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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual Learning;Neural Tangent Kernel;Optimisation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Non-Local Aggregation;Attention;Disassortative Graph
| null | 0 | null | null |
iclr
| -0.166667 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Non-Local Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
4;5;2;5
| null |
null |
Unknown Affiliation(s)
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge graph embeddings;Quaternion;Hamilton product
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
| null | null | 0 | 4.5 |
Withdraw
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Transformers;Metric Learning;Proteomics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
| null | null |
Deep Learning Proteins using a Triplet-BERT network
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
Google Research, Mountain View, California
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2967; None
| null | 0 | null | null | null | null | null |
Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan
|
https://iclr.cc/virtual/2021/poster/2967
|
Transformers;Multi-Task Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2967
|
HyperGrid Transformers: Towards A Single Model for Multiple Tasks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
School of Computer Science, Hebrew University, Jerusalem, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2557; None
| null | 0 | null | null | null | null | null |
Eran Malach, Shai Shalev-Shwartz
|
https://iclr.cc/virtual/2021/poster/2557
|
Neural Networks;Deep Learning;Convolutional Networks;Fully-Connected Networks;Gradient Descent
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 6.75 |
5;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2557
|
Computational Separation Between Convolutional and Fully-Connected Networks
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Self-supervised learning;Multi-task learning;Graph Convolutional Networks;Semi-supervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
University of Illinois at Urbana-Champaign; Virginia Tech; Microsoft Dynamics 365 AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3123; None
| null | 0 | null | null | null | null | null |
Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu
|
https://iclr.cc/virtual/2021/poster/3123
|
adversarial robustness;information theory;BERT;adversarial training;NLI;QA
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
4;6;8
| null |
https://iclr.cc/virtual/2021/poster/3123
|
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
|
https://github.com/AI-secure/InfoBERT
| null | 0 | 3 |
Poster
|
4;3;2
| null |
null |
Bosch Center for Artificial Intelligence, Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2629; None
| null | 0 | null | null | null | null | null |
Jan Hendrik Metzen, Maksym Yatsura
|
https://iclr.cc/virtual/2021/poster/2629
|
robustness;certified defense;adversarial patch;aversarial examples
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2629
|
Efficient Certified Defenses Against Patch Attacks on Image Classifiers
| null | null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null |
AI Labs, Didi Chuxing, Beijing, China
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Computer Science and Engineering, University of California, San Diego, San Diego, CA 92093; Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA 92093
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
large scale learning;square loss vs cross-entropy;classification;experimental evaluation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;7;7;8
| null | null |
EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS
| null | null | 0 | 4 |
Poster
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-play;asymmetric self-play;automatic curriculum;automatic goal generation;robotic learning;robotic manipulation;reinforcement learning
| null | 0 | null | null |
iclr
| -0.301511 | 0 |
https://robotics-self-play.github.io
|
main
| 6.5 |
6;6;7;7
| null | null |
Asymmetric self-play for automatic goal discovery in robotic manipulation
| null | null | 0 | 3.25 |
Reject
|
3;4;2;4
| null |
null |
University of Toronto and Vector Institute; IIT Delhi
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2745; None
| null | 0 | null | null | null | null | null |
Pratyush Maini, Mohammad Yaghini, Nicolas Papernot
|
https://iclr.cc/virtual/2021/poster/2745
|
model ownership;model extraction;MLaaS
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2745
|
Dataset Inference: Ownership Resolution in Machine Learning
|
github.com/cleverhans-lab/dataset-inference
| null | 0 | 4 |
Spotlight
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;confidence estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Semi-supervised learning by selective training with pseudo labels via confidence estimation
| null | null | 0 | 3 |
Reject
|
3;2;4;3
| null |
null |
Princeton University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3139; None
| null | 0 | null | null | null | null | null |
Dipendra Kumar Misra, Qinghua Liu, Chi Jin, John Langford
|
https://iclr.cc/virtual/2021/poster/3139
|
