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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Computer Science, UCLA; DiDi AI Labs
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2787; None
| null | 0 | null | null | null | null | null |
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh
|
https://iclr.cc/virtual/2021/poster/2787
| null | null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7.75 |
7;7;7;10
| null |
https://iclr.cc/virtual/2021/poster/2787
|
Rethinking Architecture Selection in Differentiable NAS
| null | null | 0 | 4.5 |
Oral
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
scene representation;compression;neural rendering;entropy coding
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
3D Scene Compression through Entropy Penalized Neural Representation Functions
| null | null | 0 | 3.5 |
Reject
|
5;3;4;2
| null |
null |
University of Electronic Science and Technology of China; Not specified; SenseTime Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2596; None
| null | 0 | null | null | null | null | null |
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, fengwei yu, Wei Wang, Shi Gu
|
https://iclr.cc/virtual/2021/poster/2596
|
Post Training Quantization;Mixed Precision;Second-order analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2596
|
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
|
https://github.com/yhhhli/BRECQ
| null | 0 | 3.25 |
Poster
|
4;4;1;4
| null |
null |
UMass Amherst; Peking University; CMU; CMU & HKUST; HKUST
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2869; None
| null | 0 | null | null | null | null | null |
Zhiqiang Shen, Zhiqiang Shen, Dejia Xu, Zitian Chen, Kwang-Ting Cheng, Marios Savvides
|
https://iclr.cc/virtual/2021/poster/2869
|
label smoothing;knowledge distillation;image classification;neural machine translation;binary neural networks
| null | 0 | null | null |
iclr
| 0.174078 | 0 |
http://zhiqiangshen.com/projects/LS_and_KD/index.html
|
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2869
|
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
| null | null | 0 | 3.75 |
Poster
|
3;5;3;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neuroscience;Connectomics;Human perception;EM dataset;Membrane segmentation;Evaluation criterion
| null | 0 | null | null |
iclr
| -0.953463 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT;Deep Learning;Natural Language Processing;Transformer;Knowledge Distillation;Parameter Sharing
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Pea-KD: Parameter-efficient and accurate Knowledge Distillation
| 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 |
Neural Sequence Labeling;Neural Sequence Segmentation;Dynamic Programming
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Segmenting Natural Language Sentences via Lexical Unit Analysis
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Question Generation;Question Answering;Data Augmentation;Machine Reading Comprehension
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Learning to Generate Questions by Recovering Answer-containing Sentences
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Boston University; MIT CSAIL
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2687; None
| null | 0 | null | null | null | null | null |
Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas
|
https://iclr.cc/virtual/2021/poster/2687
|
compositional generalization;data augmentation;language processing;sequence models;generative modeling
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2687
|
Learning to Recombine and Resample Data For Compositional Generalization
| 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 |
text clustering;text classification;latent variable model
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Neural Text Classification by Jointly Learning to Cluster and Align
| null | null | 0 | 3 |
Withdraw
|
4;3;2;3
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchical Reinforcement Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Graduate School of Information Science and Technology, The University of Tokyo; Center for Advanced Intelligence Project, RIKEN; PRESTO, Japan Science and Technology Agency; Graduate School of Information Science and Technology, The University of Tokyo; Center for Advanced Intelligence Project, RIKEN
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2592; None
| null | 0 | null | null | null | null | null |
Atsushi Nitanda, Taiji Suzuki
|
https://iclr.cc/virtual/2021/poster/2592
|
stochastic gradient descent;two-layer neural network;over-parameterization;neural tangent kernel
| null | 0 | null | null |
iclr
| 0.490098 | 0 | null |
main
| 7.6 |
7;7;8;8;8
| null |
https://iclr.cc/virtual/2021/poster/2592
|
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
| null | null | 0 | 3.2 |
Oral
|
3;2;2;4;5
| null |
null |
DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2571; None
| null | 0 | null | null | null | null | null |
Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan ODonoghue, Iurii Kemaev, Satinder Singh
|
https://iclr.cc/virtual/2021/poster/2571
