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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Network Pruning;Differential Privacy
| null | 0 | null | null |
iclr
| -0.229416 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Privacy-preserving Learning via Deep Net Pruning
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-task learning;monocular depth estimation;semantic segmentation;pseudo label;cross-view consistency
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Pseudo Label-Guided Multi Task Learning for Scene Understanding
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
anomaly detection;unsupervised learning;structural similarity;generative adversarial network;deep learning
| null | 0 | null | null |
iclr
| -0.942857 | 0 | null |
main
| 4.25 |
2;4;5;6
| null | null |
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection
| null | null | 0 | 3.25 |
Reject
|
5;4;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
compositional generalization;grounding
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Grounded Compositional Generalization with Environment Interactions
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Improving Sequence Generative Adversarial Networks with Feature Statistics Alignment
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Indian Institute of Technology, Kharagpur; Indian Institute of Technology, Delhi; University of Southern California; Adobe Research; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2769; None
| null | 0 | null | null | null | null | null |
Mrigank Raman, Aaron Chan, Siddhant Agarwal, PeiFeng Wang, Hansen Wang, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren
|
https://iclr.cc/virtual/2021/poster/2769
|
neural symbolic reasoning;interpretability;model explanation;faithfulness;knowledge graph;commonsense question answering;recommender system
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2769
|
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
|
https://github.com/INK-USC/deceive-KG-models
| null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative models;injectivity of neural networks;universal approximation;inference;compressed sensing with generative priors;well-posedness;random projections
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;5;8
| null | null |
Globally Injective ReLU networks
| null | null | 0 | 3 |
Reject
|
4;2;3;3
| null |
null |
Université de Montréal, Mila, CIFAR Fellow; Université de Montréal, Mila; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2649; None
| null | 0 | null | null | null | null | null |
Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville
|
https://iclr.cc/virtual/2021/poster/2649
|
Systematic generalisation;invariance penalty;semantic anomaly detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2649
|
Systematic generalisation with group invariant predictions
| null | null | 0 | 3.5 |
Spotlight
|
3;4;3;4
| null |
null |
Department of Mathematics, Department of Physics and Department of Chemistry, Duke University; Department of Mathematics, Duke University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3208; None
| null | 0 | null | null | null | null | null |
Andrea Agazzi, Jianfeng Lu
|
https://iclr.cc/virtual/2021/poster/3208
|
policy gradient;entropy regularization;mean-field dynamics;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3208
|
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime
| null | null | 0 | 3.25 |
Poster
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Video Generation;Video Forecasting;Autoregressive Models;VQVAE;Computer Vision
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Predicting Video with VQVAE
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null |
Skolkovo Institute of Science and Technology, Moscow, Russia; ITMO University, Saint Petersburg, Russia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2664; None
| null | 0 | null | null | null | null | null |
Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev
|
https://iclr.cc/virtual/2021/poster/2664
|
wasserstein-2 distance;optimal transport maps;non-minimax optimization;cycle-consistency regularization;input-convex neural networks
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
| null |
https://iclr.cc/virtual/2021/poster/2664
|
Wasserstein-2 Generative Networks
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Attack;Deep Ranking;Relative Order;Black-Box Attack
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Practical Order Attack in Deep Ranking
| null | null | 0 | 3.75 |
Withdraw
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Cross-Modal Learning;Voice-Face Matching;Voice-Face Retrieval
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
A Benchmark for Voice-Face Cross-Modal Matching and Retrieval
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active learning;uncertainty sampling;disagreement region;variation ratio;deep learning;distance;decision boundary
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Least Probable Disagreement Region for Active Learning
| 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 |
Latent space;Bayesian Optimization;Collision
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Learning Collision-free Latent Space for Bayesian Optimization
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image translation;single image;conditional generation;image manipulation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Single Image Manipulation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Regularization;Graph-based Representation
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
| null | null | 0 | 2.5 |
Reject
|
3;4;1;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
On the Effect of Consensus in Decentralized Deep Learning
| null | null | 0 | 4.5 |
Reject
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Learning;Imitation Learning;Inverse Reinforcement Learning;Reinforcement Learning;Transfer Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Unbiased Learning with State-Conditioned Rewards in Adversarial Imitation Learning
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
taylorglo;loss function;metalearning;evolution;deep networks;evolutionary strategies;taylor polynomials;glo
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization
| null | null | 0 | 3.5 |
Reject
|
4;4;2;4
| null |
null |
University of Freiburg, Freiburg, Germany; IBM Research, Dublin, Ireland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3163; None
| null | 0 | null | null | null | null | null |
Martin Wistuba, Josif Grabocka
|
https://iclr.cc/virtual/2021/poster/3163
|
bayesian optimization;metalearning;few-shot learning;automl
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5.25 |
4;5;6;6
| null |
https://iclr.cc/virtual/2021/poster/3163
|
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
| null | null | 0 | 4 |
Poster
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
attention;self-attention;bert;multi-head;tensor factorization
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Multi-Head Attention: Collaborate Instead of Concatenate
|
https://github.com/...