Reinforcement learning theory;Rich observation;Noise-contrastive learning;State abstraction;Factored MDP
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3139
|
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
| null | null | 0 | 3 |
Poster
|
3;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Machine Learning;Robot Motion Learning;DQN;Robot Manipulator;Target Reaching;Network Pruning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GCNN;graph;semi-supervised;node classification;convolutional neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
New GCNN-Based Architecture for Semi-Supervised Node Classification
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stability;neural networks;generalization bounds;normalized loss
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Stability analysis of SGD through the normalized loss function
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT;Transformer;NLP;Efficient;Faster;Smaller;Accurate
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models
| 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 |
Unsupervised Neural Machine Translation;Marginal Likelihood Maximization;Mutual Information
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Demystifying Learning of Unsupervised Neural Machine Translation
| null | null | 0 | 3.25 |
Reject
|
3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Action Recognition;Temporal Adaptive Module;Temporal Adaptive Network
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
TAM: Temporal Adaptive Module for Video Recognition
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Technion, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2575; None
| null | 0 | null | null | null | null | null |
Uri Alon, Eran Yahav
|
https://iclr.cc/virtual/2021/poster/2575
|
graphs;GNNs;limitations;understanding;bottleneck;over-squashing
| null | 0 | null | null |
iclr
| 0.884652 | 0 | null |
main
| 5.6 |
4;5;5;6;8
| null |
https://iclr.cc/virtual/2021/poster/2575
|
On the Bottleneck of Graph Neural Networks and its Practical Implications
|
https://github.com/tech-srl/bottleneck
| null | 0 | 4.2 |
Poster
|
4;4;4;4;5
| null |
null |
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; Hong Kong Baptist University, Hong Kong SAR, China; National University of Singapore, Singapore; The University of Tokyo, Tokyo, Japan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2758; None
| null | 0 | null | null | null | null | null |
Jingfeng Zhang, Jianing ZHU, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli
|
https://iclr.cc/virtual/2021/poster/2758
|
Adversarial robustness
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.666667 |
7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2758
|
Geometry-aware Instance-reweighted Adversarial Training
| null | null | 0 | 4.666667 |
Oral
|
4;5;5
| null |
null |
The University of Melbourne, VIC, Australia; Deakin University, Geelong, VIC, Australia; Key Lab. of Machine Perception (MoE), School of EECS, Peking University, Beijing, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2831; None
| null | 0 | null | null | null | null | null |
Hanxun Huang, Xingjun Ma, Sarah Erfani, James Bailey, Yisen Wang
|
https://iclr.cc/virtual/2021/poster/2831
|
Unlearnable Examples;Data Protection;Adversarial Machine Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2831
|
Unlearnable Examples: Making Personal Data Unexploitable
|
https://github.com/HanxunH/Unlearnable-Examples
| null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Novel Policy Seeking;Reinforcement Learning;Constrained Optimization
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Novel Policy Seeking with Constrained Optimization
| 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 |
adversarial training;activation function;spatially attentive activation
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Sparta: Spatially Attentive and Adversarially Robust Activations
| null | null | 0 | 3.75 |
Withdraw
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
A General Computational Framework to Measure the Expressiveness of Complex Networks using a Tight Upper Bound of Linear Regions
| null | null | 0 | 2.75 |
Reject
|
4;2;2;3
| null |
null |
Tsinghua Shenzhen International Graduate School, Tsinghua University; School of Data Science, The Chinese University of Hong Kong, Shenzhen and Secure Computing Lab of Big Data, Shenzhen Research Institute of Big Data; Tsinghua Shenzhen International Graduate School, Tsinghua University and PCL Research Center of Networks and Communications, Peng Cheng Laboratory; Tencent AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2631; None
| null | 0 | null | null | null | null | null |
Jiawang Bai, Baoyuan Wu, Yong Zhang, Yiming Li, Zhifeng Li, Shu-Tao Xia
|
https://iclr.cc/virtual/2021/poster/2631
|
targeted attack;bit-flip;weight attack
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2631
|
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
|
https://github.com/jiawangbai/TA-LBF
| null | 0 | 3.25 |
Poster
|
4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robotics;Robot Manipulation;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Attention-driven Robotic Manipulation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Large Graphs;Heterogeneous Graphs
