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stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Disentangled representation learning;Spatio-temporal data;principle of relevant information
| null | 1.5 | null | null |
iclr
| -0.870388 | 0.816497 | null |
main
| 3.5 |
3;3;3;5
|
3;3;2;4
| null |
Spatio-temporal Disentangled representation learning for mobility prediction
| null | null | 3 | 4.25 |
Reject
|
5;5;4;3
|
2;0;2;2
|
null |
CAPTAIN, Wuhan University
|
2022
| 2.6 |
https://iclr.cc/virtual/2022/poster/7018; None
| null | 0 | null | null | null |
3;3;2;3;2
| null |
Ziming Wang, Nan Xue, Ling Lei, Gui-Song Xia
|
https://iclr.cc/virtual/2022/poster/7018
|
partial Wasserstein discrepancy;partial distribution matching;point set registration
| null | 2.8 | null |
https://openreview.net/forum?id=2ggNjUisGyr
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6;6
|
3;4;4;4;3
|
https://iclr.cc/virtual/2022/poster/7018
|
Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration
| null | null | 3.6 | 3.4 |
Poster
|
3;4;3;4;3
|
3;2;3;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2;2
| null | null | null |
Defect synthesis;Generative Adversarial Networks;Content transfer;Automated visual inspection;Data augmentation
| null | 2.2 | null | null |
iclr
| -0.25 | -1 | null |
main
| 3.4 |
3;3;3;3;5
|
3;3;3;3;2
| null |
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
| null | null | 2.8 | 4.2 |
Reject
|
5;4;4;4;4
|
2;3;2;2;2
|
null | null |
2022
| 3.333333 | null | null | 0 | null | null | null |
4;3;3
| null | null | null |
Domain Adaptation;Transfer Learning;Information Theory
| null | 0.666667 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
|
3;4;3
| null |
Domain Adaptation via Maximizing Surrogate Mutual Information
| null | null | 3.333333 | 3.666667 |
Withdraw
|
3;4;4
|
0;2;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
optimization;network pruning
| null | 1.75 | null | null |
iclr
| 0 | 0.57735 | null |
main
| 2.5 |
1;3;3;3
|
2;3;3;2
| null |
Network Pruning Optimization by Simulated Annealing Algorithm
| null | null | 2.5 | 4 |
Reject
|
4;4;4;4
|
2;2;2;1
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;3;3;2
| null | null | null |
adversarial attack;decision boundary;riemannian geometry;differential geometry;interpretability;adversarial training
| null | 3 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;4;4;3
| null |
The Geometry of Adversarial Subspaces
| null | null | 3.5 | 4 |
Reject
|
4;4;4;4
|
3;3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null |
Deep Learning;Differential Privacy;Optimization Algorithms;Convergence Theory;Calibration
| null | 3 | null | null |
iclr
| 0.366508 | 0.478861 | null |
main
| 4.75 |
3;3;5;8
|
2;4;3;4
| null |
On the Convergence and Calibration of Deep Learning with Differential Privacy
| null | null | 3.25 | 3.5 |
Reject
|
4;3;3;4
|
2;2;4;4
|
null |
Department of Computer Science, University of Oxford; Department of Computer Science and Technology, University of Cambridge; Laboratoire Informatique d’Avignon, Avignon Université
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/6036; None
| null | 0 | null | null | null |
2;3;2
| null |
Xinchi Qiu, Javier Fernandez-Marques, Pedro Porto Buarque de Gusmao, Yan Gao, Titouan Parcollet, Nicholas Lane
|
https://iclr.cc/virtual/2022/poster/6036
|
Federated Learning;sparse training
| null | 3 | null |
https://openreview.net/forum?id=2sDQwC_hmnM
|
iclr
| -0.5 | -0.5 | null |
main
| 5.666667 |
5;6;6
|
4;4;2
|
https://iclr.cc/virtual/2022/poster/6036
|
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
| null | null | 3.333333 | 3.666667 |
Poster
|
4;4;3
|
3;3;3
|
null |
Center on Frontiers of Computing Studies, School of Computer Science, Peking University; Center for Data Science, Peking University; School of Artificial Intelligence, Peking University
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/6578; None
| null | 0 | null | null | null |
3;2;3
| null |
Yuanfei Wang, Fangwei Zhong, Jing Xu, Yizhou Wang
|
https://iclr.cc/virtual/2022/poster/6578
|
Theory of Mind;Target-oriented Multi-Agent Cooperation;Multi-agent Communication
| null | 2.666667 | null |
https://openreview.net/forum?id=2t7CkQXNpuq
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
|
4;3;3
|
https://iclr.cc/virtual/2022/poster/6578
|
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
| null | null | 3.333333 | 3.666667 |
Poster
|
4;3;4
|
3;3;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
reinforcement learning;constrained Markov decision processes;safety learning
| null | 1.5 | null | null |
iclr
| -0.207514 | -0.927173 | null |
main
| 4.75 |
3;5;5;6
|
4;3;3;3
| null |
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
| null | null | 3.25 | 3.25 |
Withdraw
|
3;4;4;2
|
2;2;0;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
multi-party computation;privacy;cryptography;privacy-preserving machine learning
| null | 1.75 | null | null |
iclr
| -0.426401 | 1 | null |
main
| 3.5 |
1;3;5;5
|
1;2;3;3
| null |
HD-cos Networks: Efficient Neural Architechtures for Secure Multi-Party Computation
| null | null | 2.25 | 4 |
