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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6202; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Alex Morehead, Chen Chen, Jianlin Cheng
|
https://iclr.cc/virtual/2022/poster/6202
|
Geometric Deep Learning;Graph Transformers;Protein Bioinformatics;Invariance
| null | 2.5 | null |
https://openreview.net/forum?id=CS4463zx6Hi
|
iclr
| 0.927173 | 0.57735 | null |
main
| 5.75 |
5;6;6;6
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6202
|
Geometric Transformers for Protein Interface Contact Prediction
|
https://github.com/BioinfoMachineLearning/DeepInteract
| null | 3.5 | 3.75 |
Poster
|
2;5;4;4
|
1;3;3;3
|
null |
School of Microelectronics, Fudan University, Shanghai, China; Microsoft Research Asia, Shanghai, China; Department of Electrical Engineering and Computer Science, University of Michigan, Michigan, United States; Department of Computer Science, University of Colorado Boulder, Boulder, United States; Department of Computing, Imperial College London, London, United Kingdom; China and Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6663; None
| null | 0 | null | null | null |
3;3;3;4
| null |
Yixuan Chen, Yubin Shi, Dongsheng Li, Yujiang Wang, Mingzhi Dong, Yingying Zhao, Robert Dick, Qin Lv, Fan Yang, Li Shang
|
https://iclr.cc/virtual/2022/poster/6663
|
disentanglement;representation learning;compositional
| null | 3 | null |
https://openreview.net/forum?id=CSfcOznpDY
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/6663
|
Recursive Disentanglement Network
| null | null | 3.75 | 3.75 |
Poster
|
5;4;3;3
|
3;3;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
collaboration equilibrium
| null | 2.5 | null | null |
iclr
| -0.12666 | 0.080845 | null |
main
| 5.25 |
3;5;5;8
|
4;4;3;4
| null |
Learning to Collaborate
| null | null | 3.75 | 2.75 |
Reject
|
2;3;4;2
|
2;2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null | null | null | 2.75 | null | null |
iclr
| -0.727607 | 0.889297 | null |
main
| 5.25 |
3;5;5;8
|
3;3;3;4
| null |
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate
| null | null | 3.25 | 3.25 |
Reject
|
4;3;3;3
|
3;3;2;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null | null | null | 2 | null | null |
iclr
| 0.5 | 0.866025 | null |
main
| 4.333333 |
3;5;5
|
2;4;3
| null |
Training with Worst-Case Distributional Shift causes Overestimation and Inaccuracies in State-Action Value Functions
| null | null | 3 | 4.333333 |
Withdraw
|
4;5;4
|
2;2;2
|
null |
NAVER LABS Europe
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/6121; None
| null | 0 | null | null | null |
2;3;2
| null |
Ginger Delmas, Rafael S Rezende, Gabriela Csurka, Diane Larlus
|
https://iclr.cc/virtual/2022/poster/6121
| null | null | 2 | null |
https://openreview.net/forum?id=CVfLvQq9gLo
|
iclr
| 0 | 1 | null |
main
| 7.333333 |
6;8;8
|
3;4;4
|
https://iclr.cc/virtual/2022/poster/6121
|
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
|
https://github.com/naver/artemis
| null | 3.666667 | 4 |
Poster
|
4;4;4
|
0;4;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;3;2
| null | null | null |
Neural Architecture Search;Bit-wise Shift and Add;Hardware Acceleration;Multiplication-Reduced Networks
| null | 2.75 | null | null |
iclr
| 0 | 0.333333 | null |
main
| 5.75 |
5;6;6;6
|
3;3;4;3
| null |
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
| null | null | 3.25 | 4 |
Reject
|
4;5;4;3
|
2;3;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null |
meta-learning
| null | 2.5 | null | null |
iclr
| 0.333333 | 1 | null |
main
| 5.25 |
5;5;5;6
|
2;2;2;3
| null |
Multi-Subspace Structured Meta-Learning
| null | null | 2.25 | 3.75 |
Withdraw
|
4;3;4;4
|
2;3;2;3
|
null |
University of Tübingen; University of Tübingen & IMPRS-IS
|
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/5966; None
| null | 0 | null | null | null |
3;4;3;4
| null |
Kristof Meding, Luca Schulze Buschoff, Robert Geirhos, Felix Wichmann
|
https://iclr.cc/virtual/2022/poster/5966
|
CNNs;Cognitive Science;Vision Science;Psychophysics;Neuroscience;Visual perception;Inductive bias;ImageNet;CIFAR;RSA;Representation similarity analysis;Error consistency;Datasets
| null | 3.25 | null |
https://openreview.net/forum?id=C_vsGwEIjAr
|
iclr
| -0.57735 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
2;4;4;4
|
https://iclr.cc/virtual/2022/poster/5966
|
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
| null | null | 3.5 | 4 |
Poster
|
3;5;5;3
|
3;3;3;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;3;2;2
| null | null | null | null | null | 2.5 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
2;3;3;3
| null |
DAAS: Differentiable Architecture and Augmentation Policy Search
| null | null | 2.75 | 4.5 |
Withdraw
|
4;5;5;4
|
2;3;2;3
|
null | null |
2022
| 1.666667 | null | null | 0 | null | null | null |
3;1;1
| null | null | null |
Video Prediction;Non-Parametric Models;Gaussian Processes;Confidence Aware
| null | 1.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
3;3;4
| null |
Towards Non-Parametric Models for Confidence Aware Video Prediction on Smooth Dynamics
| null | null | 3.333333 | 3 |
Withdraw
|
3;2;4
|
2;1;1
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Universit ´e Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6309; None
| null | 0 | null | null | null |
3;3;4;2
| null |
Louis Rouillard, Demian Wassermann
|
https://iclr.cc/virtual/2022/poster/6309
|
Bayesian inference;Hierarchical Bayesian Models;structured Variational Inference;Simulation Based Inference;Inference amortization;Neuroimaging
| null | 1 | null |
https://openreview.net/forum?id=CgIEctmcXx1
|
iclr
| 0.229416 | 1 | null |
main
| 5.5 |
5;5;6;6
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6309
|
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