| null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2571
|
Discovering a set of policies for the worst case reward
| null | null | 0 | 3.75 |
Spotlight
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;adversarial defense;adversarial attack;shape;background subtraction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Shape Defense
|
https://github.com/[masked]
| null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning to learn;neural optimizer
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Learning to Learn with Smooth Regularization
| null | null | 0 | 3 |
Withdraw
|
4;3;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed optimization;federated learning;client selection
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
| 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;privacy;generative adversarial networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Adversarial representation learning for synthetic replacement of private attributes
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null |
Microsoft Research Redmond, [email protected]; Microsoft Research Redmond, [email protected]; University of Waterloo, [email protected]; IST Austria, [email protected]
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3312; None
| null | 0 | null | null | null | null | null |
Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh
|
https://iclr.cc/virtual/2021/poster/3312
|
distributed machine learning;distributed deep learning;robust deep learning;non-convex optimization;Byzantine resilience
| null | 0 | null | null |
iclr
| 0.13484 | 0 |
https://arxiv.org/abs/2012.14368
|
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3312
|
Byzantine-Resilient Non-Convex Stochastic Gradient Descent
| null | null | 0 | 3.25 |
Poster
|
4;2;3;4
| null |
null |
Computer Science Department, Carnegie Mellon University & Bosch Center for AI; Computer Science Department, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3293; None
| null | 0 | null | null | null | null | null |
Asher Trockman, Zico Kolter
|
https://iclr.cc/virtual/2021/poster/3293
|
orthogonal layers;Lipschitz constrained networks;adversarial robustness
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3293
|
Orthogonalizing Convolutional Layers with the Cayley Transform
|
https://github.com/locuslab/orthogonal-convolutions
| null | 0 | 3.75 |
Spotlight
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;deep learning;self-supervised learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks
| 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 | null | null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
CAFE: Catastrophic Data Leakage in Federated Learning
| null | null | 0 | 3.5 |
Reject
|
5;4;3;2
| null |
null |
Mila, Université de Montréal; Mila, Université de Montréal, CIFAR Fellow
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3302; None
| null | 0 | null | null | null | null | null |
Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville
|
https://iclr.cc/virtual/2021/poster/3302
|
iterated learning;cultural transmission;neural module network;clevr;shapes;vqa;visual question answering;systematic generalization;compositionality
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3302
|
Iterated learning for emergent systematicity in VQA
| null | null | 0 | 3.333333 |
Oral
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Predictive Coding;Backprop;Biological plausibility;neural networks
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
| 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;Population-based Search;Policy Gradient;Combining PG with PS
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
PGPS : Coupling Policy Gradient with Population-based Search
| 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 | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
iPTR: Learning a representation for interactive program translation retrieval
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimizers;meta-learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
TaskSet: A Dataset of Optimization Tasks
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
latent dynamics;temporal abstraction;video prediction;probabilistic modeling;variational inference;deep learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Video Prediction with Variational Temporal Hierarchies
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
analog computing-in-memory;quantization algorithm;deep neural networks
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Relational learning;unsupervised learning;variational inference;probabilistic graphical model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Relational Learning with Variational Bayes
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Network;Symbolic Proofs;Graph-to-Sequence
| null | 0 | null | null |
iclr
| -0.090909 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Learning Axioms to Compute Verifiable Symbolic Expression Equivalence Proofs Using Graph-to-Sequence Networks
| null | null | 0 | 3.75 |
Reject
|
