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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Towards Robustness against Unsuspicious Adversarial Examples
| 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 |
Semi-supervised learning;Adversarial;regression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Semi-supervised regression with skewed data via adversarially forcing the distribution of predicted values
| null | null | 0 | 3 |
Reject
|
3;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
VAE;RL;PPO
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 2.5 |
1;2;3;4
| null | null |
Guiding Representation Learning in Deep Generative Models with Policy Gradients
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;pruning after training;weight pruning;proximal operator;fixed point iteration
| null | 0 | null | null |
iclr
| 0.345857 | 0 | null |
main
| 6.5 |
5;5;7;9
| null | null |
Sparsifying Networks via Subdifferential Inclusion
| null | null | 0 | 3.25 |
Reject
|
2;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph neural networks;Graph attention networks;graph sparsification;spectral sparsification
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
FAST GRAPH ATTENTION NETWORKS USING EFFECTIVE RESISTANCE BASED GRAPH SPARSIFICATION
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null |
MIT CSAIL, Cambridge, MA 02139, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2871; None
| null | 0 | null | null | null | null | null |
Lucy Chai, Jonas Wulff, Phillip Isola
|
https://iclr.cc/virtual/2021/poster/2871
|
Image Synthesis;Composition;Generative Adversarial Networks;Image Editing;Interpretability
| null | 0 | null | null |
iclr
| 0 | 0 |
https://chail.github.io/latent-composition/
|
main
| 6.25 |
5;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/2871
|
Using latent space regression to analyze and leverage compositionality in GANs
|
https://github.com/chail/latent-composition
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
incremental learning;continual learning;class-incremental learning;meta learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Meta-Aggregating Networks for Class-Incremental Learning
| null | null | 0 | 4.25 |
Withdraw
|
4;4;4;5
| null |
null |
School of Computer Science, McGill University; School of Computer Science, McGill University, Mila
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3086; None
| null | 0 | null | null | null | null | null |
Zichao Yan, William Hamilton, Mathieu Blanchette
|
https://iclr.cc/virtual/2021/poster/3086
|
Graph neural network;Deep generative modeling;Machine learning;Drug discovery;RNA structure;RNA structure embedding;RNA-protein interaction prediction
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3086
|
Neural representation and generation for RNA secondary structures
| null | null | 0 | 3.25 |
Poster
|
3;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Cox process;variational inference;stochastic differential equation;smoothing posterior density
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Variational inference for diffusion modulated Cox processes
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Robustness
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Azimuthal Rotational Equivariance in Spherical CNNs
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Orthogonal Subspace Decomposition: A New Perspective of Learning Discriminative Features for Face Clustering
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Network;Departure From Normality;Schur Decomposition;Spectrogram Synthesis
| null | 0 | null | null |
iclr
| -0.207514 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Conditioning Trick for Training Stable GANs
| null | null | 0 | 3.25 |
Reject
|
5;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
pretrained language models;multi-vocab pretraining;Chinese BERT
| null | 0 | null | null |
iclr
| -0.632456 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
MVP-BERT: Redesigning Vocabularies for Chinese BERT and Multi-Vocab Pretraining
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null |
Gatsby Unit; University of Washington; DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2995; None
| null | 0 | null | null | null | null | null |
Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
|
https://iclr.cc/virtual/2021/poster/2995
|
Causal Inference;Instrumental Variable Regression;Deep Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.8 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2995
|
Learning Deep Features in Instrumental Variable Regression
| null | null | 0 | 3.5 |
Poster
|
3;2;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Generalization;Domain Representation;Multi-source Domain Generalization
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Batch Normalization Embeddings for Deep Domain Generalization
| null | null | 0 | 4.5 |
Withdraw
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
MARL;multi-agent reinforcement learning;value function factorization;attention
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive Gradient Methods;Deep Learning;Nonconvex Optimization
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Adam$^+$: A Stochastic Method with Adaptive Variance Reduction
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
University of Cambridge, The Alan Turing Institute; University of Cambridge; Imperial College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2803; None