Reject
|
5;3;4;4
|
0;2;2;3
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;1;2
| null | null | null | null | null | 2 | null | null |
iclr
| -0.57735 | -0.333333 | null |
main
| 3.5 |
3;3;3;5
|
2;2;4;2
| null |
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation
| null | null | 2.5 | 3.5 |
Reject
|
3;4;4;3
|
2;2;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
deep networks;kernels on the sphere;nonlinear classification
| null | 2.25 | null | null |
iclr
| -0.57735 | 0.904534 | null |
main
| 4.5 |
3;3;6;6
|
3;2;4;4
| null |
Model-Efficient Deep Learning with Kernelized Classification
| null | null | 3.25 | 3.75 |
Reject
|
4;4;3;4
|
2;2;2;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;3;3;3
| null | null | null |
Differential Privacy;Empirical Risk Minimization;Lower bounds
| null | 1 | null | null |
iclr
| -0.396059 | 0.080845 | null |
main
| 5.25 |
3;5;5;8
|
4;2;4;4
| null |
Tight lower bounds for Differentially Private ERM
| null | null | 3.5 | 3 |
Reject
|
4;3;2;3
|
0;0;3;1
|
null |
Cognitive Computing Lab, Baidu Research, 10900 NE 8th St. Bellevue, WA 98004, USA
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6153; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Jianwen Xie, yaxuan zhu, Jun Li, Ping Li
|
https://iclr.cc/virtual/2022/poster/6153
|
Langevin dynamics;energy-based model;normalizing flow;cooperative learning;short-run MCMC
| null | 2.75 | null |
https://openreview.net/forum?id=31d5RLCUuXC
|
iclr
| -0.70014 | 0.727607 | null |
main
| 5.75 |
3;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6153
|
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model
| null | null | 3.25 | 3.5 |
Poster
|
4;3;4;3
|
2;3;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
feature pyramid;network architecture;object detection;deep learning
| null | 2.5 | null | null |
iclr
| -0.816497 | -0.816497 | null |
main
| 5 |
3;5;6;6
|
4;4;3;3
| null |
Trident Pyramid Networks: The importance of processing at the feature pyramid level for better object detection
| null | null | 3.5 | 4.5 |
Reject
|
5;5;4;4
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
Graph Neural Network;Subgraph;Mutual Information Maximization
| null | 2.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
3;3;2
| null |
Learning Representations of Partial Subgraphs by Subgraph InfoMax
| null | null | 2.666667 | 4 |
Withdraw
|
4;4;4
|
3;2;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
1;2;2;2
| null | null | null |
deep learning;expert system;sequential learning
| null | 1.75 | null | null |
iclr
| 0 | -0.816497 | null |
main
| 3 |
1;3;3;5
|
3;1;1;1
| null |
DL-based prediction of optimal actions of human experts
| null | null | 1.5 | 3.75 |
Reject
|
4;4;3;4
|
1;2;2;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Computer Go;Monte-Carlo Tree Search;Reinforcement learning;Adaptive;Acceleration
| null | 2.333333 | null | null |
iclr
| -0.802955 | 0.802955 | null |
main
| 5.333333 |
3;5;8
|
2;3;3
| null |
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
| null | null | 2.666667 | 4.333333 |
Reject
|
5;4;4
|
2;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Long-tail image classification;Semantic augmentation
| null | 2.25 | null | null |
iclr
| -1 | 0.522233 | null |
main
| 3.5 |
3;3;3;5
|
3;2;1;3
| null |
Sample-specific and Context-aware Augmentation for Long Tail Image Classification
| null | null | 2.25 | 3.75 |
Withdraw
|
4;4;4;3
|
3;2;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
bayesian neural network;uncertainty;uncertainty estimation;uncertainty quantification
| null | 2.5 | null | null |
iclr
| -0.870388 | 0.333333 | null |
main
| 5.75 |
5;5;5;8
|
2;3;3;3
| null |
Blur Is an Ensemble: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
| null | null | 2.75 | 3.25 |
Reject
|
4;3;4;2
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3;2
| null | null | null |
Anomaly detection;Anomaly segmentation;Self-Supervised learning
| null | 2.75 | null | null |
iclr
| 0.207514 | 0.927173 | null |
main
| 4.75 |
3;5;5;6
|
1;3;3;3
| null |
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning
| null | null | 2.5 | 3.75 |
Withdraw
|
4;3;3;5
|
1;2;4;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
dataset bias;out of distribution;meta-learning;data augmentation
| null | 2.25 | null | null |
iclr
| -0.688247 | 0.324443 | null |
main
| 4.75 |
3;5;5;6
|
3;2;3;4
| null |
From Biased Data to Unbiased Models: a Meta-Learning Approach
| null | null | 3 | 3.5 |
Withdraw
|
4;3;4;3
|
2;2;2;3
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null |
Representation Learning;Transformer;Autoencoder;Binary Code Similarity Detection
| null | 2.666667 | null | null |
iclr
| -0.917663 | 0 | null |
main
| 3.333333 |
1;3;6
|
3;3;3
| null |
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection
| null | null | 3 | 4 |
Reject
|
5;5;2
|
2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Adversarial Training;Robust Accuracy
| null | 2.5 | null | null |
iclr
| 0.555556 | 0.754337 | null |
main
| 4.25 |
3;3;5;6
|
2;2;4;3
| null |
Can standard training with clean images outperform adversarial one in robust accuracy?
