi
|
https://iclr.cc/virtual/2021/poster/2845
|
Off-policy policy evaluation;autoregressive models;offline reinforcement learning;policy optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2845
|
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Word Alignment;Neural Machine Translation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Efficient Neural Machine Translation with Prior Word Alignment
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SGD;Data-parallel;Asynchronous;Optimization;Non-convex;Deep Neural Network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Local SGD Meets Asynchrony
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
IST Austria
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2889; None
| null | 0 | null | null | null | null | null |
Mary Phuong, Christoph H Lampert
|
https://iclr.cc/virtual/2021/poster/2889
|
inductive bias;implicit bias;gradient descent;ReLU networks;max-margin;extremal sector
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 7 |
5;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2889
|
The inductive bias of ReLU networks on orthogonally separable data
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
Nanyang Technological University, Singapore; VinAI Research, Vietnam; Xiamen University, China; National Institute of Informatics, Japan; Nanyang Technological University, Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2924; None
| null | 0 | null | null | null | null | null |
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu
|
https://iclr.cc/virtual/2021/poster/2924
|
Natural Language Processing;Adversarial Defense
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2924
|
Towards Robustness Against Natural Language Word Substitutions
|
https://github.com/dongxinshuai/ASCC
| null | 0 | 3.333333 |
Spotlight
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visual model-based reinforcement learning;visual planning;long-horizon planning;collocation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Model-Based Reinforcement Learning via Latent-Space Collocation
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Edge Servers;Wireless Edge Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
FedMes: Speeding Up Federated Learning with Multiple Edge Servers
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Technion – Israel Institute of Technology; Idiap Research Institute, Switzerland; EPFL, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2560; None
| null | 0 | null | null | null | null | null |
Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson
|
https://iclr.cc/virtual/2021/poster/2560
|
Transfer learning;NLP;large-scale pre-trained language models;over-fitting;robust;biases;variational information bottleneck
| null | 0 | null | null |
iclr
| -0.404226 | 0 | null |
main
| 5.75 |
4;4;7;8
| null |
https://iclr.cc/virtual/2021/poster/2560
|
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
|
https://github.com/rabeehk/vibert
| null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial networks;Evaluation
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Measuring GAN Training in Real Time
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null |
§Salesforce Research; †Yale University, §Salesforce Research; †Yale University; ‡University of Edinburgh
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3307; None
| null | 0 | null | null | null | null | null |
Tao Yu, Chien-Sheng Wu, Xi V Lin, bailin wang, Yi Tan, Xinyi Yang, Dragomir Radev, Richard Socher, Caiming Xiong
|
https://iclr.cc/virtual/2021/poster/3307
|
text-to-sql;semantic parsing;pre-training;nlp
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3307
|
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
IIMAS, Universidad Nacional Autónoma de México, UNAM, Mexico City, Mexico; Department of Biostatistics, Wisconsin Institute for Discovery, UW-Madison, WI, 53715
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2525; None
| null | 0 | null | null | null | null | null |
Alejandro Pimentel-Alarcón, Daniel L Pimentel-Alarcón
|
https://iclr.cc/virtual/2021/poster/2525
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2525
|
Mixed-Features Vectors and Subspace Splitting
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3251; None
| null | 0 | null | null | null | null | null |
Timothy Nguyen, Zhourong Chen, Jaehoon Lee
|
https://iclr.cc/virtual/2021/poster/3251
|
dataset distillation;dataset compression;meta-learning;kernel-ridge regression;neural kernels;infinite-width networks;dataset corruption
| null | 0 | null | null |
iclr
| -0.229416 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3251
|
Dataset Meta-Learning from Kernel Ridge-Regression
| null | null | 0 | 3.5 |
Poster
|
4;3;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
| 4 |
3;4;4;5
| null | null |
Transferable Feature Learning on Graphs Across Visual Domains
| null | null | 0 | 4 |
Withdraw
|
5;2;5;4
| null |
null |
Mechanical Engineering Department and the Center of Control, Dynamical Systems and Computation, UC Santa Barbara, CA 93106-5070, USA; U.S. Army Research Lab, Adelphi, MD 20783, USA; Computer Science Department, UC Santa Barbara, CA 93106-5110, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3316; None
| null | 0 | null | null | null | null | null |
Arlei Lopes da Silva, Furkan Kocayusufoglu, Saber Jafarpour, Francesco Bullo, Ananthram Swami, Ambuj K Singh
|
https://iclr.cc/virtual/2021/poster/3316
|
graphs;networks;bilevel optimization;metalearning;flow graphs
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3316
|
Combining Physics and Machine Learning for Network Flow Estimation
| null | null | 0 | 4 |
Poster
|
5;5;3;3
| null |
null |
Ulm University; Tübingen University; University of Catalonia; University College London; Masaryk University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2878; None
| null | 0 | null | null | null | null | null |
Pedro Hermosilla Casajus, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlikova, Michael Krone, Tobias Ritschel, Timo Ropinski
|
https://iclr.cc/virtual/2021/poster/2878
|
classification;bioinformatics
| null | 0 | null | null |
iclr
| 0.184637 | 0 | null |
main
| 7.4 |
5;6;8;9;9
| null |
https://iclr.cc/virtual/2021/poster/2878
