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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Reinforcement Learning;Multi-Agent Reinforcement Learning;Bounded Rationality;Rational Inattention;Simulations
| null | 2.25 | null | null |
iclr
| -0.622543 | 0.927173 | null |
main
| 4.75 |
3;5;5;6
|
3;4;4;4
| null |
Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning
| null | null | 3.75 | 2.75 |
Reject
|
4;2;2;3
|
2;3;0;4
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
2;1;1;2
| null | null | null |
Out of distribution;deep learning;gradient;backpropagation
| null | 1.5 | null | null |
iclr
| -0.333333 | -0.57735 | null |
main
| 3.5 |
3;3;3;5
|
2;3;3;2
| null |
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation
| null | null | 2.5 | 4.25 |
Reject
|
4;5;4;4
|
2;1;1;2
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3;3
| null | null | null |
spiking neural networks;back-propagationthrough time;parameter initialization
| null | 3 | null | null |
iclr
| 0.333333 | 0.333333 | null |
main
| 5.75 |
5;6;6;6
|
3;3;4;3
| null |
Accelerating Training of Deep Spiking Neural Networks with Parameter Initialization
| null | null | 3.25 | 4.25 |
Reject
|
4;5;4;4
|
3;3;3;3
|
null | null |
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6876; None
| null | 0 | null | null | null |
2;4;1;3
| null |
Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
|
https://iclr.cc/virtual/2022/poster/6876
|
domain randomization;sim-to-real transfer;learning theory
| null | 0 | null |
https://openreview.net/forum?id=T8vZHIRTrY
|
iclr
| 0.727607 | -0.342997 | null |
main
| 7.75 |
5;8;8;10
|
4;4;1;3
|
https://iclr.cc/virtual/2022/poster/6876
|
Understanding Domain Randomization for Sim-to-real Transfer
| null | null | 3 | 2.25 |
Spotlight
|
2;2;2;3
| null |
null | null |
2022
| 3.5 |
https://iclr.cc/virtual/2022/poster/6379; None
| null | 0 | null | null | null |
3;3;4;4
| null |
Adrián Javaloy and Isabel Valera
|
https://iclr.cc/virtual/2022/poster/6379
|
multitask learning;conflicting gradients;negative transfer
| null | 3.25 | null |
https://openreview.net/forum?id=T8wHz4rnuGL
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6379
|
RotoGrad: Gradient Homogenization in Multitask Learning
| null | null | 3.75 | 3.75 |
Spotlight
|
4;3;4;4
|
3;3;4;3
|
null | null |
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/7085; None
| null | 0 | null | null | null |
3;2;3;3
| null |
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu
|
https://iclr.cc/virtual/2022/poster/7085
|
Learning with noisy labels;benchmark;real-world label noise;human annotations
| null | 3 | null |
https://openreview.net/forum?id=TBWA6PLJZQm
|
iclr
| -0.57735 | 1 | null |
main
| 7 |
6;6;8;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/7085
|
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
| null | null | 3.5 | 4.75 |
Poster
|
5;5;4;5
|
3;2;4;3
|
null | null |
2022
| 2.8 |
https://iclr.cc/virtual/2022/poster/6593; None
| null | 0 | null | null | null |
2;3;3;3;3
| null |
Wenyong Huang, Zhenhe Zhang, Yu Ting Yeung, Xin Jiang, Qun Liu
|
https://iclr.cc/virtual/2022/poster/6593
|
Speech Representation Learning;Speech Pre-training;Speech Recognition;Self-supervised Representation Learning
| null | 3.2 | null |
https://openreview.net/forum?id=TBpg4PnXhYH
|
iclr
| 0.327327 | 1 | null |
main
| 7.2 |
6;6;8;8;8
|
3;3;4;4;4
|
https://iclr.cc/virtual/2022/poster/6593
|
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
| null | null | 3.6 | 3.8 |
Poster
|
4;3;3;4;5
|
4;3;3;3;3
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;3;3;3
| null | null | null |
Pretrained Vision-language Models;Prompt Tuning;Visual Grounding
| null | 2.75 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
|
3;3;3;3
| null |
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
2;3;3;3
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Ensembles;Sparse MoEs;Robustness;Uncertainty Calibration;OOD detection;Efficient Ensembles;Large scale;Computer vision
| null | 2 | null | null |
iclr
| -0.755929 | 0.944911 | null |
main
| 4.666667 |
3;5;6
|
3;4;4
| null |
Sparse MoEs meet Efficient Ensembles
| null | null | 3.666667 | 4.333333 |
Reject
|
5;5;3
|
2;1;3
|
null | null |
2022
| 1.5 | null | null | 0 | null | null | null |
1;2;1;2
| null | null | null |
fMRI encoding;Vision Transformers;Multi-Modal Transformers
| null | 2.75 | null | null |
iclr
| -0.522233 | 0.333333 | null |
main
| 2.5 |
1;3;3;3
|
2;2;2;3
| null |
Visio-Linguistic Brain Encoding
| null | null | 2.25 | 4.25 |
Withdraw
|
5;4;5;3
|
2;3;4;2
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
2;2;4;4
| null | null | null |
Single-step Adversarial Training;Catastrophic Overfitting;Adversarial Robustness;Adversarial Example
| null | 2 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
3;5;6;6
|
3;4;3;3
| null |
I-PGD-AT: Efficient Adversarial Training via Imitating Iterative PGD Attack
| null | null | 3.25 | 4.5 |
Reject
|
5;5;4;4
|
2;2;4;0
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Knowledge Graph;Tensor Decomposition;Low-rank Tensor Completion
| null | 2.333333 | null | null |
iclr
| -0.5 | -0.5 | null |
main
| 4 |
3;3;6
|
4;3;3
| null |
Knowledge Graph Completion as Tensor Decomposition: A Genreal Form and Tensor N-rank Regularization
| null | null | 3.333333 | 3.333333 |
Withdraw
|
3;4;3
|
2;2;3
|
null | null |
2022
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
On the Effect of Input Perturbations for Graph Neural Networks
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null | null | null | 2.25 | null | null |
iclr
| 0 | 0.471405 | null |
main
