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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Programming Language;Reverse engineering;neural machine translation;machine learning for system
| null | 0 | null | null |
iclr
| -0.845154 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
N-Bref : A High-fidelity Decompiler Exploiting Programming Structures
| 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 |
Gaussian noise;input noise;adversarial defense;black-box attack;adversarial attack;query-based attack
| null | 0 | null | null |
iclr
| -0.948683 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Small Input Noise is Enough to Defend Against Query-based Black-box Attacks
| null | null | 0 | 4 |
Reject
|
5;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feature Fusion;Object Detection;Multi-Scale
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.25 |
3;3;3;4
| null | null |
MSFM: Multi-Scale Fusion Module for Object Detection
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;reinforcement learning;information theory
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Non-Markovian Predictive Coding For Planning In Latent Space
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian neural network;earthquake rupture;simulation;Explainable neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Bayesian neural network parameters provide insights into the earthquake rupture physics.
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty estimation;deep ensemble;dataset shift;robustness;uncertainty calibration
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
| 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 |
Model selection;Neural Network;Regularization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
THE EFFICACY OF L1 REGULARIZATION IN NEURAL NETWORKS
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Duke University; NVIDIA Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3011; None
| null | 0 | null | null | null | null | null |
Nadav Dym, Haggai Maron
|
https://iclr.cc/virtual/2021/poster/3011
|
3D deep learning;Rotation invariance;Invariant and equivariant deep networks;Universal approximation;Point clouds
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3011
|
On the Universality of Rotation Equivariant Point Cloud Networks
| null | null | 0 | 2.5 |
Poster
|
2;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Few-shot;SVD;PCA
| null | 0 | null | null |
iclr
| 0.87831 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Few-shot Adaptation of Generative Adversarial Networks
| null | null | 0 | 4.25 |
Withdraw
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Mean-field games;Fictitious play;Entropy regularization;Nash equilibrium
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Provable Fictitious Play for General Mean-Field Games
| null | null | 0 | 4 |
Reject
|
5;3;3;5
| null |
null |
Blueshift, Alphabet; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2782; None
| null | 0 | null | null | null | null | null |
Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur
|
https://iclr.cc/virtual/2021/poster/2782
|
Sharpness Minimization;Generalization;Regularization;Training Method;Deep Learning
| null | 0 | null | null |
iclr
| 0.636364 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2782
|
Sharpness-aware Minimization for Efficiently Improving Generalization
|
https://github.com/google-research/sam
| null | 0 | 3.25 |
Spotlight
|
2;4;4;3
| null |
null |
Seoul National University; Microsoft Research; NVIDIA Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2901; None
| null | 0 | null | null | null | null | null |
Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, Yale Song
|
https://iclr.cc/virtual/2021/poster/2901
|
Self-supervised learning;audio-visual representation learning;video representation learning
| null | 0 | null | null |
iclr
| 0.229416 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2901
|
Parameter Efficient Multimodal Transformers for Video Representation Learning
| null | null | 0 | 4 |
Poster
|
5;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformers;sequence modeling;machine translation;efficiency
| null | 0 | null | null |
iclr
| 1 | 0 |
https://u.pcloud.link/publink/show?code=kZSlqJXZ8gzUB1PdOfHmzsda8Bo1HQN8O46k
|
main
| 4.666667 |
4;4;6
| null | null |
Subformer: A Parameter Reduced Transformer
| null | null | 0 | 4.333333 |
Withdraw
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolutional Network;vertex classification;graph signal processing;adaptive graph filter
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Adaptive Stacked Graph Filter
| null | null | 0 | 4.75 |
Reject
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;semi-supervised learning;novel class discovery;clustering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Open-world Semi-supervised Learning
| 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 |
Unsupervised domain adaptation;3D vision;object detection;autonomous driving
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null |
Department of Computer Science and Engineering, The Chinese University of Hong Kong; Department of Statistics, Iowa State University; Dept of Data Science and AI, Faculty of IT, Monash University; Department of Computer Science, Princeton University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2846; None
| null | 0 | null | null | null | null | null |
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou
|
https://iclr.cc/virtual/2021/poster/2846
|
Federated Learning;Non-IID;Batch Normalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/2846
|
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
|
https://github.com/med-air/FedBN
| null | 0 | 4.5 |
Poster
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Radio astronomy;Calibration;Radio interferometry;ska;kat-7;MeerKat
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
ZCal: Machine learning methods for calibrating radio interferometric data
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Redesigning the Classification Layer by Randomizing the Class Representation Vectors
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regression;weakly-supervised learning;healthcare
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Regression from Upper One-side Labeled Data
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;generative adversarial networks;generative modeling;memorization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
A Large-scale Study on Training Sample Memorization in Generative Modeling
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Out-Of-Distribution;DNN;image classification
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
| 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 |
Multi-Source Domain Adaptation;Label-wise Moment Matching;Pseudolabel;Ensemble of Feature Representation
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Multi-EPL: Accurate Multi-source Domain Adaptation
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representation learning;dynamic learning;Variational Autoencoder;PID contoller
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
| null | null | 0 | 4.5 |
Reject
|
4;5;4;5
| null |
null |
Harvard University; Carnegie Mellon University; University of Mannheim
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3202; None
| null | 0 | null | null | null | null | null |
Marcel Neunhoeffer, Steven Wu, Cynthia Dwork
|
https://iclr.cc/virtual/2021/poster/3202
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3202
|
Private Post-GAN Boosting
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian methods;logistic regression;regret;online learning;MDL.