| null | null | 2.75 | 3.75 |
Reject
|
4;3;4;4
|
2;2;4;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
deep learning;large-batch training
| null | 2 | null | null |
iclr
| 0.57735 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
3;2;3;3
| null |
Achieving Small-Batch Accuracy with Large-Batch Scalability via Adaptive Learning Rate Adjustment
| null | null | 2.75 | 4.25 |
Withdraw
|
4;4;4;5
|
2;2;2;2
|
null | null |
2022
| 2.8 | null | null | 0 | null | null | null |
3;2;3;3;3
| null | null | null |
federated learning;data heterogeneity;hardware heterogeneity;security heterogeneity;adversarial robustness
| null | 2.6 | null | null |
iclr
| -0.089087 | 0.285714 | null |
main
| 5.6 |
3;3;6;8;8
|
2;4;3;4;3
| null |
Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
| null | null | 3.2 | 3.8 |
Reject
|
4;4;3;4;4
|
3;2;2;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Normalization-Free ResNets;Weights Initialization;Exploding Gradient;Residual Blocks
| null | 2.25 | null | null |
iclr
| 0.522233 | -1 | null |
main
| 3.5 |
3;3;3;5
|
3;3;3;2
| null |
A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization
| null | null | 2.75 | 4.25 |
Withdraw
|
5;3;4;5
|
2;2;2;3
|
null | null |
2022
| 1 | null | null | 0 | null | null | null |
1;1;1
| null | null | null |
the condition number of modules;median principal angle;network mathematical description
| null | 1.333333 | null | null |
iclr
| 0.5 | 0.5 | null |
main
| 2.333333 |
1;3;3
|
1;2;1
| null |
Understanding ResNet from a Discrete Dynamical System Perspective
| null | null | 1.333333 | 3.666667 |
Withdraw
|
3;3;5
|
1;1;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;4;2;2
| null | null | null |
Local Augmentation;Graph Neural Networks
| null | 2 | null | null |
iclr
| -0.816497 | 0.57735 | null |
main
| 4.5 |
3;5;5;5
|
2;3;3;2
| null |
Local Augmentation for Graph Neural Networks
| null | null | 2.5 | 3 |
Reject
|
4;3;3;2
|
1;3;2;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;1;2
| null | null | null |
Anytime Learning;Mixture of experts;growing architectures
| null | 2.25 | null | null |
iclr
| -0.662266 | 1 | null |
main
| 4.75 |
3;5;5;6
|
1;3;3;4
| null |
On Anytime Learning at Macroscale
| null | null | 2.75 | 3.75 |
Reject
|
4;4;4;3
|
2;2;2;3
|
null |
Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6023; None
| null | 0 | null | null | null |
3;2;3;3
| null |
SunWoo Lee, Jeongwoo Park, Dongsuk Jeon
|
https://iclr.cc/virtual/2022/poster/6023
|
low-precision training;quantized training;logarithmic weight;data format optimization;hysteresis quantization
| null | 2.75 | null |
https://openreview.net/forum?id=3HJOA-1hb0e
|
iclr
| 0.408248 | 0.471405 | null |
main
| 6 |
5;5;6;8
|
3;2;3;3
|
https://iclr.cc/virtual/2022/poster/6023
|
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
| null | null | 2.75 | 4.5 |
Poster
|
4;5;4;5
|
3;2;3;3
|
null |
UC San Diego; NVIDIA; UC Merced
|
2022
| 2 |
https://iclr.cc/virtual/2022/poster/6783; None
| null | 0 | null | null | null |
1;2;3
| null |
Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz
|
https://iclr.cc/virtual/2022/poster/6783
|
Continuous Scene Representation;Implicit Neural Networks
| null | 2 | null |
https://openreview.net/forum?id=3ILxkQ7yElm
|
iclr
| -0.907841 | 0.998625 | null |
main
| 5 |
1;6;8
|
1;3;4
|
https://iclr.cc/virtual/2022/poster/6783
|
Learning Continuous Environment Fields via Implicit Functions
| null | null | 2.666667 | 3.666667 |
Poster
|
5;4;2
|
1;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null | null | null | 2 | null | null |
iclr
| -0.57735 | 0.904534 | null |
main
| 4 |
3;3;5;5
|
2;1;3;3
| null |
Robust Weight Perturbation for Adversarial Training
| null | null | 2.25 | 4.25 |
Withdraw
|
4;5;4;4
|
2;2;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
Deep learning theory;convolutional neural networks
| null | 1.75 | null | null |
iclr
| 0.333333 | -0.132453 | null |
main
| 4.5 |
3;5;5;5
|
3;1;3;4
| null |
Provable Learning of Convolutional Neural Networks with Data Driven Features
| null | null | 2.75 | 3.5 |
Reject
|
3;5;3;3
|
0;1;2;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
model-based reinforcement learning;representation learning
| null | 2.5 | null | null |
iclr
| -0.132453 | 0.899229 | null |
main
| 4.75 |
3;5;5;6
|
2;4;3;4
| null |
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
| null | null | 3.25 | 3.75 |
Reject
|
4;4;3;4
|
2;3;3;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
Explanation methods;Interpretability;Robustness;Adversarial attacks
| null | 2.5 | null | null |
iclr
| 0 | 0.707107 | null |
main
| 5.5 |
5;5;6;6
|
2;3;3;4
| null |
An evaluation of quality and robustness of smoothed explanations
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
2;2;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;2;4
| null | null | null |
Domain Generalization;IRM;Adversarial Training
| null | 1.75 | null | null |
iclr
| -0.648886 | -0.688247 | null |
main
| 6.25 |
5;6;6;8
|
4;3;4;3
| null |
Domain-wise Adversarial Training for Out-of-Distribution Generalization
| null | null | 3.5 | 4 |
Reject
|
4;5;4;3
|
0;2;2;3
|
null |
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France.