| 5 |
3;5;6;6
|
3;3;3;4
| null |
Revisiting Locality-Sensitive Binary Codes from Random Fourier Features
| null | null | 3.25 | 3 |
Reject
|
3;3;4;2
|
2;1;3;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;2;3
| null | null | null | null | null | 2 | null | null |
iclr
| 0.522233 | -0.57735 | null |
main
| 5.75 |
5;6;6;6
|
3;2;3;2
| null |
Fully Online Meta-Learning Without Task Boundaries
| null | null | 2.5 | 3.75 |
Reject
|
3;4;3;5
|
2;2;2;2
|
null | null |
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6537; None
| null | 0 | null | null | null |
3;3;3;4
| null |
Tim Salimans, Jonathan Ho
|
https://iclr.cc/virtual/2022/poster/6537
|
Diffusion Models;Generative Models;fast sampling
| null | 3 | null |
https://openreview.net/forum?id=TIdIXIpzhoI
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8;8
|
4;3;4;4
|
https://iclr.cc/virtual/2022/poster/6537
|
Progressive Distillation for Fast Sampling of Diffusion Models
| null | null | 3.75 | 3.75 |
Spotlight
|
4;4;3;4
|
3;3;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null | null | null | 2.5 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4 |
3;3;5;5
|
3;3;3;3
| null |
Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution
| null | null | 3 | 3.75 |
Withdraw
|
4;3;4;4
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
Diffusion models;CLIP;Image manipulation;Image to image translation
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
|
3;3;3
| null |
DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models
| null | null | 3 | 4 |
Withdraw
|
4;4;4
|
2;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
prototypes;fairness;hierarchy;neural network;encoding
| null | 2.5 | null | null |
iclr
| 0.57735 | 1 | null |
main
| 4.5 |
3;3;6;6
|
2;2;3;3
| null |
Prototype Based Classification from Hierarchy to Fairness
| null | null | 2.5 | 3.25 |
Reject
|
3;3;4;3
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;3;2;2
| null | null | null |
self-supervised learning;unsupervised learning;representation learning
| null | 1.25 | null | null |
iclr
| -0.57735 | 0.333333 | null |
main
| 4.5 |
3;5;5;5
|
3;4;3;3
| null |
Self-Supervised Learning by Estimating Twin Class Distributions
| null | null | 3.25 | 4.5 |
Withdraw
|
5;5;4;4
|
1;0;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;2;2;3
| null | null | null |
Deep Reinforcemenet Learning;Travelling salesman problem;Curriculum Learning;Equivariance;Local Search
| null | 2.5 | null | null |
iclr
| 0.927173 | 0 | null |
main
| 6.25 |
5;6;6;8
|
3;3;3;3
| null |
Generalization in Deep RL for TSP Problems via Equivariance and Local Search
| null | null | 3 | 4.25 |
Reject
|
4;4;4;5
|
3;3;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
3;1;1;3
| null | null | null |
Optimal Transport;Generative Models;Quantile Functions;Time-Series Forecasting;Image Generation
| null | 2 | null | null |
iclr
| 0.333333 | 0.522233 | null |
main
| 3.5 |
3;3;3;5
|
2;4;3;4
| null |
Conditional Generative Quantile Networks via Optimal Transport and Convex Potentials
| null | null | 3.25 | 3.5 |
Reject
|
2;4;4;4
|
2;2;1;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
4;2;3;3
| null | null | null |
Deep Boltzmann machine;mean-field inference;deep equilibrium model
| null | 1.25 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.75 |
3;5;5;6
|
4;4;4;4
| null |
Monotone deep Boltzmann machines
| null | null | 4 | 4.25 |
Reject
|
4;4;5;4
|
1;2;2;0
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null |
representation learning;disentangled representations;generative models
| null | 2.666667 | null | null |
iclr
| -1 | 0.866025 | null |
main
| 3.666667 |
3;3;5
|
2;3;4
| null |
Leveraging Relational Information for Learning Weakly Disentangled Representations
| null | null | 3 | 3.666667 |
Withdraw
|
4;4;3
|
2;3;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
1;3;3;3
| null | null | null |
Neural Networks;Topological Data Analysis;learning;evolution;Persistent Homology
| null | 2.25 | null | null |
iclr
| 0.333333 | 0.333333 | null |
main
| 2.5 |
1;3;3;3
|
1;1;2;1
| null |
Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set
| null | null | 1.25 | 4.25 |
Reject
|
4;4;5;4
|
2;2;2;3
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null |
implicit differentiation;bilevel optimization;autodiff;jax
| null | 2.666667 | null | null |
iclr
| -0.960769 | -0.240192 | null |
main
| 7 |
3;8;10
|
4;3;4
| null |
Efficient and Modular Implicit Differentiation
| null | null | 3.666667 | 3.333333 |
Reject
|
4;3;3
|
2;3;3
|
null | null |
2022
| 2.6 | null | null | 0 | null | null | null |
2;3;3;2;3
| null | null | null |
Scene Priors;Modular Training;Reinforcement Learning;Audio-Visual;Robot Navigation;Embodied
| null | 2.6 | null | null |
iclr
| 0.166667 | 0.456435 | null |
main
| 4.2 |
3;3;5;5;5
|
3;2;2;4;4
| null |
Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation
| null | null | 3 | 3.6 |
Withdraw
|
3;4;4;3;4
|
2;2;3;2;4
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;1;1;3
| null | null | null |
Reinforcement Learning;Action Selection;Cost Optimization;Shapely
| null | 2 | null | null |
iclr
| -0.57735 | 0.707107 | null |
main
| 2 |
1;1;3;3
|
2;1;2;3
| null |
DATA-DRIVEN EVALUATION OF TRAINING ACTION SPACE FOR REINFORCEMENT LEARNING
| null | null | 2 | 3.75 |
Reject
|
4;4;4;3
|
2;1;2;3
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;3;3
| null | null | null |
computer vision;vision transformer;mixer;patch embeddings;convolution;convolutional neural network
| null | 3 | null | null |
iclr
| -0.5 | 0.5 | null |
main
| 6 |
5;5;8
|
3;2;3
| null |
Patches Are All You Need?