| null | 0 | 3.75 |
Reject
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regret matching;counterfactual regret minimization;imperfect-information games;regret updating
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 3.666667 |
2;3;6
| null | null |
Temperature Regret Matching for Imperfect-Information Games
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Boston University; MIT-IBM Watson AI Lab; Massachusetts Institute of Technology; IBM Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3252; None
| null | 0 | null | null | null | null | null |
Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogerio Feris
|
https://iclr.cc/virtual/2021/poster/3252
| null | null | 0 | null | null |
iclr
| 0.522233 | 0 |
https://mengyuest.github.io/AdaFuse/
|
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3252
|
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null |
Tencent AI LAB, China; NLPR&CRIPAC, Institute of Automation, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China and Tencent AI LAB, China; NLPR&CRIPAC, Institute of Automation, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China and Center for Excellence in Brain Science and Intelligence Technology, CAS, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2997; None
| null | 0 | null | null | null | null | null |
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He
|
https://iclr.cc/virtual/2021/poster/2997
| null | null | 0 | null | null |
iclr
| -0.236525 | 0 | null |
main
| 5 |
2;3;7;8
| null |
https://iclr.cc/virtual/2021/poster/2997
|
Graph Information Bottleneck for Subgraph Recognition
| null | null | 0 | 2.75 |
Poster
|
2;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
offline reinforcement learning;behavior regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement 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 |
Causal Discovery;Reinforcement Learning;Ordering Search
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Ordering-Based Causal Discovery with Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.948683 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Unsupervised Domain Adaptation via Minimized Joint Error
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian active learning;geometric interpretation;core-set construction;model uncertainty;ellipsoid.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
On the Geometry of Deep Bayesian Active Learning
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regularization;loss;loss function;metalearning;meta-learning;optimization;theory;robustness;adversarial attacks
| null | 0 | null | null |
iclr
| 0.390567 | 0 | null |
main
| 5.75 |
3;5;7;8
| null | null |
Effective Regularization Through Loss-Function Metalearning
| 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 |
Robustness;Adversarial Risk;Neural Networks;Machine Learning Security
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.5 |
3;4;4;7
| null | null |
With False Friends Like These, Who Can Have Self-Knowledge?
| null | null | 0 | 3.75 |
Reject
|
3;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.225494 | 0 | null |
main
| 4.75 |
3;4;4;8
| null | null |
Exploiting Verified Neural Networks via Floating Point Numerical Error
| null | null | 0 | 4.5 |
Reject
|
5;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Cross-Probe BERT for Efficient and Effective Cross-Modal Search
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-shot learning;transductive learning;unsupervised learning;self-supervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
SHOT IN THE DARK: FEW-SHOT LEARNING WITH NO BASE-CLASS LABELS
| null | null | 0 | 4 |
Withdraw
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Explicit homography estimation improves contrastive self-supervised learning
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
algorithmic fairness
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Withdraw
| null | null | 0 | 4.25 |
Withdraw
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visualization;t-SNE;UMAP;dimensionality reduction;nonlinear dimensionality reduction
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum
| null | null | 0 | 3.5 |
Reject
|
5;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot learning;meta-learning;uncertainty estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Task Calibration for Distributional Uncertainty in Few-Shot Classification
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Label Smoothing;Non-convex Optimization;Deep Learning Theory
| null | 0 | null | null |
iclr
| -0.916949 | 0 | null |
main
| 4.25 |
1;4;6;6
| null | null |
Towards Understanding Label Smoothing
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sentiment classification;reinforcement learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Aspect-based Sentiment Classification via Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3025; None
| null | 0 | null | null | null | null | null |
Sven Gowal, Po-Sen Huang, Aaron v den, Timothy A Mann, Pushmeet Kohli
|
https://iclr.cc/virtual/2021/poster/3025
|
self-supervised;adversarial training;robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3025
|
Self-supervised Adversarial Robustness for the Low-label, High-data Regime
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
MIT BCS, CBMM, CSAIL; The University of Hong Kong; MIT-IBM Watson AI Lab; Stanford University; MIT CSAIL
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2994; None
| null | 0 | null | null | null | null | null |
Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee K Wong, Joshua B Tenenbaum, Chuang Gan
|
https://iclr.cc/virtual/2021/poster/2994
|
Concept Learning;Neuro-Symbolic Learning;Video Reasoning;Visual Reasoning
| null | 0 | null | null |
iclr
| 0.301511 | 0 |
http://dcl.csail.mit.edu
|
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2994
|
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