| null | null | 3.5 | 3.25 |
Poster
|
3;3;2;5
|
2;2;0;0
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Text-to-Speech;Speech Synthesis;DDPM;TTS;Untranscribed speech
| null | 2.5 | null | null |
iclr
| -0.080845 | 0.080845 | null |
main
| 5.25 |
3;5;5;8
|
3;2;3;3
| null |
Guided-TTS:Text-to-Speech with Untranscribed Speech
| null | null | 2.75 | 4.25 |
Reject
|
4;5;4;4
|
1;3;3;3
|
null | null |
2022
| 1.333333 | null | null | 0 | null | null | null |
2;1;1
| null | null | null |
Deep Learning;Data Augmentation;Image Classification;Supervised Learning;Generative models
| null | 1.666667 | null | null |
iclr
| -0.5 | 0.5 | null |
main
| 2.333333 |
1;3;3
|
3;3;4
| null |
Mistake-driven Image Classification with FastGAN and SpinalNet
| null | null | 3.333333 | 4.666667 |
Reject
|
5;5;4
|
2;2;1
|
null |
Stanford University; Stanford University | Meta AI
|
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/6283; None
| null | 0 | null | null | null |
3;4;3;4
| null |
Brett Larsen, Stanislav Fort, Nic Becker, Surya Ganguli
|
https://iclr.cc/virtual/2022/poster/6283
|
loss landscape;high-dimensional geometry;random hyperplanes;optimization
| null | 3.25 | null |
https://openreview.net/forum?id=ChMLTGRjFcU
|
iclr
| 0 | 0.333333 | null |
main
| 6.5 |
6;6;6;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6283
|
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
| null | null | 3.75 | 4 |
Poster
|
4;4;4;4
|
3;3;3;4
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null |
activation functions;logits
| null | 2.25 | null | null |
iclr
| 0.333333 | 0.777778 | null |
main
| 4.25 |
3;3;5;6
|
3;3;3;4
| null |
Logical Activation Functions: Logit-space equivalents of Boolean Operators
| null | null | 3.25 | 3.25 |
Reject
|
3;3;4;3
|
2;2;2;3
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;2;4
| null | null | null |
Differential privacy;complex-valued deep learning
| null | 3.333333 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
|
4;4;4
| null |
Complex-valued deep learning with differential privacy
| null | null | 4 | 3.666667 |
Reject
|
4;3;4
|
2;4;4
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;1;3;3
| null | null | null |
Out-of-Distribution Generalization;Generalization;Spurious Correlations;Bi-level Optimization
| null | 2.25 | null | null |
iclr
| 1 | 0.57735 | null |
main
| 4.5 |
3;5;5;5
|
2;3;2;3
| null |
BLOOD: Bi-level Learning Framework for Out-of-distribution Generalization
| null | null | 2.5 | 3.75 |
Reject
|
3;4;4;4
|
2;2;2;3
|
null |
Huawei UK R&D; Huawei UK R&D and Honorary Lecturer at UCL; Huawei UK R&D, University of Cambridge; University College London
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6691; None
| null | 0 | null | null | null |
3;3;2;4;3
| null |
Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou Ammar
|
https://iclr.cc/virtual/2022/poster/6691
|
context-dependent Reinforcement Learning;model-based reinforcement learning;hierarchical Dirichlet process
| null | 1.8 | null |
https://openreview.net/forum?id=CmsfC7u054S
|
iclr
| -0.763763 | 0.166667 | null |
main
| 6.8 |
6;6;6;8;8
|
4;3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6691
|
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
| null | null | 3.4 | 3.2 |
Poster
|
4;4;3;3;2
|
2;2;2;3;0
|
null | null |
2022
| 3.5 | null | null | 0 | null | null | null |
3;3;4;4
| null | null | null |
neural networks;uncertainity calibration;out of distribution detection
| null | 3 | null | null |
iclr
| 0.57735 | 1 | null |
main
| 5.5 |
5;5;6;6
|
3;3;4;4
| null |
AdaFocal: Calibration-aware Adaptive Focal Loss
| null | null | 3.5 | 3.25 |
Reject
|
3;3;3;4
|
3;2;3;4
|
null |
Institute for AI, Peking University & BIGAI; University College London; Huawei Technologies; Shanghaitech University; Imperial College London
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6617; None
| null | 0 | null | null | null |
3;3;3;4
| null |
David Mguni, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Oliver Slumbers, Feifei Tong, , Jiangcheng Zhu, Yaodong Yang, Jun Wang
|
https://iclr.cc/virtual/2022/poster/6617
|
multi-agent;reinforcement learning;intrinsic rewards;exploration
| null | 2.25 | null |
https://openreview.net/forum?id=CpTuR2ECuW
|
iclr
| -0.942809 | 0.942809 | null |
main
| 6 |
5;5;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6617
|
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
| null | null | 3.25 | 3.75 |
Poster
|
4;4;4;3
|
3;2;0;4
|
null | null |
2022
| 1.8 | null | null | 0 | null | null | null |
1;2;2;1;3
| null | null | null |
characteristics;characteristics extraction;characteristics evaluation;voice characteristics;label refining;refined labels;semi-supervision
| null | 1.6 | null | null |
iclr
| -0.25 | 0.612372 | null |
main
| 3.4 |
3;3;3;3;5
|
3;2;2;2;3
| null |
Label Refining: a semi-supervised method to extract voice characteristics without ground truth
| null | null | 2.4 | 3.2 |
Reject
|
3;4;3;3;3
|
1;2;2;1;2
|
null |
Department of Informatics, University of Oslo, Norway; Department of Computer Science, University of Oxford, UK
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/5985; None
| null | 0 | null | null | null |
2;2;3;3
| null |
David Jaime Tena Cucala, Bernardo Grau, Egor Kostylev, Boris Motik
|
https://iclr.cc/virtual/2022/poster/5985
| null | null | 2.25 | null |
https://openreview.net/forum?id=CrCvGNHAIrz
|
iclr
| -0.927173 | 0.688247 | null |
main
| 6.25 |
5;6;6;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/5985
|
Explainable GNN-Based Models over Knowledge Graphs
| null | null | 3.5 | 3.75 |
Poster
|
4;4;4;3
|
2;2;3;2
|
null | null |
2022
| 1.2 | null | null | 0 | null | null | null |
1;1;1;1;2
| null | null | null |
Conversational AI;Lifelong Learning;Machine Learning;Natural Language Processing;Neural Network.
