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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;non-stationary environment;model-free approach;regret analysis
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Computation & Neural Systems, California Institute of Technology; Department of Computer Science, UC Irvine
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2626; None
| null | 0 | null | null | null | null | null |
Ruihan Yang, Yibo Yang, Joe Marino, Stephan Mandt
|
https://iclr.cc/virtual/2021/poster/2626
|
Compression;Video Compression;Generative Models;Autoregressive Models
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2626
|
Hierarchical Autoregressive Modeling for Neural Video Compression
| null | null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;word embedding;hierarchical representations;polar coordinates
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Polar Embedding
| null | null | 0 | 4.25 |
Withdraw
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalization;Theoretical Machine Learning;Convolutional Neural Networks
| null | 0 | null | null |
iclr
| 0.471405 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Co-complexity: An Extended Perspective on Generalization Error
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
School of Statistics, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA; Department of Electrical and Computer Engineering, Duke University, Durhm, NC 27705, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3165; None
| null | 0 | null | null | null | null | null |
Enmao Diao, Jie Ding, VAHID TAROKH
|
https://iclr.cc/virtual/2021/poster/3165
|
Federated Learning;Internet of Things;Heterogeneity
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3165
|
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
| null | null | 0 | 4.25 |
Poster
|
5;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Routing Networks;Multi-task Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.272166 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
Re-examining Routing Networks for Multi-task Learning
| null | null | 0 | 3 |
Withdraw
|
3;3;4;2
| null |
null |
DeepMind, London, United Kingdom
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2639; None
| null | 0 | null | null | null | null | null |
Dan A. Calian, Daniel J Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A Mann
|
https://iclr.cc/virtual/2021/poster/2639
|
reinforcement learning;meta-gradients;constraints
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2639
|
Balancing Constraints and Rewards with Meta-Gradient D4PG
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
University of Toronto, Vector Institute, Canadian Institute for Advanced Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2789; None
| null | 0 | null | null | null | null | null |
Renjie Liao, Raquel Urtasun, Richard Zemel
|
https://iclr.cc/virtual/2021/poster/2789
|
PAC Bayes;Generalization Bounds;Graph Neural Networks;Graph Convolutional Neural Networks;Message Passing GNNs
| null | 0 | null | null |
iclr
| -0.454545 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2789
|
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
| null | null | 0 | 3.25 |
Poster
|
4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
denoising;image processing;deep learning;applications;scientific discovery;microscopy;material science
| null | 0 | null | null |
iclr
| 0.316228 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy
| null | null | 0 | 4 |
Withdraw
|
3;5;4;4
| null |
null |
Stanford University; Massachusetts Institute of Technology; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3304; None
| null | 0 | null | null | null | null | null |
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
|
https://iclr.cc/virtual/2021/poster/3304
| null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3304
|
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
|
https://github.com/Newbeeer/Anytime-Auto-Regressive-Model
| null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse Representation;Inverse Problem;Deep Generative Models;Compressed Sensing
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
On the Inversion of Deep Generative Models
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;representation learning;segmentation;biocomputing
| null | 0 | null | null |
iclr
| 0.565916 | 0 | null |
main
| 5.25 |
3;4;7;7
| null | null |
Learning Hyperbolic Representations for Unsupervised 3D Segmentation
| null | null | 0 | 3.5 |
Reject
|
3;3;3;5
| null |
null |
University of Oxford; Institute for Interdisciplinary Information Sciences, Tsinghua University; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2717; None
| null | 0 | null | null | null | null | null |
Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang
|
https://iclr.cc/virtual/2021/poster/2717
|
Multi-Agent Reinforcement Learning;Role-Based Learning;Hierarchical Multi-Agent Learning;Multi-Agent Transfer Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://sites.google.com/view/rode-marl
|
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2717
|
RODE: Learning Roles to Decompose Multi-Agent Tasks
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
A New Variant of Stochastic Heavy ball Optimization Method for Deep Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
memory;meta learning;learn from failures
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/learn-from-failures
|
main
| 4 |
4;4;4;4
| null | null |
Learning to Recover from Failures using Memory
| 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 |