3;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Graph Representations;Disentanglement Learning;GNN;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.196116 | 0 | null |
main
| 4.6 |
3;4;5;5;6
| null | null |
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations
| null | null | 0 | 3.8 |
Reject
|
4;4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;planning;PDDL;multitask;transfer;objects
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://sites.google.com/view/mine-pddl
|
main
| 4 |
3;4;4;5
| null | null |
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
| null | null | 0 | 3 |
Reject
|
4;3;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
temporal difference learning;gradient-descent based temporal difference;Off-policy;regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Gradient descent temporal difference-difference learning
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;probabilistic model;neuro-symbolic learning
| null | 0 | null | null |
iclr
| -0.96833 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null |
University of Cambridge, UK; University of California, Los Angeles, USA; Cambridge Center for AI in Medicine, UK; The Alan Turing Institute, UK; University of California, Los Angeles, USA; University of Cambridge, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2750; None
| null | 0 | null | null | null | null | null |
Ahmed Alaa, Alex Chan, Mihaela van der Schaar
|
https://iclr.cc/virtual/2021/poster/2750
| null | null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2750
|
Generative Time-series Modeling with Fourier Flows
| 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 |
Multi-agent coverage;Multi-resource coverage;Areal coverage;Differentiable approximations
| null | 0 | null | null |
iclr
| -0.98644 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Differentiable Approximations for Multi-resource Spatial Coverage Problems
| null | null | 0 | 2.75 |
Reject
|
4;4;2;1
| null |
null |
The University of British Columbia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3022; None
| null | 0 | null | null | null | null | null |
Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, Z. J Wang
|
https://iclr.cc/virtual/2021/poster/3022
|
Conditional generative adversarial networks;image generation;continuous and scalar conditions
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3022
|
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation
| 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 |
Adversarial Training;Generative Modeling;Out-of-Distribution Detection;GANs;Generative adversarial networks
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Analyzing and Improving Generative Adversarial Training for Generative Modeling and Out-of-Distribution Detection
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Institute of Cognitive Neuroscience, UCL, London, UK; Centre for Artificial Intelligence, UCL, London, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Sainsbury Wellcome Centre, UCL, London, UK; Institute of Cognitive Neuroscience, UCL, London, UK; Sainsbury Wellcome Centre, UCL, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3259; None
| null | 0 | null | null | null | null | null |
Changmin Yu, Timothy Behrens, Neil Burgess
|
https://iclr.cc/virtual/2021/poster/3259
|
Computational neuroscience;grid cells;normative models
| null | 0 | null | null |
iclr
| 0.25 | 0 | null |
main
| 5.6 |
4;5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3259
|
Prediction and generalisation over directed actions by grid cells
| null | null | 0 | 3.4 |
Poster
|
4;1;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Normalizing Flow;Density Estimation;low-dimensional manifolds;noise;normal space
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Density estimation on low-dimensional manifolds: an inflation-deflation approach
| null | null | 0 | 3 |
Reject
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Causal Inference;Robust Reinforcement Learning;Adversarial Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
Causal Inference Q-Network: Toward Resilient Reinforcement Learning
| 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 |
Feature selection;neural networks;high-dimension;small number of samples;biology
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
FsNet: Feature Selection Network on High-dimensional Biological Data
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Deep Learning Theory;Graph Connectivity;Minimum Spanning Trees
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Beyond GNNs: A Sample Efficient Architecture for Graph Problems
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Stanford University; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3177; None
| null | 0 | null | null | null | null | null |
Yang Song, Jascha Sohl-Dickstein, Durk Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
|
https://iclr.cc/virtual/2021/poster/3177
|
generative models;score-based generative models;stochastic differential equations;score matching;diffusion
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 8 |
7;8;8;9
| null |
https://iclr.cc/virtual/2021/poster/3177
|