| null | null | 0 | 3.25 |
Poster
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regularization;deep learning;adversarial robustness
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Manifold Regularization for Locally Stable Deep Neural Networks
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;policy evaluation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Adaptive N-step Bootstrapping with Off-policy Data
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null |
Peking University; JD AI Research; UCLA
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;certified robustness;certfied robust training
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Learning Contextual Perturbation Budgets for Training Robust Neural Networks
| null | null | 0 | 3.75 |
Reject
|
5;5;2;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
| 6 |
5;6;7
| null | null |
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of Radiology, Stanford University, Stanford CA, USA; National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda MD, USA; Department of Computer Science, Stanford University, Stanford CA, USA; Department of Electrical Engineering, Stanford University, Stanford CA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2918; None
| null | 0 | null | null | null | null | null |
Sarah Hooper, Michael Wornow, Ying Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re
|
https://iclr.cc/virtual/2021/poster/2918
|
Weak supervision;segmentation;CNN;latent variable;medical imaging
| null | 0 | null | null |
iclr
| 0.196116 | 0 | null |
main
| 5.6 |
4;5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2918
|
Cut out the annotator, keep the cutout: better segmentation with weak supervision
| null | null | 0 | 3.4 |
Poster
|
3;4;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
nuclear fusion;physics;differential equations;dynamical systems;control;dynamics
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Yale University, New Haven, CT, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2945; None
| null | 0 | null | null | null | null | null |
Juntang Zhuang, Nicha C Dvornek, sekhar tatikonda, James s Duncan
|
https://iclr.cc/virtual/2021/poster/2945
|
neural ode;memory efficient;reverse accuracy;gradient estimation
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2945
|
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
|
https://github.com/jzkay12/TorchDiffEqPack
| null | 0 | 2.75 |
Poster
|
2;4;3;2
| null |
null |
University of Texas at Austin; University of Texas at Austin, VMware Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3341; None
| null | 0 | null | null | null | null | null |
Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl
|
https://iclr.cc/virtual/2021/poster/3341
|
memory optimized training;memory efficient training;checkpointing;deep network training
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3341
|
Memory Optimization for Deep Networks
|
https://github.com/utsaslab/MONeT
| null | 0 | 3.75 |
Spotlight
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Inference;Natural Language Understanding;Natural Language Processing;Gender Bias;Societal Bias;Bias;Ethics;Debiasing Techniques;Data Augmentation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Evaluating Gender Bias in Natural Language Inference
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning
| null | null | 0 | 4 |
Reject
|
3;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Robustness;Equilibrium Networks;Neural ODE
| null | 0 | null | null |
iclr
| 0.818182 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Lipschitz-Bounded Equilibrium Networks
| null | null | 0 | 3.25 |
Reject
|
2;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
upper bound of error;feature separability;classifier discrepancy;few-shot learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Towards Understanding the Cause of Error in Few-Shot Learning
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Experimental design;generalization;data collection
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Predicting the impact of dataset composition on model performance
| 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 |
neural differential equations;neural ordinary differential equations;adjoint
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code
| null | null | 0 | 4.25 |
Reject
|
3;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Autoencoders;Noise Contrastive Estimation;Sampling
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning from Demonstrations;Energy based Models;Inverse Reinforcement Learning;Imitation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning
| null | null | 0 | 3.5 |
Reject
|
2;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.288675 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria
| null | null | 0 | 3 |
Reject
|
3;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;teacher-student setting;theoretical analysis;hierarchical models;representation learning
| null | 0 | null | null |
iclr
| -0.332205 | 0 | null |
main
| 5.8 |
3;5;6;7;8
| null | null |
Understanding Self-supervised Learning with Dual Deep Networks
| null | null | 0 | 3.2 |
Reject
|
4;2;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Generalizing Tree Models for Improving Prediction Accuracy
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Poisoning;backdoor;attack;benchmark
| null | 0 | null | null |
iclr
| -0.730297 | 0 | null |
main
| 6 |
4;5;7;8
| null | null |
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks
| 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 |
Generative Adverarial Networks;Sampling;Markov chain Monte Carlo;Reparameterization
| null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 6.25 |
5;5;7;8
| null | null |
Efficient Sampling for Generative Adversarial Networks with Reparameterized Markov Chains