reinforcement learning;attention;partial observability;mutual information;information theory
| null | 0 | null | null |
iclr
| -0.870388 | 0 |
https://sites.google.com/view/hard-attention-control
|
main
| 4.5 |
4;4;4;6
| null | null |
Hard Attention Control By Mutual Information Maximization
| 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 |
Causal Inference;Generative Modelling;Distributional Shift
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 6.5 |
6;6;7;7
| null | null |
Variational Auto-Encoder Architectures that Excel at Causal Inference
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null |
X, the Moonshot Factory, Mountain View, CA; DeepMind, London, UK; X, the Moonshot Factory, Mountain View, CA, USA; DeepMind, Mountain View, CA, USA; Florida State University, Tallahassee, FL, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3197; None
| null | 0 | null | null | null | null | null |
Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, Greg Hajcak
|
https://iclr.cc/virtual/2021/poster/3197
|
EEG;ERP;electroencephalography;depression;representation learning;disentanglement;beta-VAE
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3197
|
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bee;beehive;audio;sound;computational ethology;deep learning;representation learning;semi-supervised learning;modeling;population;disease
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Salesforce Research Asia; Singapore Management University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3376; None
| null | 0 | null | null | null | null | null |
wu xiongwei, Doyen Sahoo, Steven HOI
|
https://iclr.cc/virtual/2021/poster/3376
|
Object Detection;Deep Learning
| null | 0 | null | null |
iclr
| -0.140028 | 0 | null |
main
| 5.75 |
3;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3376
|
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection
|
https://github.com/XiongweiWu/PolarNetV1
| null | 0 | 4.5 |
Poster
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 |
https://www.dropbox.com/s/wcg8kbq5btl4gm0/code_data_pickle_files.zip?dl=0
|
main
| 4.666667 |
4;4;6
| null | null |
SkillBERT: “Skilling” the BERT to classify skills!
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Institute of Computer Science, Johannes-Gutenberg University Mainz, Staudingerweg 9, 55122 Mainz, Germany; Institute of Computer Science, Johannes Gutenberg-University of Mainz, Staudingerweg 9, 55128 Mainz, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3043; None
| null | 0 | null | null | null | null | null |
Christian Ali Mehmeti-Göpel, David Hartmann, Michael Wand
|
https://iclr.cc/virtual/2021/poster/3043
|
deep learning theory;loss landscape;harmonic distortion analysis;network trainability
| null | 0 | null | null |
iclr
| -0.565916 | 0 | null |
main
| 6.25 |
4;5;8;8
| null |
https://iclr.cc/virtual/2021/poster/3043
|
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks
| 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 |
Adversarial Robustness;Provable Adversarial Defense;Sample-wise Randomized Smoothing.
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Pretrain-to-Finetune Adversarial Training via Sample-wise Randomized Smoothing
| null | null | 0 | 3.5 |
Reject
|
4;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
$k$-nearest neighbors;neural networks;label smoothing;churn;reproducibility;stability;robustness
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Deep $k$-NN Label Smoothing Improves Reproducibility of Neural Network Predictions
| null | null | 0 | 3.25 |
Reject
|
4;3;4;2
| null |
null |
Microsoft, Redmond, Washington, USA; Duke University, Durham, North Carolina, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3306; None
| null | 0 | null | null | null | null | null |
Siyang Yuan, Pengyu Cheng, Ruiyi Zhang, Weituo Hao, Zhe Gan, Lawrence Carin
|
https://iclr.cc/virtual/2021/poster/3306
|
Style Transfer;Mutual Information;Zero-shot Learning;Disentanglement
| null | 0 | null | null |
iclr
| -0.404226 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3306
|
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning
| null | null | 0 | 3.25 |
Poster
|
5;5;1;2
| null |
null |
Department of Computer Science and Engineering, State University of New York at Buffalo
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2848; None
| null | 0 | null | null | null | null | null |
Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, Jinhui Xu
|
https://iclr.cc/virtual/2021/poster/2848
|
meta-learning;neural tangent kernel
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2848
|
Meta-Learning with Neural Tangent Kernels
| null | null | 0 | 3.75 |
Poster
|
4;5;2;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
| 5.5 |
5;5;5;7
| null | null |
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning
| null | null | 0 | 2.75 |
Reject
|
2;2;3;4
| null |
null |
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3053; None
| null | 0 | null | null | null | null | null |
Huanrui Yang, Lin Duan, Yiran Chen, Hai Li
|
https://iclr.cc/virtual/2021/poster/3053
|
Mixed-precision quantization;bit-level sparsity;DNN compression
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3053
|
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
kernel-learning;gaussian-process;Bayesian;ensemble
| null | 0 | null | null |
iclr
| -0.392232 | 0 | null |
main
| 5.5 |
3;5;6;8
| null | null |
Deep Ensemble Kernel Learning
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
noisy annotation;object detection;label noise
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Towards Noise-resistant Object Detection with Noisy Annotations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent reinforcement learning