Score-Based Generative Modeling through Stochastic Differential Equations
| null | null | 0 | 3.5 |
Oral
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Contrastive learning;self-supervised learning;video representation learning;audio-visual representation learning;multimodal representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Google LLC; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3075; None
| null | 0 | null | null | null | null | null |
Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara Sainath, Yonghui Wu, Ruoming Pang
|
https://iclr.cc/virtual/2021/poster/3075
|
Speech Recognition;Streaming ASR;Low-latency ASR;Dual-mode ASR
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3075
|
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling
| null | null | 0 | 4.75 |
Poster
|
5;4;5;5
| null |
null |
University of Cambridge; DeepMind; University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3049; None
| null | 0 | null | null | null | null | null |
Wenda Li, Lei Yu, Yuhuai Wu, Lawrence Paulson
|
https://iclr.cc/virtual/2021/poster/3049
|
mathematical reasoning;dataset;benchmark;reasoning;transformer
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3049
|
IsarStep: a Benchmark for High-level Mathematical Reasoning
|
https://github.com/Wenda302/IsarStep
| null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Topological data analysis;fuzzy clustering
| null | 0 | null | null |
iclr
| -0.19245 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
Fuzzy c-Means Clustering for Persistence Diagrams
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null |
School of Mathematical Science, Research Institute of Intelligent Complex Systems and ISTBI, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3120; None
| null | 0 | null | null | null | null | null |
Qunxi Zhu, Yao Guo, Wei Lin
|
https://iclr.cc/virtual/2021/poster/3120
|
Delay differential equations;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3120
|
Neural Delay Differential Equations
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Causal Representation Learning;Unsupervised/Self-Supervised Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
HCL America
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2746; None
| null | 0 | null | null | null | null | null |
Ali Borji
|
https://iclr.cc/virtual/2021/poster/2746
|
object recognition;deep learning;ObjectNet;Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2746
|
Contemplating Real-World Object Classification
|
https://github.com/aliborji/ObjectNetReanalysis.git
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
National Advanced School of Engineering Yaounde, Cameroon; MILA-Quebec Artificial Intelligence Institute
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Translation;Multilingualism;Linguistic similarity;Dataset;African languages;Multi-task learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
On the use of linguistic similarities to improve Neural Machine Translation for African Languages
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;deep learning;adversarial learning;theory
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
| null | null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multimodal machine translation;interpretability
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Good for Misconceived Reasons: Revisiting Neural Multimodal Machine Translation
| null | null | 0 | 4.25 |
Withdraw
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.733333 | 0 | null |
main
| 5.75 |
3;5;6;9
| null | null |
On the Explicit Role of Initialization on the Convergence and Generalization Properties of Overparametrized Linear Networks
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Polytechnique Montreal; Mila, Polytechnique Montreal; Mila, McGill University; Mila, University of Montreal
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3078; None
| null | 0 | null | null | null | null | null |
Yann Bouteiller, Simon Ramstedt, Giovanni Beltrame, Chris J Pal, Jonathan Binas
|
https://iclr.cc/virtual/2021/poster/3078
|
Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.70014 | 0 |
Not provided
|
main
| 5.75 |
3;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3078
|
Reinforcement Learning with Random Delays
|
Not provided
| null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theorem proving;Pre-training;Inductive bias;Reasoning.
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
| null | null | 0 | 3.5 |
Reject
|
2;4;4;4
| null |
null |
New York University; UC San Diego; Shanghai Jiao Tong University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2817; None
| null | 0 | null | null | null | null | null |
Qiang Zhang, Tete Xiao, Alexei Efros, Lerrel Pinto, Xiaolong Wang
|
https://iclr.cc/virtual/2021/poster/2817
|
self-supervised learning;robotics
| null | 0 | null | null |
iclr
| 0.956183 | 0 |
https://sjtuzq.github.io/cycle_dynamics.html
|
main
| 7.75 |
6;7;8;10
| null |
https://iclr.cc/virtual/2021/poster/2817
|
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
| null | null | 0 | 3 |
Oral
|
