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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
University of Minnesota (UMN); Korea Advanced Institute of Science and Technology (KAIST); Pohang University of Science and Technology (POSTECH)
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6037; None
| null | 0 | null | null | null |
2;2;2;4
| null |
Jaehyung Kim, Dongyeop Kang, Sungsoo Ahn, Jinwoo Shin
|
https://iclr.cc/virtual/2022/poster/6037
|
NLP;data augmentation;learning augmentation policy;text classification
| null | 3.25 | null |
https://openreview.net/forum?id=Ucx3DQbC9GH
|
iclr
| 0 | 1 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6037
|
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
| null | null | 3.25 | 4 |
Poster
|
5;3;4;4
|
3;3;3;4
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
3;2;2;2
| null | null | null |
Vision Transformer;Adversarial Robustness
| null | 2.25 | null | null |
iclr
| -0.57735 | -0.301511 | null |
main
| 4 |
3;3;5;5
|
4;2;2;3
| null |
Are Vision Transformers Robust to Patch-wise Perturbations?
| null | null | 2.75 | 3.75 |
Withdraw
|
4;4;3;4
|
3;1;2;3
|
null |
Google Research, India; Princeton University; University of Washington, Seattle
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6354; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J Ratliff
|
https://iclr.cc/virtual/2022/poster/6354
|
Minimax optimization;two player zero sum games;generative adversarial networks;adversarial training
| null | 3 | null |
https://openreview.net/forum?id=UdxJ2fJx7N0
|
iclr
| -0.707107 | 1 | null |
main
| 7 |
6;6;8;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6354
|
Minimax Optimization with Smooth Algorithmic Adversaries
| null | null | 3.5 | 4 |
Poster
|
5;4;4;3
|
3;3;3;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
Open-ended data;machine learning;supervised learning;data conflict
| null | 2.75 | null | null |
iclr
| 0.132453 | 0.229416 | null |
main
| 6.25 |
5;6;6;8
|
4;3;3;4
| null |
Subjective Learning for Open-Ended Data
| null | null | 3.5 | 2.75 |
Reject
|
3;2;3;3
|
3;2;3;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
graph neural networks;adversarial robustness;low-rank approximation;spectral graph theory
| null | 2.666667 | null | null |
iclr
| 0 | 0.944911 | null |
main
| 4.666667 |
3;5;6
|
2;3;3
| null |
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks
| null | null | 2.666667 | 4 |
Reject
|
4;4;4
|
2;2;4
|
null |
University of Oxford
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6368; None
| null | 0 | null | null | null |
2;3;3;4
| null |
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi
|
https://iclr.cc/virtual/2022/poster/6368
|
unsupervised;generative;deep learning;segmentation;object segmentation
| null | 2.5 | null |
https://openreview.net/forum?id=Ug-bgjgSlKV
|
iclr
| 0.927173 | 0.927173 | null |
main
| 6.25 |
5;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6368
|
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models
| null | null | 3.25 | 3.25 |
Poster
|
3;3;3;4
|
2;3;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Deep Neural Networks;Gradient Boosting classifiers;NN architecture optimization
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
3;2;3;2
| null |
Gradient Boosting Neural Networks: GrowNet
| null | null | 2.5 | 4.25 |
Withdraw
|
4;4;5;4
|
2;2;2;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;2;1
| null | null | null |
missing data;modulation;DNN layer;neuromodulation;robustness
| null | 1.75 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
3;3;1;2
| null |
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
| null | null | 2.25 | 4.25 |
Reject
|
5;3;5;4
|
2;2;1;2
|
null | null |
2022
| 3.5 | null | null | 0 | null | null | null |
3;4;3;4
| null | null | null |
transformers;attention
| null | 2.75 | null | null |
iclr
| 0.5547 | 0.5547 | null |
main
| 5.5 |
3;5;6;8
|
3;4;3;4
| null |
Inductive Biases and Variable Creation in Self-Attention Mechanisms
| null | null | 3.5 | 3.5 |
Reject
|
3;4;3;4
|
3;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;3;1
| null | null | null |
neuro-symbolic AI;differentiable logic;object-centric reasoning
| null | 1 | null | null |
iclr
| 0.333333 | 0.57735 | null |
main
| 3.5 |
3;3;3;5
|
2;4;2;4
| null |
Neuro-Symbolic Forward Reasoning
| null | null | 3 | 3.75 |
Reject
|
4;3;4;4
|
2;1;0;1
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3;2
| null | null | null |
Graph;spike;energy;neural network
| null | 2.25 | null | null |
iclr
| 0 | 0.904534 | null |
main
| 4 |
3;3;5;5
|
2;1;3;3
| null |
Spiking Graph Convolutional Networks
| null | null | 2.25 | 3.5 |
Withdraw
|
5;2;3;4
|
2;2;3;2
|
null |
Paper under double-blind review
|
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial neural networks;Collaborative filtering;Empirical process;Recommender system;Two-tower model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
On strong convergence of the two-tower model for recommender system
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
molecule;molecular conformation;loss function
| null | 2 | null | null |
iclr
| -0.904534 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
3;3;4;3
| null |
Equivalent Distance Geometry Error for Molecular Conformation Comparison
| null | null | 3.25 | 4.25 |
Reject
|
5;5;4;3
|
3;0;2;3
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;1;2
| null | null | null | null | null | 2.5 | null | null |
iclr
| -0.333333 | -0.174078 | null |
main
| 3.5 |
3;3;3;5
|
4;2;4;3
| null |
DM-CT: Consistency Training with Data and Model Perturbation
| null | null | 3.25 | 4.25 |
Withdraw
|
5;4;4;4
|
3;2;2;3
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Nearest neighbor search;tree algorithms;graph cuts;random projections
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Cluster Tree for Nearest Neighbor Search
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Mila, Quebec Artificial Intelligence Institute, Universit ´e de Montr ´eal; Northwestern University; Google Brain; New York University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6966; None
| null | 0 | null | null | null |
3;3;3
| null |
Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Timotheus Cooijmans, Aaron Courville, Anna Huang, Jesse Engel
|
https://iclr.cc/virtual/2022/poster/6966
|
Audio Synthesis;Generative Model;Hierarchical;DDSP;Music;Audio;Structured Models
| null | 3.666667 | null |
https://openreview.net/forum?id=UseMOjWENv
|
iclr
| 0 | 0 |
https://midi-ddsp.github.io/
|
main
| 8 |
8;8;8
|
4;3;4
|
https://iclr.cc/virtual/2022/poster/6966
|
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
|
https://github.com/magenta/midi-ddsp
| null | 3.666667 | 4 |
Oral
|
4;3;5
|
4;3;4
|
null |
University of Warsaw, Warsaw, Poland; University of Warsaw, Google, Oxford, U.K.; DeepMind, London, U.K.