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Parallel Computing Lab - India, Intel Labs; RIKEN Center for Advanced Intelligence Project; Chugai Pharmaceutical Co., Ltd
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2528; None
| null | 0 | null | null | null | null | null |
Xiufeng Yang, Tanuj Aasawat, Kazuki Yoshizoe
|
https://iclr.cc/virtual/2021/poster/2528
|
parallel Monte Carlo Tree Search (MCTS);Upper Confidence bound applied to Trees (UCT);molecular design
| null | 0 | null | null |
iclr
| 0.353553 | 0 | null |
main
| 6 |
3;5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2528
|
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design
|
https://github.com/yoshizoe/mp-chemts
| null | 0 | 3 |
Poster
|
3;2;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph;deep;learning
| null | 0 | null | null |
iclr
| 0.365148 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Graph Structural Aggregation for Explainable Learning
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null |
Snap Inc.; Rutgers University; University of Delaware
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2810; None
| null | 0 | null | null | null | null | null |
Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris Metaxas, Sergey Tulyakov
|
https://iclr.cc/virtual/2021/poster/2810
|
high-resolution video generation;contrastive learning;cross-domain video generation
| null | 0 | null | null |
iclr
| 0.96225 | 0 | null |
main
| 7 |
6;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2810
|
A Good Image Generator Is What You Need for High-Resolution Video Synthesis
|
https://github.com/snap-research/MoCoGAN-HD
| null | 0 | 3.75 |
Spotlight
|
3;2;5;5
| null |
null |
Amazon.com
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2732; None
| null | 0 | null | null | null | null | null |
Mohammad Taha Bahadori, David Heckerman
|
https://iclr.cc/virtual/2021/poster/2732
|
Interpretability;Concept-based Explanation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2732
|
Debiasing Concept-based Explanations with Causal Analysis
| null | null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Backdoor attack;Deep Neural Networks security
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Dynamic Backdoor Attacks Against Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
School of Electrical and Computer Engineering, Georgia Institute of Technology, USA; Center for Machine Learning, School of Aerospace Engineering, Georgia Institute of Technology, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2702; None
| null | 0 | null | null | null | null | null |
Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
|
https://iclr.cc/virtual/2021/poster/2702
|
deep learning training;optimal control;trajectory optimization;differential dynamica programming
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2702
|
DDPNOpt: Differential Dynamic Programming Neural Optimizer
| null | null | 0 | 3 |
Spotlight
|
3;4;3;2
| null |
null |
Department of ECE, University of Utah, Salt Lake City, UT 84112; Department of EE, University at Buffalo, Buffalo, NY 14260
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2968; None
| null | 0 | null | null | null | null | null |
Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou
|
https://iclr.cc/virtual/2021/poster/2968
|
Optimization;Reinforcement Learning;Machine Learning
| null | 0 | null | null |
iclr
| -0.235702 | 0 | null |
main
| 6 |
3;5;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2968
|
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
| null | null | 0 | 4 |
Poster
|
5;3;4;5;3
| null |
null |
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Surrey, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2738; None
| null | 0 | null | null | null | null | null |
Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang
|
https://iclr.cc/virtual/2021/poster/2738
|
Domain Generalization;Style Mixing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2738
|
Domain Generalization with MixStyle
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;robustness;non-robust features
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Non-robust Features through the Lens of Universal Perturbations
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null |
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, AUS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2795; None
| null | 0 | null | null | null | null | null |
Sean Fox, Seyedramin Rasoulinezhad, Julian Faraone, david boland, Philip Leong
|
https://iclr.cc/virtual/2021/poster/2795
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2795
|
A Block Minifloat Representation for Training Deep Neural Networks
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Department of Statistics, University of Michigan; IBM Research, MIT-IBM Watson AI Lab; Department of Mathematics, University of Michigan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2627; None
| null | 0 | null | null | null | null | null |
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun
|
https://iclr.cc/virtual/2021/poster/2627
|
algorithmic fairness;learning to rank;optimal transport
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.75 |
4;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2627
|
Individually Fair Rankings
| null | null | 0 | 3 |
Poster
|
4;3;3;2
| null |
null |
Google; Technion - Israel Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2539; None
| null | 0 | null | null | null | null | null |
Liran Katzir, Gal Elidan, Ran El-Yaniv
