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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture search;stabilize DARTS;noise injection
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
2;5;5;5
| null | null |
Noisy Differentiable Architecture Search
| null | null | 0 | 4 |
Withdraw
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Time Series Analysis;Counterfactual Inference;Differential Equations.
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Time Series Counterfactual Inference with Hidden Confounders
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
School of Electrical and Data Engineering, University of Technology Sydney; Australian Artificial Intelligence Institute, University of Technology Sydney
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2633; None
| null | 0 | null | null | null | null | null |
Shuo Yang, Lu Liu, Min Xu
|
https://iclr.cc/virtual/2021/poster/2633
|
few-shot learning;image classification;distribution estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2633
|
Free Lunch for Few-shot Learning: Distribution Calibration
|
https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration
| null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Out-of-Distribution Detection;Novelty Detection
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
UNSUPERVISED ANOMALY DETECTION FROM SEMANTIC SIMILARITY SCORES
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer learning;graph neural networks
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null |
Huazhong University of Science and Technology; Sun Yat-sen University; Shanghai Jiao Tong University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3349; None
| null | 0 | null | null | null | null | null |
Hao Zhang, Sen Li, YinChao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang
|
https://iclr.cc/virtual/2021/poster/3349
|
Dropout;Interpretability;Interactions
| null | 0 | null | null |
iclr
| 0.968496 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3349
|
Interpreting and Boosting Dropout from a Game-Theoretic View
| null | null | 0 | 3.75 |
Poster
|
1;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
objective functions;reinforcement learning;information theory;probabilistic modeling;control as inference;exploration;intrinsic motivation;world models
| null | 0 | null | null |
iclr
| -0.666667 | 0 | null |
main
| 5.5 |
3;6;6;7
| null | null |
Action and Perception as Divergence Minimization
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
MIT-IBM Watson AI Lab, IBM Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3236; None
| null | 0 | null | null | null | null | null |
Veronika Thost, Jie Chen
|
https://iclr.cc/virtual/2021/poster/3236
|
Graph Neural Networks;Graph Representation Learning;Directed Acyclic Graphs;DAG;Inductive Bias
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3236
|
Directed Acyclic Graph Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Fourier Representations for Black-Box Optimization over Categorical Variables
| null | null | 0 | 2.5 |
Reject
|
3;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolutional Networks;Filter Representation Power;Graph Polynomial Filters
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
SoGCN: Second-Order Graph Convolutional Networks
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual reinforcement learning;lifelong learning;deep reinforcement learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Self-Activating Neural Ensembles for Continual Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;generalization;regularization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
University of Oxford, OATML, Department of Computer Science; University of Oxford, Department of Statistics
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3037; None
| null | 0 | null | null | null | null | null |
Sebastian Farquhar, Yarin Gal, Tom Rainforth
|
https://iclr.cc/virtual/2021/poster/3037
|
Active Learning;Monte Carlo;Risk Estimation
| null | 0 | null | null |
iclr
| -0.19245 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3037
|
On Statistical Bias In Active Learning: How and When to Fix It
| null | null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null |
Vrije Universiteit Amsterdam; ´Ecole Polytechnique F ´ed´erale de Lausanne (EPFL)
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3118; None
| null | 0 | null | null | null | null | null |
David W. Romero, Jean-Baptiste Cordonnier
|
https://iclr.cc/virtual/2021/poster/3118
|
group equivariant transformers;group equivariant self-attention;group equivariance;self-attention;transformers
| null | 0 | null | null |
iclr
| 0.454545 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3118
|
Group Equivariant Stand-Alone Self-Attention For Vision
| null | null | 0 | 4.25 |
Poster
|
5;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Conditional Generative Adversarial Network;Multimodal Image-to-Image Translation
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Diversity Augmented Conditional Generative Adversarial Network for Enhanced Multimodal Image-to-Image Translation
|
https://github.com/anomymous-gan/DivAugGAN
| null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null |