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/6011; None
| null | 0 | null | null | null |
3;3;2
| null |
Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski
|
https://iclr.cc/virtual/2022/poster/6011
|
Visual Reasoning;Visual Question Answering;Black Box Testing;Computer Vision
| null | 3 | null |
https://openreview.net/forum?id=UtGtoS4CYU
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
|
3;3;3
|
https://iclr.cc/virtual/2022/poster/6011
|
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models
| null | null | 3 | 4 |
Poster
|
4;4;4
|
3;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
Attention mechanism;sequence to sequence learning;reinforcement learning
| null | 2.25 | null | null |
iclr
| -0.333333 | 0.555556 | null |
main
| 4.25 |
3;3;5;6
|
3;2;3;3
| null |
Sharp Attention for Sequence to Sequence Learning
| null | null | 2.75 | 3.75 |
Reject
|
4;4;3;4
|
2;2;3;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;2;4
| null | null | null |
Deep Neural Networks;Network Pruning;Ramanujan Graphs;Eigenvalue bounds;Spectral Gap
| null | 2.25 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
|
3;3;3;3
| null |
Revisiting the Lottery Ticket Hypothesis: A Ramanujan Graph Perspective
| null | null | 3 | 3.5 |
Reject
|
5;3;3;3
|
2;2;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Reinforcement learning;partial differential equation;reduced order modeling;closure models;state prediction;state estimation;dynamic mode decomposition.
| null | 2.25 | null | null |
iclr
| 0.493742 | 0.308304 | null |
main
| 4.75 |
3;3;5;8
|
3;3;1;4
| null |
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems
| null | null | 2.75 | 3.75 |
Reject
|
4;3;4;4
|
2;2;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Physical simulations;graph neural network
| null | 2.25 | null | null |
iclr
| -0.132453 | 0.132453 | null |
main
| 4.75 |
3;5;5;6
|
3;3;4;3
| null |
Constraint-based graph network simulator
| null | null | 3.25 | 3.75 |
Reject
|
4;3;4;4
|
2;2;3;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;3;2;2
| null | null | null | null | null | 1.75 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
4;3;3;2
| null |
Object-Centric Neural Scene Rendering
| null | null | 3 | 3.5 |
Reject
|
4;3;3;4
|
1;2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
Min-max Optimization;Non-convex Optimization;Multi-agent learning;Multi-agent GANs;Game Theory;Duality Gap
| null | 1.25 | null | null |
iclr
| -0.333333 | -0.57735 | null |
main
| 5.25 |
3;6;6;6
|
4;3;3;4
| null |
Teamwork makes von Neumann work:Min-Max Optimization in Two-Team Zero-Sum Games
| null | null | 3.5 | 3.75 |
Reject
|
4;4;3;4
|
1;2;2;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
weakly-supervised learning;long-tailed learning;learning with noisy labels;semi-supervised learning;multi-label learning
| null | 2 | null | null |
iclr
| 1 | 0.5 | null |
main
| 3.666667 |
3;3;5
|
3;2;3
| null |
Robust Long-Tailed Learning under Label Noise
| null | null | 2.666667 | 4.333333 |
Withdraw
|
4;4;5
|
2;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;3;2;2
| null | null | null |
dataset bias;debiasing;representation bias
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
|
3;3;3;3
| null |
Mitigating Dataset Bias Using Per-Sample Gradients From A Biased Classifier
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
2;2;3;2
|
null |
Department of Computer Science, University of Maryland, College Park, MD 20740, USA
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6003; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Xiaoyu Liu, Jiahao Su, Furong Huang
|
https://iclr.cc/virtual/2022/poster/6003
|
Attention Modules;Transformers;Data-driven Model Design;Trainable Heads;Expressive Power;Tensor Methods.