| null | 1 | null | null |
iclr
| -0.408248 | 0.408248 | null |
main
| 1.4 |
1;1;1;1;3
|
2;1;1;2;2
| null |
Conversational Artificial Intelligence in Natural Language Processing Application with Lifelong Learning
| null | null | 1.6 | 4.4 |
Withdraw
|
5;5;4;4;4
|
1;1;1;1;1
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
dropout;stochastic gradient descent;loss landscape;flatness;neural network
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
3;2;3;3
| null |
A Variance Principle Explains why Dropout Finds Flatter Minima
| null | null | 2.75 | 3 |
Reject
|
5;2;3;2
|
3;2;2;2
|
null |
Google Research
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6296; None
| null | 0 | null | null | null |
3;2;2;3
| null |
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
|
https://iclr.cc/virtual/2022/poster/6296
|
self-supervised learning;tabular data;pre-training;contrastive learning;openML
| null | 3.25 | null |
https://openreview.net/forum?id=CuV_qYkmKb3
|
iclr
| 0 | 0 | null |
main
| 7 |
6;6;8;8
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6296
|
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption
| null | null | 3.5 | 4 |
Spotlight
|
4;4;4;4
|
3;3;4;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3
| null | null | null |
sequential text-to-image retrieval;story-to-image retrieval;scene graph embedding;dual learning
| null | 2 | null | null |
iclr
| -0.866025 | 0.866025 | null |
main
| 3 |
1;3;5
|
2;2;4
| null |
Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval
| null | null | 2.666667 | 4.333333 |
Reject
|
5;5;3
|
2;2;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Neural language models;Unconditional Text Generation;Transformer
| null | 2 | null | null |
iclr
| 0.5 | 1 | null |
main
| 3.666667 |
1;5;5
|
2;3;3
| null |
Topic Aware Neural Language Model: Domain Adaptation of Unconditional Text Generation Models
| null | null | 2.666667 | 2.333333 |
Reject
|
2;3;2
|
1;2;3
|
null |
HDSI, UC, San Diego; Depart. of Computer Science, UC, San Diego; Depart. of Computer Science, Ohio State University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/7200; None
| null | 0 | null | null | null |
2;4;3
| null |
Chaoyue Liu, Libin Zhu, Mikhail Belkin
|
https://iclr.cc/virtual/2022/poster/7200
|
Assembling;linearity;Transition to linearity;wide neural networks
| null | 0 | null |
https://openreview.net/forum?id=CyKHoKyvgnp
|
iclr
| 1 | 1 | null |
main
| 7.333333 |
6;8;8
|
3;4;4
|
https://iclr.cc/virtual/2022/poster/7200
|
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
| null | null | 3.666667 | 2.666667 |
Spotlight
|
2;3;3
| null |
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
molecular representation learning;knowledge distillation
| null | 2.25 | null | null |
iclr
| 0.080845 | 0.12666 | null |
main
| 5.25 |
3;5;5;8
|
4;3;2;4
| null |
Stepping Back to SMILES Transformers for Fast Molecular Representation Inference
| null | null | 3.25 | 3.75 |
Reject
|
4;3;4;4
|
3;2;0;4
|
null | null |
2022
| 2.4 | null | null | 0 | null | null | null |
2;3;3;2;2
| null | null | null |
Neural Architecture Search
| null | 2.4 | null | null |
iclr
| -0.25 | 0.25 | null |
main
| 4.6 |
3;5;5;5;5
|
3;3;3;3;4
| null |
IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search
| null | null | 3.2 | 3.8 |
Withdraw
|
4;4;3;4;4
|
2;3;3;2;2
|
null |
NVIDIA, University of Waterloo, Vector Institute; NVIDIA
|
2022
| 4 |
https://iclr.cc/virtual/2022/poster/6687; None
| null | 0 | null | null | null |
4;4;4;4
| null |
Tim Dockhorn, Arash Vahdat, Karsten Kreis
|
https://iclr.cc/virtual/2022/poster/6687
|
Score-based generative modeling;denoising diffusion models;image synthesis
| null | 3.5 | null |
https://openreview.net/forum?id=CzceR82CYc
|
iclr
| 0.816497 | 0.57735 |
https://nv-tlabs.github.io/CLD-SGM
|
main
| 8.5 |
8;8;8;10
|
4;3;3;4
|
https://iclr.cc/virtual/2022/poster/6687
|
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
|
https://github.com/nv-tlabs/CLD-SGM
| null | 3.5 | 4 |
Spotlight
|
3;4;4;5
|
4;3;3;4
|
null |
Korea Advanced Institute of Science and Technology (KAIST); NAVER AI Lab
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/5954; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, Jinwoo Shin
|
https://iclr.cc/virtual/2022/poster/5954
|
video generation;implicit neural representations;generative adversarial networks
| null | 2.75 | null |
https://openreview.net/forum?id=Czsdv-S4-w9
|
iclr
| 0.826811 | 0.676481 |
https://sihyun-yu.github.io/digan/
|
main
| 7.25 |
5;6;8;10
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/5954
|
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
|
https://github.com/sihyun-yu/digan
| null | 3.75 | 4.25 |
Poster
|
4;4;4;5
|
2;3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
optimization;generalization;machine learning theory
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.5 |
5;5;6;6
|
3;4;4;3
| null |
Short optimization paths lead to good generalization
| null | null | 3.5 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null | null | null | 2 | null | null |
iclr
| -1 | 0.57735 | null |
main
| 3.5 |
3;3;3;5
|
2;2;3;3
| null |
Enhanced countering adversarial attacks via input denoising and feature restoring
|
https://github.com/ID-FR/IDFR
| null | 2.5 | 3.75 |
Withdraw
|
4;4;4;3
|
2;2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;1;3
| null | null | null |
equivariant;symmetry;contrastive loss;world models;transition;representation theory;generalization
| null | 2.5 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
2;3;3;3
| null |
Learning Symmetric Representations for Equivariant World Models
| null | null | 2.75 | 3 |
Reject
|
3;4;3;2
|
2;2;3;3
|
null |
Mohamed bin Zayed University of AI; Qualcomm USA; Stony Brook University; Linköping University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6341; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Khan, Fatih Porikli
|
https://iclr.cc/virtual/2022/poster/6341
|
Vision Transformers;Adversarial Perturbations
| null | 3.25 | null |
https://openreview.net/forum?id=D6nH3719vZy
|
iclr
| -0.174078 | -0.333333 | null |
main
| 7.5 |
6;8;8;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6341
|
On Improving Adversarial Transferability of Vision Transformers
|
https://t.ly/hBbW
| null | 3.75 | 3.75 |
Spotlight
|
4;5;3;3
|
3;3;3;4
|
null |
Yonsei University; Yonsei University, NAVER AI Lab
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6017; None
| null | 0 | null | null | null |
3;2;3;4
| null |
Namuk Park, Songkuk Kim
|
https://iclr.cc/virtual/2022/poster/6017
|
vision transformer;self-attention;multi-head self-attention;loss landscape
| null | 3.5 | null |
https://openreview.net/forum?id=D78Go4hVcxO
|
iclr
| -0.57735 | 0.870388 | null |
main
| 7.25 |
5;8;8;8
|
2;4;4;3
|
https://iclr.cc/virtual/2022/poster/6017
|
How Do Vision Transformers Work?