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Purdue University; California Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3281; None
| null | 0 | null | null | null | null | null |
Zongyi Li, Nikola B Kovachki, Kamyar Azizzadenesheli, Burigede liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
|
https://iclr.cc/virtual/2021/poster/3281
|
Partial differential equation;Fourier transform;Neural operators
| null | 0 | null | null |
iclr
| -0.2 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3281
|
Fourier Neural Operator for Parametric Partial Differential Equations
| null | null | 0 | 3.5 |
Poster
|
5;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-output gaussian processes;exact inference;directed acyclic graphs;conditional independence;structure learning;negative transfer
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes
| null | null | 0 | 3.25 |
Withdraw
|
4;2;3;4
| null |
null |
Mila, Quebec Artificial Intelligence Institute, Universite de Montreal; Imagia Cybernetics; Mila, Quebec Artificial Intelligence Institute, Universite de Montreal; CIFAR Senior Fellow
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3280; None
| null | 0 | null | null | null | null | null |
Joseph Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
|
https://iclr.cc/virtual/2021/poster/3280
|
Feature Attribution;Generalization;Saliency
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3280
|
Saliency is a Possible Red Herring When Diagnosing Poor Generalization
| null | null | 0 | 4 |
Poster
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3D scene representation;novel view synthesis;neural rendering
| null | 0 | null | null |
iclr
| 0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep neural networks;deep learning understanding;backdoor vulnerability;adversarial vulnerability
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
| 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 |
visible and invisible;causal discovery;invariant causal prediction;colorectal cancer
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;3;7
| null | null |
Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study
| null | null | 0 | 2.666667 |
Withdraw
|
2;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
theory of overparameterized learning;statistics;double descent;transfer learning;regression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
explainability;fairness;Shapley
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Explainability for fair machine 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 |
Hypergraph;Representation Learning;Inductive Learning;Geometric Deep Learning;Aggregation Methods
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;resisting adversarial examples
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2 |
1;2;2;3
| null | null |
Towards Counteracting Adversarial Perturbations to Resist Adversarial Examples
| null | null | 0 | 5 |
Reject
|
5;5;5;5
| null |
null |
Stanford University; Cornell University; Penn State University; ASAPP Inc.
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2678; None
| null | 0 | null | null | null | null | null |
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Weinberger, Yoav Artzi
|
https://iclr.cc/virtual/2021/poster/2678
|
Fine-tuning;Optimization;BERT
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2678
|
Revisiting Few-sample BERT Fine-tuning
| null | null | 0 | 4 |
Poster
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;benchmarks;datasets;convolutional neural networks;interpretability;robustness;overinterpretation
| null | 0 | null | null |
iclr
| -0.67082 | 0 | null |
main
| 4 |
2;3;5;6
| null | null |
Overinterpretation reveals image classification model pathologies
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Stanford University; Georgia Institute of Technology; Simon Fraser University; Intel Labs
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3314; None
| null | 0 | null | null | null | null | null |
Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian
|
https://iclr.cc/virtual/2021/poster/3314
|
reinforcement learning;simulation
| null | 0 | null | null |
iclr
| -0.18334 | 0 | null |
main
| 5.8 |
4;5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3314
|
Large Batch Simulation for Deep Reinforcement Learning
| null | null | 0 | 3.2 |
Poster
|
4;3;2;4;3
| null |
null |
Stanford; University of Pennsylvania; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3047; None
| null | 0 | null | null | null | null | null |
Glen Berseth, Daniel Geng, Coline M Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/3047
|
Reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3047
|
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
| null | null | 0 | 4 |
Oral
|
4;4;4;4
| null |
null |
Korea Advanced Institute of Science and Technology
|
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 |
Depth Completion using Plane-Residual Representation
| null | null | 0 | 5 |
Withdraw
|
5;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Withdraw
| null | null | 0 | 3 |
Withdraw
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model-Based Optimization;Benchmark;Offline
| null | 0 | null | null |
iclr
| 0.522233 | 0 |
https://sites.google.com/view/design-bench
|
main
| 5.75 |
5;5;6;7
| null | null |
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null |
UT Austin, Facebook AI Research; UT Austin
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2814; None
| null | 0 | null | null | null | null | null |
Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh Kumar Ramakrishnan, Kristen Grauman
|
https://iclr.cc/virtual/2021/poster/2814
|
visual navigation;audio visual learning;embodied vision
| null | 0 | null | null |
iclr
| -0.333333 | 0 |
http://vision.cs.utexas.edu/projects/audio_visual_waypoints
|
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2814
|
Learning to Set Waypoints for Audio-Visual Navigation