2;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta Continual Learning;Supervised Learning;Dynamic Programming;Catastrophic Forgetting;Generalization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Meta-Continual Learning Via Dynamic Programming
| null | null | 0 | 3.25 |
Withdraw
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchical Reinforcement Learning;Off-Policy;Abstractions;Data-Efficiency
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Data-efficient Hindsight Off-policy Option Learning
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
Affiliation
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multiagent reinforcement learning;MARL;decentralized actor-critic algorithm
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;5;6
| null | null |
Decentralized Deterministic Multi-Agent Reinforcement Learning
| null | null | 0 | 3.8 |
Reject
|
3;5;4;4;3
| null |
null |
Institute of Psychology and Center for Cognitive Science, Technische Universität Darmstadt; University of Tübingen, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3153; None
| null | 0 | null | null | null | null | null |
Judith Borowski, Roland Zimmermann, Judith Schepers, Robert Geirhos, Thomas S Wallis, Matthias Bethge, Wieland Brendel
|
https://iclr.cc/virtual/2021/poster/3153
|
evaluation of interpretability;feature visualization;activation maximization;human psychophysics;understanding CNNs;explanation method
| null | 0 | null | null |
iclr
| 0.316228 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3153
|
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
| null | null | 0 | 4 |
Poster
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Reducing Class Collapse in Metric Learning with Easy Positive Sampling
| null | null | 0 | 4.75 |
Reject
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Higher-order;graph simplicial complex;link prediction
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Higher-order Structure Prediction in Evolving Graph Simplicial Complexes
| null | null | 0 | 2.666667 |
Reject
|
4;2;2
| 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
| 4.666667 |
4;4;6
| null | null |
Decoupled Greedy Learning of Graph Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
VinAI Research, Vietnam; University of Texas, Austin; VinAI Research, Vietnam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3072; None
| null | 0 | null | null | null | null | null |
Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui
|
https://iclr.cc/virtual/2021/poster/3072
|
Deep generative models;Sliced Wasserstein;Optimal Transport
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3072
|
Distributional Sliced-Wasserstein and Applications to Generative Modeling
| null | null | 0 | 4.666667 |
Spotlight
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent Reinforcement Learning;Moving Target Defense;Stackelberg Security
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Learning Movement Strategies for Moving Target Defense
| null | null | 0 | 3.25 |
Reject
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Network;Real-time Image Projection;Image Manipulation;Local Editing;Deep Learning
| null | 0 | null | null |
iclr
| -0.207514 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
A StyleMap-Based Generator for Real-Time Image Projection and Local Editing
| null | null | 0 | 4.25 |
Withdraw
|
5;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta learning;deep learning
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Meta Gradient Boosting Neural Networks
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantum Machine Learning;Qubits;Kernel Methods;Deep Neural Network
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
GenQu: A Hybrid System for Learning Classical Data in Quantum States
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
control;Lyapunov stability;REINFORCE;finite-sample bounds
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Reinforcement Learning for Control with Probabilistic Stability Guarantee
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null |
Tencent Jarvis Lab, Shenzhen, China; Tencent AI Lab, Shenzhen, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3344; None
| null | 0 | null | null | null | null | null |
Gege Qi, Lijun GONG, Yibing Song, Kai Ma, Yefeng Zheng
|
https://iclr.cc/virtual/2021/poster/3344
|
Healthcare;Biometrics
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3344
|
Stabilized Medical Image Attacks
|
https://github.com/imogenqi/SMA
| null | 0 | 4 |
Spotlight
|
4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantization;Pruning;Model Compression;AutoML
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
ABS: Automatic Bit Sharing for Model Compression
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
PRECISE Center, University of Pennsylvania
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3264; None
| null | 0 | null | null | null | null | null |
Sangdon Park, Shuo Li, Insup Lee, Osbert Bastani
|
https://iclr.cc/virtual/2021/poster/3264
|
classification;calibration;probably approximated correct guarantee;fast DNN inference;safe planning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3264