| null | 0 | null | null | null | null | null |
Wenbo Gong, Yingzhen Li, José Miguel Hernández Lobato
|
https://iclr.cc/virtual/2021/poster/2803
|
kernel methods;variational inference;particle inference
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2803
|
Sliced Kernelized Stein Discrepancy
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dataset sampling;batch selection;mini-batch SGD;reinforcement learning;policy gradient;optimal sample sequence
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Adaptive Dataset Sampling by Deep Policy Gradient
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multimodal representation learning;Probabilistic representation;image caption retrieval
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Probabilistic Multimodal Representation Learning
| 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 |
Spectral Graph Theory;Undirected Graphical models;Gaussian Markov Random Fields
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Graph Learning via Spectral Densification
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.862662 | 0 | null |
main
| 3.25 |
2;2;3;6
| null | null |
Certified Distributional Robustness via Smoothed Classifiers
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent Reinforcement Learning;Benchmarking
| null | 0 | null | null |
iclr
| -0.688247 | 0 |
https://sites.google.com/view/marlbenchmarks
|
main
| 4.25 |
3;4;4;6
| null | null |
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
IST Austria & NeuralMagic; IST Austria; IIT Bombay
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3360; None
| null | 0 | null | null | null | null | null |
Peter Davies, Vijaykrishna Gurunathan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh
|
https://iclr.cc/virtual/2021/poster/3360
|
distributed machine learning;mean estimation;variance reduction;lattices
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3360
|
New Bounds For Distributed Mean Estimation and Variance Reduction
| 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 |
representation learning;contrastive learning;neural networks;event sequiences
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
CoLES: Contrastive learning for event sequences with self-supervision
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Microsoft Research Asia; City University of Hong Kong
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2828; None
| null | 0 | null | null | null | null | null |
Nanxuan Zhao, Zhirong Wu, Rynson W Lau, Stephen Lin
|
https://iclr.cc/virtual/2021/poster/2828
|
Transfer Learning;Unsupervised Learning;Self-supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
http://nxzhao.com/projects/good_transfer/
|
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2828
|
What Makes Instance Discrimination Good for Transfer 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 | null | null | 0 | null | null |
iclr
| -0.632456 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Training By Vanilla SGD with Larger Learning Rates
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian deep learning;Gaussian processes;uncertainty quantification
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolution;Anchor Space;Anisotropic Convolution;Graph Classification;Node Classification
| null | 0 | null | null |
iclr
| 0.688247 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Graph Deformer Network
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null |
Mila - Quebec AI Institute, HEC Montreal, Canadian Institute for Advanced Research (CIFAR); Mila - Quebec AI Institute, Universite de Montreal, Canadian Institute for Advanced Research (CIFAR); Mila - Quebec AI Institute, Universite de Montreal; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3084; None
| null | 0 | null | null | null | null | null |
Meng Qu, Junkun Chen, Louis-Pascal A Xhonneux, Yoshua Bengio, Jian Tang
|
https://iclr.cc/virtual/2021/poster/3084
|
Knowledge Graph Reasoning;Logic Rules;EM
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3084
|
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
| null | null | 0 | 2.75 |
Poster
|
2;4;4;1
| null |
null |
MIT; Yahoo! Research NYC
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3273; None
| null | 0 | null | null | null | null | null |
Kyriakos Axiotis, Maxim Sviridenko
|
https://iclr.cc/virtual/2021/poster/3273
|
low rank;rank-constrained convex optimization;matrix completion
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3273
|
Local Search Algorithms for Rank-Constrained Convex Optimization
| null | null | 0 | 3.5 |
Poster
|
3;3;3;5
| null |
null |
Facebook AI Research; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2950; None
| null | 0 | null | null | null | null | null |
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E Gonzalez, Marcus Rohrbach, trevor darrell
|
https://iclr.cc/virtual/2021/poster/2950
|
Continual Learning;Lifelong Learning;Catastrophic Forgetting;XAI;Explainability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2950
|
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
|
https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons
| null | 0 | 3.5 |
Poster
|
4;2;4;4
| null |
null |
Department of Mathemathcal Sciences, Tsinghua University; Hisilicon; Yau Mathematical Sciences Center, Tsinghua University; Institute for Interdisciplinary Information Science, Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2927; None