|
https://github.com/tmp-iclr/convmixer
| null | 2.666667 | 4.333333 |
Reject
|
4;5;4
|
3;3;3
|
null |
Paper under double-blind review
|
2022
| 1.666667 | null | null | 0 | null | null | null |
2;1;2
| null | null | null |
continuous reinforcement learning;deep q-learning;optimal control problems;normalized advantage functions
| null | 1.333333 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
|
4;3;3
| null |
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis
| null | null | 3.333333 | 3.666667 |
Reject
|
4;5;2
|
2;0;2
|
null |
Department of Computer Sciences, University of Wisconsin - Madison
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6052; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li
|
https://iclr.cc/virtual/2022/poster/6052
| null | null | 2.75 | null |
https://openreview.net/forum?id=TW7d65uYu5M
|
iclr
| -0.19245 | -0.555556 | null |
main
| 6.75 |
5;6;8;8
|
4;4;3;4
|
https://iclr.cc/virtual/2022/poster/6052
|
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
|
https://github.com/deeplearning-wisc/vos
| null | 3.75 | 4 |
Poster
|
5;3;3;5
|
2;3;3;3
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
3;2;3
| null | null | null |
ensemble;boosting;regularization;clusterization
| null | 2 | null | null |
iclr
| -0.5 | -0.5 | null |
main
| 5.333333 |
5;5;6
|
4;3;3
| null |
Learn Together, Stop Apart: a Novel Approach to Ensemble Pruning
| null | null | 3.333333 | 4.333333 |
Reject
|
5;4;4
|
3;0;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2
| null | null | null |
synthetic data;causal inference;EHR;healthcare;deep generative modeling;treatment effects;model validation;observational patient data;patient privacy
| null | 2 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
|
3;3;3
| null |
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments
| null | null | 3 | 4 |
Reject
|
5;3;4
|
2;2;2
|
null | null |
2022
| 2.6 | null | null | 0 | null | null | null |
3;2;3;3;2
| null | null | null |
Extreme-Scale Pretraining;Language Modeling;Natural Language Processing
| null | 2 | null | null |
iclr
| -0.395285 | 0.353553 | null |
main
| 4 |
3;3;3;5;6
|
2;4;2;4;3
| null |
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
| null | null | 3 | 4.2 |
Reject
|
5;4;4;4;4
|
2;1;2;3;2
|
null |
Stony Brook University; SRI International
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6614; None
| null | 0 | null | null | null |
3;3;3;4
| null |
Xiaoling Hu, Xiao Lin, Michael Cogswell, Yi Yao, Susmit Jha, Chao Chen
|
https://iclr.cc/virtual/2022/poster/6614
|
Trojan detection;diversity loss;topological prior
| null | 2.75 | null |
https://openreview.net/forum?id=TXsjU8BaibT
|
iclr
| -0.904534 | 0.57735 | null |
main
| 6.5 |
5;5;8;8
|
3;2;3;3
|
https://iclr.cc/virtual/2022/poster/6614
|
Trigger Hunting with a Topological Prior for Trojan Detection
| null | null | 2.75 | 3.75 |
Poster
|
5;4;3;3
|
3;2;3;3
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;2;1;2
| null | null | null | null | null | 1.75 | null | null |
iclr
| -1 | 0.870388 | null |
main
| 3.5 |
3;3;3;5
|
3;2;2;4
| null |
Space Time Recurrent Memory Network
| null | null | 2.75 | 4.75 |
Withdraw
|
5;5;5;4
|
2;1;2;2
|
null |
MIT-IBM Watson AI Lab; Massachusetts Institute of Technology
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6878; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Han Cai, Chuang Gan, Ji Lin, Song Han
|
https://iclr.cc/virtual/2022/poster/6878
|
Tiny Deep Learning
| null | 2 | null |
https://openreview.net/forum?id=TYw3-OlrRm-
|
iclr
| 0 | 0.990148 |
https://tinyml.mit.edu
|
main
| 5.75 |
3;6;6;8
|
2;3;3;4
|
https://iclr.cc/virtual/2022/poster/6878
|
Network Augmentation for Tiny Deep Learning
| null | null | 3 | 4 |
Poster
|
4;3;5;4
|
2;3;0;3
|
null |
AWS AI Labs
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6924; None
| null | 0 | null | null | null |
2;3;4
| null |
Yifei Ma, Ge Liu, Anoop Deoras
|
https://iclr.cc/virtual/2022/poster/6924
|
Recommender systems;marketing;push notifications;temporal point processes;sequence models
| null | 2.333333 | null |
https://openreview.net/forum?id=TZeArecH2Nf
|
iclr
| -0.944911 | 0.944911 | null |
main
| 6.333333 |
5;6;8
|
3;3;4
|
https://iclr.cc/virtual/2022/poster/6924
|
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
|
https://github.com/awslabs/recurrent-intensity-model-experiments
| null | 3.333333 | 3.666667 |
Poster
|
4;4;3
|
2;3;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;2;3
| null | null | null |
Multi-Agent Reinforcement Learning;Coordination Graphs;Polynomial-time DCOP
| null | 1.666667 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
3;3;8
|
3;3;3
| null |
Self-Organized Polynomial-time Coordination Graphs
| null | null | 3 | 3.666667 |
Reject
|
4;4;3
|
2;0;3
|
null |
MIT-IBM Watson AI Lab
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6345; None
| null | 0 | null | null | null |
3;3;2;3
| null |
Chun-Fu (Richard) Chen, Rameswar Panda, Quanfu Fan
|
https://iclr.cc/virtual/2022/poster/6345
|
vision transformer;image recognition;multi-scale feature
| null | 2.5 | null |
https://openreview.net/forum?id=T__V3uLix7V
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6345
|
RegionViT: Regional-to-Local Attention for Vision Transformers
|
https://github.com/IBM/RegionViT
| null | 3 | 3.5 |
Poster
|
3;4;3;4
|
3;3;2;2
|
null | null |
2022
| 2.6 | null | null | 0 | null | null | null |
2;2;2;3;4
| null | null | null |
neural implicit representations;physics learning;video interpretation;physical parameter estimation
| null | 2.2 | null | null |
iclr
| -0.75 | 0.395285 | null |
main
| 4 |
3;3;3;5;6
|
3;2;3;3;3
| null |