| null | 2.5 | null |
https://openreview.net/forum?id=V0A5g83gdQ_
|
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
|
3;2;3;2
|
https://iclr.cc/virtual/2022/poster/6003
|
Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency
|
https://github.com/umd-huang-lab/tuformer
| null | 2.5 | 4 |
Poster
|
4;4;4;4
|
3;3;2;2
|
null |
Paper under double-blind review
|
2022
| 1.5 | null | null | 0 | null | null | null |
1;2;1;2
| null | null | null |
Importance Sampling;Normalizing Flows;High-Energy-Physics
| null | 2 | null | null |
iclr
| 0.57735 | 0.333333 | null |
main
| 3.5 |
3;3;3;5
|
4;4;3;4
| null |
Accelerating HEP simulations with Neural Importance Sampling
| null | null | 3.75 | 3.5 |
Reject
|
3;4;3;4
|
2;2;1;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
1;2;4
| null | null | null |
influence function;interpretability;model pruning;feature importance ranking
| null | 2 | null | null |
iclr
| 0 | 0.907841 | null |
main
| 4 |
1;3;8
|
1;3;4
| null |
Mask and Understand: Evaluating the Importance of Parameters
| null | null | 2.666667 | 5 |
Withdraw
|
5;5;5
|
1;2;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
1;3;3;3
| null | null | null |
Lasso;nonlinear regression;model selection
| null | 1.5 | null | null |
iclr
| -0.662266 | 0.899229 | null |
main
| 4.75 |
3;5;5;6
|
2;3;4;4
| null |
Provable Identifiability of ReLU Neural Networks via Lasso Regularization
| null | null | 3.25 | 2.75 |
Withdraw
|
3;3;3;2
|
1;2;3;0
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
1;3;3
| null | null | null |
Transformers;attention;redundancy;reuse;efficient
| null | 2 | null | null |
iclr
| -0.981981 | 0.654654 | null |
main
| 4 |
1;5;6
|
3;3;4
| null |
Leveraging Redundancy in Attention with Reuse Transformers
| null | null | 3.333333 | 4.333333 |
Withdraw
|
5;4;4
|
1;2;3
|
null |
Google Research
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6231; None
| null | 0 | null | null | null |
3;3;2;2
| null |
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi
|
https://iclr.cc/virtual/2022/poster/6231
|
Scaling law;Pre-training;Transfer learning;Large Scale;Vision Transformer;Few Shot;Empirical Investigation
| null | 3.25 | null |
https://openreview.net/forum?id=V3C8p78sDa
|
iclr
| 0 | -0.333333 | null |
main
| 7.5 |
6;8;8;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6231
|
Exploring the Limits of Large Scale Pre-training
| null | null | 3.75 | 3 |
Spotlight
|
3;3;3;3
|
3;3;3;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Machine Learning Interpretability;Bias;ImageNet;AlexNet;ResNet;VGG-16;Inception;CNNs;MNIST
| null | 2 | null | null |
iclr
| 0 | 0.57735 | null |
main
| 2.5 |
1;3;3;3
|
2;3;3;2
| null |
Beyond Pixels: A Sample Based Method for understanding the decisions of Neural Networks
| null | null | 2.5 | 3 |
Withdraw
|
3;2;4;3
|
2;2;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null |
Continual Learnig;Reinforcement learning;Class-incremental Continual Learning;Online Learning
| null | 2.75 | null | null |
iclr
| 0 | 0.648886 | null |
main
| 4.75 |
3;5;5;6
|
2;3;4;3
| null |
Closed-loop Control for Online Continual Learning
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
2;3;3;3
|
null | null |
2022
| 1.333333 | null | null | 0 | null | null | null |
2;1;1
| null | null | null |
COVID-19;Sequence Classification;Spike Sequences;k-mers;Deep Learning
| null | 1.666667 | null | null |
iclr
| 0.5 | 0.5 | null |
main
| 1.666667 |
1;1;3
|
2;1;2
| null |
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification
| null | null | 1.666667 | 4.666667 |
Reject
|
5;4;5
|
2;2;1
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
3;2;2
| null | null | null |
adversarial training;adversarial robustness
| null | 2.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
|
3;4;3
| null |
Biased Multi-Domain Adversarial Training
| null | null | 3.333333 | 3.666667 |
Withdraw
|
4;3;4
|
3;2;2
|
null | null |
2022
| 2.6 | null | null | 0 | null | null | null |
2;2;2;3;4
| null | null | null |
Learning under group actions;Neural networks;Group representations;Characters;Class functions
| null | 1.4 | null | null |
iclr
| -0.395285 | 0.408248 | null |
main
| 3.4 |
3;3;3;3;5
|
3;3;4;4;4
| null |
Conjugation Invariant Learning with Neural Networks
| null | null | 3.6 | 3 |
Reject
|
1;4;4;4;2
|
2;1;1;2;1
|
null |
Paper under double-blind review
|
2022
| 1.333333 | null | null | 0 | null | null | null |
1;2;1
| null | null | null | null | null | 1.666667 | null | null |
iclr
| -0.866025 | 0.944911 | null |
main
| 1.666667 |
1;1;3
|
2;1;4
| null |
Network robustness as a mathematical property: training, evaluation and attack
| null | null | 2.333333 | 4 |
Reject
|
4;5;3
|
1;2;2
|
null |
University of Texas at Austin; JD Explore Academy; Eindhoven University of Technology; University of Twente
|
2022
| 2.25 |
https://iclr.cc/virtual/2022/poster/6925; None
| null | 0 | null | null | null |
2;2;2;3
| null |
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Mocanu, Zhangyang Wang, Mykola Pechenizkiy
|
https://iclr.cc/virtual/2022/poster/6925
|
random pruning;sparse training;static sparse training;layer-wise sparsities;dynamic sparse training
| null | 3 | null |
https://openreview.net/forum?id=VBZJ_3tz-t
|
iclr
| -0.57735 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
4;3;4;4
|
https://iclr.cc/virtual/2022/poster/6925
|
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
|
https://github.com/VITA-Group/Random_Pruning
| null | 3.75 | 4.5 |
Poster
|
5;5;4;4
|
3;2;3;4
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;1;3;4
| null | null | null |
Generative Models;Causality
| null | 2.5 | null | null |
iclr
| 0 | 0.707107 | null |
main
| 4.5 |
3;3;6;6
|
2;3;4;3
| null |
CAGE: Probing Causal Relationships in Deep Generative Models
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
3;0;3;4
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
3;2;1;3
| null | null | null |