| null | null | 3.25 | 3.5 |
Spotlight
|
4;3;4;3
|
2;4;4;4
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
continual learning;task-free continual learning;self-supervised learning;pre-training;ensemble methods
| null | 2 | null | null |
iclr
| 0.707107 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
2;3;3;3
| null |
Ensembles and Encoders for Task-Free Continual Learning
| null | null | 2.75 | 4 |
Withdraw
|
4;3;4;5
|
2;2;2;2
|
null |
Paper under double-blind review
|
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;1;2
| null | null | null |
maximum mean discrepancy;data shift;covariate shift;representation learning;missing data
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
3;4;2;3
| null |
Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift
| null | null | 3 | 3.5 |
Reject
|
3;5;4;2
|
2;2;2;2
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
2;1;2;1
| null | null | null |
Single-Cell RNA-sequencing;cell type classification;capsule network;attention;interpretable model
| null | 1.5 | null | null |
iclr
| 0.707107 | -0.301511 | null |
main
| 2 |
1;1;3;3
|
4;2;3;2
| null |
Single-Cell Capsule Attention : an interpretable method of cell type classification for single-cell RNA-sequencing data
| null | null | 2.75 | 4 |
Reject
|
3;4;4;5
|
1;1;2;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null |
Lifelong Learning;Continual Learning;Catastrophic Forgetting;Pre-training
| null | 2.333333 | null | null |
iclr
| 0 | 1 | null |
main
| 5.333333 |
5;5;6
|
3;3;4
| null |
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
| null | null | 3.333333 | 4 |
Reject
|
4;4;4
|
2;3;2
|
null | null |
2022
| 3.333333 | null | null | 0 | null | null | null |
2;4;4
| null | null | null |
Generative adversarial networks;non-convex optimization
| null | 3 | null | null |
iclr
| 0.5 | 0.5 | null |
main
| 6 |
5;5;8
|
3;4;4
| null |
Adam is no better than normalized SGD: Dissecting how adaptivity improves GAN performance
| null | null | 3.666667 | 3.666667 |
Reject
|
3;4;4
|
2;3;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
long-tailed recognition;Out-of-Distribution;open-set noisy labels;deep learning
| null | 2.25 | null | null |
iclr
| -0.345857 | 0.927173 | null |
main
| 4.75 |
3;5;5;6
|
2;3;3;3
| null |
Open-sampling: Re-balancing Long-tailed Datasets with Out-of-Distribution Data
| null | null | 2.75 | 3.75 |
Withdraw
|
4;3;5;3
|
2;2;2;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;3;2
| null | null | null | null | null | 2.5 | null | null |
iclr
| -1 | 1 | null |
main
| 4.5 |
3;3;6;6
|
2;2;3;3
| null |
SiT: Simulation Transformer for Particle-based Physics Simulation
| null | null | 2.5 | 3.5 |
Reject
|
4;4;3;3
|
3;2;3;2
|
null |
The University of Texas at Austin, Facebook AI Research; The University of Texas at Austin
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6109; None
| null | 0 | null | null | null |
3;2;2;3
| null |
Santhosh Kumar Ramakrishnan, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman
|
https://iclr.cc/virtual/2022/poster/6109
|
Self-supervised learning;visual navigation;representation learning
| null | 3 | null |
https://openreview.net/forum?id=DBiQQYWykyy
|
iclr
| 0.942809 | -1 | null |
main
| 7.5 |
6;8;8;8
|
4;3;3;3
|
https://iclr.cc/virtual/2022/poster/6109
|
Environment Predictive Coding for Visual Navigation
|
https://vision.cs.utexas.edu/projects/epc/
| null | 3.25 | 4 |
Poster
|
2;5;5;4
|
3;3;3;3
|
null | null |
2022
| 1.333333 | null | null | 0 | null | null | null |
1;1;2
| null | null | null |
Fine-tuning;Query-feedback;Transfer learning;Weakly supervised learning
| null | 2 | null | null |
iclr
| 0 | 0.866025 | null |
main
| 3.666667 |
3;3;5
|
2;1;3
| null |
Fine-Tuning from Limited Feedbacks
| null | null | 2 | 4 |
Withdraw
|
4;4;4
|
2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
3;1;2;2
| null | null | null |
Federated inference;local latent representation;feature alignment;graph structure learning;Gumbel softmax
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
2;2;2;3
| null |
Federated Inference through Aligning Local Representations and Learning a Consensus Graph
| null | null | 2.25 | 4.25 |
Reject
|
4;5;5;3
|
2;2;2;2
|
null | null |
2022
| 2.2 | null | null | 0 | null | null | null |
2;1;2;3;3
| null | null | null |
Self-Supervised Learning;Autoencoders;Variational Autoencoders;Data Augmentation
| null | 2.2 | null | null |
iclr
| -0.763763 | 0.080064 | null |
main
| 3.8 |
3;3;3;5;5
|
4;1;2;2;3
| null |
AAVAE: Augmentation-Augmented Variational Autoencoders
| null | null | 2.4 | 4.2 |
Reject
|
5;5;4;3;4
|
2;1;3;2;3
|
null |
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6557; None
| null | 0 | null | null | null |
3;3;3
| null |
Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola
|
https://iclr.cc/virtual/2022/poster/6557
|
orthogonal classifier;invariance
| null | 3 | null |
https://openreview.net/forum?id=DIjCrlsu6Z
|
iclr
| 0.5 | 1 | null |
main
| 7.333333 |
6;8;8
|
3;4;4
|
https://iclr.cc/virtual/2022/poster/6557
|
Controlling Directions Orthogonal to a Classifier
|
https://github.com/Newbeeer/orthogonal_classifier
| null | 3.666667 | 3.333333 |
Spotlight
|
3;4;3
|
3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;3;2;3
| null | null | null |
physics-aware neural networks;partial differential equations;advection-diffusion equations;learning constituents;out-of-distribution generalization
| null | 2 | null | null |
iclr
| 0 | -0.57735 | null |
main
| 5.25 |
3;6;6;6
|
4;3;4;3
| null |
Composing Partial Differential Equations with Physics-Aware Neural Networks
| null | null | 3.5 | 3 |
Reject
|
3;2;3;4
|
2;3;0;3
|
null |
Department of Computer Science, University of Illinois Urbana-Champaign
|
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/6221; None
| null | 0 | null | null | null |
3;4;4;3
| null |