Neural Implicit Representations for Physical Parameter Inference from a Single Video
| null | null | 2.8 | 3 |
Reject
|
3;4;3;3;2
|
2;1;2;3;3
|
null | null |
2022
| 2.4 | null | null | 0 | null | null | null |
2;2;2;2;4
| null | null | null | null | null | 2.4 | null | null |
iclr
| -0.408248 | 0.612372 | null |
main
| 5.2 |
5;5;5;5;6
|
3;3;4;3;4
| null |
Local Calibration: Metrics and Recalibration
| null | null | 3.4 | 4.4 |
Reject
|
5;4;4;5;4
|
2;2;2;2;4
|
null | null |
2022
| 2.666667 | null | null | 0 | null | null | null |
2;3;3
| null | null | null |
Latent Discontinuity;Variational Auto-Encoder;Natural Language Generation;Generative Model
| null | 2.666667 | null | null |
iclr
| -0.5 | 1 | null |
main
| 4 |
3;3;6
|
2;2;3
| null |
On the Latent Holes 🧀 of VAEs for Text Generation
| null | null | 2.333333 | 3.333333 |
Reject
|
4;3;3
|
2;3;3
|
null |
Massachusetts Institute of Technology; Microsoft Research
|
2022
| 2.333333 |
https://iclr.cc/virtual/2022/poster/6930; None
| null | 0 | null | null | null |
2;2;3
| null |
Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry
|
https://iclr.cc/virtual/2022/poster/6930
|
model debugging;vision transformers;missingness
| null | 3 | null |
https://openreview.net/forum?id=Te5ytkqsnl
|
iclr
| -0.5 | -0.5 | null |
main
| 5.333333 |
5;5;6
|
4;3;3
|
https://iclr.cc/virtual/2022/poster/6930
|
Missingness Bias in Model Debugging
|
https://github.com/madrylab/missingness
| null | 3.333333 | 4.333333 |
Poster
|
5;4;4
|
3;3;3
|
null |
KAIST; UC Berkeley; University of Michigan, LG AI Research
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/6054; None
| null | 0 | null | null | null |
3;3;2
| null |
Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee
|
https://iclr.cc/virtual/2022/poster/6054
|
preference-based reinforcement learning;human-in-the-loop reinforcement learning;deep reinforcement learning;semi-supervised learning
| null | 2.666667 | null |
https://openreview.net/forum?id=TfhfZLQ2EJO
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
|
3;4;3
|
https://iclr.cc/virtual/2022/poster/6054
|
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
| null | null | 3.333333 | 3.666667 |
Poster
|
4;3;4
|
3;3;2
|
null | null |
2022
| 2.4 | null | null | 0 | null | null | null |
2;2;3;3;2
| null | null | null | null | null | 2.4 | null | null |
iclr
| -0.408248 | 0.666667 | null |
main
| 4.2 |
3;3;5;5;5
|
2;2;2;3;3
| null |
On the exploitative behavior of adversarial training against adversarial attacks
| null | null | 2.4 | 3.8 |
Reject
|
4;4;4;4;3
|
2;2;3;3;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Reinforcement Learning;Representation Learning;Pixel-based Control
| null | 2.5 | null | null |
iclr
| -1 | 0.57735 | null |
main
| 4.5 |
3;3;6;6
|
3;2;3;3
| null |
Learning Representations for Pixel-based Control: What Matters and Why?
| null | null | 2.75 | 3.5 |
Reject
|
4;4;3;3
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
Deep learning;Neural networks;Computation Efficiency;Weight pruning;Overfitting;Softmax;Log likelihood ratio (LLR)
| null | 1.75 | null | null |
iclr
| 0 | 0.816497 | null |
main
| 3 |
1;3;3;5
|
2;2;2;3
| null |
On the Efficiency of Deep Neural Networks
| null | null | 2.25 | 3.75 |
Withdraw
|
4;3;4;4
|
1;2;2;2
|
null |
Wyze Labs Inc.; Yale Institute for Network Science, Yale University; Department of Computer Science & Engineering, The Pennsylvania State University
|
2022
| 2.666667 |
https://iclr.cc/virtual/2022/poster/6784; None
| null | 0 | null | null | null |
2;3;3
| null |
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Amin Karbasi
|
https://iclr.cc/virtual/2022/poster/6784
|
Composite Optimization;Distributionally Robust Optimization
| null | 2.666667 | null |
https://openreview.net/forum?id=To-R742x7se
|
iclr
| 0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
|
4;4;4
|
https://iclr.cc/virtual/2022/poster/6784
|
Learning Distributionally Robust Models at Scale via Composite Optimization
| null | null | 4 | 2.666667 |
Poster
|
2;2;4
|
2;2;4
|
null |
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027, USA
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6678; None
| null | 0 | null | null | null |
2;2;3;3
| null |
Daniel R Kepple, Rainer Engelken, Kanaka Rajan
|
https://iclr.cc/virtual/2022/poster/6678
|
curriculum learning;neuroscience
| null | 3 | null |
https://openreview.net/forum?id=TpJMvo0_pu-
|
iclr
| -0.688247 | 0.973329 | null |
main
| 6.25 |
5;6;6;8
|
2;3;3;4
|
https://iclr.cc/virtual/2022/poster/6678
|
Curriculum learning as a tool to uncover learning principles in the brain
| null | null | 3 | 3.5 |
Poster
|
4;4;3;3
|
2;3;4;3
|
null |
MIT, Google; MIT; Microsoft
|
2022
| 3.333333 |
https://iclr.cc/virtual/2022/poster/6983; None
| null | 0 | null | null | null |
4;3;3
| null |
Mark Hamilton, Scott Lundberg, Stephanie Fu, Lei Zhang, William Freeman
|
https://iclr.cc/virtual/2022/poster/6983
|
Model Interpretability;Shapley Values;Search Engines;Information Retrieval;Visual Search;Similarity Learning;Metric Learning;Black-box explanations
| null | 2.666667 | null |
https://openreview.net/forum?id=TqNsv1TuCX9
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
|
3;3;4
|
https://iclr.cc/virtual/2022/poster/6983
|
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
| null | null | 3.333333 | 3.666667 |
Poster
|
4;5;2
|
3;2;3
|
null |
Google
|
2022
| 2.5 |
https://iclr.cc/virtual/2022/poster/6064; None
| null | 0 | null | null | null |
2;2;2;4
| null |
Yuhuai Wu, Markus Rabe, DeLesley Hutchins, Christian Szegedy
|
https://iclr.cc/virtual/2022/poster/6064
|
Transformer;architecture;memorization.