| null | 0 | null | null | null | null | null |
Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao
|
https://iclr.cc/virtual/2021/poster/2927
|
Real-world image denoising;unsupervised image denoising
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2927
|
An Unsupervised Deep Learning Approach for Real-World Image Denoising
| null | null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Object-Centric Representation;Planning;Discrete VAE
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Generating Plannable Lifted Action Models for Visually Generated Logical Predicates
| null | null | 0 | 2.666667 |
Reject
|
1;3;4
| null |
null |
阿里巴巴集团; 哈尔滨工业大学; 北京大学
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2657; None
| null | 0 | null | null | null | null | null |
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David Kreil, Michael K Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
|
https://iclr.cc/virtual/2021/poster/2657
|
Modern Hopfield Network;Energy;Attention;Convergence;Storage Capacity;Hopfield layer;Associative Memory
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2657
|
Hopfield Networks is All You Need
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;distribution shift;ensembling;uncertainty estimation
| null | 0 | null | null |
iclr
| -0.187317 | 0 | null |
main
| 5 |
3;3;5;9
| null | null |
Robustness via Probabilistic Cross-Task Ensembles
| null | null | 0 | 3.75 |
Withdraw
|
5;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recommendation;implicit trust;explicit trust;collaborative filtering;multi-faceted;trust;trust metrics;similarity;recommender
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Multi-Faceted Trust Based Recommendation System
| null | null | 0 | 4.5 |
Withdraw
|
5;4;4;5
| null |
null |
The Swiss AI Lab IDSIA, USI, SUPSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3336; None
| null | 0 | null | null | null | null | null |
Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2021/poster/3336
|
Reinforcement Learning;Off-Policy Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3336
|
Parameter-Based Value Functions
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null |
UChicago and TTIC; Blueshift, Alphabet
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2744; None
| null | 0 | null | null | null | null | null |
Xiaoxia (Shirley) Wu, Ethan Dyer, Behnam Neyshabur
|
https://iclr.cc/virtual/2021/poster/2744
|
Curriculum Learning;Understanding Deep Learning;Empirical Investigation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2744
|
When Do Curricula Work?
|
https://github.com/google-research/understanding-curricula
| null | 0 | 3.333333 |
Oral
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
ahmad
| null |
Machine Learning;Computer Vision;Data Augmentation;Background Removal
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 3.25 |
2;3;3;5
| null | null |
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null |
Department of CS, Emory University, Atlanta, GA 30322, USA; Department of IST, George Mason University, Fairfax, VA 22030, USA; Department of CS, George Mason University, Fairfax, VA 22030, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2938; None
| null | 0 | null | null | null | null | null |
Xiaojie Guo, Yuanqi Du, Liang Zhao
|
https://iclr.cc/virtual/2021/poster/2938
|
deep generative models;interpretable latent representation;disentangled representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2938
|
Property Controllable Variational Autoencoder via Invertible Mutual Dependence
|
https://github.com/xguo7/PCVAE
| null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Language modeling;Transformers;Recurrence;Gradient checkpointing;Pretraining
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Adding Recurrence to Pretrained Transformers
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
federated learning;kernel $k$-means;communication efficient
| null | 0 | null | null |
iclr
| -0.866154 | 0 | null |
main
| 4.25 |
1;5;5;6
| null | null |
A Communication Efficient Federated Kernel $k$-Means
| null | null | 0 | 3.25 |
Reject
|
5;2;3;3
| null |
null |
UC Berkeley, Google Brain; CMU, Google Brain; CMU
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2583; None
| null | 0 | null | null | null | null | null |
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2583
|
reinforcement learning;goal reaching;density estimation;Q-learning;hindsight relabeling
| null | 0 | null | null |
iclr
| -0.115663 | 0 | null |
main
| 6.4 |
4;6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2583
|
C-Learning: Learning to Achieve Goals via Recursive Classification
|
https://ben-eysenbach.github.io/c_learning/
| null | 0 | 3.4 |
Poster
|
4;2;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
knowledge distillation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Understanding Knowledge Distillation
| null | null | 0 | 4.333333 |
Withdraw
|
5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;variational autoencoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Symmetric Wasserstein Autoencoders
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
University of Toronto & Vector Institute; University of Montreal, Mila & CIFAR Fellow; University of Montreal & Mila
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3181; None
| null | 0 | null | null | null | null | null |