Saba Ghaffari, Ehsan Saleh, David Forsyth, Yu-Xiong Wang
|
https://iclr.cc/virtual/2022/poster/6221
|
Few-shot Classification;Firth Regularization;MLE Bias
| null | 3.25 | null |
https://openreview.net/forum?id=DNRADop4ksB
|
iclr
| -0.174078 | -0.333333 | null |
main
| 7.5 |
6;8;8;8
|
4;3;4;4
|
https://iclr.cc/virtual/2022/poster/6221
|
On the Importance of Firth Bias Reduction in Few-Shot Classification
|
https://github.com/ehsansaleh/firth_bias_reduction
| null | 3.75 | 3.75 |
Spotlight
|
4;5;3;3
|
3;4;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Representation Learning;Audiovisual;Self-Supervised Learning;Spatiotemporal Augmentations
| null | 2 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4 |
3;3;5;5
|
2;3;3;2
| null |
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning
| null | null | 2.5 | 4 |
Withdraw
|
5;4;3;4
|
2;2;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null | null | null | 1.25 | null | null |
iclr
| -0.942809 | 0.568535 | null |
main
| 4 |
1;3;6;6
|
2;4;3;4
| null |
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
| null | null | 3.25 | 4.5 |
Reject
|
5;5;4;4
|
0;1;2;2
|
null |
Peking University; Shanghai Jiao Tong University; Shanghai University of Finance and Economics
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/7174; None
| null | 0 | null | null | null |
3;3;3;3;3;3
| null |
Shaofeng Jiang, Erzhi Liu, You Lyu, Zhihao Tang, Yubo Zhang
|
https://iclr.cc/virtual/2022/poster/7174
|
online algorithms;facility location;prediction;learning-augmented
| null | 2.333333 | null |
https://openreview.net/forum?id=DSQHjibtgKR
|
iclr
| -0.316228 | 0.316228 | null |
main
| 6.666667 |
6;6;6;6;8;8
|
3;4;4;4;4;4
|
https://iclr.cc/virtual/2022/poster/7174
|
Online Facility Location with Predictions
| null | null | 3.833333 | 4.166667 |
Poster
|
4;4;5;4;4;4
|
2;4;1;2;2;3
|
null |
Peking University, Beijing, China; University of Science and Technology of China, Hefei, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Tencent AI Lab, Shenzhen, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6627; None
| null | 0 | null | null | null |
3;2;3;3
| null |
Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, QIANG FU, Yang Wei
|
https://iclr.cc/virtual/2022/poster/6627
|
Policy Optimization;Nash Equilibrium;Mahjong AI
| null | 3 | null |
https://openreview.net/forum?id=DTXZqTNV5nW
|
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
5;6;8;8
|
4;4;4;4
|
https://iclr.cc/virtual/2022/poster/6627
|
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
| null | null | 4 | 4.25 |
Poster
|
3;5;5;4
|
3;2;3;4
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
1;3;3
| null | null | null |
Noise Synthesis;Self-Supervised Learning;Contrastive Learning
| null | 1.666667 | null | null |
iclr
| -1 | 1 | null |
main
| 3.666667 |
1;5;5
|
1;3;3
| null |
Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis
| null | null | 2.333333 | 3.333333 |
Withdraw
|
4;3;3
|
1;2;2
|
null |
TAU, LISN-CNRS–INRIA, Université Paris-Saclay, Orsay, France; MISA, LMI, Université d’Antananarivo, Ankatso, Madagascar
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6788; None
| null | 0 | null | null | null |
2;3;3;3;4
| null |
Herilalaina Rakotoarison, Louisot Milijaona, Andry RASOANAIVO, Michele Sebag, Marc Schoenauer
|
https://iclr.cc/virtual/2022/poster/6788
|
AutoML;Meta-features;Hyper-parameter Optimization;Optimal Transport
| null | 3 | null |
https://openreview.net/forum?id=DTkEfj0Ygb8
|
iclr
| 0.979958 | 0.583333 | null |
main
| 6.6 |
5;6;6;8;8
|
3;3;3;4;3
|
https://iclr.cc/virtual/2022/poster/6788
|
Learning meta-features for AutoML
|
https://github.com/luxusg1/metabu
| null | 3.2 | 3.2 |
Spotlight
|
2;3;3;4;4
|
3;3;3;3;3
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
energy-based models;dynamic inference;joint language models;super model optimization;NLP;BERT;T5
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
E-LANG: Energy-based Joint Inferencing of Super and Swift Language Models
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Computer Science and Engineering, University of California, Santa Cruz
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6926; None
| null | 0 | null | null | null |
2;4;3;3
| null |
Zhaowei Zhu, Tianyi Luo, Yang Liu
|
https://iclr.cc/virtual/2022/poster/6926
|
semi-supervised learning;fairness;disparate impact;Matthew effect;consistency regularization
| null | 3.25 | null |
https://openreview.net/forum?id=DXPftn5kjQK
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6926
|
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
| null | null | 3.25 | 3.75 |
Poster
|
4;4;4;3
|
3;4;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
1;3;3;3
| null | null | null |
reinforcement learning;curriculum learning;sparse rewards
| null | 1.75 | null | null |
iclr
| 0.816497 | 0.522233 | null |
main
| 3.5 |
3;3;3;5
|
3;1;2;3
| null |
Reachability Traces for Curriculum Design in Reinforcement Learning
| null | null | 2.25 | 4 |
Reject
|
4;4;3;5
|
2;1;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
unsupervised representation learning;disentanglement;Variational Autoencoders;Generative Adversarial Networks
| null | 2.25 | null | null |
iclr
| 0.522233 | 0.333333 | null |
main
| 3.5 |
3;3;3;5
|
3;3;2;3
| null |
Disentangling One Factor at a Time
|
https://github.com/OATFactor/OATFactor
| null | 2.75 | 3.25 |
Reject
|
4;3;2;4
|
2;2;3;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
distillation;self-distillation;distribution distillation;uncertainty;robustness
| null | 2.5 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
4;2;3;4
| null |
Self-Distribution Distillation: Efficient Uncertainty Estimation
| null | null | 3.25 | 3.5 |
Reject
|
3;3;5;3
|
3;2;2;3
|
null |
University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Cambridge, UK