| null | 2.5 | null |
https://openreview.net/forum?id=TrjbxzRcnf-
|
iclr
| 0.927173 | -0.229416 | null |
main
| 6.25 |
5;6;6;8
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6064
|
Memorizing Transformers
| null | null | 3.5 | 4.25 |
Spotlight
|
4;4;4;5
|
2;2;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
3;1;2;2
| null | null | null |
aBCIs;EEG-based emotion recognition;domain adaptation;domain generalization;meta-learning;adversarial learning
| null | 2 | null | null |
iclr
| 0.57735 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
2;3;3;3
| null |
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition
| null | null | 2.75 | 4.75 |
Reject
|
4;5;5;5
|
2;1;3;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
2;3;2
| null | null | null |
antitransitivity;parasiamese network;antonymy-synonymy discrimination
| null | 2.333333 | null | null |
iclr
| -1 | 0.5 | null |
main
| 5 |
3;6;6
|
3;4;3
| null |
Antonymy-Synonymy Discrimination through the Repelling Parasiamese Neural Network
| null | null | 3.333333 | 3.333333 |
Reject
|
4;3;3
|
2;3;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
2;1;2;2
| null | null | null |
Metric Learning;open set;deep learning
| null | 1.75 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
3;3;2;3
| null |
Hyperspherical embedding for novel class classification
| null | null | 2.75 | 3.75 |
Reject
|
4;4;4;3
|
1;2;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;3;3;3
| null | null | null |
Riemannian Geometry;Point Cloud;Autoencoders
| null | 2.5 | null | null |
iclr
| -0.919866 | -0.160128 | null |
main
| 5.5 |
3;5;6;8
|
4;4;3;4
| null |
A Statistical Manifold Framework for Point Cloud Data
| null | null | 3.75 | 3.75 |
Reject
|
5;4;3;3
|
3;2;2;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;3;2
| null | null | null |
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
| null | 2.75 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
|
4;3;4;3
| null |
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
| null | null | 3.5 | 4.25 |
Reject
|
5;4;4;4
|
3;3;3;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
domain adaptation;object detection;discriminative feature mining
| null | 2.5 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
4;2;2;3
| null |
Decouple and Reconstruct: Mining Discriminative Features for Cross-domain Object Detection
| null | null | 2.75 | 3 |
Reject
|
3;3;4;2
|
4;2;2;2
|
null |
School of Cyber Science and Technology, Zhejiang University; Tsinghua Shenzhen International Graduate School, Tsinghua University; School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6519; None
| null | 0 | null | null | null |
2;3;3;3
| null |
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, Kui Ren
|
https://iclr.cc/virtual/2022/poster/6519
|
Backdoor Defense;Backdoor Learning
| null | 2.75 | null |
https://openreview.net/forum?id=TySnJ-0RdKI
|
iclr
| -0.333333 | 0 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;3
|
https://iclr.cc/virtual/2022/poster/6519
|
Backdoor Defense via Decoupling the Training Process
|
https://github.com/SCLBD/DBD
| null | 3 | 4.25 |
Poster
|
5;4;4;4
|
2;3;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
compositional learning;few-shot;few referential compositions
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
|
3;3;2;2
| null |
Reference-Limited Compositional Learning: A Realistic Assessment for Human-level Compositional Generalization
| null | null | 2.5 | 3.75 |
Withdraw
|
4;4;3;4
|
2;3;2;2
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;3;2;2
| null | null | null |
Graph Representation Learning;Latent Space Model;Complex Networks;Scalable Network embeddings;Link prediction;Low dimension graph representations
| null | 2.25 | null | null |
iclr
| 0.333333 | -0.870388 | null |
main
| 3.5 |
3;3;3;5
|
4;4;3;2
| null |
Scalable Hierarchical Embeddings of Complex Networks
| null | null | 3.25 | 3.75 |
Reject
|
4;4;3;4
|
2;3;2;2
|
null | null |
2022
| 3.25 | null | null | 0 | null | null | null |
3;3;4;3
| null | null | null |
interpretability;concept-based explanations;counterfactual explanations
| null | 2.25 | null | null |
iclr
| 0 | -0.57735 |
Not provided
|
main
| 5.75 |
5;6;6;6
|
4;4;3;3
| null |
Meaningfully Explaining Model Mistakes Using Conceptual Counterfactuals
|
Not provided
| null | 3.5 | 4 |
Reject
|
4;4;4;4
|
2;3;2;2
|
null |
DeepMind; University of Toronto, Vector Institute
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6162; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Guodong Zhang, Aleksandar Botev, James Martens
|
https://iclr.cc/virtual/2022/poster/6162
|
Neural Network Training;Kernel Approximation for Neural Networks;Neural Network Initialization;Generalization
| null | 2.75 | null |
https://openreview.net/forum?id=U0k7XNTiFEq
|
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
|
3;4;4;4
|
https://iclr.cc/virtual/2022/poster/6162
|
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
| null | null | 3.75 | 3 |
Poster
|
4;3;2;3
|
3;2;3;3
|
null | null |
2022
| 3.5 | null | null | 0 | null | null | null |
3;4;3;4
| null | null | null |
distributed training;model-parallel;model parallelism;pipeline;fault tolerance;communication efficiency;volunteer computing
| null | 1.75 | null | null |
iclr
| -0.57735 | 0.816497 | null |
main
| 5.25 |
3;6;6;6
|
2;3;3;4
| null |
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