VAE;Variational Auto Encoder;Time Series;Data Generation;GAN;Generative Adversarial Network
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
4;3;3;3
| null |
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
| null | null | 3.25 | 4 |
Reject
|
4;4;4;4
|
3;2;2;2
|
null |
Google Research, Brain Team
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6174; None
| null | 0 | null | null | null |
3;2;3;3
| null |
Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi
|
https://iclr.cc/virtual/2022/poster/6174
| null | null | 3 | null |
https://openreview.net/forum?id=VFBjuF8HEp
|
iclr
| -1 | 0.57735 | null |
main
| 6.5 |
6;6;6;8
|
4;3;3;4
|
https://iclr.cc/virtual/2022/poster/6174
|
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
| null | null | 3.5 | 3.75 |
Poster
|
4;4;4;3
|
4;2;3;3
|
null | null |
2022
| 2.2 | null | null | 0 | null | null | null |
2;3;2;2;2
| null | null | null |
Recursive Operation;Vision Transformer;Efficient Model;Approximating Self-Attention;Sliced Group Self-Attention
| null | 2 | null | null |
iclr
| -0.102062 | 0.666667 | null |
main
| 4.8 |
3;5;5;5;6
|
2;3;3;2;3
| null |
Sliced Recursive Transformer
| null | null | 2.6 | 3.8 |
Withdraw
|
4;4;3;4;4
|
2;2;2;2;2
|
null |
Brookhaven National Laboratory; Language Technologies Institute, Carnegie Mellon University
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/5999; None
| null | 0 | null | null | null |
2;2;3
| null |
Zhiqing Sun, Yiming Yang, Shinjae Yoo
|
https://iclr.cc/virtual/2022/poster/5999
|
Sparse Attention;Transformer;Learning-to-Hash;Natural Language Processing
| null | 3 | null |
https://openreview.net/forum?id=VGnOJhd5Q1q
|
iclr
| -0.327327 | 0 | null |
main
| 6.333333 |
5;6;8
|
3;3;3
|
https://iclr.cc/virtual/2022/poster/5999
|
Sparse Attention with Learning to Hash
| null | null | 3 | 4 |
Poster
|
5;3;4
|
3;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
representation learning;structural bioinformatics;proteins
| null | 2.25 | null | null |
iclr
| 0.942809 | 0.738549 | null |
main
| 5 |
3;5;6;6
|
2;4;4;3
| null |
Contrastive Representation Learning for 3D Protein Structures
| null | null | 3.25 | 3.75 |
Reject
|
3;4;4;4
|
2;2;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
optimization;average-case;first-order;random matrix theory;nesterov
| null | 2.5 | null | null |
iclr
| 0.333333 | 0.333333 | null |
main
| 5.75 |
5;5;5;8
|
4;4;3;4
| null |
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
| null | null | 3.75 | 3.75 |
Reject
|
3;4;4;4
|
2;3;2;3
|
null |
UCLA Departments of Mathematics and Statistics and MPI MIS; UCLA Departments of Mathematics
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/7090; None
| null | 0 | null | null | null |
3;3;2;3
| null |
Benjamin Bowman, Guido Montufar
|
https://iclr.cc/virtual/2022/poster/7090
|
underparameterized regime;spectral bias;neural tangent kernel;implicit bias;implicit regularization;gradient flow
| null | 1 | null |
https://openreview.net/forum?id=VLgmhQDVBV
|
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;6;8
|
4;4;4;4
|
https://iclr.cc/virtual/2022/poster/7090
|
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
| null | null | 4 | 3 |
Poster
|
3;3;3;3
|
0;0;0;4
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
2;3;3;4
| null | null | null |
Data Poisoning;Poisoning Defenses;Adversarial Training;Empirical Defenses;Robustness;Security
| null | 2.75 | null | null |
iclr
| -0.594089 | 0.54886 | null |
main
| 5.75 |
3;6;6;8
|
2;4;4;3
| null |
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning
| null | null | 3.25 | 3 |
Reject
|
4;2;3;3
|
2;3;2;4
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null |
NMF;clustering;linking data;chaining data
| null | 1.666667 | null | null |
iclr
| 0 | 1 | null |
main
| 4.333333 |
3;5;5
|
2;3;3
| null |
Chaining Data - A Novel Paradigm in Artificial Intelligence Exemplified with NMF based Clustering
| null | null | 2.666667 | 4 |
Reject
|
4;4;4
|
1;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null |
Generalizable Person Re-Identification;Distributionally robust optimization
| null | 2.5 | null | null |
iclr
| -0.160128 | 0.919866 | null |
main
| 5.5 |
3;5;6;8
|
2;2;3;4
| null |
Generalizable Person Re-identification Without Demographics
| null | null | 2.75 | 3.75 |
Reject
|
4;4;3;4
|
2;3;2;3
|
null |
School of Software, BNRist, Tsinghua University, China
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6215; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Junguang Jiang, baixu chen, Jianmin Wang, Mingsheng Long
|
https://iclr.cc/virtual/2022/poster/6215
|
Object Detection;Domain Adaptation;Object Localization;Deep Learning;Transfer Learning
| null | 3.5 | null |
https://openreview.net/forum?id=VNqaB1g9393
|
iclr
| -0.57735 | 0.333333 | null |
main
| 7.5 |
6;8;8;8
|
3;3;4;3
|
https://iclr.cc/virtual/2022/poster/6215
|
Decoupled Adaptation for Cross-Domain Object Detection
| null | null | 3.25 | 3.5 |
Poster
|
4;3;3;4
|
3;3;4;4
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;1;2;2
| null | null | null |
Deep Reinforcement Learning;Fourier features;interference;sparsity;expressiveness;preprocessing
| null | 2.5 | null | null |
iclr
| 0.333333 | 1 | null |
main
| 3.5 |
3;3;3;5
|
2;2;2;4
| null |
Fourier Features in Reinforcement Learning with Neural Networks
| null | null | 2.5 | 3.75 |
Withdraw
|
3;4;4;4
|
2;3;2;3
|
null |
AI4Science, AMLab, University of Amsterdam, The Netherlands; AI4Science, AMLab, Anton Pannekoek Institute, University of Amsterdam, The Netherlands
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6606; None
| null | 0 | null | null | null |
3;3;3;3
| null |
David Ruhe, Patrick Forré
|
https://iclr.cc/virtual/2022/poster/6606
|
self-supervision;inference;state-space model;Kalman filter;recurrent neural network
| null | 2.25 | null |
https://openreview.net/forum?id=VPjw9KPWRSK
|
iclr
| -0.333333 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6606