Qualcomm AI Research; Qualcomm AI Research, University of Amsterdam; QUV A Lab, University of Amsterdam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3367; None
| null | 0 | null | null | null | null | null |
Pim De Haan, Maurice Weiler, Taco Cohen, Max Welling
|
https://iclr.cc/virtual/2021/poster/3367
|
symmetry;equivariance;mesh;geometric;convolution
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.5 |
7;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3367
|
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
| null | null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null |
National University of Singapore, Singapore; Institute for Infocomm Research, A*STAR, Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3369; None
| null | 0 | null | null | null | null | null |
Kangkang Lu, Cuong Nguyen, Xun Xu, Kiran Chari, Yu Jing Goh, Chuan-Sheng Foo
|
https://iclr.cc/virtual/2021/poster/3369
|
Adversarial Robustness;Semi-supervised Learning;Multi-view Learning;Diversity Regularization;Entropy Maximization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3369
|
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
| null | null | 0 | 4.25 |
Poster
|
4;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conditional text generation;hallucination detection;sequence generation evaluation;neural machine translation;abstractive text summarization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Detecting Hallucinated Content in Conditional Neural Sequence Generation
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural network;place cell;hippocampus;neural dynamics
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dialogue System;Persuasion;Conversation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robust;Multi-agent;Reinforcement Learning;Correlated Equilibrium
| null | 0 | null | null |
iclr
| -0.320256 | 0 | null |
main
| 4.4 |
3;4;4;5;6
| null | null |
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium
| null | null | 0 | 3.4 |
Reject
|
4;3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Task-similarity;Meta-learning;Kernel regression;Nonparametric regression;Task-descriptors
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Optimistic Policy Optimization with General Function Approximations
| null | null | 0 | 3.25 |
Reject
|
4;4;1;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Generalization;Molecular Property Prediction
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Enforcing Predictive Invariance across Structured Biomedical Domains
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty;confidence;out of distribution;outlier exposure;classification
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Uncertainty for deep image classifiers on out of distribution data.
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNNs;complex;hypercomplex
| null | 0 | null | null |
iclr
| 0.4842 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Removing Dimensional Restrictions on Complex/Hyper-complex Convolutions
| null | null | 0 | 3.75 |
Reject
|
4;2;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Physical simulation;Quantum chemistry;Catalysis
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Invariant Causal Representation Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI; CMU
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2679; None
| null | 0 | null | null | null | null | null |
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
|
https://iclr.cc/virtual/2021/poster/2679
|
exponential tilting;models of learning and generalization;label noise robustness;fairness
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2679
|
Tilted Empirical Risk Minimization
| null | null | 0 | 3.25 |
Poster
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised machine translation;NMT;machine translation
| null | 0 | null | null |
iclr
| -0.090909 | 0 |
https://tinyurl.com/y2ru8res
|
main
| 6.25 |
5;6;7;7
| null | null |
Cross-model Back-translated Distillation for Unsupervised Machine Translation
| null | null | 0 | 4.25 |
Reject
|
5;3;5;4
| null |
null |
Knowledge Representation and Management, FAU Erlangen-Nürnberg; Computational Logic, University of Innsbruck; Computational Logic, University of Innsbruck; Institute of Computer science, Warsaw University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2931; None
| null | 0 | null | null | null | null | null |
Dennis Müller, Cezary Kaliszyk
|
https://iclr.cc/virtual/2021/poster/2931
| null | null | 0 | null | null |
iclr
| 0.845154 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2931
|
Disambiguating Symbolic Expressions in Informal Documents
| null | null | 0 | 3.5 |
Poster
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Memory Augmented Design of Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
SenseTime Research, CUHK-Sensetime Joint Lab, CUHK; SenseTime Research; CUHK-Sensetime Joint Lab, CUHK; NLPR, CASIA; Northwestern University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3174; None
| null | 0 | null | null | null | null | null |
Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, Hongsheng Li
|
https://iclr.cc/virtual/2021/poster/3174
|
sparsity;efficient training and inference.