| null | null | 3 | 4.5 |
Reject
|
5;4;5;4
|
0;3;2;2
|
null |
Department of Computer Science & Engineering, The Chinese University of Hong Kong; School of Nursing, The Hong Kong Polytechnic University
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6803; None
| null | 0 | null | null | null |
3;3;3;4
| null |
Minhao LIU, Ailing Zeng, Qiuxia LAI, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu
|
https://iclr.cc/virtual/2022/poster/6803
| null | null | 2.25 | null |
https://openreview.net/forum?id=U4uFaLyg7PV
|
iclr
| 0 | 1 | null |
main
| 6.5 |
6;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6803
|
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
| null | null | 3.25 | 3 |
Poster
|
4;3;2;3
|
3;0;3;3
|
null |
UC Berkeley
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6075; None
| null | 0 | null | null | null |
3;3;3;3
| null |
Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, trevor darrell
|
https://iclr.cc/virtual/2022/poster/6075
| null | null | 3.5 | null |
https://openreview.net/forum?id=U8pbd00cCWB
|
iclr
| -0.662266 | 0.927173 | null |
main
| 6.25 |
5;6;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6075
|
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
|
https://github.com/zhusz/ICLR22-DGS
| null | 3.25 | 4.25 |
Poster
|
5;4;4;4
|
3;4;3;4
|
null | null |
2022
| 3 | null | null | 0 | null | null | null |
3;2;4;3
| null | null | null |
Randomized Smoothing;Adversarial Robustness;Semantic Transformations;Machine Learning
| null | 3 | null | null |
iclr
| 0.852803 | 0.333333 | null |
main
| 6 |
3;5;8;8
|
2;4;3;3
| null |
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
| null | null | 3 | 3.75 |
Reject
|
3;3;5;4
|
2;3;4;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;3;2;3
| null | null | null |
Robust Estimation;Imitation Learning;Reinforcement Learning
| null | 2.25 | null | null |
iclr
| -1 | 0.904534 | null |
main
| 4 |
3;3;5;5
|
2;2;3;4
| null |
Robust Imitation Learning from Corrupted Demonstrations
| null | null | 2.75 | 3.5 |
Reject
|
4;4;3;3
|
2;2;3;2
|
null | null |
2022
| 1.75 | null | null | 0 | null | null | null |
1;2;2;2
| null | null | null |
Graph Neural Network;Pooling;Extrapolation
| null | 2 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4 |
3;3;5;5
|
3;3;3;3
| null |
Learning to Pool in Graph Neural Networks for Extrapolation
| null | null | 3 | 3.75 |
Reject
|
4;4;4;3
|
2;2;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
2;3;3;3
| null | null | null |
randomized smoothing;anisotropic certification;deep neural network certification;certified defenses
| null | 2.75 | null | null |
iclr
| 0 | 0.662266 | null |
main
| 6.25 |
5;6;6;8
|
3;4;4;4
| null |
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
| null | null | 3.75 | 4 |
Reject
|
4;5;3;4
|
2;3;3;3
|
null | null |
2022
| 1.6 | null | null | 0 | null | null | null |
2;1;2;1;2
| null | null | null |
neural networks;trainable activation function;function approximation;image classification
| null | 1 | null | null |
iclr
| 0 | -0.645497 | null |
main
| 2.2 |
1;1;3;3;3
|
2;3;2;1;2
| null |
Neural networks with trainable matrix activation functions
| null | null | 2 | 4 |
Reject
|
4;4;5;3;4
|
1;1;1;1;1
|
null | null |
2022
| 1.25 | null | null | 0 | null | null | null |
1;1;1;2
| null | null | null |
uncertainty estimation;prior networks;posterior networks;conjugate priors;classification;regression;evidential deep learning;dirichlet
| null | 0.25 | null | null |
iclr
| 0.870388 | -0.301511 | null |
main
| 3.5 |
1;3;5;5
|
4;3;4;3
| null |
A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation
| null | null | 3.5 | 3.75 |
Reject
|
3;4;4;4
|
0;0;1;0
|
null | null |
2022
| 2.4 | null | null | 0 | null | null | null |
2;2;3;2;3
| null | null | null |
representation learning
| null | 2.2 | null | null |
iclr
| -0.322749 | -0.166667 | null |
main
| 4.8 |
3;5;5;5;6
|
3;3;2;2;3
| null |
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
| null | null | 2.6 | 4 |
Withdraw
|
4;4;4;5;3
|
1;2;3;2;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
influence maximization;graph neural networks
| null | 2.25 | null | null |
iclr
| -0.98644 | 0.333333 | null |
main
| 4.25 |
3;3;5;6
|
3;3;4;3
| null |
Learning Graph Representations for Influence Maximization
| null | null | 3.25 | 3.25 |
Reject
|
4;4;3;2
|
2;2;3;2
|
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/6394; None
| null | 0 | null | null | null |
3;3;3;2
| null |
Ge Liu, Alexander Dimitrakakis, Brandon Carter, David Gifford
|
https://iclr.cc/virtual/2022/poster/6394
|
computational biology;vaccine design;COVID-19;maximum n-times coverage;combinatorial optimization;integer linear programming
| null | 2.5 | null |
https://openreview.net/forum?id=ULfq0qR25dY
|
iclr
| -0.333333 | 0.816497 | null |
main
| 6.5 |
6;6;6;8
|
3;3;2;4
|
https://iclr.cc/virtual/2022/poster/6394
|
Maximum n-times Coverage for Vaccine Design
| null | null | 3 | 3.25 |
Poster
|
3;3;4;3
|
3;2;2;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3
| null | null | null |
time delay estimation;transfer entropy;traffic congestion
| null | 2 | null | null |
iclr
| -1 | -0.5 | null |
main
| 4 |
3;3;6
|
4;3;3
| null |
Time Delay Estimation of Traffic Congestion Based on Statistical Causality
| null | null | 3.333333 | 3.666667 |
Withdraw
|
4;4;3
|
1;2;3
|
null |