|
Self-Supervised Inference in State-Space Models
| null | null | 3.75 | 3.25 |
Poster
|
4;3;3;3
|
2;2;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
graph tree networks;graph tree convolution networks;graph tree attention networks;GNNs
| null | 2 | null | null |
iclr
| -0.97714 | 0.526152 | null |
main
| 4.25 |
1;5;5;6
|
3;3;3;4
| null |
DEEP GRAPH TREE NETWORKS
| null | null | 3.25 | 4.25 |
Reject
|
5;4;4;4
|
1;2;2;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
3;2;2
| null | null | null |
spiking neural network;equilibrium state;spike-based training method;neuromorphic engineering
| null | 2.666667 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
|
3;3;3
| null |
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
| null | null | 3 | 3 |
Reject
|
3;2;4
|
3;3;2
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;4;2;2;4
| null | null | null |
Variational Autoencoders;Riemannian geometry
| null | 2.6 | null | null |
iclr
| -0.25 | 0.534522 | null |
main
| 6.4 |
6;6;6;6;8
|
2;3;4;3;4
| null |
A Geometric Perspective on Variational Autoencoders
| null | null | 3.2 | 3.2 |
Reject
|
4;3;3;3;3
|
2;3;2;2;4
|
null | null |
2022
| 2.4 | null | null | 0 | null | null | null |
2;3;2;3;2
| null | null | null |
Reinforcement learning;Optical simulation;Machine Learning for Optics
| null | 1.4 | null | null |
iclr
| -0.263523 | -0.133631 | null |
main
| 4.4 |
3;3;5;5;6
|
4;2;2;3;3
| null |
An Optics Controlling Environment and Reinforcement Learning Benchmarks
| null | null | 2.8 | 3 |
Withdraw
|
3;3;3;4;2
|
2;3;2;0;0
|
null |
Amazon; Shanghai Jiao Tong University; University of Maryland; Fudan University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6772; None
| null | 0 | null | null | null |
3;3;2;3;4
| null |
Yangkun Wang, Jiarui Jin, Weinan Zhang, Yang Yongyi, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf
|
https://iclr.cc/virtual/2022/poster/6772
| null | null | 2.2 | null |
https://openreview.net/forum?id=VTNjxbFRKly
|
iclr
| -0.771744 | 0.490098 | null |
main
| 5.8 |
5;5;5;6;8
|
4;3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6772
|
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
| null | null | 3.4 | 3.4 |
Poster
|
4;4;3;4;2
|
2;3;2;1;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
3;2;2;2
| null | null | null |
perceptual similarity metrics;computer vision;adversarial robustness;image quality assessment;transferable adversarial examples
| null | 2.75 | null | null |
iclr
| 0.345547 | -0.070535 | null |
main
| 4.75 |
3;3;5;8
|
4;4;2;4
| null |
Attacking Perceptual Similarity Metrics
| null | null | 3.5 | 4 |
Withdraw
|
4;3;5;4
|
3;2;3;3
|
null | null |
2022
| 1.666667 | null | null | 0 | null | null | null |
2;1;2
| null | null | null |
Computer Vision;Perceptual Similarity Metric;Image Quality Assessment;Robustness;Convolutional Neural Networks;Anti-aliasing
| null | 3 | null | null |
iclr
| -0.802955 | 0.993399 | null |
main
| 5.666667 |
3;6;8
|
2;3;4
| null |
Shift-tolerant Perceptual Similarity Metric
| null | null | 3 | 4.666667 |
Reject
|
5;5;4
|
2;3;4
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
no-box attack;training-free;hybrid image transformation
| null | 2.5 | null | null |
iclr
| -0.816497 | 0.544331 | null |
main
| 6 |
3;5;8;8
|
3;3;4;3
| null |
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
| null | null | 3.25 | 4.25 |
Reject
|
5;4;4;4
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
3D Point Cloud Classification and segmentation;Neural Architecture Search
| null | 2.5 | null | null |
iclr
| -0.547723 | 0.471405 | null |
main
| 5 |
3;5;6;6
|
3;3;3;4
| null |
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor
| null | null | 3.25 | 3.5 |
Reject
|
5;2;4;3
|
2;2;3;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
meta-learning;causality;intervention;memorization;overfitting
| null | 2.5 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;3
| null |
Minimizing Memorization in Meta-learning: A Causal Perspective
| null | null | 3 | 3.75 |
Withdraw
|
4;4;3;4
|
3;3;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;3;2;2;2
| null | null | null | null | null | 1.4 | null | null |
iclr
| 0 | 0.784465 | null |
main
| 3.4 |
3;3;3;3;5
|
1;2;3;2;4
| null |
Adversarial Robustness via Adaptive Label Smoothing
| null | null | 2.4 | 4 |
Withdraw
|
4;4;4;4;4
|
1;0;2;1;3
|
null |
Department of Computer Science, University of Georgia; Department of Computer Science, Rice University; Department of Computer Science and Engineering, Texas A&M University
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6059; None
| null | 0 | null | null | null |
2;3;2;3
| null |
Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu
|
https://iclr.cc/virtual/2022/poster/6059
|
XAI;GNN
| null | 2.75 | null |
https://openreview.net/forum?id=Ve0Wth3ptT_
|
iclr
| 1 | 1 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6059
|
DEGREE: Decomposition Based Explanation for Graph Neural Networks
| null | null | 3.25 | 3.25 |
Poster
|
3;3;3;4
|
3;3;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Pareto optimal;Multi-objective optimization;Multi-task learning;Evolutionary strategy
| null | 2.25 | null | null |
iclr
| -0.522233 | 0.522233 | null |
main
| 4.25 |
3;3;5;6
|
2;3;2;4
| null |
Self-evolutionary optimization for Pareto front learning
| null | null | 2.75 | 3.25 |
Withdraw
|
3;4;4;2
|
1;2;3;3
|
null |
Stanford University; Google Research
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6652; None
| null | 0 | null | null | null |
2;2;3;4
| null |
Honglin Yuan, Warren Morningstar, Lin Ning, Karan Singhal
|
https://iclr.cc/virtual/2022/poster/6652
|
Federated Learning;generalization;heterogeneity
| null | 2.5 | null |
https://openreview.net/forum?id=VimqQq-i_Q
|
iclr
| 0.57735 | 0.57735 | null |
main
| 6.5 |
6;6;6;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6652
|
What Do We Mean by Generalization in Federated Learning?