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3174
|
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
|
https://github.com/NM-sparsity/NM-sparsity
| null | 0 | 3.75 |
Poster
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural architecture search;Flat minima
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Neighborhood-Aware Neural Architecture Search
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null |
MIT, Adobe Research; MIT, Google; MIT, Microsoft
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive Losses;Outlier Detection;Adaptive Regularization;Recalibration;Robust Modelling
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
It Is Likely That Your Loss Should be a Likelihood
| null | null | 0 | 3.75 |
Reject
|
4;5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;episodic memory;GEM;experience replay;deep metric learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
privacy;private learning;dynamic policy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
On Dynamic Noise Influence in Differential Private Learning
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;Ito Process;Stochastic
| null | 0 | null | null |
iclr
| -0.985844 | 0 | null |
main
| 4.75 |
1;5;6;7
| null | null |
Robust Ensembles of Neural Networks using Itô Processes
| null | null | 0 | 3 |
Withdraw
|
5;3;2;2
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2813; None
| null | 0 | null | null | null | null | null |
Thao Nguyen, Maithra Raghu, Simon Kornblith
|
https://iclr.cc/virtual/2021/poster/2813
|
Representation learning
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2813
|
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
| null | null | 0 | 3.5 |
Poster
|
3;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Contrastive learning;Self-supervised learning;Unsupervised learning;Stronger augmentations
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Contrastive Learning with Stronger Augmentations
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null |
University of Chinese Academy of Sciences; Shanghai Jiao Tong University; Meituan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2641; None
| null | 0 | null | null | null | null | null |
Xiangxiang Chu, Victor Wang, Bo Zhang, Shun Lu, Xiaolin Wei, Junchi Yan
|
https://iclr.cc/virtual/2021/poster/2641
|
neural architecture search;DARTS stability
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2641
|
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
|
https://github.com/Meituan-AutoML/DARTS-
| null | 0 | 3.75 |
Poster
|
5;3;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Meta-Model-Based Meta-Policy Optimization
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multiple instance learning;mil;mil pooling filters;distribution pooling;point estimate based pooling
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Distribution Based MIL Pooling Filters are Superior to Point Estimate Based Counterparts
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.8044 | 0 | null |
main
| 4.5 |
2;3;6;7
| null | null |
Intervention Generative Adversarial Nets
| null | null | 0 | 4.25 |
Reject
|
5;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised representation learning;deep image compression
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
| null | null | 0 | 2.5 |
Reject
|
3;4;2;1
| null |
null |
Carnegie Mellon University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2913; None
| null | 0 | null | null | null | null | null |
Mikhail Khodak, Neil Tenenholtz, Lester Mackey, Nicolo Fusi
|
https://iclr.cc/virtual/2021/poster/2913
|
model compression;knowledge distillation;multi-head attention;matrix factorization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2913
|
Initialization and Regularization of Factorized Neural Layers
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null |
Department of Computer Science, University of Bonn
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3004; None
| null | 0 | null | null | null | null | null |
Nils Wandel, Michael Weinmann, Reinhard Klein
|
https://iclr.cc/virtual/2021/poster/3004
|
Unsupervised Learning;Fluid Dynamics;U-Net
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3004
|
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize
| null | null | 0 | 4 |
Spotlight
|
3;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
density estimation;self-attention
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
TraDE: A Simple Self-Attention-Based Density Estimator
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Interpretability;Counterfactual;Explanations;Black-Box
| null | 0 | null | null |
iclr
| -0.272166 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null |
Department of Computer Science, Purdue University; Department of Computer Science, Stanford University; Electrical Engineering, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2651; None
| null | 0 | null | null | null | null | null |
Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, Pan Li
|
https://iclr.cc/virtual/2021/poster/2651
|
temporal networks;inductive representation learning;anonymous walk;network motif
| null | 0 | null | null |
iclr
| 0 | 0 |
http://snap.stanford.edu/caw/
|
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2651
|
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
cGAN;RNN;MIDI generation;music
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.5 |
2;2;3;3
| null | null |
FLAGNet : Feature Label based Automatic Generation Network for symbolic music
| null | null | 0 | 5 |
Reject
|
5;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic optimization;Riemannian geometry;Riemannian gradient flows;convolutional neural nets
| null | 0 | null | null |
iclr
| -0.489956 | 0 | null |
main
| 5.4 |
4;5;6;6;6
| null | null |
Channel-Directed Gradients for Optimization of Convolutional Neural Networks
| null | null | 0 | 2.8 |
Reject
|
3;5;3;1;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few shot learning;Semi-supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian Deep Learning;Approximate Inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GANs;ensembles;disconnected data
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Ensembles of Generative Adversarial Networks for Disconnected Data
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Out-of-Distribution Generalization Analysis via Influence Function
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Node selection;Realistic Propagation;Graph neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME
|
https://github.com/superlouis/NODE-SELECT
| null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
On Flat Minima, Large Margins and Generalizability
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-attention;neural network architecture;image classification;semantic segmentation