Chin-Wei Huang, Tian Qi Chen, Christos Tsirigotis, Aaron Courville
|
https://iclr.cc/virtual/2021/poster/3181
|
Normalizing flows;generative models;variational inference;invertible neural networks;universal approximation;optimal transport;convex optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3181
|
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
| 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 learning;Neural networks;Relational and structured data;Aggregation functions
| null | 0 | null | null |
iclr
| -0.738549 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Learning Aggregation Functions
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network interpretation;chain graph;deep learning theory;probabilistic graphical model
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
A Chain Graph Interpretation of Real-World Neural Networks
| null | null | 0 | 3.25 |
Reject
|
4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;self-supervised learning;contrastive learning;regularization;theory
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null |
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Microsoft Research, Redmond, WA 98052, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2622; None
| null | 0 | null | null | null | null | null |
Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner
|
https://iclr.cc/virtual/2021/poster/2622
|
support estimation;sublinear;learning-based;distinct elements;chebyshev polynomial
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2622
|
Learning-based Support Estimation in Sublinear Time
| null | null | 0 | 3.75 |
Spotlight
|
4;4;4;3
| null |
null |
Courant Institute of Mathematical Sciences, New York University, New York, NY; Courant Institute of Mathematical Sciences, Center for Data Science, New York University, New York, NY
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3074; None
| null | 0 | null | null | null | null | null |
Lei Chen, Zhengdao Chen, Joan Bruna
|
https://iclr.cc/virtual/2021/poster/3074
|
Graph Neural Networks;expressive power;feature propagation;rooted graphs;attributed walks;community detection;depth separation
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3074
|
On Graph Neural Networks versus Graph-Augmented MLPs
|
https://github.com/leichen2018/GNN_vs_GAMLP
| null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null |
Google Research, USA; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA, 90095
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2714; None
| null | 0 | null | null | null | null | null |
Weitong ZHANG, Dongruo Zhou, Lihong Li, Quanquan Gu
|
https://iclr.cc/virtual/2021/poster/2714
|
Deep Learning;Contextual Bandits;Thompson sampling
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2714
|
Neural Thompson Sampling
| null | null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Trusted Execution Environments;Integrity-Preserving Computation;Intel SGX
| null | 0 | null | null |
iclr
| -0.169031 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
GINN: Fast GPU-TEE Based Integrity for Neural Network Training
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Dept. of Electrical and Computer Engineering, Technical University of Munich; Dept. of Electrical and Computer Engineering, Rice University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2647; None
| null | 0 | null | null | null | null | null |
Reinhard Heckel, Fatih Furkan Yilmaz
|
https://iclr.cc/virtual/2021/poster/2647
|
early stopping;double descent
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2647
|
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
N/A
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semantic Segmentation;Unsupervised Domain Adaptation;Knowledge Distillation;Pseudo Labeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion
| null | null | 0 | 4 |
Withdraw
|
4;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 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Gradient flow encoding with distance optimization adaptive step size
| null | null | 0 | 4 |
Withdraw
|
4;3;5;4
| null |
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2933; None
| null | 0 | null | null | null | null | null |
Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
|
https://iclr.cc/virtual/2021/poster/2933
|
conformal prediction;uncertainty estimation;efficient inference methods;natural language processing;chemistry
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2933
|
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
|
https://github.com/ajfisch/conformal-cascades
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Controllable Graph Generation;Explainability;Conditional Generative Adversarial Network
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
SHADOWCAST: Controllable Graph Generation with Explainability
| 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 |
representation learning;node embeddings;temporal graphs;tensor factorization;disease spreading
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
FNRS research fellows, ICTEAM, Universit catholique de Louvain, Louvain-La-Neuve, Belgium
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2802; None
| null | 0 | null | null | null | null | null |
Simon Carbonnelle, Christophe De Vleeschouwer
|
https://iclr.cc/virtual/2021/poster/2802
|
deep learning;generalization;implicit regularization