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Scalable Graph Neural Networks for Heterogeneous Graphs
| null | null | 0 | 4.75 |
Reject
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Certified robustness;adversarial perturbation
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
| null | null | 0 | 3.25 |
Withdraw
|
4;4;2;3
| null |
null |
Department of Mathematics, Purdue University, West Lafayette, IN, USA; Departments of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA; Departments of Statistics, Purdue University, West Lafayette, IN, USA; Department of Mathematics, University of Southern California, Los Angeles, CA, USA; Department of Mathematical Sciences, Durham University, Durham, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2654; None
| null | 0 | null | null | null | null | null |
Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang
|
https://iclr.cc/virtual/2021/poster/2654
|
variance reduction;replica exchange;parallel tempering;stochastic gradient Langevin dynamics;uncertainty quantification;change of measure;generalized Girsanov theorem;Dirichlet form;Markov jump process
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2654
|
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3258; None
| null | 0 | null | null | null | null | null |
Jing An, Lexing Ying, Yuhua Zhu
|
https://iclr.cc/virtual/2021/poster/3258
|
biased sampling;reweighting;resampling;stability;stochastic asymptotics
| null | 0 | null | null |
iclr
| -0.801784 | 0 | null |
main
| 6.2 |
5;6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3258
|
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
| null | null | 0 | 3.2 |
Poster
|
4;3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
understanding deep learning;generalization;interpolating methods;empirical investigation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Distributional Generalization: A New Kind of Generalization
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual Learning;Domain Generalization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Online Continual Learning Under Domain Shift
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Non-Euclidean data completion;Sparse matrices;Recommender systems;Recommendation systems;Sparse representations
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.25 |
3;3;3;4
| null | null |
Matrix Data Deep Decoder - Geometric Learning for Structured Data Completion
| null | null | 0 | 4.25 |
Reject
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fokker Planck Equation;weak form;Wasserstein GAN
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Learning Stochastic Behaviour from Aggregate Data
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Carnegie Mellon University; University of California, Berkeley; University of Southern California
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3271; None
| null | 0 | null | null | null | null | null |
Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard H Hovy
|
https://iclr.cc/virtual/2021/poster/3271
|
Generative Models;Generative Flow;Normalizing Flow;Image Generation;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3271
|
Decoupling Global and Local Representations via Invertible Generative Flows
|
https://github.com/XuezheMax/wolf
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
University of California Berkeley; University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2600; None
| null | 0 | null | null | null | null | null |
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg
|
https://iclr.cc/virtual/2021/poster/2600
|
Safe exploration;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.688247 | 0 |
https://sites.google.com/view/conservative-safety-critics/
|
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2600
|
Conservative Safety Critics for Exploration
| null | null | 0 | 3.25 |
Poster
|
5;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantization;Constrained Optimization;Mu-law;8-bit training
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
End-to-end Quantized Training via Log-Barrier Extensions
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null |
ICSI and UC Berkeley; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2975; None
| null | 0 | null | null | null | null | null |
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W Mahoney
|
https://iclr.cc/virtual/2021/poster/2975
|
transfer learning;adversarial training;influence functions;limited data
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2975
|
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
cooperative MARL;value function factorization;stochastic policy;continuous tasks
| null | 0 | null | null |
iclr
| -0.642857 | 0 | null |
main
| 2.8 |
2;2;3;3;4
| null | null |
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning
| null | null | 0 | 4.2 |
Reject
|
5;4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Unsupervised Learning;Video Analytics
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Watching the World Go By: Representation Learning from Unlabeled Videos
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