| null | null | 3.5 | 3.5 |
Poster
|
3;4;3;4
|
2;2;2;4
|
null |
Inria Paris, IRIF, Paris, France; Inria Sophia Antipolis, Sophia Antipolis, France
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/5981; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Arthur da Cunha, Emanuele Natale, Laurent Viennot
|
https://iclr.cc/virtual/2022/poster/5981
|
lottery ticket hypothesis;convolutional neural network;network pruning;random subset sum;random neural network
| null | 1 | null |
https://openreview.net/forum?id=Vjki79-619-
|
iclr
| 0.058026 | 0 | null |
main
| 6.75 |
5;6;8;8
|
4;4;4;4
|
https://iclr.cc/virtual/2022/poster/5981
|
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks
| null | null | 4 | 3.25 |
Poster
|
4;2;3;4
|
0;0;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
1;3;2;3
| null | null | null | null | null | 2.25 | null | null |
iclr
| -0.688247 | 0.688247 | null |
main
| 4.75 |
3;5;5;6
|
2;2;3;3
| null |
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
| null | null | 2.5 | 3.5 |
Reject
|
4;4;3;3
|
3;0;3;3
|
null |
Paper under double-blind review
|
2022
| 3 | null | null | 0 | null | null | null |
3;2;4
| null | null | null |
Anomaly Detection;Time-Series forecasting;Residual Temporal Convolutional Networks
| null | 2 | null | null |
iclr
| -0.5 | 0.5 | null |
main
| 5.333333 |
5;5;6
|
3;2;3
| null |
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection
| null | null | 2.666667 | 4.333333 |
Reject
|
5;4;4
|
2;2;2
|
null |
Renmin University of China; Northeastern University
|
2022
| 2.6 |
https://iclr.cc/virtual/2022/poster/6855; None
| null | 0 | null | null | null |
2;3;2;3;3
| null |
Chengping Rao, Pu Ren, Yang Liu, Hao Sun
|
https://iclr.cc/virtual/2022/poster/6855
|
Data-driven equation discovery;dynamical system modeling;physics-encoded learning
| null | 1.8 | null |
https://openreview.net/forum?id=Vog_3GXsgmb
|
iclr
| -0.910182 | 0.645497 | null |
main
| 5.4 |
5;5;5;6;6
|
2;3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6855
|
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
| null | null | 3 | 3.8 |
Poster
|
5;5;4;2;3
|
2;2;2;3;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
dimensionality reduction;manifold learning;image retrieval;document retrieval;PCA
| null | 2.75 | null | null |
iclr
| -0.816497 | 0.19245 | null |
main
| 4.25 |
3;3;5;6
|
4;3;3;4
| null |
TLDR: Twin Learning for Dimensionality Reduction
| null | null | 3.5 | 4 |
Withdraw
|
4;5;4;3
|
2;2;3;4
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3
| null | null | null |
Bayesian posterior;deep ensembles;submodular optimization
| null | 2.666667 | null | null |
iclr
| -1 | 0 |
Not provided
|
main
| 5 |
3;6;6
|
3;3;3
| null |
Greedy Bayesian Posterior Approximation with Deep Ensembles
|
Not provided
| null | 3 | 3.333333 |
Reject
|
4;3;3
|
2;2;4
|
null | null |
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6427; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang
|
https://iclr.cc/virtual/2022/poster/6427
|
Learning to Optimize;Knowledge Amalgamation;Stability-Aware Training
| null | 2.75 | null |
https://openreview.net/forum?id=VqzXzA9hjaX
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6427
|
Optimizer Amalgamation
| null | null | 3 | 3.75 |
Poster
|
4;3;4;4
|
2;3;3;3
|
null |
Stanford University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6736; None
| null | 0 | null | null | null |
3;2;3;4
| null |
Ashwin Paranjape, Omar Khattab, Christopher Potts, Peter Bailis, Christopher Manning
|
https://iclr.cc/virtual/2022/poster/6736
|
retrieval;generation;retrieval-augmented generation;open-ended generation;informative conversations;free-form QA;posterior distribution;ELBo
| null | 1.5 | null |
https://openreview.net/forum?id=Vr_BTpw3wz
|
iclr
| 0.57735 | 1 | null |
main
| 7 |
6;6;8;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6736
|
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
| null | null | 3.5 | 3.75 |
Poster
|
3;4;4;4
|
2;2;2;0
|
null |
Alibaba Group, Bellevue, WA, 98004, USA; Alibaba Group, Hangzhou, China
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/7022; None
| null | 0 | null | null | null |
2;2;3
| null |
Yichen Qian, Xiuyu Sun, Ming Lin, Zhiyu Tan, Rong Jin
|
https://iclr.cc/virtual/2022/poster/7022
|
Image compression;Entropy Model;Global Dependencies
| null | 2.666667 | null |
https://openreview.net/forum?id=VrjOFfcnSV8
|
iclr
| -0.866025 | 1 | null |
main
| 6.666667 |
6;6;8
|
3;3;4
|
https://iclr.cc/virtual/2022/poster/7022
|
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
|
https://github.com/damo-cv/entroformer
| null | 3.333333 | 4 |
Poster
|
4;5;3
|
2;3;3
|
null |
NAVER CLOVA; Samsung Research
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6553; None
| null | 0 | null | null | null |
2;3;4;3
| null |
baeseong park, Se Jung Kwon, Daehwan Oh, Byeonguk Kim, Dongsoo Lee
|
https://iclr.cc/virtual/2022/poster/6553
|
Sparse Neural Network;Fixed-to-fixed data compression;Unstructured Pruning
