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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
healthcare;medical application
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Learning Blood Oxygen from Respiration Signals
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language models;transformer;model extraction;security
| null | 0 | null | null |
iclr
| -0.29277 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Grey-box Extraction of Natural Language Models
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural network;NAS;FPGA
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
FGNAS: FPGA-Aware Graph Neural Architecture Search
| 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 |
implicit regularization;implicit bias;algorithmic regularization;over-parameterization;learning theory
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
Texas A&M University; The University of Texas at Austin
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2698; None
| null | 0 | null | null | null | null | null |
XINJIE FAN, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, Mingyuan Zhou
|
https://iclr.cc/virtual/2021/poster/2698
|
Efficient Inference Methods;Probabilistic Methods;Supervised Deep Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2698
|
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Point Cloud Classification;Adversarial Training
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
On The Adversarial Robustness of 3D Point Cloud Classification
| null | null | 0 | 3.25 |
Reject
|
2;4;4;3
| null |
null |
Stanford University; Facebook AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3275; None
| null | 0 | null | null | null | null | null |
Beliz Gunel, Jingfei Du, Alexis Conneau, Veselin Stoyanov
|
https://iclr.cc/virtual/2021/poster/3275
|
pre-trained language model fine-tuning;supervised contrastive learning;natural language understanding;few-shot learning;robustness;generalization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3275
|
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
| null | null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalization;Robustness;Neural Network;Weight Perturbation;Rademacher complexity
| null | 0 | null | null |
iclr
| -0.088045 | 0 | null |
main
| 5.75 |
3;6;7;7
| null | null |
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
| 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;Unsupervised Learning;Entropy Maximization;Contrastive Learning;Self-supervised Learning;Exploration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Unsupervised Active Pre-Training for Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentanglement;image translation;latent optimization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Learning Disentangled Representations for Image Translation
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Statistical distance;Divergence;Optimal Transport;Implicit Distribution;Deep Generative Models;GANs
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
ACT: Asymptotic Conditional Transport
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian process;variational inference;variational autoencoders;Bayesian inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Sparse Gaussian Process Variational Autoencoders
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3375; None
| null | 0 | null | null | null | null | null |
Yann Dauphin, Ekin Cubuk
|
https://iclr.cc/virtual/2021/poster/3375
|
deep learning;batch normalization;regularization;understanding neural networks
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 5.666667 |
4;6;7
| null |
https://iclr.cc/virtual/2021/poster/3375
|
Deconstructing the Regularization of BatchNorm
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning
| null | 0 | null | null |
iclr
| -0.161165 | 0 | null |
main
| 4.8 |
3;4;5;5;7
| null | null |
PAC-Bayesian Randomized Value Function with Informative Prior
| null | null | 0 | 3.2 |
Withdraw
|
4;2;3;4;3
| null |
null |
UIUC; UChicago; Columbia University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2962; None
| null | 0 | null | null | null | null | null |
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt
|
https://iclr.cc/virtual/2021/poster/2962
|
multitask;few-shot
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2962
|
Measuring Massive Multitask Language Understanding
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
University of Toronto; IIT Kanpur
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2663; None
| null | 0 | null | null | null | null | null |
Avik Pal, Jonah Philion, Yuan-Hong Liao, Sanja Fidler
|
https://iclr.cc/virtual/2021/poster/2663
| null | null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2663
|
Emergent Road Rules In Multi-Agent Driving Environments
| null | null | 0 | 2.5 |
Poster
|
2;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Learning;Deep Learning;Optimization;Time-Series;Offer Personalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
OFFER PERSONALIZATION USING TEMPORAL CONVOLUTION NETWORK AND OPTIMIZATION
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Bidirectionally Self-Normalizing Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
University of Maryland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2860; None
| null | 0 | null | null | null | null | null |
Cassidy Laidlaw, Sahil Singla, Soheil Feizi
|
https://iclr.cc/virtual/2021/poster/2860
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2860
|
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
| null | null | 0 | 4 |
Poster
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge Distillation;Ensemble Learning;Neural Machine Translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Knowledge Distillation based Ensemble Learning for Neural Machine Translation
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
Affiliation; Second Affiliation
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Continuous Transfer Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Texas at Austin; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2875; None
| null | 0 | null | null | null | null | null |
Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
|
https://iclr.cc/virtual/2021/poster/2875
|
Non-asymptotic Confidence Intervals;Off Policy Evaluation;Reinforcement Learnings
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2875
|
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