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Which Model to Transfer? Finding the Needle in the Growing Haystack
| null | null | 0 | 4.25 |
Reject
|
3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformers;interpretability
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Thinking Like Transformers
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adaptive optimizers;sparse group lasso;DNN models;online optimization
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Adaptive Optimizers with Sparse Group Lasso
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;policy search;offline RL;control
| null | 0 | null | null |
iclr
| -0.408248 | 0 |
sites.google.com/view/awr-supp/
|
main
| 4 |
3;3;4;6
| null | null |
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Department of Computer Science / Campus Institute Data Science, University of Göttingen, Germany; Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany; Bernstein Center for Computational Neuroscience, University of Tübingen, Germany; Department for Neuroscience, Baylor College of Medicine, Houston, TX, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3042; None
| null | 0 | null | null | null | null | null |
Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay Jagadish, Eric Wang, Edgar Walker, Santiago Cadena, Taliah Muhammad, Erick M Cobos, Andreas Tolias, Alexander S Ecker, Fabian Sinz
|
https://iclr.cc/virtual/2021/poster/3042
|
neuroscience;cognitive science;multitask learning;transfer learning;representation learning;network architecture;computational biology;visual perception
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3042
|
Generalization in data-driven models of primary visual cortex
| null | null | 0 | 4 |
Spotlight
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Budgeted training;importance sampling;data augmentation;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
How Important is Importance Sampling for Deep Budgeted Training?
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
infectious diseases;neural networks;healthcare;regularization;structured data
| null | 0 | null | null |
iclr
| -0.68313 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Exploiting structured data for learning contagious diseases under incomplete testing
| null | null | 0 | 2.5 |
Reject
|
4;2;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;prototypical learning;online learning;incremental learning;deep learning;representation learning;catastrophic forgetting;concept drift
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 6 |
3;7;8
| null | null |
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Univ. de Lille, CNRS, Inria Scool, UMR 9189 CRIStAL; Google Research, Brain Team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2569; None
| null | 0 | null | null | null | null | null |
Robert Dadashi, Léonard Hussenot-Desenonges, Matthieu Geist, Olivier Pietquin
|
https://iclr.cc/virtual/2021/poster/2569
|
Reinforcement Learning;Inverse Reinforcement Learning;Imitation Learning;Optimal Transport;Wasserstein distance
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2569
|
Primal Wasserstein Imitation Learning
| null | null | 0 | 4 |
Poster
|
3;4;4;5
| null |
null |
Microsoft Research; The Swiss AI Lab IDSIA / USI / SUPSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2988; None
| null | 0 | null | null | null | null | null |
Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2021/poster/2988
|
memory-augmented neural networks;tensor product;fast weights
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2988
|
Learning Associative Inference Using Fast Weight Memory
|
github.com/ischlag/Fast-Weight-Memory-public
| null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;numerical optimization;transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Convergence Analysis of Homotopy-SGD for Non-Convex 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 |
Meta-Learning;Episodic Training;Pre-training;Disentanglement
| null | 0 | null | null |
iclr
| -0.478091 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
On the Role of Pre-training for Meta Few-Shot Learning
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NAS;cGAN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5;5
| null | null |
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
| null | null | 0 | 3.6 |
Reject
|
4;4;4;3;3
| null |
null |
Shanghai Jiao Tong University; Johns Hopkins University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3354; None
| null | 0 | null | null | null | null | null |
Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille
|
https://iclr.cc/virtual/2021/poster/3354
|
unsupervised representation learning;contrastive learning;consistency regularization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3354
|
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
| null | null | 0 | 4.25 |
Poster
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
proteins;potts model;unsupervised learning;amortized optimization;structure prediction
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
Neural Potts Model
| 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 |
Decision Trees;Explainability;Interpretability
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Succinct Explanations with Cascading Decision Trees
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continuous-time Stochastic RNN;Neural SDE
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Learning Continuous-Time Dynamics by Stochastic Differential Networks
| null | null | 0 | 3.25 |
Reject