| null | 3 | null |
https://openreview.net/forum?id=Vs5NK44aP9P
|
iclr
| -0.132453 | 0.229416 | null |
main
| 6.25 |
5;6;6;8
|
4;3;3;4
|
https://iclr.cc/virtual/2022/poster/6553
|
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
| null | null | 3.5 | 3.25 |
Poster
|
3;4;3;3
|
2;3;4;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
3;2;2
| null | null | null |
multimodal learning;missing modality;maximum likelihood estimation
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
3;3;2
| null |
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
| null | null | 2.666667 | 4 |
Reject
|
4;4;4
|
2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Temporal Action Localization;Transformer;Global Contextual Learning;Self-attention Learning
| null | 2.25 | null | null |
iclr
| -0.98644 | -0.333333 | null |
main
| 4.25 |
3;3;5;6
|
3;3;2;3
| null |
Temporal Action Localization with Global Segmentation Mask Transformers
| null | null | 2.75 | 4.25 |
Withdraw
|
5;5;4;3
|
2;2;3;2
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;2;4
| null | null | null | null | null | 3 | null | null |
iclr
| 1 | 0.5 | null |
main
| 5.333333 |
5;5;6
|
2;3;3
| null |
Protecting Your NLG Models with Semantic and Robust Watermarks
| null | null | 2.666667 | 3.333333 |
Withdraw
|
3;3;4
|
4;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
least squares;convex optimization;iterative Hessian sketch;subspace embedding;learning-augmented sketch
| null | 2.5 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
4;4;4;3
| null |
Learning-Augmented Sketches for Hessians
| null | null | 3.75 | 3 |
Reject
|
3;3;3;3
|
3;2;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
label smoothing;knowledge distillation;systematic diffusion;semantically similar classes
| null | 2.5 | null | null |
iclr
| 0.57735 | -0.57735 | null |
main
| 5.75 |
5;6;6;6
|
4;3;4;3
| null |
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation
| null | null | 3.5 | 3.5 |
Withdraw
|
3;4;3;4
|
3;3;0;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null | null | null | 2.5 | null | null |
iclr
| -0.160128 | -0.83205 | null |
main
| 5.5 |
3;5;6;8
|
4;4;3;3
| null |
Safe Opponent-Exploitation Subgame Refinement
| null | null | 3.5 | 3.75 |
Reject
|
4;4;3;4
|
2;2;2;4
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
2;1;1;2
| null | null | null |
Language modeling ·Document embedding ·Natural language processing ·Machine learning
| null | 1.5 | null | null |
iclr
| -0.333333 | 0.870388 | null |
main
| 3.5 |
3;3;3;5
|
2;2;3;4
| null |
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING
| null | null | 2.75 | 4.25 |
Reject
|
4;5;4;4
|
2;1;1;2
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
3;2;3
| null | null | null |
Self-Supervised Learning;Speech Processing;Representation Learning
| null | 2 | null | null |
iclr
| -0.5 | 1 | null |
main
| 4 |
3;3;6
|
2;2;3
| null |
Pretext Tasks Selection for Multitask Self-Supervised Speech Representation Learning
| null | null | 2.333333 | 3.666667 |
Reject
|
3;5;3
|
2;2;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null |
Learned Index;Dynamic $\epsilon$
| null | 2.333333 | null | null |
iclr
| -0.5 | 0.5 | null |
main
| 4.666667 |
3;3;8
|
3;2;3
| null |
Learned Index with Dynamic $\epsilon$
| null | null | 2.666667 | 3.333333 |
Reject
|
3;4;3
|
3;2;2
|
null |
Carnegie Mellon University (CMU); Not provided; Google Research, previously DeepMind; Google Research
|
2022
| 2.25 |
https://iclr.cc/virtual/2022/poster/7043; None
| null | 0 | null | null | null |
2;2;2;3
| null |
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler
|
https://iclr.cc/virtual/2022/poster/7043
|
Natural Language Processing;Transfer Learning;Multi-task Learning
| null | 3 | null |
https://openreview.net/forum?id=Vzh1BFUCiIX
|
iclr
| 0.555556 | 0.777778 | null |
main
| 6.75 |
5;6;8;8
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/7043
|
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
| null | null | 3.75 | 4.25 |
Poster
|
4;4;5;4
|
2;3;3;4
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
1;2;2;2
| null | null | null |
Multi-Agent Reinforcement Learning;Offline Reinforcement Learning;Machine Learning
| null | 2 | null | null |
iclr
| -0.816497 | 0.333333 | null |
main
| 3.5 |
3;3;3;5
|
3;3;1;3
| null |
Offline Pre-trained Multi-Agent Decision Transformer
| null | null | 2.5 | 4 |
Reject
|
5;4;4;3
|
3;2;0;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
Differential Privacy;Deep Learning;DP-SGD
| null | 2.5 | null | null |
iclr
| -0.57735 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
2;3;3;3
| null |
Dynamic Differential-Privacy Preserving SGD
| null | null | 2.75 | 3.75 |
Withdraw
|
4;4;3;4
|
2;2;3;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null |
Vision Transformers;CNN;expressive power;multi-head self-attention
| null | 2 | null | null |
iclr
| -0.866025 | 0.654654 | null |
main
| 3 |
1;3;5
|
1;4;3
| null |
Can Vision Transformers Perform Convolution?