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/7211; None
| null | 0 | null | null | null |
3;2;3
| null |
Alex Chan, Alicia Curth, Mihaela van der Schaar
|
https://iclr.cc/virtual/2022/poster/7211
|
Decision Modelling;Imitation Learning;Inverse Online Learning
| null | 2.666667 | null |
https://openreview.net/forum?id=DYypjaRdph2
|
iclr
| 0 | 0.5 | null |
main
| 6.666667 |
6;6;8
|
3;2;3
|
https://iclr.cc/virtual/2022/poster/7211
|
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
| null | null | 2.666667 | 3 |
Poster
|
3;3;3
|
3;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
Self-supervised learning;representation learning;graph neural networks
| null | 2.25 | null | null |
iclr
| 0 | -0.57735 | null |
main
| 5.5 |
5;5;6;6
|
4;3;3;3
| null |
Self-Supervised Representation Learning via Latent Graph Prediction
| null | null | 3.25 | 3 |
Reject
|
3;3;3;3
|
2;3;2;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Local Intrinsic Dimensionality;Graph Neural Networks
| null | 2.333333 | null | null |
iclr
| -0.755929 | 0.981981 | null |
main
| 4 |
1;5;6
|
1;4;4
| null |
Understanding Graph Learning with Local Intrinsic Dimensionality
| null | null | 3 | 4 |
Reject
|
5;3;4
|
0;3;4
|
null |
School of Computer Science and Engineering, Nanyang Technological University, Singapore
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6584; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Yi Huang, Adams Kong
|
https://iclr.cc/virtual/2022/poster/6584
| null | null | 2.5 | null |
https://openreview.net/forum?id=DesNW4-5ai9
|
iclr
| 0.408248 | 0.866025 | null |
main
| 6 |
5;5;6;8
|
3;2;3;4
|
https://iclr.cc/virtual/2022/poster/6584
|
Transferable Adversarial Attack based on Integrated Gradients
|
https://github.com/yihuang2016/TAIG
| null | 3 | 3.5 |
Poster
|
3;4;3;4
|
3;0;3;4
|
null |
Center for Systems Biology Dresden, Max Planck Institute (CBG), Fondazione Human Technopole; Google Research; Center for Systems Biology Dresden, Max Planck Institute (CBG)
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/5977; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug
|
https://iclr.cc/virtual/2022/poster/5977
|
Interpretable Unsupervised Image Restoration;Diversity Image Restoration;Unsupervised Image Denoising;Unsupervised Artefact Removal
| null | 2.75 | null |
https://openreview.net/forum?id=DfMqlB0PXjM
|
iclr
| 0.57735 | 0 | null |
main
| 7.5 |
6;8;8;8
|
4;4;4;4
|
https://iclr.cc/virtual/2022/poster/5977
|
Interpretable Unsupervised Diversity Denoising and Artefact Removal
| null | null | 4 | 3.5 |
Spotlight
|
3;4;3;4
|
2;3;3;3
|
null |
Carnegie Mellon University; Google Research, Brain Team, University of California Berkeley; Google Research, Brain Team
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6014; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine
|
https://iclr.cc/virtual/2022/poster/6014
|
model-based reinforcement learning;visual distractors;empowerment
| null | 2.5 | null |
https://openreview.net/forum?id=DfUjyyRW90
|
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
6;8;8;8
|
4;4;4;4
|
https://iclr.cc/virtual/2022/poster/6014
|
Information Prioritization through Empowerment in Visual Model-based RL
| null | null | 4 | 3.5 |
Poster
|
4;3;3;4
|
3;0;3;4
|
null |
Dept. of Computer & Info. Science, PRECISE Center, University of Pennsylvania; Dept. of Statistics & Data Science, The Wharton School, University of Pennsylvania
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6314; None
| null | 0 | null | null | null |
3;2;3;3
| null |
Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
|
https://iclr.cc/virtual/2022/poster/6314
|
probably approximately correct;prediction set;covariate shift;importance weight;calibration;Clopper-Pearson binomial interval;rejection sampling
| null | 3 | null |
https://openreview.net/forum?id=DhP9L8vIyLc
|
iclr
| 0.816497 | -0.333333 | null |
main
| 6.5 |
6;6;6;8
|
3;4;3;3
|
https://iclr.cc/virtual/2022/poster/6314
|
PAC Prediction Sets Under Covariate Shift
| null | null | 3.25 | 3 |
Poster
|
3;2;3;4
|
3;3;2;4
|
null |
Stanford University; Salesforce Research
|
2022
| 2.8 |
https://iclr.cc/virtual/2022/poster/6900; None
| null | 0 | null | null | null |
2;3;3;3;3
| null |
Benjamin Newman, Prafulla Kumar Choubey, Nazneen Rajani
|
https://iclr.cc/virtual/2022/poster/6900
|
NLP;Prompting;Commonsense;information extraction;factual extraction;Large Language Models
| null | 2.6 | null |
https://openreview.net/forum?id=DhzIU48OcZh
|
iclr
| -0.272166 | 0.952579 | null |
main
| 6.6 |
5;6;6;8;8
|
3;3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6900
|
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
| null | null | 3.4 | 3.6 |
Poster
|
4;4;3;3;4
|
2;2;3;3;3
|
null |
Under double-blind review
|
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
Metric learning;Adversarial learning
| null | 2.75 | null | null |
iclr
| 0.408248 | 0.942809 | null |
main
| 6 |
5;5;6;8
|
3;3;3;4
| null |
Understanding Metric Learning on Unit Hypersphere and Generating Better Examples for Adversarial Training
| null | null | 3.25 | 3.5 |
Reject
|
3;4;3;4
|
2;3;3;3
|
null |
DeepMind, London, UK; Google
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/7002; None
| null | 0 | null | null | null |
2;4;3
| null |
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, Ali Taylan Cemgil
|
https://iclr.cc/virtual/2022/poster/7002
|
robustness;distribution shifts
| null | 3.333333 | null |
https://openreview.net/forum?id=Dl4LetuLdyK
|
iclr
| 0 | 0.866025 | null |
main
| 8.666667 |
8;8;10
|
2;3;4
|
https://iclr.cc/virtual/2022/poster/7002
|
A Fine-Grained Analysis on Distribution Shift
|
github.com/deepmind/distribution_shift_framework
| null | 3 | 4 |
Oral
|
4;4;4
|
3;4;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
Video Prediction;RNNs
| null | 1.75 | null | null |
iclr
| 0 | 0.760886 | null |
main