|
2022
| 2.6 |
https://iclr.cc/virtual/2022/poster/6785; None
| null | 0 | null | null | null |
2;3;2;3;3
| null |
Michael Arbel, Julien Mairal
|
https://iclr.cc/virtual/2022/poster/6785
|
bilevel optimization;stochastic optimization
| null | 2 | null |
https://openreview.net/forum?id=3PN4iyXBeF
|
iclr
| -0.6875 | 0.534522 | null |
main
| 5.8 |
3;6;6;6;8
|
2;4;3;4;3
|
https://iclr.cc/virtual/2022/poster/6785
|
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
| null | null | 3.2 | 2.8 |
Poster
|
3;3;3;3;2
|
2;2;2;2;2
|
null |
Columbia University, New York, NY, USA; Northeastern University, Boston, MA, USA
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/6005; None
| null | 0 | null | null | null |
2;2;3
| null |
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, Yun Fu
|
https://iclr.cc/virtual/2022/poster/6005
|
point cloud representation;local relation;mlp
| null | 1.333333 | null |
https://openreview.net/forum?id=3Pbra-_u76D
|
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
|
3;3;3
|
https://iclr.cc/virtual/2022/poster/6005
|
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
|
https://github.com/ma-xu/pointMLP-pytorch
| null | 3 | 3.333333 |
Poster
|
2;4;4
|
2;2;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;1;2;2;3
| null | null | null |
time-to-event analysis;survival analysis;semiparametric method;accelerated failure time
| null | 1.8 | null | null |
iclr
| -0.632195 | 0.889757 | null |
main
| 3.6 |
1;3;3;5;6
|
2;2;2;3;3
| null |
Towards simple time-to-event modeling: optimizing neural networks via rank regression
| null | null | 2.4 | 3.8 |
Reject
|
5;4;4;2;4
|
2;2;1;1;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;3;3;2
| null | null | null |
3D scene understanding;self-attention;transformer
| null | 2 | null | null |
iclr
| 0.333333 | -0.333333 | null |
main
| 5.25 |
5;5;5;6
|
4;3;3;3
| null |
Efficient Point Transformer for Large-scale 3D Scene Understanding
| null | null | 3.25 | 3.75 |
Withdraw
|
4;3;4;4
|
3;3;0;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
1;2;1;3
| null | null | null |
self-adaptive learning rate
| null | 1.75 | null | null |
iclr
| 0 | 0.683586 | null |
main
| 3.75 |
1;3;3;8
|
3;3;2;4
| null |
Differentiable Self-Adaptive Learning Rate
| null | null | 3 | 5 |
Reject
|
5;5;5;5
|
1;2;1;3
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null | null | null | 2 | null | null |
iclr
| -0.755929 | 0.981981 | null |
main
| 4.666667 |
3;5;6
|
2;3;4
| null |
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
| null | null | 3 | 3.333333 |
Withdraw
|
4;4;2
|
1;2;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null | null | null | 2.333333 | null | null |
iclr
| -0.188982 | 0.755929 | null |
main
| 4.666667 |
3;5;6
|
3;3;4
| null |
AID-PURIFIER: A LIGHT AUXILIARY NETWORK FOR BOOSTING ADVERSARIAL DEFENSE
| null | null | 3.333333 | 3.666667 |
Withdraw
|
4;3;4
|
2;2;3
|
null | null |
2022
| 1.2 | null | null | 0 | null | null | null |
1;1;1;2;1
| null | null | null |
vision transformers;medical image analysis
| null | 2.2 | null | null |
iclr
| 0.790569 | 0 | null |
main
| 4 |
3;3;3;5;6
|
4;2;2;2;3
| null |
Should we Replace CNNs with Transformers for Medical Images?
| null | null | 2.6 | 4.2 |
Reject
|
4;4;4;4;5
|
2;2;2;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;2;3;3;4
| null | null | null |
knowledge integration;graph convolution;language model;interpretation;knowledge graph;mutual information
| null | 3 | null | null |
iclr
| -0.80403 | 0.756644 | null |
main
| 5.6 |
3;5;6;6;8
|
2;3;3;2;4
| null |
Understanding Knowledge Integration in Language Models with Graph Convolutions
| null | null | 2.8 | 3.4 |
Reject
|
4;4;3;3;3
|
2;3;4;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Object cropping;Self-Supervised learning for multi-object dataset.
| null | 1.75 | null | null |
iclr
| -0.70014 | 0.781504 | null |
main
| 3.25 |
1;3;3;6
|
1;4;2;4
| null |
Object-Aware Cropping for Self-Supervised Learning
| null | null | 2.75 | 4.5 |
Withdraw
|
5;5;4;4
|
1;2;2;2
|
null |
Department of Computer Science, Emory University, Atlanta, GA 30329, USA; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
|
2022
| 2.25 |
https://iclr.cc/virtual/2022/poster/6099; None
| null | 0 | null | null | null |
2;1;3;3
| null |
Huan He, Shifan Zhao, Yuanzhe Xi, Joyce Ho, Yousef Saad
|
https://iclr.cc/virtual/2022/poster/6099
| null | null | 2.5 | null |
https://openreview.net/forum?id=3YqeuCVwy1d
|
iclr
| -0.207514 | 0.927173 | null |
main
| 6.25 |
5;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6099
|
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
|
https://github.com/hehuannb/GDA-AM
| null | 3.25 | 3.75 |
Poster
|
3;5;4;3
|
2;3;3;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
privacy preserving machine learning;private data release;privacy for computer vision
| null | 1.5 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
2;2;2;2
| null |
Sanitizer: Sanitizing data for anonymizing sensitive information
| null | null | 2 | 3.5 |
Withdraw
|
3;4;4;3
|
0;2;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
interaction identification;Monte Carlo tree search;decision tree
| null | 1.5 | null | null |
iclr
| 0 | 0.904534 | null |
main
| 4 |
3;3;5;5
|
2;2;4;3
| null |
Identifying Interactions among Categorical Predictors with Monte-Carlo Tree Search
| null | null | 2.75 | 3 |
Withdraw
|
3;3;3;3
|
2;0;2;2
|
null |
Meta AI; University of Edinburgh
|
2022
| 2.25 |
https://iclr.cc/virtual/2022/poster/7213; None
| null | 0 | null | null | null |
3;2;2;2
| null |
Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles
|
https://iclr.cc/virtual/2022/poster/7213
|
membership inference attack;privacy
| null | 2.5 | null |
https://openreview.net/forum?id=3eIrli0TwQ
|
iclr
| 0.662266 | 1 | null |
main
| 5.75 |
5;5;5;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/7213
|
On the Importance of Difficulty Calibration in Membership Inference Attacks
| null | null | 3.25 | 3.75 |
Poster
|
2;4;4;5
|
2;2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;3;2