|
PAC Confidence Predictions for Deep Neural Network Classifiers
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Unsupervised Word Translation Pairing using Refinement based Point Set Registration
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null |
Zhejiang Lab, Key Lab. of Machine Perception (MoE), School of EECS, Peking University; Key Lab. of Machine Perception (MoE), School of EECS, Peking University, Pazhou Lab, Guangzhou, China; Beijing Institute of Big Data Research, Beijing, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2536; None
| null | 0 | null | null | null | null | null |
Ke Sun, Zhanxing Zhu, Zhouchen Lin
|
https://iclr.cc/virtual/2021/poster/2536
|
Graph Neural Networks;AdaBoost
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2536
|
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
|
https://github.com/datake/AdaGCN
| null | 0 | 3.75 |
Poster
|
4;5;3;3
| null |
null |
University of Maryland, College Park
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2565; None
| null | 0 | null | null | null | null | null |
Gaurav Shrivastava, Abhinav Shrivastava
|
https://iclr.cc/virtual/2021/poster/2565
|
video synthesis;future frame generation;video generation;gaussian process priors;diverse video generation
| null | 0 | null | null |
iclr
| 0 | 0 |
http://www.cs.umd.edu/~gauravsh/dvg.html
|
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2565
|
Diverse Video Generation using a Gaussian Process Trigger
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
University of Washington; University of Washington, Allen Institute for AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2586; None
| null | 0 | null | null | null | null | null |
Kiana Ehsani, Daniel Gordon, Thomas H Nguyen, Roozbeh Mottaghi, Ali Farhadi
|
https://iclr.cc/virtual/2021/poster/2586
|
representation learning;computer vision
| null | 0 | null | null |
iclr
| -0.911322 | 0 | null |
main
| 6.75 |
4;6;8;9
| null |
https://iclr.cc/virtual/2021/poster/2586
|
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions
|
https://github.com/ehsanik/muscleTorch
| null | 0 | 4.5 |
Poster
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;alphazero;muzero;mcts;planning;search
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
3;4;5;6;7
| null | null |
Playing Nondeterministic Games through Planning with a Learned Model
| null | null | 0 | 3.4 |
Reject
|
4;4;4;4;1
| null |
null |
University of Geneva & Geneva School of Business Administration, HES-SO; Currently at Apple; Deepmind; University of Geneva & Geneva School of Business Administration, HES-SO
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2961; None
| null | 0 | null | null | null | null | null |
Jason Ramapuram, Yan Wu, Alexandros Kalousis
|
https://iclr.cc/virtual/2021/poster/2961
|
memory;generative model;latent variable;heap allocation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2961
|
Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
School of Computing, KAIST, Daejeon, Republic of Korea; Gauss Labs Inc., Seoul, Republic of Korea; School of Computing, KAIST, Daejeon, Republic of Korea; Graduate School of AI, KAIST, Daejeon, Republic of Korea; School of Computing, KAIST, Daejeon, Republic of Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2653; None
| null | 0 | null | null | null | null | null |
Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim
|
https://iclr.cc/virtual/2021/poster/2653
|
Reinforcement Learning;Model-based Reinforcement Learning;Offline Reinforcement Learning;Batch Reinforcement Learning;Off-policy policy evaluation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2653
|
Representation Balancing Offline Model-based Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;restless bandits;Whittle index
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
NeurWIN: Neural Whittle Index Network for Restless Bandits via Deep RL
| 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 |
robustness;invariance;data augmentation;consistency loss
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Referring Expression Understanding;Language-Vision Problems;Grounded Language Understanding
| null | 0 | null | null |
iclr
| 0.244851 | 0 | null |
main
| 5.25 |
2;4;5;10
| null | null |
Language Controls More Than Top-Down Attention: Modulating Bottom-Up Visual Processing with Referring Expressions
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
North Carolina State University, Raleigh, NC, USA; Alibaba Group, Sunnyvale, CA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3162; None
| null | 0 | null | null | null | null | null |
Lin Ning, Guoyang Chen, Weifeng Zhang, Xipeng Shen
|
https://iclr.cc/virtual/2021/poster/3162
|
Reinforcement Learning;Quantization;mixed precision;augmented deep reinforcement learning;DNN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3162
|
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
| null | null | 0 | 3 |
Poster
|
3;3;3
| 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
| 4.75 |
4;5;5;5
| null | null |
Backdoor Attacks to Graph Neural Networks
| null | null | 0 | 4 |
Withdraw
|
4;5;4;3