|
https://iclr.cc/virtual/2021/poster/2539
|
Neural Networks;Architectures;Tabular Data;Predictive Modeling
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2539
|
Net-DNF: Effective Deep Modeling of Tabular Data
| null | null | 0 | 3 |
Poster
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent rl;reinforcement learning;social dilemma;policy gradient;game theory
| null | 0 | null | null |
iclr
| -0.4 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Status-Quo Policy Gradient in Multi-agent Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
4;3;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3D point cloud;GAN;sampling pattern;evaluation metrics;discriminator
| null | 0 | null | null |
iclr
| 0.628971 | 0 | null |
main
| 5.6 |
4;5;6;6;7
| null | null |
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
| null | null | 0 | 4.2 |
Reject
|
4;3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;few-shot learning;generalization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Robust Meta-learning with Noise via Eigen-Reptile
| null | null | 0 | 4 |
Reject
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
object-centric;representation learning;reinforcement learning;sparse reward
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments
| null | null | 0 | 3.5 |
Withdraw
|
4;3;4;3
| null |
null |
Weizmann Institute of Science
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3221; None
| null | 0 | null | null | null | null | null |
Matan Atzmon, Yaron Lipman
|
https://iclr.cc/virtual/2021/poster/3221
|
implicit neural representations;3D shapes learning;sign agnostic learning
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3221
|
SALD: Sign Agnostic Learning with Derivatives
| null | null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null |
Department of Statistics and Applied Probability, National University of Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3176; None
| null | 0 | null | null | null | null | null |
Atin Ghosh, alexandre thiery
|
https://iclr.cc/virtual/2021/poster/3176
|
Semi-Supervised Learning;Regularization;Data augmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3176
|
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null |
North Carolina State University, Raleigh, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3019; None
| null | 0 | null | null | null | null | null |
Hengrui Cai, Rui Song, Wenbin Lu
|
https://iclr.cc/virtual/2021/poster/3019
|
Causal network;Constrained optimization;COVID-19;Individual mediation effects;Structure learning
| null | 0 | null | null |
iclr
| 0.207514 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3019
|
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning
| null | null | 0 | 3.25 |
Poster
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-RL;Offline RL;Bayesian RL
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Offline Meta Learning of Exploration
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fairness;missing data;adversary;classification;disentanglement
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Zero-shot Fairness with Invisible Demographics
| null | null | 0 | 3.75 |
Reject
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Byzantine robustness;distributed training;heterogeneous dataset
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-attention;efficient;linear complexity;language model;transformer;BERT
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
2;5;6;6
| null | null |
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;exploration;curiosity;episodic memory
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
Impact-driven Exploration with Contrastive Unsupervised Representations
| 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 |
deep Q-network;DQN;switching cost;deep Q-learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Deep Q-Learning with Low Switching Cost
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null |
Stanford University; Harvard University; Microsoft Research & University of Washington
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2540; None
| null | 0 | null | null | null | null | null |
Preetum Nakkiran, Prayaag Venkat, Sham M Kakade, Tengyu Ma
|
https://iclr.cc/virtual/2021/poster/2540
|
double descent;generalization;regularization;regression;monotonicity
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2540
|
Optimal Regularization can Mitigate Double Descent
| null | null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Universal Approximation Theorem;CNN;Deep Learning;Symmetry
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Universal Approximation Theorem for Equivariant Maps by Group CNNs
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Connection- and Node-Sparse Deep Learning: Statistical Guarantees
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
particle hydrodynamics;graph neural networks;Lagrangian fluids
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Learning Lagrangian Fluid Dynamics with Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi task learning;computer vision
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Multi-Task Learning by a Top-Down Control Network
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
classification;imbalance;long-tailed;likelihood;focal loss
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
High-Likelihood Area Matters --- Rewarding Correct,Rare Predictions Under Imbalanced Distributions
| null | null | 0 | 3.5 |