| 4.75 |
3;5;5;6
|
2;3;2;4
| null |
CDNet: A cascaded decoupling architecture for video prediction
| null | null | 2.75 | 4 |
Withdraw
|
4;4;4;4
|
2;0;2;3
|
null |
Intel Labs China; CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6455; None
| null | 0 | null | null | null |
2;4;4;3
| null |
Chao Li, Aojun Zhou, Anbang Yao
|
https://iclr.cc/virtual/2022/poster/6455
|
Convolutional Neural Networks;Dynamic Convolution;Attention;Image Classification
| null | 1.75 | null |
https://openreview.net/forum?id=DmpCfq6Mg39
|
iclr
| 0 | 0.57735 | null |
main
| 7.5 |
6;8;8;8
|
3;4;3;4
|
https://iclr.cc/virtual/2022/poster/6455
|
Omni-Dimensional Dynamic Convolution
|
https://github.com/OSVAI/ODConv
| null | 3.5 | 4 |
Spotlight
|
4;5;3;4
|
3;1;3;0
|
null |
Department of Electrical and Computer Engineering, Texas A&M University
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/7199; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Xiaoning Qian
|
https://iclr.cc/virtual/2022/poster/7199
|
relational learning;data integration;multi-view learning;Bayesian generative model
| null | 2.75 | null |
https://openreview.net/forum?id=DnG75_KyHjX
|
iclr
| 0.471405 | 0 | null |
main
| 6 |
5;5;6;8
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/7199
|
MoReL: Multi-omics Relational Learning
| null | null | 3 | 2.75 |
Poster
|
2;3;3;3
|
2;3;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
Data Augmentation;Self-supervised;Contrastive Learning;Deep Learning
| null | 2.25 | null | null |
iclr
| 0.174078 | 0.96225 | null |
main
| 4.25 |
3;3;5;6
|
2;2;3;3
| null |
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
| null | null | 2.5 | 3.25 |
Withdraw
|
2;4;4;3
|
2;4;0;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;4;3
| null | null | null |
fairness;fair representation learning;adversarial fairness;trustworthy machine learning;randomized smoothing
| null | 2 | null | null |
iclr
| 0 | 1 | null |
main
| 4 |
3;3;5;5
|
2;2;3;3
| null |
Latent Space Smoothing for Individually Fair Representations
| null | null | 2.5 | 4 |
Withdraw
|
4;4;4;4
|
1;1;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Counterfactual example;Sum product networks;tractable probabilistic models;counterfactual explanation.
| null | 2.75 | null | null |
iclr
| 0.600994 | 0.863868 | null |
main
| 4.75 |
3;3;5;8
|
2;3;3;4
| null |
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
| null | null | 3 | 3.5 |
Reject
|
2;3;5;4
|
3;2;3;3
|
null |
Georgetown University; Columbia University; Google Research
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6703; None
| null | 0 | null | null | null |
3;2;3;4
| null |
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng
|
https://iclr.cc/virtual/2022/poster/6703
|
shuffle privacy;stochastic convex optimization;differential privacy
| null | 0 | null |
https://openreview.net/forum?id=DrZXuTGg2A-
|
iclr
| -1 | 0.57735 | null |
main
| 7 |
6;6;8;8
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/6703
|
Shuffle Private Stochastic Convex Optimization
| null | null | 3.75 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
few-shot learning;representation learning
| null | 1.666667 | null | null |
iclr
| 0.5 | 1 | null |
main
| 5.333333 |
5;5;6
|
3;3;4
| null |
Meta-free few-shot learning via representation learning with weight averaging
| null | null | 3.333333 | 3.666667 |
Reject
|
3;4;4
|
0;2;3
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
1;1;2;2
| null | null | null |
SGD;distributed training;hide communication cost;convergence
| null | 1.75 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
4;4;3;3
| null |
Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
| null | null | 3.5 | 3.75 |
Reject
|
4;3;3;5
|
1;2;2;2
|
null |
2Mila, Quebec AI Institute, 6McGill University, 7École de technologie supérieure, 8Canada CIFAR AI Chair; 2Mila, Quebec AI Institute, 4Independent Robotics; 1Polytechnique Montréal, 2Mila, Quebec AI Institute; 1Polytechnique Montréal, 2Mila, Quebec AI Institute, 3ElementAI / Service Now, 8Canada CIFAR AI Chair; 5Algolux; 2Mila, Quebec AI Institute, 3ElementAI / Service Now; 5Algolux, 9Princeton University
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6106; None
| null | 0 | null | null | null |
2;3;2;3
| null |
Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim D'Souza, Samira Ebrahimi Kahou, Felix Heide, Chris J Pal
|
https://iclr.cc/virtual/2022/poster/6106
|
trajectory prediction;motion forecasting;transformers;latent variable models
| null | 2.75 | null |
https://openreview.net/forum?id=Dup_dDqkZC5
|
iclr
| 0 | 0.57735 |
https://fgolemo.github.io/autobots/
|
main
| 7.5 |
6;8;8;8
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6106
|
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
|
https://github.com/fgolemo/autobots
| null | 3.5 | 4 |
Spotlight
|
4;4;3;5
|
3;3;2;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Transformer;GAN;symbolic reasoning;temporal logic
| null | 2 | null | null |
iclr
| 0.080845 | 0.594089 | null |
main
| 5.25 |
3;5;5;8
|
3;2;3;4
| null |
Generating Symbolic Reasoning Problems with Transformer GANs
| null | null | 3 | 3.75 |
Reject
|
4;4;3;4
|
3;0;2;3
|
null |
Under double-blind review
|
2022
| 2.75 | null | null | 0 | null | null | null |
2;2;3;4
| null | null | null |
multi-modal learning;deep neural networks;multi-view learning
| null | 3 | null | null |
iclr
| 0 | 0.855186 | null |
main
| 4.75 |
3;3;5;8
|
2;2;3;3
| null |
Recognizing and overcoming the greedy nature of learning in multi-modal deep neural networks
| null | null | 2.5 | 4 |
Reject
|
4;4;4;4
|
3;2;3;4
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
1;1;2;2
| null | null | null |
calibration;deep learning;curriculum learning;image classification
| null | 2 | null | null |
iclr
| 0 | 0.904534 | null |
main
| 3.5 |
1;3;5;5
|
2;2;3;3
| null |