| null | null | null |
Multi-Task learning;Classification
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
1;2;2;3
| null |
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
| null | null | 2 | 3.5 |
Reject
|
4;4;3;3
|
2;2;2;2
|
null |
Vrije Universiteit Amsterdam; Delft University of Technology; University of Amsterdam
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6763; None
| null | 0 | null | null | null |
2;2;3;3
| null |
David W. Romero, Robert-Jan Bruintjes, Jakub Tomczak, Erik Bekkers, Mark Hoogendoorn, Jan Gemert
|
https://iclr.cc/virtual/2022/poster/6763
|
Convolutional neural networks;learnable kernel size;continuous convolutional kernels;alias-free convolutional networks;implicit neural representations;resolution-agnostic representations;time series;sequential data;computer vision
| null | 2.25 | null |
https://openreview.net/forum?id=3jooF27-0Wy
|
iclr
| 0.57735 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
3;2;3;3
|
https://iclr.cc/virtual/2022/poster/6763
|
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes
| null | null | 2.75 | 3.5 |
Poster
|
3;3;4;4
|
2;2;3;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
vanilla transfer learning;topological machine learning;linear homeomorphism
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
2;3;4
| null |
Topological Vanilla Transfer Learning
| null | null | 3 | 3.666667 |
Withdraw
|
4;3;4
|
2;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;3;3;3
| null | null | null |
contrastive learning;f-divergence;mutual information
| null | 3 | null | null |
iclr
| -0.333333 | 0.870388 | null |
main
| 5.75 |
5;6;6;6
|
2;3;4;4
| null |
$f$-Mutual Information Contrastive Learning
| null | null | 3.25 | 3.75 |
Reject
|
4;3;4;4
|
3;3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;3;3;2
| null | null | null |
reinforcement learning;biomechanical model;cumulative fatigue;animation;bioinspired models;physics-based simulation;locomotion
| null | 1.75 | null | null |
iclr
| -0.870388 | -0.333333 | null |
main
| 5.75 |
5;6;6;6
|
3;3;3;1
| null |
Learning Symmetric Locomotion using Cumulative Fatigue for Reinforcement Learning
| null | null | 2.5 | 3.75 |
Reject
|
5;3;4;3
|
3;2;2;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
deep generative models;object-centric representation learning;segmentation
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
3;3;3;3
| null |
Learning Global Spatial Information for Multi-View Object-Centric Models
| null | null | 3 | 4 |
Reject
|
4;5;3;4
|
2;3;2;2
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
3;2;3
| null | null | null |
training data memorization;Byte-Pair Encoding;Transformers
| null | 3 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
|
3;3;3
| null |
How BPE Affects Memorization in Transformers
| null | null | 3 | 4.333333 |
Reject
|
5;4;4
|
3;3;3
|
null |
University of Cambridge; University of Cambridge, Invenia Labs
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/6738; None
| null | 0 | null | null | null |
2;3;2
| null |
Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan, Richard E Turner
|
https://iclr.cc/virtual/2022/poster/6738
|
conditional neural processes;neural processes;meta-learning;convolutional conditional neural processes;Gaussian neural processes
| null | 2.666667 | null |
https://openreview.net/forum?id=3pugbNqOh5m
|
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
|
4;4;4
|
https://iclr.cc/virtual/2022/poster/6738
|
Practical Conditional Neural Process Via Tractable Dependent Predictions
| null | null | 4 | 3.666667 |
Poster
|
3;4;4
|
2;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Systematic Generalization;Iterated Learning;Linear Neural Networks
| null | 2 | null | null |
iclr
| -0.57735 | -1 | null |
main
| 4.5 |
3;5;5;5
|
4;3;3;3
| null |
The Role of Learning Regime, Architecture and Dataset Structure on Systematic Generalization in Simple Neural Networks
| null | null | 3.25 | 3.5 |
Reject
|
4;3;3;4
|
2;2;2;2
|
null |
National University of Singapore, Singapore
|
2022
| 2.8 |
https://iclr.cc/virtual/2022/poster/6474; None
| null | 0 | null | null | null |
2;3;3;3;3
| null |
Yingtian Zou, Fusheng Liu, Qianxiao Li
|
https://iclr.cc/virtual/2022/poster/6474
|
Meta-Learning;Learning rate;Optimization
| null | 1.4 | null |
https://openreview.net/forum?id=3rULBvOJ8D2
|
iclr
| -0.748455 | -0.408248 | null |
main
| 5.4 |
5;5;5;6;6
|
3;3;4;3;3
|
https://iclr.cc/virtual/2022/poster/6474
|
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
| null | null | 3.2 | 2.6 |
Poster
|
4;2;4;1;2
|
2;3;2;0;0
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Graph Learning;Multilayer Perceptrons;Consistency Constraints
| null | 2.25 | null | null |
iclr
| 0.777778 | 0.555556 | null |
main
| 4.25 |
3;3;5;6
|
3;2;3;3
| null |
Beyond Message Passing Paradigm: Training Graph Data with Consistency Constraints
| null | null | 2.75 | 4.25 |
Withdraw
|
4;4;4;5
|
2;2;2;3
|
null |
Georgia Institute of Technology
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6679; None
| null | 0 | null | null | null |
2;3;4;3
| null |
Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
|
https://iclr.cc/virtual/2022/poster/6679
|
large learning rate;gradient descent;matrix factorization;implicit regularization;convergence;balancing;alignment
| null | 1.25 | null |
https://openreview.net/forum?id=3tbDrs77LJ5
|
iclr
| -0.19245 | -0.19245 | null |
main
| 6.75 |
5;6;8;8
|
4;3;4;3
|
https://iclr.cc/virtual/2022/poster/6679
|
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
| null | null | 3.5 | 3.5 |
Poster
|
4;3;3;4
|
2;3;0;0
|
null |
Department of Robotics, University of Maryland, College Park, MD, USA; Department of Computer Science, University of Maryland, College Park, MD, USA; Department of Mathematics, University of Maryland, College Park, MD, USA
|
2022
| 2 |
https://iclr.cc/virtual/2022/poster/6385; None
| null | 0 | null | null | null |
1;2;2;3
| null |
Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein
|
https://iclr.cc/virtual/2022/poster/6385
|