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Global Self-Attention Networks for Image Recognition
| null | null | 0 | 4.25 |
Reject
|
4;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Disentanglement;Content and Style Disentanglement;Inductive Bias;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.125988 | 0 | null |
main
| 5.5 |
3;4;6;9
| null | null |
Improving Generalizability of Protein Sequence Models via Data Augmentations
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot learning;sample propagation;feature calibration;outlier removal;noisy label
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
MASP: Model-Agnostic Sample Propagation for Few-shot learning
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null |
Salesforce Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3090; None
| null | 0 | null | null | null | null | null |
Junnan Li, Pan Zhou, Caiming Xiong, Steven Hoi
|
https://iclr.cc/virtual/2021/poster/3090
|
self-supervised learning;unsupervised learning;representation learning;contrastive learning
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3090
|
Prototypical Contrastive Learning of Unsupervised Representations
|
https://github.com/salesforce/PCL
| null | 0 | 4 |
Poster
|
4;5;4;3
| null |
null |
Facebook AI; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington and Allen Institute for AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3096; None
| null | 0 | null | null | null | null | null |
Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah Smith
|
https://iclr.cc/virtual/2021/poster/3096
|
Machine Translation;Sequence Modeling;Natural Language Processing
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 7 |
5;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3096
|
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
|
https://github.com/jungokasai/deep-shallow
| null | 0 | 3.75 |
Poster
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Learning a Non-Redundant Collection of Classifiers
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.207514 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Energy-based Out-of-distribution Detection for Multi-label Classification
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null |
MIT CSAIL, MIT CBMM; Broad Institute of MIT and Harvard, Division of Genetics, Brigham and Women’s Hospital; MIT CSAIL, MIT Department of Mathematics; MIT CSAIL, Department of Physics, University of Pennsylvania; MIT CSAIL, Broad Institute of MIT and Harvard, Division of Genetics, Brigham and Women’s Hospital
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3170; None
| null | 0 | null | null | null | null | null |
Adam Yaari, Maxwell Sherman, Oliver C Priebe, Po-Ru Loh, Boris Katz, Andrei Barbu, Bonnie Berger
|
https://iclr.cc/virtual/2021/poster/3170
|
Computational Biology;non-stationary stochastic processes;cancer research;deep learning;probabelistic models;graphical models
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3170
|
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer
| null | null | 0 | 2.333333 |
Poster
|
1;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transformers;memorization;question answering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Modifying Memories in Transformer Models
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Department of Mathematics, Stanford University; The Voleon Group
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2699; None
| null | 0 | null | null | null | null | null |
Huy Tuan Pham, Phan-Minh Nguyen
|
https://iclr.cc/virtual/2021/poster/2699
|
deep learning theory
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
7;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2699
|
Global Convergence of Three-layer Neural Networks in the Mean Field Regime
| null | null | 0 | 2.5 |
Oral
|
3;2;3;2
| null |
null |
Unknown Affiliation
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretable;RNN;prototypes
| null | 0 | null | null |
iclr
| -0.40452 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Interpretable Sequence Classification Via Prototype Trajectory
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
informativeness measure;incidental supervision;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;5;7;8
| null | null |
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta learning;Continual Learning;Sequential Domain Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Towards Learning to Remember in Meta Learning of Sequential Domains
|
https://github.com/ICLR20210927/Sequential-domain-meta-learning.git
| null | 0 | 4.5 |
Reject
|
5;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep metric learning;adversarial robustness;adversarial examples;adversarial perturbations;adversarial training
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Adversarial Deep Metric Learning
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;ConvNets;parameter sharing;model compression;convolutional neural networks;recursive networks
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Learning Deeply Shared Filter Bases for Efficient ConvNets
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
layout generation;layout synthesis;multimodal attention;transformers;document layouts;generative model;3D
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Multimodal Attention for Layout Synthesis in Diverse Domains
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantization;dnn inference;data free quantization;synthetic data;model compression
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Hybrid and Non-Uniform DNN quantization methods using Retro Synthesis data for efficient inference
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning;Learning from Observation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.8 |
5;5;6;6;7
| null | null |
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity
| null | null | 0 | 4 |
Reject
|
3;5;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continuous representations;neuroscience;convolutional neural networks;gaussian scale-space;learnable scale;receptive field size;neural ODEs;pattern completion
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Continuous Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph representation learning;graph neural networks;expressiveness;universality;random node initialization;Weisfeiler-Lehman heuristic;higher-order graph neural networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
The Surprising Power of Graph Neural Networks with Random Node Initialization
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
Institute of Evaluation and Assessment Research, Academy of Military Science, Beijing, China; Institute of Automation CAS, School of Artificial Intelligence, University of CAS, Beijing, China; Department of Information Engineering, Army Academy of Artillery and Air Defense, Hefei, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2771; None