| 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 |
Neural Processes;Meta-learning;Variational Inference
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.75 |
5;5;5;8
| null | null |
Uncertainty in Neural Processes
| 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 |
equivariance;group convolutional neural network;inductive bias;group equivariant architecture;Lie group;Lie algebra
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Alibaba Group, Bellevue, WA, 98004, USA; Alibaba Group, Hangzhou, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2829; None
| null | 0 | null | null | null | null | null |
Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Li Hao, Rong Jin
|
https://iclr.cc/virtual/2021/poster/2829
|
Image compression;Entropy Model;Global Reference
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2829
|
Learning Accurate Entropy Model with Global Reference for Image Compression
| null | null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
cross-modal;multilingual;unsupervised translation;visual similarity
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Globetrotter: Unsupervised Multilingual Translation from Visual Alignment
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Heavy-tailed Gradients;Proximal Policy Optimization;Robust Estimation;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 6.25 |
5;5;7;8
| null | null |
On Proximal Policy Optimization's Heavy-Tailed Gradients
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;natural language processing
| null | 0 | null | null |
iclr
| -0.285714 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
| null | null | 0 | 3.8 |
Reject
|
5;3;3;4;4
| null |
null |
Montreal Robotics and Embodied AI Lab, Université de Montréal; University of Toronto, Vector Institute; Mila, Université de Montréal; Mila, McGill; NVIDIA; University of Copenhagen; NVIDIA, University of Toronto, Vector Institute; Mila
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3082; None
| null | 0 | null | null | null | null | null |
Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jérôme Parent-Lévesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler
|
https://iclr.cc/virtual/2021/poster/3082
|
Differentiable simulation;System identification;Physical parameter estimation;3D scene understanding;3D vision;Differentiable rendering;Differentiable physics
| null | 0 | null | null |
iclr
| 0 | 0 |
https://gradsim.github.io
|
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3082
|
gradSim: Differentiable simulation for system identification and visuomotor control
|
https://github.com/gradsim
| null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Symbolic mathematics;neuro-symbolic computation;differential equations;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
A neural method for symbolically solving partial differential equations
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Disentanglement;Equivariance;Topology;Representation theory;Character theory
| null | 0 | null | null |
iclr
| -0.221163 | 0 |
https://anonymous.4open.science/r/5b7e2cbb-54dc-4fde-bc2c-8f75d29fc15a/
|
main
| 5.4 |
3;5;6;6;7
| null | null |
Addressing the Topological Defects of Disentanglement
| 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;Trust region methods;Maximum Entropy Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control
| null | null | 0 | 2.5 |
Reject
|
2;3;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Video Textures;Audio Conditioned Video Synthesis;Self-Supervised learning;Contrastive Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Contrastive Video Textures
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
AITRICS, Seoul, South Korea; School of Computing, KAIST, Daejeon, South Korea; Graduate School of AI, KAIST, Seoul, South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2819; None
| null | 0 | null | null | null | null | null |
Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang
|
https://iclr.cc/virtual/2021/poster/2819
|
Federated Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2819
|
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
|
https://github.com/wyjeong/FedMatch
| null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pruning;Compression;CNN;LSTM;Image classification
| null | 0 | null | null |
iclr
| -0.738549 | 0 | null |
main
| 3.8 |
2;3;4;4;6
| null | null |
More Side Information, Better Pruning: Shared-Label Classification as a Case Study
| null | null | 0 | 3.6 |
Reject
|
4;4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multimodal Learning;Representations;Noise;Audio;Visual;Fusion;Biologically Inspired
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
On the Benefits of Early Fusion in Multimodal Representation Learning
| null | null | 0 | 4 |
Withdraw
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Regularize;ensemble;variance;reduction
| null | 0 | null | null |
iclr
| 0.68313 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
Inria; Sorbonne Universit ´e
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3190; None
| null | 0 | null | null | null | null | null |
Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed CHETOUANI, Olivier Sigaud
|
https://iclr.cc/virtual/2021/poster/3190
|
Deep reinforcement learning;intrinsic motivations;symbolic representations;autonomous learning
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3190
|
Grounding Language to Autonomously-Acquired Skills via Goal Generation
| null | null | 0 | 3.5 |
Poster
|
3;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-supervised learning;visual navigation
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Environment Predictive Coding for Embodied Agents
| null | null | 0 | 3.75 |
Reject
|
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
| -1 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
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