Deep learning;recurrent networks;depth
| null | 2.5 | null |
https://openreview.net/forum?id=3wNcr5nq56
|
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6385
|
The Uncanny Similarity of Recurrence and Depth
| null | null | 3 | 4.5 |
Poster
|
5;4;5;4
|
2;2;3;3
|
null |
Carnegie Mellon University; Google Brain, UC Berkeley
|
2022
| 4 |
https://iclr.cc/virtual/2022/poster/6206; None
| null | 0 | null | null | null |
4;4;4
| null |
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
|
https://iclr.cc/virtual/2022/poster/6206
|
unsupervised skill learning;reward-free RL;mutual information;DIAYN
| null | 0 | null |
https://openreview.net/forum?id=3wU2UX0voE
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
|
4;4;4
|
https://iclr.cc/virtual/2022/poster/6206
|
The Information Geometry of Unsupervised Reinforcement Learning
| null | null | 4 | 3.666667 |
Oral
|
4;3;4
| null |
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
deep learning;generalization error;stochastic gradient descent;functional form;hyperparameter;batch size;learning rate
| null | 2.5 | null | null |
iclr
| 0.57735 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
2;2;2;4
| null |
A Theoretical and Empirical Model of the Generalization Error under Time-Varying Learning Rate
| null | null | 2.5 | 3.75 |
Reject
|
3;4;4;4
|
2;2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null |
deep learning;tabular data;gradient boosting;Hopfield networks;associative memory
| null | 2.5 | null | null |
iclr
| 0.333333 | 0.57735 | null |
main
| 3.75 |
3;3;3;6
|
2;3;2;3
| null |
Hopular: Modern Hopfield Networks for Tabular Data
| null | null | 2.5 | 3.75 |
Withdraw
|
4;4;3;4
|
3;2;2;3
|
null |
Vector Institute, University of Waterloo; Vector Institute, University of Toronto, Nvidia; Vector Institute, University of Toronto
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6704; None
| null | 0 | null | null | null |
3;3;3;4
| null |
Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand
|
https://iclr.cc/virtual/2022/poster/6704
|
model-based reinforcement learning;reinforcment learning;objective mismatch;value function;sensitivity
| null | 2.75 | null |
https://openreview.net/forum?id=4-D6CZkRXxI
|
iclr
| 0 | 0 | null |
main
| 7 |
6;6;8;8
|
3;4;3;4
|
https://iclr.cc/virtual/2022/poster/6704
|
Value Gradient weighted Model-Based Reinforcement Learning
| null | null | 3.5 | 5 |
Spotlight
|
5;5;5;5
|
4;2;3;2
|
null |
Stanford University
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/5938; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher Manning, Jure Leskovec
|
https://iclr.cc/virtual/2022/poster/5938
|
language models;commonsense;question answering;knowledge graphs;KG augmentation
| null | 3 | null |
https://openreview.net/forum?id=41e9o6cQPj
|
iclr
| 0 | 0.57735 | null |
main
| 7 |
6;6;8;8
|
4;3;4;4
|
https://iclr.cc/virtual/2022/poster/5938
|
GreaseLM: Graph REASoning Enhanced Language Models
|
https://github.com/snap-stanford/GreaseLM
| null | 3.75 | 3.5 |
Spotlight
|
4;3;4;3
|
3;3;3;3
|
null |
Pennsylvania State University; MIT-IBM Watson AI Lab, IBM Research
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6770; None
| null | 0 | null | null | null |
2;3;3;4
| null |
Enyan Dai, Jie Chen
|
https://iclr.cc/virtual/2022/poster/6770
|
Anomaly Detection;Normalizing Flow;DAG;Multiple Time Series
| null | 2.5 | null |
https://openreview.net/forum?id=45L_dgP48Vd
|
iclr
| -0.57735 | 0.229416 | null |
main
| 7 |
6;6;8;8
|
1;4;3;3
|
https://iclr.cc/virtual/2022/poster/6770
|
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
| null | null | 2.75 | 3.75 |
Spotlight
|
4;4;4;3
|
1;3;3;3
|
null |
Brigham and Women’s Hospital, Channing Division of Network Medicine, Boston, MA, USA; Northeastern University, Department of Electrical and Computer Engineering, Boston, MA, USA
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6731; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Aria Masoomi, Davin Hill, Zhonghui Xu, Craig Hersh, Edwin Silverman, Peter Castaldi, Stratis Ioannidis, Jennifer Dy
|
https://iclr.cc/virtual/2022/poster/6731
|
Explainability;Shapley values;Interpretability;Directional interaction;feature interaction
| null | 2.75 | null |
https://openreview.net/forum?id=45Mr7LeKR9
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6731
|
Explanations of Black-Box Models based on Directional Feature Interactions
| null | null | 3.75 | 3.5 |
Spotlight
|
3;3;4;4
|
3;3;3;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
explainability;rationalism;deep learning
| null | 1.75 | null | null |
iclr
| 0.160128 | 0 | null |
main
| 5.5 |
3;5;6;8
|
3;3;3;3
| null |
Explanatory Learning: Beyond Empiricism in Neural Networks
| null | null | 3 | 3.5 |
Reject
|
4;2;4;4
|
2;2;3;0
|
null |
University of Georgia
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6139; None
| null | 0 | null | null | null |
3;3;2;3
| null |
Ronghang Zhu, Sheng Li
|
https://iclr.cc/virtual/2022/poster/6139
|
Single Domain Generalization;Open-Set Recognition
| null | 2.75 | null |
https://openreview.net/forum?id=48RBsJwGkJf
|
iclr
| 0 | 0 | null |
main
| 6 |
5;5;6;8
|
4;2;4;3
|
https://iclr.cc/virtual/2022/poster/6139
|
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
| null | null | 3.25 | 4.25 |
Poster
|
4;4;5;4
|
3;2;3;3
|
null |
MIT-IBM Watson AI Lab; Princeton University; Wechat AI; MIT
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6044; None
| null | 0 | null | null | null |
2;3;3;4
| null |
Shunyu Yao, Mo Yu, Yang Zhang, Karthik Narasimhan, Joshua B Tenenbaum, Chuang Gan
|
https://iclr.cc/virtual/2022/poster/6044
|
Emergent Language;Emergent Communication;Transfer Learning
| null | 3 | null |
https://openreview.net/forum?id=49A1Y6tRhaq
|
iclr
| 0.070535 | 0.916949 | null |
main
| 6.25 |
3;6;8;8
|
2;3;3;3
|
https://iclr.cc/virtual/2022/poster/6044
|
Linking Emergent and Natural Languages via Corpus Transfer
|
https://github.com/ysymyth/ec-nl
| null | 2.75 | 3.75 |
Spotlight
|
4;3;4;4
|
2;3;3;4
|
null |