|
3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Decision Boundary;Robustness;Deep Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Studying relationship between geometry of decision boundaries with network complexity for robustness analysis
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural-Symbolic Model;Inductive Logic Programming;Abduction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Abductive Knowledge Induction from Raw Data
| 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 |
graph neural networks;graph pooling;representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Neural Pooling for Graph Neural Networks
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null |
Duke University; State University of New York at Buffalo; Duke University, Facebook AI; Microsoft Dynamics 365 AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2554; None
| null | 0 | null | null | null | null | null |
Kevin Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, Lawrence Carin
|
https://iclr.cc/virtual/2021/poster/2554
|
Natural Language Processing;Representation Learning
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2554
|
MixKD: Towards Efficient Distillation of Large-scale Language Models
| null | null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph convolutional networks;graph filtering;Laplacian smooth;ADMM
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
LAYER SPARSITY IN NEURAL NETWORKS
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.320256 | 0 | null |
main
| 4.6 |
4;4;5;5;5
| null | null |
Adaptive Gradient Method with Resilience and Momentum
| null | null | 0 | 3.4 |
Withdraw
|
2;4;3;3;5
| null |
null |
Massachusetts Institute of Technology (MIT); University of Washington; National Institute of Informatics (NII); University of Maryland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3229; None
| null | 0 | null | null | null | null | null |
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Du, Ken-Ichi Kawarabayashi, Stefanie Jegelka
|
https://iclr.cc/virtual/2021/poster/3229
|
extrapolation;deep learning;out-of-distribution;graph neural networks;deep learning theory
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 8.75 |
8;9;9;9
| null |
https://iclr.cc/virtual/2021/poster/3229
|
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
| null | null | 0 | 3.75 |
Oral
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble learning;Few shot learning;Multi-representaion
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Multi-Representation Ensemble in Few-Shot Learning
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null |
Technical University of Munich, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2727; None
| null | 0 | null | null | null | null | null |
Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
|
https://iclr.cc/virtual/2021/poster/2727
|
Robustness certificates;Adversarial robustness;Graph neural networks
| null | 0 | null | null |
iclr
| 0.6742 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2727
|
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
| null | null | 0 | 1.75 |
Poster
|
1;1;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Object Detection，Dense Connection，Context Aware，Attention Mechanism
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Dense Global Context Aware RCNN for Object Detection
| null | null | 0 | 4.75 |
Withdraw
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;uncertainty;model-based;MCTS
| null | 0 | null | null |
iclr
| -0.516047 | 0 | null |
main
| 4.4 |
3;3;4;6;6
| null | null |
Structure and randomness in planning and reinforcement learning
| null | null | 0 | 3.4 |
Reject
|
4;4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Siamese trackers;cross-attention;light structure;anchor-free
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
SiamCAN:Simple yet Effective Method to enhance Siamese Short-Term Tracking
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null |
Amazon Web Services
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2952; None
| null | 0 | null | null | null | null | null |
Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, RISHITA ANUBHAI, Cicero Nogueira dos Santos, Bing Xiang, Stefano Soatto
|
https://iclr.cc/virtual/2021/poster/2952
|
language models;few-shot learning;transfer learning;structured prediction;generative modeling;sequence to sequence;multi-task learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2952
|
Structured Prediction as Translation between Augmented Natural Languages
| null | null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null |
Department of Computer Science, ETH Zurich, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3359; None
| null | 0 | null | null | null | null | null |
Mark Niklas Müller, Mislav Balunovic, Martin Vechev
|
https://iclr.cc/virtual/2021/poster/3359
|
Provable Robustness;Network Architecture;Robustness;Adversarial Accuracy;Certified Robustness
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3359
|
Certify or Predict: Boosting Certified Robustness with Compositional Architectures
| null | null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null |
University of Toronto, Toronto, Canada; University of Amsterdam, Amsterdam, The Netherlands; Vector Institute, Toronto, Canada
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep generative model;relational inference;trajectory modeling;multi-agent learning
| null | 0 | null | null |
iclr
| -0.207514 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Dynamic Relational Inference in Multi-Agent Trajectories
| null | null | 0 | 4.25 |
Reject