news-driven stock prediction;equity state representation;recurrent state transition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
News-Driven Stock Prediction Using Noisy Equity State Representation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-shot learning;Object detection
| null | 0 | null | null |
iclr
| -0.239046 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Cooperating RPN's Improve Few-Shot Object Detection
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transferability;deep reinforcement learning;generalization;adversarial
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Exploring Transferability of Perturbations in Deep 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 |
Deep learning;speech processing;multilingual modeling;cross-lingual
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Unsupervised Cross-lingual Representation Learning for Speech Recognition
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Experience Replay;Off-Policy Optimization;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Experience Replay with Likelihood-free Importance Weights
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Manifold Learning;Inverse Model;Representation Learing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Invertible Manifold Learning for Dimension Reduction
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2891; None
| null | 0 | null | null | null | null | null |
Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu
|
https://iclr.cc/virtual/2021/poster/2891
|
Layer order;Transformers;Instance-wise Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2891
|
IOT: Instance-wise Layer Reordering for Transformer Structures
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ML privacy;split inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Private Split Inference of Deep Networks
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
University of Toronto & Vector Institute; University of Toronto & Vector Institute, Google Research; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2597; None
| null | 0 | null | null | null | null | null |
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
|
https://iclr.cc/virtual/2021/poster/2597
|
Generative Models;EBM;Energy-Based Models;Energy Based Models;semi-supervised learning;JEM
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
4;7;8
| null |
https://iclr.cc/virtual/2021/poster/2597
|
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
|
github.com/wgrathwohl/VERA
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty decomposition;epistemic uncertainty;online learning;real-time control
| null | 0 | null | null |
iclr
| 0.68313 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Real-time Uncertainty Decomposition for Online Learning Control
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficiency;Model Compression;Unstructured Pruning;Differentiable Pruning
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Learned Threshold Pruning
| 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 |
few-shot learning;class imbalance
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Class Imbalance in Few-Shot Learning
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null |
Nvidia Research & Technion; Technion; Nvidia Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3370; None
| null | 0 | null | null | null | null | null |
Esther Derman, Gal Dalal, Shie Mannor
|
https://iclr.cc/virtual/2021/poster/3370
|
reinforcement learning;delay
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3370
|
Acting in Delayed Environments with Non-Stationary Markov Policies
|
https://github.com/galdl/rl_delay_basic.git
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
shape bias;texture bias;interpretability;smoothness;visualization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual learning;Energy-based model
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Energy-Based Models for Continual Learning
| 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 |
Adversarial Training;Image Recognition;Generalization
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
ALFA: Adversarial Feature Augmentation for Enhanced Image Recognition
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Meta-Learning;Pruning
| null | 0 | null | null |
iclr
| -0.090909 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
LINGUINE: LearnIng to pruNe on subGraph convolUtIon 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 |
Natural Language Processing;Summarization;Abstractive Summarization;Memory Compression;Hierarchical models
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Mem2Mem: Learning to Summarize Long Texts with Memory Compression and Transfer
| null | null | 0 | 4 |
Withdraw
|
5;3;4
| null |
null |
Machine Learning Research Group, University of Oxford, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3361; None
| null | 0 | null | null | null | null | null |
Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne
|
https://iclr.cc/virtual/2021/poster/3361
| null | null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 7 |
5;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3361
|
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
|
https://github.com/xingchenwan/nasbowl