State Key Laboratory for Novel Software Technology, Nanjing University; The Ohio State University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6294; None
| null | 0 | null | null | null |
2;3;3;4
| null |
Han-Jia Ye, Wei-Lun Chao
|
https://iclr.cc/virtual/2022/poster/6294
|
meta-learning;few-shot learning;classification;MAML
| null | 3.25 | null |
https://openreview.net/forum?id=49h_IkpJtaE
|
iclr
| -0.57735 | 0.57735 | null |
main
| 6.75 |
3;8;8;8
|
3;4;3;4
|
https://iclr.cc/virtual/2022/poster/6294
|
How to Train Your MAML to Excel in Few-Shot Classification
| null | null | 3.5 | 4.5 |
Poster
|
5;4;4;5
|
2;4;3;4
|
null |
Stanford University; Toyota Research Institute
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6413; None
| null | 0 | null | null | null |
2;3;3;2
| null |
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma
|
https://iclr.cc/virtual/2022/poster/6413
|
self-supervised learning;dataset imbalance;representation learning;long-tailed recognition
| null | 2.75 | null |
https://openreview.net/forum?id=4AZz9osqrar
|
iclr
| 0.870388 | 0.816497 | null |
main
| 7.25 |
5;8;8;8
|
2;4;3;3
|
https://iclr.cc/virtual/2022/poster/6413
|
Self-supervised Learning is More Robust to Dataset Imbalance
| null | null | 3 | 3.25 |
Spotlight
|
2;4;4;3
|
2;4;3;2
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null |
noisy labels;sample selection;high variances
| null | 3.333333 | null | null |
iclr
| 0.755929 | 0.944911 | null |
main
| 4.666667 |
3;5;6
|
3;4;4
| null |
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples
| null | null | 3.666667 | 4.333333 |
Withdraw
|
4;4;5
|
2;4;4
|
null |
Department of Statistics, UCLA; Department of Statistics, UCLA; Beijing Institute for General Artificial Intelligence (BIGAI); Google Research; Department of Statistics, UCLA; Salesforce Research
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6004; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Yingnian Wu
|
https://iclr.cc/virtual/2022/poster/6004
|
Generative models;energy-based models;MCMC
| null | 2.5 | null |
https://openreview.net/forum?id=4C93Qvn-tz
|
iclr
| -0.57735 | 0 | null |
main
| 7 |
6;6;8;8
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6004
|
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
| null | null | 3 | 3.5 |
Poster
|
4;4;2;4
|
2;2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;3;2
| null | null | null |
propagating distributions;uncertainty quantification
| null | 2.25 | null | null |
iclr
| -0.333333 | -0.333333 | null |
main
| 5.25 |
3;6;6;6
|
4;4;3;4
| null |
Propagating Distributions through Neural Networks
| null | null | 3.75 | 3.5 |
Reject
|
4;4;4;2
|
2;3;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
1;3;3;3
| null | null | null |
Equivariance;Invariance;Representation learning;Reinforcement learning;Symmetric MDPs;MDP homomorphism;Lie parameterization.
| null | 2.5 | null | null |
iclr
| 0 | 0.98644 | null |
main
| 6.75 |
5;6;8;8
|
2;3;4;4
| null |
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
| null | null | 3.25 | 3 |
Reject
|
3;3;3;3
|
2;2;4;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;2;1
| null | null | null | null | null | 2 | null | null |
iclr
| 0 | 0.707107 | null |
main
| 4 |
3;3;5;5
|
3;2;3;4
| null |
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data
| null | null | 3 | 3.5 |
Reject
|
3;4;3;4
|
2;2;2;2
|
null |
Facebook AI Research; CNRS-ISIR, Sorbonne University, Paris, France; Ubisoft
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6408; None
| null | 0 | null | null | null |
2;3;3;2
| null |
Jean-Baptiste Gaya, Laure Soulier, Ludovic Denoyer
|
https://iclr.cc/virtual/2022/poster/6408
|
Deep Reinforcement Learning;Online adaptation
| null | 3 | null |
https://openreview.net/forum?id=4Muj-t_4o4
|
iclr
| -0.140028 | 0.990148 | null |
main
| 5.75 |
3;6;6;8
|
2;3;3;4
|
https://iclr.cc/virtual/2022/poster/6408
|
Learning a subspace of policies for online adaptation in Reinforcement Learning
|
https://github.com/facebookresearch/salina/tree/main/salina_examples/rl/subspace_of_policies
| null | 3 | 4.5 |
Poster
|
5;4;4;5
|
2;4;3;3
|
null |
Institute of Information Science, Academia Sinica; Department of Computer Science, National Central University
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6458; None
| null | 0 | null | null | null |
3;3;2;2
| null |
Dennis Wu, Di-Nan Lin, Vincent Chen, Hung-Hsuan Chen
|
https://iclr.cc/virtual/2022/poster/6458
|
pipeline training;parallel training;backpropagation;associated learning
| null | 2.5 | null |
https://openreview.net/forum?id=4N-17dske79
|
iclr
| 0 | 0.57735 | null |
main
| 5.5 |
5;5;6;6
|
3;3;4;3
|
https://iclr.cc/virtual/2022/poster/6458
|
Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer
|
https://github.com/Hibb-bb/AL
| null | 3.25 | 3.5 |
Poster
|
4;3;4;3
|
3;3;2;2
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Disentangled Representations using Trained Models
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
1;4;3;3
| null | null | null |
Complementary Learning Systems;continual learning;Parallel Distributed Processing
| null | 2.25 | null | null |
iclr
| -0.688247 | 0.973329 | null |
main
| 4.75 |
3;5;5;6
|
2;3;3;4
| null |
Cognitively Inspired Learning of Incremental Drifting Concepts
| null | null | 3 | 3.5 |
Reject
|
4;3;4;3
|
1;3;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;2;3;4
| null | null | null | null | null | 2.25 | null | null |
iclr
| 0.57735 | 0.816497 | null |
main
| 6 |
5;5;6;8
|
3;3;4;4
| null |
Sharper Utility Bounds for Differentially Private Models
| null | null | 3.5 | 4 |
Reject
|
4;4;3;5
|
3;2;1;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null |
hierarchical vae;variational inference;multimodal learning
| null | 1.5 | null | null |
iclr
| -1 | 0.57735 | null |
main
| 5.5 |
5;5;6;6
|
3;3;3;4
| null |
Hierarchical Multimodal Variational Autoencoders
| null | null | 3.25 | 3.5 |
Reject
|
4;4;3;3
|
2;1;3;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
time series;forecasting;model selection;multiobjective optimization;transfer-learning;tabular dataset.