Reject
|
3;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;DARTS
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
3;4;5
| null | null |
FTSO: Effective NAS via First Topology Second Operator
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative model;variational autoencoders;posterior collapse;regularization
| null | 0 | null | null |
iclr
| 0.927173 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
Graduate School of AI, KAIST, Daejeon, Republic of Korea; School of Computing, KAIST, Daejeon, Republic of Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3128; None
| null | 0 | null | null | null | null | null |
Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim
|
https://iclr.cc/virtual/2021/poster/3128
|
natural language processing;Monte-Carlo tree search;reinforcement learning;interactive fiction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3128
|
Monte-Carlo Planning and Learning with Language Action Value Estimates
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse Neural Networks;Pruning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;5;5;7
| null | null |
PHEW: Paths with Higher Edge-Weights give ''winning tickets'' without training data
| null | null | 0 | 4.6 |
Withdraw
|
4;5;5;5;4
| null |
null |
Carnegie Mellon University & Determined AI; University of Washington; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2706; None
| null | 0 | null | null | null | null | null |
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
|
https://iclr.cc/virtual/2021/poster/2706
|
Interpretability;Learning Theory;Local Explanations;Generalization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2706
|
A Learning Theoretic Perspective on Local Explainability
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
School of Computer Science and Engineering, Sun Yat-sen University; Microsoft Research Asia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2834; None
| null | 0 | null | null | null | null | null |
Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu
|
https://iclr.cc/virtual/2021/poster/2834
|
privacy preserving machine learning;differentially private deep learning;gradient redundancy
| null | 0 | null | null |
iclr
| -0.478091 | 0 | null |
main
| 6.75 |
5;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2834
|
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
| null | null | 0 | 4 |
Poster
|
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
| 6 |
5;6;6;7
| null | null |
Intention Propagation for Multi-agent Reinforcement Learning
| 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 |
uncertainty;contrastive learning;unsupervised learning;anomaly detection;out of distribution;corruption
| null | 0 | null | null |
iclr
| 0.454545 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
A Simple Framework for Uncertainty in Contrastive Learning
| null | null | 0 | 3.75 |
Withdraw
|
3;4;3;5
| null |
null |
Department of Computer Science, University of Illinois Urbana-Champaign; Department of Computer Science & Engineering, University of Minnesota; School of Computer Science, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3266; None
| null | 0 | null | null | null | null | null |
Yingxue Zhou, Steven Wu, Arindam Banerjee
|
https://iclr.cc/virtual/2021/poster/3266
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3266
|
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Spectral Graph Theory;Spectral Sparsification;Directed Graphs;Laplacian Solver;PageRank Vectors
| null | 0 | null | null |
iclr
| 0.662541 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
A Unified Spectral Sparsification Framework for Directed Graphs
| null | null | 0 | 3.25 |
Reject
|
3;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Models of Code;Black-box Adversarial Attacks;Adversarial Robustness
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
STRATA: Simple, Gradient-free Attacks for Models of Code
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;offline;batch reinforcement learning;off-policy;uncertainty estimation;dropout;actor-critic;bootstrap error
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;5;6;7;8
| null | null |
Uncertainty Weighted Offline Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT-based models;vulnerabilities;attribute inference;transferability
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
EXPLORING VULNERABILITIES OF BERT-BASED APIS
| null | null | 0 | 3.75 |
Reject
|
3;5;3;4
| null |
null |
King Abdullah University of Science and Technology (KAUST), Saudi Arabia; Moscow Institute of Physics and Technology (MIPT), Russia; King Abdullah University of Science and Technology (KAUST), Saudi Arabia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3288; None
| null | 0 | null | null | null | null | null |
Ivan Skorokhodov, Mohamed Elhoseiny
|
https://iclr.cc/virtual/2021/poster/3288
|
zero-shot learning;normalization;continual learning;initialization
| null | 0 | null | null |
iclr
| 0.97714 | 0 | null |
main
| 6.25 |
3;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3288
|
Class Normalization for (Continual)? Generalized Zero-Shot Learning
|
https://github.com/universome/class-norm
| null | 0 | 4.75 |
Poster
|
4;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Entropy Regularization;Exploration
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Department of Computer Science, Purdue University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3239; None
| null | 0 | null | null | null | null | null |