| null | null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multilinguality;science for NLP;fundamental science in the era of AI/DL;representation learning for language;conditional language modeling;Transformer;Double Descent;non-monotonicity;fairness;meta evaluation;visualization or interpretation of learned representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble Q-Learning;Representation Diversity;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity
| null | null | 0 | 3 |
Reject
|
1;4;4;3
| null |
null |
Microsoft; UChicago; Columbia University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2960; None
| null | 0 | null | null | null | null | null |
Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt
|
https://iclr.cc/virtual/2021/poster/2960
|
value learning;human preferences;alignment
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2960
|
Aligning AI With Shared Human Values
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null |
Google Research, Mountain View, CA, USA; CISPA Helmholtz Center for Information Security, Saarbrücken, 66123 Saarland, Germany; Saarland University, Saarbrücken, 66123 Saarland, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3332; None
| null | 0 | null | null | null | null | null |
Christopher Hahn, Frederik Schmitt, Jens Kreber, Markus Rabe, Bernd Finkbeiner
|
https://iclr.cc/virtual/2021/poster/3332
|
Logic;Verification;Transformer
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3332
|
Teaching Temporal Logics to Neural Networks
| 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 |
continual learning;unsupervised learning;representation learning;online learning
| null | 0 | null | null |
iclr
| -0.896421 | 0 | null |
main
| 5 |
2;5;5;6;7
| null | null |
Unsupervised Progressive Learning and the STAM Architecture
| null | null | 0 | 3.4 |
Reject
|
5;3;3;3;3
| null |
null |
Facebook AI Research; Inria; Facebook AI Research, Inria; Facebook AI Research, LORIA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2772; None
| null | 0 | null | null | null | null | null |
Pierre Stock, Angela Fan, Benjamin Graham, Edouard Grave, Rémi Gribonval, Hervé Jégou, Armand Joulin
|
https://iclr.cc/virtual/2021/poster/2772
|
Compression;Efficiency;Product Quantization
| null | 0 | null | null |
iclr
| 0.567962 | 0 | null |
main
| 5.8 |
4;4;5;6;10
| null |
https://iclr.cc/virtual/2021/poster/2772
|
Training with Quantization Noise for Extreme Model Compression
|
https://github.com/pytorch/fairseq/tree/master/examples/quant_noise
| null | 0 | 4 |
Poster
|
4;4;4;3;5
| null |
null |
Idiap Research Institute & EPFL; University of Geneva
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2943; None
| null | 0 | null | null | null | null | null |
Suraj Srinivas, François Fleuret
|
https://iclr.cc/virtual/2021/poster/2943
|
Interpretability;saliency maps;score-matching
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
5;7;9;9
| null |
https://iclr.cc/virtual/2021/poster/2943
|
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
| null | null | 0 | 4 |
Oral
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language generation;structure representation;structure controlling;conditional language model;structure aware transformer
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3 |
2;2;3;5
| null | null |
Structure Controllable Text Generation
| 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 |
Contrastive Divergence;Energy Based Modeling
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Improved Contrastive Divergence Training of Energy Based Models
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conditional image generation;generative models;polynomial neural networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
MVP: Multivariate polynomials for conditional generation
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Numerical analysis;Deep learning;Partial differential equation;Machine learning;Predictive modeling
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Neural Time-Dependent Partial Differential Equation
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;3;4;4;6
| null | null |
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment
| null | null | 0 | 3.8 |
Withdraw
|
4;4;4;3;4
| null |
null |
Element AI, Canada CIFAR AI Chair; Polytechnique Montreal & Mila
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2893; None
| null | 0 | null | null | null | null | null |
Jonathan Pilault, Amine EL hattami, Chris J Pal
|
https://iclr.cc/virtual/2021/poster/2893
|
Multi-Task Learning;Adaptive Learning;Transfer Learning;Natural Language Processing;Hypernetwork
| null | 0 | null | null |
iclr
| 0.612372 | 0 | null |
main
| 6.6 |
6;6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2893
|
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
|
https://github.com/CAMTL/CA-MTL
| null | 0 | 3.6 |
Poster
|
3;3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised domain adaptation;Consistency regularization;Neighbor
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Neighbor Class Consistency on Unsupervised Domain Adaptation
| null | null | 0 | 4 |
Reject
|
5;5;3;3
| null |
null |
Facebook AI Research; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2582; None
| null | 0 | null | null | null | null | null |
Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh
|
https://iclr.cc/virtual/2021/poster/2582
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2582
|
Control-Aware Representations for Model-based Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
The Ohio State University, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2944; None
| null | 0 | null | null | null | null | null |
Hong-You Chen, Wei-Lun Chao
|
https://iclr.cc/virtual/2021/poster/2944
| null | null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2944
|
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
| null | null | 0 | 3.25 |
Poster
|
2;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
OOD detection;informative outlier mining;robustness
| null | 0 | null | null |
iclr
| -0.980196 | 0 | null |
main
| 5.25 |
3;4;7;7
| null | null |
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
Computer Science & Math Departments, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3269; None
| null | 0 | null | null | null | null | null |
Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Ng, Gunnar E Carlsson, Stefano Ermon