|
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
| null | null | 0 | 4.2 |
Poster
|
4;4;5;4;4
| null |
null |
School of Data Science, Shenzhen Institute of Artificial Intelligence and Robotics for Society, the Chinese University of Hong Kong, Shenzhen; Georgia Institute of Technology; Gaoling School of Artificial Intelligence, Renmin University of China, and Beijing Key Laboratory of Big Data Management and Analysis Methods; Georgia State University; Shanghai Jiao Tong University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3186; None
| null | 0 | null | null | null | null | null |
Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha
|
https://iclr.cc/virtual/2021/poster/3186
|
Regression without correspondence;differentiable programming;first-order optimization;Sinkhorn algorithm
| null | 0 | null | null |
iclr
| 0.40452 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3186
|
A Hypergradient Approach to Robust Regression without Correspondence
| null | null | 0 | 3.75 |
Poster
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph representation learning;large-scale;sequence
| null | 0 | null | null |
iclr
| 0.662266 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
| 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 |
Inverse Reinforcement Learning;Stochastic Methods;MCEM
| null | 0 | null | null |
iclr
| -0.962533 | 0 | null |
main
| 2.8 |
2;2;3;3;4
| null | null |
Stochastic Inverse Reinforcement Learning
| null | null | 0 | 3.8 |
Reject
|
5;5;3;4;2
| 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
| 5.5 |
4;5;5;8
| null | null |
Understanding, Analyzing, and Optimizing the Complexity of Deep Models
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multigraph;Transformer;natural language process
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformer;multivariate time series;unsupervised representation learning;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
A Transformer-based Framework for Multivariate Time Series Representation Learning
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;representation learning;dimensionality reduction;UMAP;semi-supervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;4;7;9
| null | null |
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning
| null | null | 0 | 5 |
Reject
|
5;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reproducing kernel Hilbert space;GAN;neural network;statistical guarantee
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Toward Understanding Supervised Representation Learning with RKHS and GAN
| null | null | 0 | 3.5 |
Withdraw
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fast MRI;deep learning for inverse problems;data augmentation;unrolled networks;learning with limited data
| null | 0 | null | null |
iclr
| -0.918559 | 0 | null |
main
| 5.4 |
4;5;6;6;6
| null | null |
Data augmentation for deep learning based accelerated MRI reconstruction
| null | null | 0 | 4.4 |
Reject
|
5;5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pathology Visual Question Answering;Healthcare;Learning to Ignore;Self-supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Pathological Visual Question Answering
| null | null | 0 | 3.75 |
Withdraw
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;realistic semi-supervised learning;class-distribution mismatch;unsupervised learning
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Contextualized Knowledge Structures for Commonsense Reasoning
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta reinforcement learning;out-of-distribution;reinforcement learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Linear Representation Meta-Reinforcement Learning for Instant Adaptation
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Stanford University; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2998; None
| null | 0 | null | null | null | null | null |
Ishaan Gulrajani, David Lopez-Paz
|
https://iclr.cc/virtual/2021/poster/2998
|
domain generalization;reproducible research
| null | 0 | null | null |
iclr
| -0.707107 | 0 |
https://arxiv.org/abs/2007.01434
|
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2998
|
In Search of Lost Domain Generalization
| null | null | 0 | 4.5 |
Poster
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;deep learning;machine learning;deep reinforcement learning
| null | 0 | null | null |
iclr
| -0.19803 | 0 | null |
main
| 5.25 |
3;4;7;7
| null | null |
Reinforcement Learning with Latent Flow
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-supervised Learning;Bayesian Inference;Image Denoising
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
Self-supervised Bayesian Deep Learning for Image Denoising
| null | null | 0 | 4.5 |
Withdraw
|
5;5;3;5
| null |
null |
College of Compute Science, Nankai University; Peking University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2522; None
| null | 0 | null | null | null | null | null |
Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
|
https://iclr.cc/virtual/2021/poster/2522
|
Natural Language Processing;Pre-training
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2522
|
Taking Notes on the Fly Helps Language Pre-Training
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
University of Oxford; École Polytechnique de Montreal; McGill University; HEC Montréal; University of Ottawa; Max-Planck Institute for Intelligent Systems Tübingen; Université de Montreal; Mila, Québec
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2912; None
| null | 0 | null | null | null | null | null |
Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, Gaétan Marceau Caron, pierre carrier, Joumana Ghosn, satya gagne, Chris J Pal, Irina Rish, Bernhard Schoelkopf, abhinav sharma, Jian Tang, Andrew Williams
|
https://iclr.cc/virtual/2021/poster/2912
|
covid-19;contact tracing;distributed inference;set transformer;deepset;epidemiology;applications;domain randomization;retraining;simulation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2912
|
Predicting Infectiousness for Proactive Contact Tracing
| null | null | 0 | 3 |
Spotlight
|
4;2;3
| null |
null |
The Pennsylvania State University, State College, PA, USA, 16803
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2587; None