| null | 0 | null | null | null | null | null |
Wei Tao, sheng long, Gaowei Wu, Qing Tao
|
https://iclr.cc/virtual/2021/poster/2771
|
Deep learning;convex optimization;momentum methods;adaptive heavy-ball methods;optimal convergence
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2771
|
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Nonvacuous Loss Bounds with Fast Rates for Neural Networks via Conditional Information Measures
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;CNN;Medical image classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Increasing-Margin Adversarial (IMA) training to Improve Adversarial Robustness of Neural Networks
| null | null | 0 | 3 |
Reject
|
1;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;distributional shifts
| null | 0 | null | null |
iclr
| -0.918559 | 0 | null |
main
| 4.8 |
3;5;5;5;6
| null | null |
Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization
| null | null | 0 | 4.2 |
Reject
|
5;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Optimal Control;Fuel Management System;Hybrid Electric vehicles;H∞ Performance Index
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach
| null | null | 0 | 3 |
Reject
|
3;4;2;3
| null |
null |
Oregon State University; Stony Brook University; University of California, San Diego
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2910; None
| null | 0 | null | null | null | null | null |
Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen
|
https://iclr.cc/virtual/2021/poster/2910
|
Topology;Morse theory;Image segmentation
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2910
|
Topology-Aware Segmentation Using Discrete Morse Theory
| null | null | 0 | 3 |
Spotlight
|
3;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial machine learning
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Target Training: Tricking Adversarial Attacks to Fail
| null | null | 0 | 3.25 |
Reject
|
5;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
point process;set;normalizing flow;equivariance
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
Equivariant Normalizing Flows for Point Processes and Sets
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data selection;low rank approximation;column subset selection
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
| null | null | 0 | 3.5 |
Withdraw
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian neural networks;deep Gaussian processes;variational inference;inducing points
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
andrew
| null |
Graph neural networks;Subgraph matching;Order Embedding
| null | 0 | null | null |
iclr
| -0.229416 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Neural Subgraph Matching
| null | null | 0 | 4 |
Reject
|
5;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Adversarial Robustness;Adversarial Training;Generalization
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization
| null | null | 0 | 3.75 |
Reject
|
5;4;2;4
| null |
null |
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA; Department of Physics, University of Ottawa, ON, K1N 6N5, Canada; National Research Council of Canada, Ottawa, ON K1N 5A2, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada; Department of Physics, University of Ottawa, ON, K1N 6N5, Canada; National Research Council of Canada, Ottawa, ON K1N 5A2, Canada
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neuroevolution;gradient descent;theoretical description of learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Correspondence between neuroevolution and gradient descent
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-shot learning;Contrastive learning;Metric-based meta learning
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Auto-view contrastive learning for few-shot image recognition
| null | null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null |
Princeton University, Princeton, NJ; University of California Los Angeles, Los Angeles, CA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2965; None
| null | 0 | null | null | null | null | null |
Taylor Webb, Ishan Sinha, Jonathan Cohen
|
https://iclr.cc/virtual/2021/poster/2965
|
abstract rules;out-of-distribution generalization;external memory;indirection;variable binding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2965
|
Emergent Symbols through Binding in External Memory
| null | null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;graph representation learning;network analysis;network motifs;subgraph isomoprhism
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null |
Department of ECE, University of Utah, Salt Lake City, UT 84112, USA; Department of ECE, The Ohio State University, Columbus, OH 43210, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2697; None
| null | 0 | null | null | null | null | null |
Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
|
https://iclr.cc/virtual/2021/poster/2697
|
Kurdyka-Łojasiewicz geometry;minimax;nonconvex;proximal gradient descent-ascent;variable convergence
| null | 0 | null | null |
iclr
| 0.288675 | 0 | null |
main
| 7 |
5;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2697
|
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
| null | null | 0 | 4 |
Poster
|
4;3;5;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3031; None
| null | 0 | null | null | null | null | null |
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang
|
https://iclr.cc/virtual/2021/poster/3031
|
lottery tickets;winning tickets;lifelong learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null |
https://iclr.cc/virtual/2021/poster/3031
|
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Mila, University of Montreal; Max Planck Institute for Intelligent Systems; Mila, Polytechnique Montréal
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2822; None
| null | 0 | null | null | null | null | null |
Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schoelkopf, Yoshua Bengio
|
https://iclr.cc/virtual/2021/poster/2822
|
modular representations;better generalization;learning mechanisms
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2822
|
Fast And Slow Learning Of Recurrent Independent Mechanisms
| null | null | 0 | 3.75 |
Poster
|
3;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
factor normalization;ultrahigh dimensional features;adaptive learning rate;factor decomposition
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Factor Normalization for Deep Neural Network Models
|
https://github.com/HazardNeo4869/FactorNormalization
| null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
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