How Curriculum Learning Impacts Model Calibration
| null | null | 2.5 | 4 |
Withdraw
|
4;4;4;4
|
1;3;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
Continual Learning;Language Grounding;Language-Image Embeddings;Multimodal Distributional Semantics;Reference Resolution
| null | 2.5 | null | null |
iclr
| 0.648886 | 0.324443 | null |
main
| 4.75 |
3;5;5;6
|
3;2;3;4
| null |
CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings
| null | null | 3 | 4 |
Reject
|
3;5;4;4
|
2;3;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3
| null | null | null |
algebra;Abelian group;word analogy;invertible neural networks;permutation invariant;size generalization
| null | 1.666667 | null | null |
iclr
| -0.5 | -1 | null |
main
| 5 |
3;6;6
|
4;3;3
| null |
Abelian Neural Networks
| null | null | 3.333333 | 3.666667 |
Reject
|
4;3;4
|
3;0;2
|
null |
Monash University, VinAI Research; Monash University; Adobe Research
|
2022
| 2.8 |
https://iclr.cc/virtual/2022/poster/6448; None
| null | 0 | null | null | null |
2;3;3;3;3
| null |
Anh Bui, Trung Le, Quan Tran, He Zhao, Dinh Phung
|
https://iclr.cc/virtual/2022/poster/6448
|
Adversarial Machine Learning;Distributional Robustness
| null | 2.8 | null |
https://openreview.net/forum?id=Dzpe9C1mpiv
|
iclr
| 0 | -0.666667 | null |
main
| 6.6 |
5;6;6;8;8
|
4;3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6448
|
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
|
https://github.com/tuananhbui89/Unified-Distributional-Robustness
| null | 3.2 | 3 |
Poster
|
3;3;3;2;4
|
2;3;3;3;3
|
null | null |
2022
| 2.6 | null | null | 0 | null | null | null |
2;3;3;3;2
| null | null | null |
federated learning;nonconvex optimization;convex optimization;local gradient method
| null | 2.4 | null | null |
iclr
| -0.408248 | 0.612372 | null |
main
| 4.6 |
3;5;5;5;5
|
3;4;4;4;3
| null |
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
| null | null | 3.6 | 3.6 |
Reject
|
4;4;3;3;4
|
2;2;3;3;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
3;3;1
| null | null | null |
POMDP;RNN;recurrent model-free RL;baseline;meta RL;robust RL;generalization in RL
| null | 3.333333 | null | null |
iclr
| 0 | 0.755929 |
https://drive.google.com/drive/folders/1I5mLlKPf2Gmdpm0nzy9OkR494nCJll1g?usp=sharing
|
main
| 6.333333 |
5;6;8
|
2;3;3
| null |
Recurrent Model-Free RL is a Strong Baseline for Many POMDPs
| null | null | 2.666667 | 4 |
Reject
|
4;4;4
|
3;3;4
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
adversarial learning;adversarial robustness;deep learning;cnn
| null | 1.666667 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
2;3;3
| null |
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models
| null | null | 2.666667 | 4 |
Withdraw
|
4;4;4
|
2;1;2
|
null |
University of Maryland, College Park; Google
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6742; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein
|
https://iclr.cc/virtual/2022/poster/6742
|
Federated Learning;Peroredical Distribution Shift
| null | 2.25 | null |
https://openreview.net/forum?id=E4EE_ohFGz
|
iclr
| -0.699913 | 0.83205 | null |
main
| 5.5 |
3;5;6;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/6742
|
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
| null | null | 3.5 | 3.75 |
Poster
|
4;5;4;2
|
2;0;3;4
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null |
visual imitation learning;imitation learning from noisy video
| null | 2 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/iclr2022eil/home
|
main
| 3 |
1;3;3;5
|
2;2;2;2
| null |
Extraneousness-Aware Imitation Learning
| null | null | 2 | 4.25 |
Withdraw
|
4;4;5;4
|
2;3;1;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null |
theory;deep learning theory;implicit bias;generalization;interpolation;ridgeless regression
| null | 0.75 | null | null |
iclr
| 0.662266 | 0.648886 | null |
main
| 4.75 |
3;5;5;6
|
2;4;3;3
| null |
Ridgeless Interpolation with Shallow ReLU Networks in $1D$ is Nearest Neighbor Curvature Extrapolation and Provably Generalizes on Lipschitz Functions
| null | null | 3 | 3.25 |
Withdraw
|
3;3;3;4
|
2;0;1;0
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Structure Aggregation;Personalisation;Graph Neural Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Structured Federated Aggregation for Personalizing On-device Intelligence
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
2;1;2;1
| null | null | null |
neural networks;deep learning;neural network optimization;hyperparameter tuning;optimizer comparison
| null | 1.75 | null | null |
iclr
| -0.927173 | 0.927173 | null |
main
| 4.75 |
3;5;5;6
|
3;4;4;4
| null |
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
| null | null | 3.75 | 4.25 |
Reject
|
5;4;4;4
|
0;2;3;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
few-shot learning;transformer model;weight generation;supervised learning;semi-supervised learning
| null | 2.333333 | null | null |
iclr
| 0 | 0.993399 | null |
main
| 5.333333 |
3;5;8
|
2;3;4
| null |
HyperTransformer: Attention-Based CNN Model Generation from Few Samples
| null | null | 3 | 4 |
Reject
|
4;4;4
|
2;2;3
|
null |
Stanford University; University of Toronto and Vector Institute
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6513; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot
|
https://iclr.cc/virtual/2022/poster/6513
|
model extraction;model stealing;model functionality stealing;proof-of-work;adversarial machine learning;trustworthy machine learning;deep learning
| null | 2.5 | null |
https://openreview.net/forum?id=EAy7C1cgE1L
|
iclr
| -0.493742 | 0.493742 | null |
main
| 6.25 |
3;6;8;8
|
3;3;4;3
|
https://iclr.cc/virtual/2022/poster/6513
|
Increasing the Cost of Model Extraction with Calibrated Proof of Work
| null | null | 3.25 | 3.75 |
Spotlight
|
4;4;4;3
|
2;3;2;3
|
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