| null | 0 | null | null | null | null | null |
Ali Ayub, Alan Wagner
|
https://iclr.cc/virtual/2021/poster/2587
|
Continual Learning;Catastrophic Forgetting;Cognitively-inspired Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null |
https://iclr.cc/virtual/2021/poster/2587
|
EEC: Learning to Encode and Regenerate Images for Continual Learning
| null | null | 0 | 4 |
Poster
|
5;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Actor Critic;Policy Gradient;Model Free
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
3;3;5;5
| null | null |
FORK: A FORward-looKing Actor for Model-Free Reinforcement 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 |
deep learning;learning rate;generalization
| null | 0 | null | null |
iclr
| -0.512989 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
| null | null | 0 | 3.25 |
Reject
|
5;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3dconv;convolution;neural network;explainability;interpretability
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs
| 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 |
Compression;sparsity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Sparse matrix products for neural network compression
| null | null | 0 | 3.75 |
Reject
|
3;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Random coordinate descent;Langevin dynamics;Monte Carlo sampling
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Random Coordinate Langevin Monte Carlo
| 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 |
causal discovery;heterogeneous data;structure learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Score-based Causal Discovery from Heterogeneous Data
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
School of Computing, National University of Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2833; None
| null | 0 | null | null | null | null | null |
Tao Zhuo, Mohan Kankanhalli
|
https://iclr.cc/virtual/2021/poster/2833
|
abstract reasoning;raven's progressive matrices;deep learning
| null | 0 | null | null |
iclr
| -0.229416 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2833
|
Effective Abstract Reasoning with Dual-Contrast Network
|
https://github.com/visiontao/dcnet
| null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
global convolution;point cloud;graph-cnn;NUFFT
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Efficient Long-Range Convolutions for Point Clouds
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Stanford University; Harvard University; MIT
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3207; None
| null | 0 | null | null | null | null | null |
Yilun Du, Kevin A Smith, Tomer Ullman, Joshua B Tenenbaum, Jiajun Wu
|
https://iclr.cc/virtual/2021/poster/3207
|
unsupervised object discovery;surprisal;scene decomposition;physical scene understanding
| null | 0 | null | null |
iclr
| -0.333333 | 0 |
https://yilundu.github.io/podnet
|
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3207
|
Unsupervised Discovery of 3D Physical Objects from Video
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;online learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Online Learning of Graph Neural Networks: When Can Data Be Permanently Deleted
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
University of Toronto, Vector Institute, Canadian Institute for Advanced Research; University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3334; None
| null | 0 | null | null | null | null | null |
Jake Snell, Richard Zemel
|
https://iclr.cc/virtual/2021/poster/3334
|
few-shot learning;gaussian processes;bayesian deep learning;uncertainty estimation
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3334
|
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| 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
| 4.25 |
3;4;5;5
| null | null |
Transferred Discrepancy: Quantifying the Difference Between Representations
| null | null | 0 | 4 |
Withdraw
|
3;5;4;4
| null |
null |
University of Washington, Seattle
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3000; None
| null | 0 | null | null | null | null | null |
Tianyi Zhou, Shengjie Wang, Jeff Bilmes
|
https://iclr.cc/virtual/2021/poster/3000
|
curriculum learning;noisy label;robust learning;training dynamics;neural networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.5 |
5;5;6;6
| null |
https://iclr.cc/virtual/2021/poster/3000
|
Robust Curriculum Learning: from clean label detection to noisy label self-correction
| null | null | 0 | 4.5 |
Poster
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep Reinforcement Learning;Average Reward;Policy Optimization;Model Free RL
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Average Reward Reinforcement Learning with Monotonic Policy Improvement
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.467707 | 0 | null |
main
| 4.2 |
3;4;4;5;5
| null | null |
Certified Robustness of Nearest Neighbors against Data Poisoning Attacks
| null | null | 0 | 3.4 |
Reject
|
4;3;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly detection (AD);weakly supervised learning (WSL);sparse coding (SC);generative adversarial network (GAN);hyperspectral image (HSI)
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null |
Mathematical Institute, University of Oxford; The Alan Turing Institute, British Library
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2566; None
| null | 0 | null | null | null | null | null |
Patrick Kidger, Terry Lyons
|
https://iclr.cc/virtual/2021/poster/2566
|
signature;logsignature;gpu;library;open source
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2566
|
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
|
https://github.com/patrick-kidger/signatory
| null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Black-box Optimization;Model-guided sequence design;Computational biology
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design
| null | null | 0 | 2.75 |
Reject
|
3;4;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;black-box attack;bandits
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
CorrAttack: Black-box Adversarial Attack with Structured Search
| null | null | 0 | 3.5 |
Reject
|
5;2;4;3
| null |
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