University of Illinois, Urbana Champaign; ETH Zurich
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6160; None
| null | 0 | null | null | null |
2;3;4
| null |
Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev
|
https://iclr.cc/virtual/2022/poster/6160
|
Adversarial attacks;Robustness Certification;Abstract Interpretation;Deep Learning
| null | 3 | null |
https://openreview.net/forum?id=UMfhoMtIaP5
|
iclr
| 0.866025 | 1 | null |
main
| 6.666667 |
6;6;8
|
3;3;4
|
https://iclr.cc/virtual/2022/poster/6160
|
Provably Robust Adversarial Examples
| null | null | 3.333333 | 4 |
Poster
|
4;3;5
|
3;3;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3
| null | null | null |
Optimal transport;Wasserstein distance;Incomprable distributions;Generative models
| null | 0.666667 | null | null |
iclr
| -0.5 | 0.866025 | null |
main
| 4 |
3;3;6
|
3;2;4
| null |
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
| null | null | 3 | 3.666667 |
Reject
|
5;3;3
|
2;0;0
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
Pareto Optimality;Neural nets;Pareto Filter;Interpretability;Benchmarking
| null | 1.75 | null | null |
iclr
| -0.816497 | 1 | null |
main
| 3.75 |
3;3;3;6
|
2;2;2;3
| null |
A Two-Stage Neural-Filter Pareto Front Extractor and the need for Benchmarking
| null | null | 2.25 | 4 |
Reject
|
4;5;4;3
|
2;1;1;3
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
3;3;2;2
| null |
Author
| null |
semantic segmentation;segmentation from noisy annotations;weakly supervised semantic segmentation
| null | 2.25 | null | null |
iclr
| -0.174078 | 1 | null |
main
| 4.5 |
3;5;5;5
|
2;3;3;3
| null |
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
| null | null | 2.75 | 3.75 |
Withdraw
|
4;5;3;3
|
2;3;2;2
|
null | null |
2022
| 2.75 | null | null | 0 | null | null | null |
3;2;3;3
| null | null | null |
Data Augmentation;Test Time Augmentation;Uncertainty Estimation
| null | 2 | null | null |
iclr
| -0.688247 | 0.57735 | null |
main
| 4 |
3;3;5;5
|
2;3;3;3
| null |
Cyclic Test Time Augmentation with Entropy Weight Method
| null | null | 2.75 | 3.25 |
Withdraw
|
3;5;2;3
|
2;2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
gradient tampering;smoothing;softmax;prediction;image classification;neural networks
| null | 1.75 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 3 |
1;3;3;5
|
2;3;2;2
| null |
Softmax Gradient Tampering: Decoupling the Backward Pass for Improved Fitting
| null | null | 2.25 | 3.75 |
Reject
|
3;4;4;4
|
1;2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;3;2
| null | null | null |
Learning with noisy labels;label smoothing;model confidence
| null | 2 | null | null |
iclr
| 0 | 0.816497 | null |
main
| 4.5 |
3;5;5;5
|
2;3;4;3
| null |
Understanding Generalized Label Smoothing when Learning with Noisy Labels
| null | null | 3 | 4 |
Reject
|
4;3;4;5
|
1;2;4;1
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;4;2
| null | null | null |
Deep Learning;Computer Vision;Domain Generalization
| null | 2.25 | null | null |
iclr
| -0.816497 | 0.544331 | null |
main
| 6 |
3;5;8;8
|
3;3;4;3
| null |
Fishr: Invariant Gradient Variances for Out-of-distribution Generalization
|
https://anonymous.4open.science/r/fishr-anonymous-EBB6/
| null | 3.25 | 4.25 |
Withdraw
|
5;4;4;4
|
2;2;2;3
|
null |
Department of Computer Science, Rutgers University
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6200; None
| null | 0 | null | null | null |
3;3;3
| null |
Wenzheng Zhang, Wenyue Hua, Karl Stratos
|
https://iclr.cc/virtual/2022/poster/6200
|
Entity linking;open-domain question answering;dense retrieval;reading comprehension;information extraction;natural language processing
| null | 2.333333 | null |
https://openreview.net/forum?id=US2rTP5nm_
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
|
3;3;4
|
https://iclr.cc/virtual/2022/poster/6200
|
EntQA: Entity Linking as Question Answering
| null | null | 3.333333 | 3.666667 |
Spotlight
|
4;4;3
|
2;3;2
|
null |
School of CS, Peking University; National Engineering Laboratory for Big Data Analysis and Applications; Institute of Computational Social Science, Peking University (Qingdao), China; School of CS, Peking University; Apple; School of CS, Peking University; National Engineering Laboratory for Big Data Analysis and Applications
|
2022
| 3 |
https://iclr.cc/virtual/2022/poster/6074; None
| null | 0 | null | null | null |
2;3;3;4
| null |
Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin CUI
|
https://iclr.cc/virtual/2022/poster/6074
|
Active Learning;Graph;Information Gain
| null | 2 | null |
https://openreview.net/forum?id=USC0-nvGPK
|
iclr
| 0.679366 | 0.679366 | null |
main
| 5 |
1;5;6;8
|
3;3;3;4
|
https://iclr.cc/virtual/2022/poster/6074
|
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels
| null | null | 3.25 | 4.25 |
Poster
|
4;4;4;5
|
2;0;3;3
|
null |
Université de Neuchâtel, Neuchâtel, Switzerland; Yandex Research, Moscow, Russia; MIPT, Moscow, Russia; IITP RAS; MIPT, Moscow, Russia
|
2022
| 2.8 |
https://iclr.cc/virtual/2022/poster/6589; None
| null | 0 | null | null | null |
2;3;3;3;3
| null |
Liudmila Prokhorenkova, Dmitry Baranchuk, Nikolay Bogachev, Yury Demidovich, Alexander Kolpakov
|
https://iclr.cc/virtual/2022/poster/6589
|
similarity search;nearest neighbor search;hyperbolic space;graph-based nearest neighbor search
| null | 1.8 | null |
https://openreview.net/forum?id=USIgIY6TNDe
|
iclr