S Chandra Mouli, Bruno Ribeiro
|
https://iclr.cc/virtual/2021/poster/3239
|
Extrapolation;G-invariance regularization;Counterfactual inference;Invariant subspaces
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3239
|
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
| null | null | 0 | 3 |
Poster
|
4;3;2
| null |
null |
IDSIA / USI / SUPSI / NNAISENSE; IDSIA / USI / SUPSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3200; None
| null | 0 | null | null | null | null | null |
Robert Csordas, Sjoerd van Steenkiste, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2021/poster/3200
|
modularity;systematic generalization;compositionality
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3200
|
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
| null | null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null |
Stanford University; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2532; None
| null | 0 | null | null | null | null | null |
Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis
|
https://iclr.cc/virtual/2021/poster/2532
|
nearest neighbors;machine translation
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 5.5 |
4;4;6;8
| null |
https://iclr.cc/virtual/2021/poster/2532
|
Nearest Neighbor Machine Translation
| null | null | 0 | 4.25 |
Poster
|
5;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Heterogeneous model transfer;pretraining-finetuning
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Heterogeneous Model Transfer between Different Neural Networks
|
https://anonymous.4open.science/r/6ab184dc-3c64-4fdd-ba6d-1e5097623dfd/
| null | 0 | 4.75 |
Withdraw
|
5;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Introducing Sample Robustness
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
VIVO AI Lab; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; The Chinese University of Hong Kong
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2606; None
| null | 0 | null | null | null | null | null |
Pengfei Chen, Guangyong Chen, Junjie Ye, jingwei zhao, Pheng-Ann Heng
|
https://iclr.cc/virtual/2021/poster/2606
|
Noisy Labels;Robust Learning;SGD noise;Regularization
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2606
|
Noise against noise: stochastic label noise helps combat inherent label noise
|
https://github.com/chenpf1025/SLN
| null | 0 | 4 |
Spotlight
|
4;5;4;3
| null |
null |
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48823, USA; Department of Mathematics, Michigan State University, East Lansing, MI 48823, USA; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2693; None
| null | 0 | null | null | null | null | null |
Xiaorui Liu, Yao Li, Rongrong Wang, Jiliang Tang, Ming Yan
|
https://iclr.cc/virtual/2021/poster/2693
|
Decentralized Optimization;Communication Compression;Linear Convergence;Heterogeneous data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2693
|
Linear Convergent Decentralized Optimization with Compression
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Large-scale recommendation system;End-to-end training
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed Learning;Communication;Gradient Quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;2;4;4
| null | null |
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING
| 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 |
Hessian computation;Deep Learning;Loss Curvature;Lanczos
| null | 0 | null | null |
iclr
| -0.365148 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Deep Curvature Suite
| 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 |
Group Sparsity;Stochastic Learning;Half-Space Projection;Group-Sparsity Identification
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization
| null | null | 0 | 2.75 |
Reject
|
3;2;3;3
| null |
null |
Facebook Inc; Department of Electrical and Computer Engineering, Rice University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2552; None
| null | 0 | null | null | null | null | null |
Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin
|
https://iclr.cc/virtual/2021/poster/2552
|
Efficient training;low precision training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2552
|
CPT: Efficient Deep Neural Network Training via Cyclic Precision
|
https://github.com/RICE-EIC/CPT
| null | 0 | 4 |
Spotlight
|
3;3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
PPO;sparse rewards;single demonstration;3D environment
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://www.youtube.com/playlist?list=PLBeSdcnDP2WFQWLBrLGSkwtitneOelcm-1
|
main
| 5.333333 |
4;6;6
| null | null |
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Web Navigation;Adversarial Environment Generation;Web Environment Design;Minimax Regret Adversary;Auto Curriculum
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Adversarial Environment Generation for Learning to Navigate the Web
| null | null | 0 | 2.5 |
Reject
|
3;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Multi-agent Reinforcement Learning;Networked System Control
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Learning Predictive Communication by Imagination in Networked System Control
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null |
Facebook AI; Cornell University; Facebook
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3274; None
| null | 0 | null | null | null | null | null |
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin Benson
|
https://iclr.cc/virtual/2021/poster/3274
|
graphs;graph neural networks;label propagation;simple;residual