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2802
|
Intraclass clustering: an implicit learning ability that regularizes DNNs
| 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 |
transformer;efficiency;anytime prediction;sequence length;evolutionary search
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
GENERATIVE MODEL-ENHANCED HUMAN MOTION PREDICTION
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fairness;student teacher model;counterfactual generative model
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Generative Fairness Teaching
| null | null | 0 | 3.25 |
Reject
|
4;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse PCA;Principal component analysis;Randomized linear algebra;Singular value decomposition
| null | 0 | null | null |
iclr
| 0.942809 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Approximation Algorithms for Sparse Principal Component Analysis
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null |
Universit ´e Cˆote d’Azur, Inria (Maasai team), Laboratoire J.A. Dieudonn ´e, UMR CNRS 7351, France; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2776; None
| null | 0 | null | null | null | null | null |
Niels Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
|
https://iclr.cc/virtual/2021/poster/2776
| null | null | 0 | null | null |
iclr
| 0.688247 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2776
|
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
| 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 |
self-training;counterfactual inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Counterfactual Self-Training
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial learning;robust machine learning;robust optimization;meta learning
| null | 0 | null | null |
iclr
| -0.088045 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Learning to Generate Noise for Multi-Attack Robustness
| null | null | 0 | 3.75 |
Reject
|
4;1;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Affordance;affordance embedding;object representation
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Understanding Mental Representations Of Objects Through Verbs Applied To Them
| 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 |
autoML;neural architecture search;NAS;one-shot NAS;weight-sharing NAS;super-net
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS
| null | null | 0 | 4 |
Reject
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Video Synthesis;Vision and Language;Representation Learning
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Compositional Video Synthesis with Action Graphs
| null | null | 0 | 3 |
Reject
|
2;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Artificial GAN Fingerprints: Rooting Deepfake Attribution in Training Data
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
zero-shot learning;visual-semantic embedding;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Zero-Shot Recognition through Image-Guided Semantic Classification
| null | null | 0 | 4.5 |
Reject
|
4;5;5;4
| null |
null |
Berkeley Artificial Intelligence Research Lab, Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3285; None
| null | 0 | null | null | null | null | null |
Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin
|
https://iclr.cc/virtual/2021/poster/3285
|
unsupervised stabilization;representation of dynamical systems;neural networks;empowerment;intrinsic motivation
| null | 0 | null | null |
iclr
| 0.13484 | 0 |
https://sites.google.com/view/latent-gce
|
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3285
|
Efficient Empowerment Estimation for Unsupervised Stabilization
| null | null | 0 | 3.5 |
Poster
|
4;2;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graphs;GNN;adversarial;attack
| null | 0 | null | null |
iclr
| -0.514496 | 0 | null |
main
| 5.8 |
5;6;6;6;6
| null | null |
Single-Node Attack for Fooling Graph Neural Networks
|
https://github.com/gnnattack/SINGLE
| null | 0 | 3.8 |
Reject
|
5;2;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Object-centric Visual Representation Learning;Deep Generative Models;Computer Vision
| null | 0 | null | null |
iclr
| -0.917663 | 0 | null |
main
| 3.666667 |
1;4;6
| null | null |
Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
long-tailed classification;gradient distortion;asynchronous modeling
| null | 0 | null | null |
iclr
| -0.492366 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
efficient deep neural network;semantic segmentation;parameter sharing;anytime prediction;tiny network graph;massively parallel hardware systems;recurrent convolutional network
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
ItNet: iterative neural networks for fast and efficient anytime prediction
| null | null | 0 | 3.25 |
Reject
|
3;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
EqCo: Equivalent Rules for Self-supervised Contrastive 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 |
Reinforcement Learning;Multi-task;Hierarchical;Meta Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.25 |
3;3;3;4
| null | null |
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null |
Facebook Reality Labs, Pittsburgh, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2964; None
| null | 0 | null | null | null | null | null |
Alexander Richard, Dejan Markovic, Israel Gebru, Steven Krenn, Gladstone A Butler, Fernando Torre, Yaser Sheikh
|
https://iclr.cc/virtual/2021/poster/2964
|