| -0.408248 | -0.408248 | null |
main
| 5.8 |
5;6;6;6;6
|
4;4;3;3;4
|
https://iclr.cc/virtual/2022/poster/6589
|
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
| null | null | 3.6 | 3.6 |
Poster
|
4;4;3;4;3
|
2;3;0;1;3
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
reinforcement learning;hierachical reinforcement learning;options;life long reinforcement learning;skill transfer
| null | 2 | null | null |
iclr
| 0.57735 | -0.707107 | null |
main
| 5.5 |
5;5;6;6
|
4;3;2;3
| null |
Learning Diverse Options via InfoMax Termination Critic
| null | null | 3 | 3.75 |
Reject
|
4;3;4;4
|
2;2;2;2
|
null | null |
2022
| 2.5 | null | null | 0 | null | null | null |
2;2;3;3
| null | null | null |
Forecasting Model Selection;Time-series Forecasting;Meta-features
| null | 2.75 | null | null |
iclr
| -0.555556 | 0.19245 | null |
main
| 4.25 |
3;3;5;6
|
3;2;2;3
| null |
Automatic Forecasting via Meta-Learning
| null | null | 2.5 | 3.25 |
Reject
|
3;4;3;3
|
2;2;4;3
|
null | null |
2022
| 1.8 | null | null | 0 | null | null | null |
2;2;1;2;2
| null | null | null |
Knowledge Graph Embedding;Link Prediction;Representation Learning
| null | 2 | null | null |
iclr
| -0.790569 | -0.25 | null |
main
| 3.4 |
3;3;3;3;5
|
2;2;2;3;2
| null |
KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods
| null | null | 2.2 | 4 |
Withdraw
|
4;5;4;4;3
|
2;2;1;3;2
|
null | null |
2022
| 2.333333 | null | null | 0 | null | null | null |
1;3;3
| null | null | null |
Adversarial Domain Adaptation;Dynamic Domain Labels
| null | 2.333333 | null | null |
iclr
| -0.5 | 1 | null |
main
| 4.333333 |
3;5;5
|
1;3;3
| null |
Unleash the Potential of Adaptation Models via Dynamic Domain Labels
| null | null | 2.333333 | 4.333333 |
Withdraw
|
5;5;3
|
2;3;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
3;1;1;3
| null | null | null |
reinforcement learning;transfer learning;representations;dimensionality;sparsity;RSA
| null | 2.5 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.75 |
3;3;3;6
|
3;4;2;3
| null |
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
| null | null | 3 | 3.5 |
Reject
|
4;4;3;3
|
2;2;2;4
|
null | null |
2022
| 2.25 | null | null | 0 | null | null | null |
2;2;3;2
| null | null | null |
Out-of-distribution detection;Fourier analysis;Normailzing flow model
| null | 2.5 | null | null |
iclr
| -0.816497 | 0.816497 | null |
main
| 5 |
3;5;6;6
|
3;3;4;4
| null |
Decomposing Texture and Semantics for Out-of-distribution Detection
| null | null | 3.5 | 3.5 |
Reject
|
4;4;3;3
|
2;2;3;3
|
null |
Shannon.AI, Zhejiang University; Peking University; Shannon.AI; Tsinghua University; Nanyang Technological University; Zhejiang University
|
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 |
A General Framework for Defending against Backdoor Attacks via Influence Graph
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Stanford University, Computer Science Department
|
2022
| 2.75 |
https://iclr.cc/virtual/2022/poster/5945; None
| null | 0 | null | null | null |
3;3;3;2
| null |
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang
|
https://iclr.cc/virtual/2022/poster/5945
|
fine-tuning theory;transfer learning theory;fine-tuning;distribution shift;implicit regularization
| null | 2 | null |
https://openreview.net/forum?id=UYneFzXSJWh
|
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8;8
|
3;3;4;4
|
https://iclr.cc/virtual/2022/poster/5945
|
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
| null | null | 3.5 | 3.5 |
Oral
|
3;4;4;3
|
3;2;0;3
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null |
Federated learning;optimization;sketching;differential privacy
| null | 0.5 | null | null |
iclr
| 0 | 0.707107 | null |
main
| 4 |
3;3;5;5
|
3;2;4;3
| null |
Iterative Sketching and its Application to Federated Learning
| null | null | 3 | 3.5 |
Reject
|
4;3;3;4
|
0;0;2;0
|
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 |
Sampling Before Training: Rethinking the Effect of Edges in the Process of Training Graph Neural Networks
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2022
| 2 | null | null | 0 | null | null | null |
1;2;2;3
| null | null | null | null | null | 1.75 | null | null |
iclr
| 0 | 0.852803 | null |
main
| 3 |
1;3;3;5
|
2;2;3;4
| null |
Towards Robust Active Feature Acquisition
| null | null | 2.75 | 3.5 |
Withdraw
|
3;5;3;3
|
1;2;2;2
|
null | null |
2022
| 2 | null | null | 0 | null | null | null |
2;2;2;2
| null | null | null |
pruning;sparse;DNN
| null | 2.25 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
|
3;3;3;3
| null |
Learning sparse DNNs with soft thresholding of weights during training
| null | null | 3 | 4 |
Reject
|
4;4;4;4
|
3;2;2;2
|
null |
Cornell University; Carnegie Mellon University
|
2022
| 3.25 |
https://iclr.cc/virtual/2022/poster/6196; None
| null | 0 | null | null | null |
4;3;3;3
| null |
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani
|
https://iclr.cc/virtual/2022/poster/6196
|
Agent Design;Morphology Optimization;Reinforcement Learning
| null | 3 | null |
https://openreview.net/forum?id=UcDUxjPYWSr
|
iclr
| 0 | 0 |
https://sites.google.com/view/transform2act
|
main
| 8 |
8;8;8;8
|
3;4;4;3
|
https://iclr.cc/virtual/2022/poster/6196
|
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
|
Not provided
| null | 3.5 | 4.25 |
Oral
|
4;4;5;4
|
3;3;3;3
|
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