| null |
null |
Department of Computer Science, The University of Hong Kong; School of Computer Science & Engineering, Hebrew University of Jerusalem; Paul G. Allen School of Computer Science & Engineering, University of Washington; DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3213; None
| null | 0 | null | null | null | null | null |
Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith, Lingpeng Kong
|
https://iclr.cc/virtual/2021/poster/3213
|
Attention;transformers;machine translation;language modeling
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 7 |
4;8;8;8
| null |
https://iclr.cc/virtual/2021/poster/3213
|
Random Feature Attention
| null | null | 0 | 3.75 |
Spotlight
|
5;4;3;3
| null |
null |
Centre for Robotics Research, Department of Engineering, King’s College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2896; None
| null | 0 | null | null | null | null | null |
Edoardo Cetin, Oya Celiktutan
|
https://iclr.cc/virtual/2021/poster/2896
|
Imitation Learning;Reinforcement Learning;Observational Imitation;Third-Person Imitation;Mutual Information;Domain Adaption;Machine Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2896
|
Domain-Robust Visual Imitation Learning with Mutual Information Constraints
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null |
Delft University of Technology; University of Oxford; University of Oxford, Now at DeepMind, London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3156; None
| null | 0 | null | null | null | null | null |
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson
|
https://iclr.cc/virtual/2021/poster/3156
|
Reinforcement Learning;Generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/3156
|
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimistic exploration;backward bootstrapped bonus;posterior sampling;reinforcement learning
| null | 0 | null | null |
iclr
| -0.102062 | 0 | null |
main
| 5.8 |
4;6;6;6;7
| null | null |
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning
| null | null | 0 | 3.8 |
Reject
|
4;3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
side channel;model extraction;GPU;magnetic induction;sensors
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France & Criteo AI Lab; ETIS /ENSEA, Univ. de Cergy-Pontoise-CNRS, France; Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2773; None
| null | 0 | null | null | null | null | null |
Kimon ANTONAKOPOULOS, E. Belmega, Panayotis Mertikopoulos
|
https://iclr.cc/virtual/2021/poster/2773
|
min-max optimization;games;mirror-prox;adaptive methods;regime agnostic methods
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2773
|
Adaptive Extra-Gradient Methods for Min-Max Optimization and Games
| null | null | 0 | 3.5 |
Poster
|
4;2;4;4
| null |
null |
Sorbonne Universit ´e & valeo.ai, Paris, France; Sorbonne Universit ´e, Paris, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2650; None
| null | 0 | null | null | null | null | null |
Alexandre Rame, MATTHIEU CORD
|
https://iclr.cc/virtual/2021/poster/2650
|
Deep Learning;Deep Ensembles;Information Theory;Information Bottleneck;Adversarial Learning
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2650
|
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
| 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 |
Machine Learning;Graph Classification
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
Graph-Graph Similarity Network
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural network architectures;message-passing neural networks;neural network sparsification;deep learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Analysing the Update step in Graph Neural Networks via Sparsification
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Deepmind; University of Michigan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2662; None
| null | 0 | null | null | null | null | null |
Max Smith, Thomas Anthony, Michael Wellman
|
https://iclr.cc/virtual/2021/poster/2662
|
Empirical Game Theory;Reinforcement Learning;Multiagent Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2662
|
Iterative Empirical Game Solving via Single Policy Best Response
| null | null | 0 | 3 |
Spotlight
|
2;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised image translation;Geometry distortion
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative networks;adversarial generative networks
| null | 0 | null | null |
iclr
| -0.19245 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Adversarial Problems for Generative Networks
| 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 |
crowd-sourcing;calibration;dataset;uncertainty
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Dataset Curation Beyond Accuracy
| 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 |
object detection;deep neural networks;refinement
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3 |
2;2;4;4
| null | null |
BBRefinement: an universal scheme to improve precision of box object detectors
|
https://gitlab.com/irafm-ai/bb-refinement
| null | 0 | 4.75 |
Reject
|
5;5;5;4
| null |
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