| null | 0 | 3.25 |
Poster
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Revisiting Prioritized Experience Replay: A Value Perspective
| 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 |
Deep Neural Network Representation;Reliability;Selective Inference;Statistical Hypothesis Testing;p-value
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Quantifying Statistical Significance of Neural Network Representation-Driven Hypotheses by Selective Inference
| null | null | 0 | 2.75 |
Reject
|
3;4;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Medical imaging;Cross-Modal Learning
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210 USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2729; None
| null | 0 | null | null | null | null | null |
Haibo Yang, Minghong Fang, Jia Liu
|
https://iclr.cc/virtual/2021/poster/2729
|
Federated Learning;Linear Speedup;Partial Worker Participation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2729
|
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null |
Department of Engineering, University of Cambridge, Cambridge, UK; Department of Engineering, University of Cambridge, Cambridge, UK & Alan Turing Institute, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3032; None
| null | 0 | null | null | null | null | null |
Gregor Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández Lobato
|
https://iclr.cc/virtual/2021/poster/3032
|
deep reinforcement learning;molecular design;covariant neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3032
|
Symmetry-Aware Actor-Critic for 3D Molecular Design
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
normalizing flows;representational power;conditioning;depth;theory
| null | 0 | null | null |
iclr
| -0.968496 | 0 | null |
main
| 5.75 |
3;6;7;7
| null | null |
Representational aspects of depth and conditioning in normalizing flows
| 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 | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Recursive Neighborhood Pooling for Graph Representation Learning
| null | null | 0 | 3.25 |
Reject
|
4;1;4;4
| null |
null |
Mohamed bin Zayed University of AI; Galixir; Georgia Institute of Technology; Google Research, Brain Team; Georgia Institute of Technology, Shanghai Qi Zhi Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3099; None
| null | 0 | null | null | null | null | null |
Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song
|
https://iclr.cc/virtual/2021/poster/3099
|
Molecule Design;Explainable Model;Evolutionary Algorithm;Reinforcement Learning;Graph Generative Model
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3099
|
Molecule Optimization by Explainable Evolution
|
https://github.com/binghong-ml/MolEvol
| null | 0 | 4 |
Poster
|
3;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Machine Learning;Adam;Batch Normalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
A spherical analysis of Adam with Batch Normalization
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
School of Computer Science and Engineering, Sun Yat-sen University; Microsoft Research Asia; Peking University; Microsoft Devdiv; Microsoft STCA; Harbin Institute of Technology; Beihang University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2598; None
| null | 0 | null | null | null | null | null |
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou
|
https://iclr.cc/virtual/2021/poster/2598
|
Pre-training;BERT;Code Representations;Code Structure;Data Flow
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2598
|
GraphCodeBERT: Pre-training Code Representations with Data Flow
|
https://github.com/microsoft/CodeBERT
| null | 0 | 3.5 |
Poster
|
3;5;3;3
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2668; None
| null | 0 | null | null | null | null | null |
Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi
|
https://iclr.cc/virtual/2021/poster/2668
|
audio understanding;frontend;learnable;mel-filterbanks;time-frequency representations;sound classification
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2668
|
LEAF: A Learnable Frontend for Audio Classification
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null |
Seoul National University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2915; None
| null | 0 | null | null | null | null | null |
Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song
|
https://iclr.cc/virtual/2021/poster/2915
|
Compressed videos;self-supervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2915
|
Self-Supervised Learning of Compressed Video Representations
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Constrained Reinforcement Learning;Density;Safe AI
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Density Constrained Reinforcement Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;2;3
| null |
null |
Yandex, Higher School of Economics; ALTA Institute, University of Cambridge
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3015; None
| null | 0 | null | null | null | null | null |
Andrey Malinin, Mark Gales
|
https://iclr.cc/virtual/2021/poster/3015
|
ensembles;structures prediction;uncertainty estimation;knowledge uncertainty;autoregressive models;information theory;machine translation;speech recognition.