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3274
|
Combining Label Propagation and Simple Models out-performs Graph Neural Networks
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning theory;effective degrees of freedom;generalisation;posterior predictive distribution;real log canonical threshold;singular learning theory
| null | 0 | null | null |
iclr
| 0 | 0 |
Not provided
|
main
| 4.25 |
4;4;4;5
| null | null |
Deep Learning is Singular, and That's Good
|
Not available, but mentioned that the code will be released on Github
| null | 0 | 3 |
Reject
|
1;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Neuroevolution;Quality Diversity;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
Sample efficient Quality Diversity for neural continuous control
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null |
Département d’informatique de l’ENS, ENS, CNRS, PSL University, Paris, France; Collège de France, Paris, France; Flatiron Institute, New York, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2842; None
| null | 0 | null | null | null | null | null |
John Zarka, Florentin Guth, Stéphane Mallat
|
https://iclr.cc/virtual/2021/poster/2842
|
fisher ratio;neural collapse;mean separation;concentration;variance reduction;deep learning;image classification
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2842
|
Separation and Concentration in Deep Networks
| 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 |
graph neural networks;architecture design;convergence;errorneous weight links
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
A Deep Graph Neural Networks Architecture Design: From Global Pyramid-like Shrinkage Skeleton to Local Link Rewiring
|
https://github.com/xjglgjgl/SRGNN
| null | 0 | 3 |
Withdraw
|
4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Deep learning;Optimization;Kernel Method
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Graph Neural Network Acceleration via Matrix Dimension Reduction
| null | null | 0 | 2 |
Reject
|
2;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Attention mechanisms;deep learning;sample complexity;self-attention
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
Stanford University; Princeton University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2573; None
| null | 0 | null | null | null | null | null |
Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh
|
https://iclr.cc/virtual/2021/poster/2573
|
Multi-agent games;emergent behavior;transfer learning;human-AI collaboration
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2573
|
On the Critical Role of Conventions in Adaptive Human-AI Collaboration
| null | null | 0 | 3.25 |
Poster
|
2;3;4;4
| null |
null |
Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing, 100084 China; Center for Data Science, Peking University, Beijing, 100871 China; Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua University, Beijing, 100084 China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2983; None
| null | 0 | null | null | null | null | null |
Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu
|
https://iclr.cc/virtual/2021/poster/2983
|
Normalizing flows;deep generative models;probabilistic inference;implicit functions
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2983
|
Implicit Normalizing Flows
| null | null | 0 | 3.75 |
Spotlight
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multitask Learning;Multimodal Conversational Analysis
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis
| 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 |
Adversarial Attack;Robustness Evaluation;Adversarial Defense;Deep Neural Networks
| null | 0 | null | null |
iclr
| -0.288675 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness
| null | null | 0 | 4 |
Reject
|
5;2;5;4
| null |
null |
Department of Mathematics and PACM, Princeton Univeristy; Department of Mathematics, Princeton University; School of Mathematical Science, Peking University; Department of Mathematics, National University of Singapore; IHPC, A*STAR, Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3345; None
| null | 0 | null | null | null | null | null |
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
|
https://iclr.cc/virtual/2021/poster/3345
|
recurrent neural network;dynamical system;universal approximation;optimization;curse of memory
| null | 0 | null | null |
iclr
| 0.994558 | 0 | null |
main
| 6.25 |
3;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3345
|
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
| null | null | 0 | 3.25 |
Poster
|
2;3;4;4
| null |
null |
Max Planck Institute for Intelligent Systems, CIFAR Azrieli Global Scholar; Max Planck Institute for Intelligent Systems, ETH Zurich, Department for Computer Science; Technical University of Denmark; Technical University of Denmark, Copenhagen University Hospital, University of Copenhagen; Max Planck Institute for Intelligent Systems
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2681; None
| null | 0 | null | null | null | null | null |
Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schoelkopf
|
https://iclr.cc/virtual/2021/poster/2681
|
representation learning;disentanglement;real-world
| null | 0 | null | null |
iclr
| -0.483368 | 0 | null |
main
| 5.75 |
2;5;7;9
| null |
https://iclr.cc/virtual/2021/poster/2681
|
On the Transfer of Disentangled Representations in Realistic Settings
| null | null | 0 | 4.5 |
Poster
|
5;4;5;4
| null |
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