binaural audio;sound spatialization;neural sound synthesis;binaural speech;speech processing;speech generation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2964
|
Neural Synthesis of Binaural Speech From Mono Audio
|
https://github.com/facebookresearch/BinauralSpeechSynthesis
| null | 0 | 4.666667 |
Oral
|
4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
noisy labels;self-supervised learning;semi-supervised learning;label noise
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Contrast to Divide: self-supervised pre-training for learning with noisy labels
|
https://github.com/ContrastToDivide/C2D
| null | 0 | 3.75 |
Withdraw
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
probability divergence;two sample test;maximum mean discrepancy
| null | 0 | null | null |
iclr
| 0.09759 | 0 | null |
main
| 6.75 |
5;6;7;9
| null | null |
H-divergence: A Decision-Theoretic Probability Discrepancy Measure
| 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 |
reinforcement learning;text-based games;goal-driven dialogue;natural language processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
| null | null | 0 | 2.75 |
Withdraw
|
3;3;3;2
| null |
null |
Mila; Mila, Université de Montréal; Mila, Université de Montréal, CIFAR Fellow; Microsoft Research, Mila, Université de Montréal; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2797; None
| null | 0 | null | null | null | null | null |
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, Philip Bachman
|
https://iclr.cc/virtual/2021/poster/2797
|
Reinforcement Learning;Self-Supervised Learning;Representation Learning;Sample Efficiency
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2797
|
Data-Efficient Reinforcement Learning with Self-Predictive Representations
|
https://github.com/mila-iqia/spr
| null | 0 | 4.25 |
Spotlight
|
5;4;4;4
| null |
null |
Princeton University & IAS; Princeton University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3299; None
| null | 0 | null | null | null | null | null |
Zhiyuan Li, Yi Zhang, Sanjeev Arora
|
https://iclr.cc/virtual/2021/poster/3299
|
sample complexity separation;equivariance;convolutional neural networks;fully-connected
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3299
|
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
| null | null | 0 | 3.75 |
Oral
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Interpretability;Graph Neural Networks;Hard Masks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Hard Masking for Explaining Graph Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION
| 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 |
Graph Neural Networks;Graph Convolutional Networks;Full-Graph Training;Large-Graph Training;Distributed Training;Partition Parallelism;Sampling
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
BDS-GCN: Efficient Full-Graph Training of Graph Convolutional Nets with Partition-Parallelism and Boundary Sampling
| 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 |
biologically plausible;incremental learning;leaky integrator;multiscale;hierarchical processing;timescales
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Learning representations from temporally smooth data
| 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 |
invariant neural networks;universal approximation;meta-feature learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Distribution-Based Invariant Deep Networks for Learning Meta-Features
| null | null | 0 | 3 |
Reject
|
2;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
abstractive dialogue summarization;long-distance cross-sentence relation;conversational structure;factual knowledge;sparse relational graph self-attention network;dual-copy mechanism
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge
| 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 |
Reinforcement Learning;Partial Observability;Memory Representations;External Memories;POMDPs.
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
The act of remembering: A study in partially observable reinforcement learning
| 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 |
Transfer learning;Deep learning;Sequential learning;Critical learning periods;Curriculum learning
| null | 0 | null | null |
iclr
| -0.0625 | 0 | null |
main
| 4.6 |
4;4;4;5;6
| null | null |
The Negative Pretraining Effect in Sequential Deep Learning and Three Ways to Fix It
| null | null | 0 | 3.4 |
Reject
|
3;4;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNN;training;quantization;low-bit;energy efficiency
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Exploring the Potential of Low-Bit Training of Convolutional Neural Networks
| 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 |
Robustness;Multi-Agent Learning;Sim2Real;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations
| null | null | 0 | 2.5 |
Reject
|
3;1;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Spiking Neural Networks;Biologically Plausible Learning Algorithm;Energy-efficient Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;user-desired data distribution;user preference;critic
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Game of Life
| null | 0 | null | null |
iclr
| -0.648886 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
It's Hard for Neural Networks to Learn the Game of Life
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
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