| null | null | 2.666667 | 4.666667 |
Withdraw
|
5;5;4
|
1;3;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;1;1;4
| null | null | null |
uncertainty;epistemic uncertainty;uncertainty calibration
| null | 2.5 | null | null |
iclr
| 0.889297 | 0.931614 | null |
main
| 5.25 |
3;5;5;8
|
1;3;3;4
| null |
On the Practicality of Deterministic Epistemic Uncertainty
| null | null | 2.75 | 4.25 |
Reject
|
4;4;4;5
|
2;2;3;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
GNN;graph neural network;graphs;scalability;batching;local clustering
| null | 2.666667 | null | null |
iclr
| 0 | 0.866025 | null |
main
| 5.333333 |
5;5;6
|
2;3;4
| null |
Locality-Based Mini Batching for Graph Neural Networks
| null | null | 3 | 4 |
Reject
|
4;4;4
|
3;3;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;3;2
| null | null | null |
Masked speech modeling (MSM);Data selection;Self-supervision;ASR;Speech recognition
| null | 2.25 | null | null |
iclr
| 0.688247 | 0.662266 | null |
main
| 4.75 |
3;5;5;6
|
3;3;3;4
| null |
Ask2Mask: Guided Data Selection for Masked Speech Modeling
| null | null | 3.25 | 4.5 |
Reject
|
4;5;4;5
|
2;3;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
1;2;3;3
| null | null | null |
Imbalanced data classification;Evaluation metrics;Log parsing;Sentiment analysis;URL classification
| null | 2 | null | null |
iclr
| -0.852803 | 0.333333 | null |
main
| 4 |
1;3;6;6
|
2;4;3;3
| null |
Class-Weighted Evaluation Metrics for Imbalanced Data Classification
| null | null | 3 | 4.25 |
Reject
|
5;5;4;3
|
2;1;2;3
|
null |
Michigan State University; University of Minnesota; UC Santa Barbara; JD AI Research
|
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/6612; None
| null | 0 | null | null | null |
3;4;4;3
| null |
Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu
|
https://iclr.cc/virtual/2022/poster/6612
|
Zeroth-Order Optimization;Black-Box Defense;Gradient-Free;Adversarial Robustness;Certified Defense
| null | 3.25 | null |
https://openreview.net/forum?id=W9G_ImpHlQd
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8;8
|
3;4;3;3
|
https://iclr.cc/virtual/2022/poster/6612
|
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
|
https://github.com/damon-demon/Black-Box-Defense
| null | 3.25 | 4 |
Spotlight
|
3;4;5;4
|
3;3;4;3
|
null |
IvI, University of Amsterdam; Unbabel; Instituto de Telecomunicações, Instituto Superior Técnico (Lisbon ELLIS Unit); ILLC, University of Amsterdam
|
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/5895; None
| null | 0 | null | null | null |
3;3;4;4
| null |
António Farinhas, Wilker Aziz, Vlad Niculae, Andre Martins
|
https://iclr.cc/virtual/2022/poster/5895
| null | null | 3 | null |
https://openreview.net/forum?id=WAid50QschI
|
iclr
| 0.662266 | 1 |
Not provided
|
main
| 7.5 |
6;8;8;8
|
2;4;4;4
|
https://iclr.cc/virtual/2022/poster/5895
|
Sparse Communication via Mixed Distributions
|
Not provided
| null | 3.5 | 3.25 |
Oral
|
2;3;3;5
|
3;3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null | null | null | 2.75 | null | null |
iclr
| -0.471405 | 0.408248 | null |
main
| 5 |
3;5;6;6
|
3;4;3;4
| null |
Teacher's pet: understanding and mitigating biases in distillation
| null | null | 3.5 | 4.75 |
Reject
|
5;5;4;5
|
2;2;3;4
|
null |
University of Amsterdam; Qualcomm AI Research∗, University of Amsterdam
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6098; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Gabriele Cesa, Leon Lang, Maurice Weiler
|
https://iclr.cc/virtual/2022/poster/6098
|
equivariance;3D;geometric deep learning;isometries;steerable CNN
| null | 2.5 | null |
https://openreview.net/forum?id=WE4qe9xlnQw
|
iclr
| -0.57735 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/6098
|
A Program to Build E(N)-Equivariant Steerable CNNs
| null | null | 3.75 | 2.5 |
Poster
|
2;3;3;2
|
3;2;3;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
sequence representation learning;audio search;music retrieval
| null | 1.75 | null | null |
iclr
| -0.870388 | 0.301511 | null |
main
| 3.5 |
1;3;5;5
|
2;3;2;3
| null |
Seq2Tok: Deep Sequence Tokenizer for Retrieval
| null | null | 2.5 | 3.25 |
Withdraw
|
4;3;3;3
|
1;2;2;2
|
null |
The Chinese University of Hong Kong, Shenzhen; Georgia Institute of Technology; The University of Texas at Austin; Xidian University
|
2022
| 2.25 |
https://iclr.cc/virtual/2022/poster/6720; None
| null | 0 | null | null | null |
2;2;2;3
| null |
Dandan Guo, Long Tian, Minghe Zhang, Mingyuan Zhou, Hongyuan Zha
|
https://iclr.cc/virtual/2022/poster/6720
|
Summary Networks;Distribution Matching;Optimal Transport;Few-shot Classification;Meta Generative Models
| null | 2.75 | null |
https://openreview.net/forum?id=WH6u2SvlLp4
|
iclr
| 0 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
4;3;4;4
|
https://iclr.cc/virtual/2022/poster/6720
|
Learning Prototype-oriented Set Representations for Meta-Learning
| null | null | 3.75 | 3 |
Poster
|
4;2;3;3
|
3;2;3;3
|
null |
The University of Tokyo; RIKEN; The Chinese University of Hong Kong; The University of British Columbia
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6108; None
| null | 0 | null | null | null |
3;3;4;3
| null |
Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
|
https://iclr.cc/virtual/2022/poster/6108
|
unsupervised federated learning;unlabeled data;class prior shift
| null | 3 | null |
https://openreview.net/forum?id=WHA8009laxu
|
iclr
| 0.870388 | 0.57735 | null |
main
| 7.5 |
6;8;8;8
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6108
|
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
|
https://github.com/lunanbit/FedUL
| null | 3.5 | 3.25 |
Poster
|
2;3;4;4
|
2;3;4;3
|
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