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3015
|
Uncertainty Estimation in Autoregressive Structured Prediction
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Local Clustering;Random Walk on Graphs;Open Graph Benchmark
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Local Clustering Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
softmax;classification;representational similarity;transfer learning;label smoothing;dropout
| null | 0 | null | null |
iclr
| -0.632456 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Demystifying Loss Functions for Classification
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semisupervised learning;deep generative models;variational autoencoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative model;graph neural network;data release
| null | 0 | null | null |
iclr
| -0.547723 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Secure Network Release with Link Privacy
| null | null | 0 | 3.5 |
Reject
|
5;2;4;3
| null |
null |
University of California, Berkeley; University of California, Berkeley; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2984; None
| null | 0 | null | null | null | null | null |
Xuanlin Li, Brandon Trabucco, Dong Huk Park, Michael Luo, Sheng Shen, trevor darrell, Yang Gao
|
https://iclr.cc/virtual/2021/poster/2984
|
variational inference;unsupervised learning;computer vision;natural language processing;optimization;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2984
|
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
| null | null | 0 | 3.5 |
Poster
|
4;3;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;pre-training;point clouds
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Pre-Training by Completing Point Clouds
| 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 |
conditional independence testing;generative adversarial networks;high dimensionality;statistical inference
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Double Generative Adversarial Networks for Conditional Independence Testing
| null | null | 0 | 3.25 |
Reject
|
4;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.984732 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning
| null | null | 0 | 3 |
Reject
|
4;4;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sensor Network Co-design;neural architecture search
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
| null | null | 0 | 3 |
Reject
|
3;1;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Value distribution learning;reinforcement learning;deep learning;distributional reinforcement learning;distributional actor-critic
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
A Distributional Perspective on Actor-Critic Framework
| null | null | 0 | 4 |
Reject
|
5;4;2;5
| null |
null |
Cairo University; Naver Labs Europe
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2796; None
| null | 0 | null | null | null | null | null |
Muhammad Khalifa, Hady Elsahar, Marc Dymetman
|
https://iclr.cc/virtual/2021/poster/2796
|
Controlled NLG;Pretrained Language Models;Bias in Language Models;Energy-Based Models;Information Geometry;Exponential Families
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2796
|
A Distributional Approach to Controlled Text Generation
|
https://github.com/naver/gdc
| null | 0 | 3 |
Oral
|
3;3;3
| null |
null |
University of Toronto and Vector Institute; University of Toronto and Vector Institute, Google Brain; University of Toronto and Vector Institute, Nvidia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3027; None
| null | 0 | null | null | null | null | null |
Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti
|
https://iclr.cc/virtual/2021/poster/3027
|
Model-Based Reinforcement Learning;World Models;Skill Discovery;Mutual Information;Planning;Model Predictive Control;Partial Amortization
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/latent-skill-planning/
|
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3027
|
Latent Skill Planning for Exploration and Transfer
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural machine translation;non-autoregressive translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Hybrid-Regressive Neural Machine Translation
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Zero-Shot Learning;Common Sense Knowledge Graphs;Graph Neural Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Zero-Shot Learning with Common Sense Knowledge Graphs
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
q-learning;control variates;reinforcement learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Decorrelated Double Q-learning
| 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 |
Discrete Representation;Learning and Planning;Model-based RL;Hierarchical RL
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Discrete Predictive Representation for Long-horizon Planning
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Distributed Variational Inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Carnegie Mellon University; Carnegie Mellon University and Bosch AI; Carnegie Mellon University and Determined AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2577; None
| null | 0 | null | null | null | null | null |
Jeremy Cohen, Simran Kaur, Yuanzhi Li, Zico Kolter, Ameet Talwalkar
|
https://iclr.cc/virtual/2021/poster/2577
|
optimization;trajectory;stability;sharpness;implicit bias;implicit regularization;L-smoothness;deep learning theory;science of deep learning
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 6.75 |
5;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2577
|
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Data augmentation;Input attribution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;AutoML;Meta-learning
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
| null | null | 0 | 2.75 |
Reject
|
4;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
planning;reinforcement learning;combinatorial optimization;control;imitation learning
| null | 0 | null | null |
iclr
| -0.968496 | 0 | null |
main
| 4.25 |
3;3;4;7
| null | null |
Neuro-algorithmic Policies for Discrete Planning
| 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 |
Clustering;Self-supervised learning;Meta-learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Meta-k: Towards Unsupervised Prediction of Number of Clusters
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;structural biology;biomolecules
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
ATOM3D: Tasks On Molecules in Three Dimensions
|
github.com/xxxxxxx/xxxxxx
| null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
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