|
5;4;3;5
| null |
null |
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3050; None
| null | 0 | null | null | null | null | null |
James Diffenderfer, Bhavya Kailkhura
|
https://iclr.cc/virtual/2021/poster/3050
|
Binary Neural Networks;Pruning;Lottery Ticket Hypothesis
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3050
|
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
|
https://github.com/chrundle/biprop
| null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Safe reinforcement learning;Language grounding
| null | 0 | null | null |
iclr
| 0.301511 | 0 |
https://sites.google.com/view/polco-hazard-world/
|
main
| 5.75 |
5;5;6;7
| null | null |
Safe Reinforcement Learning with Natural Language Constraints
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
University of Cambridge, Alan Turing Institute; Google; DeepMind; University of Cambridge; Google, University of Cambridge
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2726; None
| null | 0 | null | null | null | null | null |
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Georgiana-Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Q Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy J Colwell, Adrian Weller
|
https://iclr.cc/virtual/2021/poster/2726
|
performer;transformer;attention;softmax;approximation;linear;bert;bidirectional;unidirectional;orthogonal;random;features;FAVOR;kernel;generalized;sparsity;reformer;linformer;protein;trembl;uniprot
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2726
|
Rethinking Attention with Performers
|
github.com/google-research/google-research/tree/master/performer
| null | 0 | 4 |
Oral
|
5;3;4;4
| null |
null |
Stanford University; University of California, Berkeley; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2599; None
| null | 0 | null | null | null | null | null |
Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2599
|
planning;model learning;distance learning;reinforcement learning;robotics
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/berkeley.edu/mbold
|
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2599
|
Model-Based Visual Planning with Self-Supervised Functional Distances
| null | null | 0 | 4 |
Spotlight
|
5;3;4;4
| null |
null |
Department of EECS, Oregon State University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3248; None
| null | 0 | null | null | null | null | null |
Zhengxian Lin, Kin-Ho Lam, Alan Fern
|
https://iclr.cc/virtual/2021/poster/3248
|
Explainable AI;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 7.333333 |
7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3248
|
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
| null | null | 0 | 3.666667 |
Oral
|
5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse coding;Learned ISTA;Convergence Analysis
| null | 0 | null | null |
iclr
| -0.239046 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding
| null | null | 0 | 3.25 |
Reject
|
4;1;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dialogue;summarization;controllable generation;natural language processing
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active learning;batch-mode active learning;deep learning;convolutional neural networks;supervised learning;regression;classification;MC dropout;computer vision;computational biology
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Active Learning in CNNs via Expected Improvement Maximization
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.5 |
3;5;7;7
| null | null |
Deep Reinforcement Learning For Wireless Scheduling with Multiclass Services
| 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 |
robustness;adversarial attack;defense;representation learning;cooperative game;feature selection;adversarial robustness;reliable machine learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness
| null | null | 0 | 3.333333 |
Withdraw
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional neural networks;wavelet packet transform;dual-tree wavelet packet transform;image classification;deep learning;image processing
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.5 |
4;4;6;8
| null | null |
Dual-Tree Wavelet Packet CNNs for Image Classification
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null |
Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society; University of Central Florida
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3012; None
| null | 0 | null | null | null | null | null |
Kunchang Li, xianhang li, Yali Wang, Jun Wang, Yu Qiao
|
https://iclr.cc/virtual/2021/poster/3012
|
Video Classification;3D Convolution;Channel Tensorization
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3012
|
CT-Net: Channel Tensorization Network for Video Classification
| 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 |
network morphism;machine translation;neural architecture search
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Improving Machine Translation by Searching Skip Connections Efficiently
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
emergence of language;reinforcement learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
What Preserves the Emergence of Language?
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2911; None
| null | 0 | null | null | null | null | null |
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
|
https://iclr.cc/virtual/2021/poster/2911
|
Learning to Rank;benchmark;neural network;gradient boosted decision trees
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6 |
2;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2911
|
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?