| null | 3.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
|
3;3;3
| null |
Multi-Objective Model Selection for Time Series Forecasting
| null | null | 3 | 4 |
Reject
|
4;4;4
|
3;3;4
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Sequential Recommendation;Self-supervised Learning;Contrastive Learning;Model Augmentation
| null | 2.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
|
3;3;3
| null |
Self-supervised Learning for Sequential Recommendation with Model Augmentation
| null | null | 3 | 4 |
Reject
|
4;4;4
|
2;2;3
|
null |
Yandex
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/6642; None
| null | 0 | null | null | null |
2;3;3
| null |
Timofey Grigoryev, Andrey Voynov, Artem Babenko
|
https://iclr.cc/virtual/2022/poster/6642
|
GAN;pretraining
| null | 3.333333 | null |
https://openreview.net/forum?id=4Ycr8oeCoIh
|
iclr
| 0.5 | -1 | null |
main
| 6.666667 |
6;6;8
|
4;4;3
|
https://iclr.cc/virtual/2022/poster/6642
|
When, Why, and Which Pretrained GANs Are Useful?
| null | null | 3.666667 | 3.666667 |
Poster
|
3;4;4
|
3;3;4
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null | null | null | 2.333333 | null | null |
iclr
| 0.944911 | -0.944911 | null |
main
| 4.666667 |
3;5;6
|
4;3;3
| null |
Learning Perceptual Compression of Facial Video
| null | null | 3.333333 | 3.666667 |
Withdraw
|
3;4;4
|
2;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
Koopman methods;sequence neural models;understanding deep learning
| null | 2 | null | null |
iclr
| -0.816497 | 0.406181 | null |
main
| 4.25 |
3;3;5;6
|
4;2;3;4
| null |
A Koopman Approach to Understanding Sequence Neural Models
| null | null | 3.25 | 4 |
Reject
|
4;5;4;3
|
2;2;2;2
|
null | null |
2022
| 3.333333 | null | null | 0 | null | null | null |
3;3;4
| null | null | null |
adversarial defense;collaboration;ensemble.
| null | 3 | null | null |
iclr
| -0.59604 | 0.737043 | null |
main
| 5.666667 |
3;6;8
|
1;4;3
| null |
Collaborate to Defend Against Adversarial Attacks
| null | null | 2.666667 | 4 |
Reject
|
5;3;4
|
2;3;4
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;3;2;2
| null | null | null |
Deep Learning;Combinatorial Optimization;Vehicle Routing
| null | 2.75 | null | null |
iclr
| 0.229416 | 0.688247 | null |
main
| 4.75 |
3;5;5;6
|
2;3;2;3
| null |
Supervised Permutation Invariant Networks for solving the CVRP with bounded fleet size
| null | null | 2.5 | 3.5 |
Reject
|
3;4;4;3
|
3;2;3;3
|
null | null |
2022
| 1.333333 | null | null | 0 | null | null | null |
2;1;1
| null | null | null |
policy learning;control;system identification;few-shot domain adaptation
| null | 1.666667 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
2;3;2
| null |
Jointly Learning Identification and Control for Few-Shot Policy Adaptation
| null | null | 2.333333 | 3.666667 |
Withdraw
|
4;3;4
|
2;2;1
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
SVD;Eigen;Interpretable;Neural Nets;Streaming;Big Data
| null | 2.25 | null | null |
iclr
| -0.718185 | 0.662266 | null |
main
| 4.75 |
3;5;5;6
|
3;3;3;4
| null |
Range-Net: A High Precision Neural SVD
| null | null | 3.25 | 3.5 |
Reject
|
5;4;2;3
|
2;2;2;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
Data poisoning;adversarial training;data privacy
| null | 3 | null | null |
iclr
| 0 | 0.57735 | null |
main
| 5.5 |
5;5;6;6
|
3;3;4;3
| null |
Fooling Adversarial Training with Induction Noise
| null | null | 3.25 | 4 |
Reject
|
4;4;4;4
|
3;2;3;4
|
null |
University of California, Los Angeles; Snap Inc.
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6711; None
| null | 0 | null | null | null |
1;3;3;4
| null |
Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah
|
https://iclr.cc/virtual/2022/poster/6711
|
Graph Neural Networks;Distillation;Node Classification;Model Inference Acceleration
| null | 2.75 | null |
https://openreview.net/forum?id=4p6_5HBWPCw
|
iclr
| -0.683586 | 0.676716 | null |
main
| 7.25 |
3;8;8;10
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6711
|
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation
|
https://github.com/snap-research/graphless-neural-networks
| null | 3.5 | 4 |
Poster
|
5;4;3;4
|
1;3;3;4
|
null | null |
2022
| 3.25 | null | null | 0 | null | null | null |
3;3;3;4
| null | null | null |
Fair node representations;fairness-aware graph data augmentations;unsupervised node representation learning;graph contrastive learning
| null | 3.25 | null | null |
iclr
| -0.939336 | 0.990148 | null |
main
| 5.75 |
3;6;6;8
|
2;3;3;4
| null |
Fair Node Representation Learning via Adaptive Data Augmentation
| null | null | 3 | 3.5 |
Reject
|
5;3;4;2
|
3;3;3;4
|
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