| null | null | 0 | 4 |
Spotlight
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;multiple adversarial peturbation types;adversarial robustness
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Machine Intelligence Technology Lab, Alibaba Group, Hangzhou, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2921; None
| null | 0 | null | null | null | null | null |
Xiangpeng Wei, Rongxiang Weng, Yue Hu, Luxi Xing, Heng Yu, Weihua Luo
|
https://iclr.cc/virtual/2021/poster/2921
|
universal representation learning;cross-lingual pretraining;hierarchical contrastive learning
| null | 0 | null | null |
iclr
| 0.5 | 0 |
https://sites.research.google/xtreme
|
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2921
|
On Learning Universal Representations Across Languages
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Allen Institute for Artificial Intelligence; University of Washington
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2685; None
| null | 0 | null | null | null | null | null |
Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi
|
https://iclr.cc/virtual/2021/poster/2685
|
representation learning;deep reinforcement learning;computer vision
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 8.25 |
8;8;8;9
| null |
https://iclr.cc/virtual/2021/poster/2685
|
Learning Generalizable Visual Representations via Interactive Gameplay
| null | null | 0 | 3.5 |
Oral
|
3;4;4;3
| null |
null |
Department of Engineering, University of Cambridge, UK; Alan Turing Institute, London, UK; Department of Computer Science, Universidad Autónoma de Madrid, Spain; Department of Computer Science and AI, University of Granada, Spain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3038; None
| null | 0 | null | null | null | null | null |
Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández Lobato
|
https://iclr.cc/virtual/2021/poster/3038
|
Gaussian Processes;Uncertainty estimation;Deep Gaussian Processes;Bayesian Neural Networks
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3038
|
Activation-level uncertainty in deep neural networks
| null | null | 0 | 4 |
Poster
|
5;4;3;4
| null |
null |
Fraunhofer IAIS, ML2R; Fraunhofer IAIS, ML2R, University of Cologne; IIT Bombay
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2788; None
| null | 0 | null | null | null | null | null |
Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee
|
https://iclr.cc/virtual/2021/poster/2788
|
Brownian motion;deep learning theory;decision boundary geometry;curvature estimates;generalization bounds;adversarial attacks/defenses
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2788
|
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretable representation learning;rule-based model;scalability
| null | 0 | null | null |
iclr
| -0.622543 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;dataset;benchmark;logic
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks
| 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 |
GANs;Spectral Bias;Convolutional Neural Networks
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Spatial Frequency Bias in Convolutional Generative Adversarial Networks
| null | null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
u6424547
| null | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning
| null | null | 0 | 4 |
Withdraw
|
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 | 0 | null |
main
| 0 | null | null | null |
Unsupervised Program Synthesis for Images By Sampling Without Replacement
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sparse training;sparsity;pruning;lottery ticket hypothesis;lottery tickets;sparse initialization;initialization;deep learning;gradient flow
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
McGill University and Mila; Carnegie Mellon University; Carnegie Mellon University, Bosch Center for AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2868; None
| null | 0 | null | null | null | null | null |
Priya Donti, David Rolnick, Zico Kolter
|
https://iclr.cc/virtual/2021/poster/2868
|
approximate constrained optimization;implicit differentiation;optimal power flow;surrogate models
| null | 0 | null | null |
iclr
| -0.87831 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2868
|
DC3: A learning method for optimization with hard constraints
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;few-shot classification;data augmentation;transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Data Augmentation for Meta-Learning
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hyperparameter Optimization
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Multi-Source Unsupervised Hyperparameter Optimization
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Value Function Approximation;Representation Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2581; None
| null | 0 | null | null | null | null | null |
Subham Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley
|
https://iclr.cc/virtual/2021/poster/2581
|
Neural Model Explanation;SMT Solvers;Symbolic Methods
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2581
|
Scaling Symbolic Methods using Gradients for Neural Model Explanation
|
https://github.com/google-research/google-research/tree/master/smug_saliency
| null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null |
MIT; Google Research; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3234; None
| null | 0 | null | null | null | null | null |
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum
|
https://iclr.cc/virtual/2021/poster/3234
|
Offline Reinforcement Learning;Primitive Discovery;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://sites.google.com/view/opal-iclr
|
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3234
|
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Department of Computer Science, University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3067; None
| null | 0 | null | null | null | null | null |
Keiran Paster, Sheila McIlraith, Jimmy Ba
|
https://iclr.cc/virtual/2021/poster/3067
|
model based reinforcement learning;deep reinforcement learning;multi-task learning;deep learning;goal-conditioned reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3067
|
Planning from Pixels using Inverse Dynamics Models
| 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 |
retrosynthesis;data transfer;transfer learninig;pre-training;fine-tuning;self-training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
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