|
https://iclr.cc/virtual/2021/poster/3269
|
generative models;evaluation;disentanglement
| null | 0 | null | null |
iclr
| 0.121268 | 0 | null |
main
| 6.2 |
5;5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3269
|
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
|
https://github.com/stanfordmlgroup/disentanglement
| null | 0 | 3 |
Poster
|
2;5;1;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Network pruning;meta learning;set representation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Rapid Neural Pruning for Novel Datasets with Set-based Task-Adaptive Meta-Pruning
| null | null | 0 | 3.333333 |
Withdraw
|
4;4;2
| null |
null |
Bielefeld University; Università della Svizzera italiana; Università della Svizzera italiana, Politecnico di Milano; The University of Sydney
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3122; None
| null | 0 | null | null | null | null | null |
Benjamin Paassen, Daniele Grattarola, Daniele Zambon, Cesare Alippi, Barbara E Hammer
|
https://iclr.cc/virtual/2021/poster/3122
|
graph neural networks;graph edit distance;time series prediction;structured prediction
| null | 0 | null | null |
iclr
| -0.578691 | 0 | null |
main
| 5.75 |
3;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3122
|
Graph Edit Networks
| null | null | 0 | 3 |
Poster
|
4;5;1;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;representation learning;computer vision;ensembles
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Deep Ensembles for Low-Data Transfer Learning
| null | null | 0 | 4 |
Reject
|
3;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Graph Representation Learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions
| null | null | 0 | 4.5 |
Reject
|
3;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;hybrid quantum-classical computing;universal approximability
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
On the Universal Approximability and Complexity Bounds of Deep Learning in Hybrid Quantum-Classical Computing
| null | null | 0 | 3.333333 |
Reject
|
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 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
An empirical study of a pruning mechanism
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversairal Robustness;Randomized Smoothing;Ensembling
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Enhancing Certified Robustness of Smoothed Classifiers via Weighted Model Ensembling
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning accelerator;Bayesian optimization;design space exploration;hardware-software co-design
| null | 0 | null | null |
iclr
| -0.2 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
LEARNED HARDWARE/SOFTWARE CO-DESIGN OF NEURAL ACCELERATORS
| null | null | 0 | 3.5 |
Reject
|
5;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Mode Collapses;Image Diversity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
InvertGAN: Reducing mode collapse with multi-dimensional Gaussian Inversion
| null | null | 0 | 3.5 |
Withdraw
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta Learning;Hyperparameters Learning;Generalization on Tasks;Optimization;LR Schedules Learning;DNNs Training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NLP;Open-Domain QA;Open-Domain Question Answering;Document Retrieval
| null | 0 | null | null |
iclr
| 0.039653 | 0 | null |
main
| 4.4 |
2;3;4;5;8
| null | null |
Is Retriever Merely an Approximator of Reader?
| null | null | 0 | 4.4 |
Withdraw
|
4;4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph convolutional network;network compression;model acceleration;k-nearest nearst neighbor;card shuffling;3D deep learning
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network
| null | null | 0 | 4.25 |
Withdraw
|
4;5;5;3
| null |
null |
University of Southern California
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3218; None
| null | 0 | null | null | null | null | null |
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
|
https://iclr.cc/virtual/2021/poster/3218
|
Saddle-point Optimization;Optimistic Mirror Decent;Optimistic Gradient Descent Ascent;Optimistic Multiplicative Weights Update;Last-iterate Convergence;Game Theory
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3218
|
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Mathematical Institute, University of Oxford
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3342; None
| null | 0 | null | null | null | null | null |
Csaba Toth, Patric Bonnier, Harald Oberhauser
|
https://iclr.cc/virtual/2021/poster/3342
|
time series;sequential data;representation learning;low-rank tensors;classification;generative modelling
| null | 0 | null | null |
iclr
| -0.365148 | 0 | null |
main
| 6 |
4;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/3342
|
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
|
https://github.com/tgcsaba/seq2tens
| null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
School of Statistics, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA; Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA; Department of Mathematics, Stanford University, Stanford, CA 94305, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2710; None
| null | 0 | null | null | null | null | null |
Xinran Wang, Yu Xiang, Jun Gao, Jie Ding
|
https://iclr.cc/virtual/2021/poster/2710
|
Adversarial Attack;Machine Learning;Model privacy;Privacy-utility tradeoff;Security
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2710
|
Information Laundering for Model Privacy
| null | null | 0 | 3.333333 |
Spotlight
|
4;2;4
| null |
null |
School of Computing Technologies, RMIT University, Melbourne, Australia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3323; None
| null | 0 | null | null | null | null | null |
Michael Dann, John Thangarajah
|
https://iclr.cc/virtual/2021/poster/3323
|
Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3323
|
Adapting to Reward Progressivity via Spectral Reinforcement Learning
| null | null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Invariants;Software Engineering;Programming Languages
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Learning to Infer Run-Time Invariants from Source code
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null |
DeepMind, London, UK
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;constraints;robustness
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
| null | null | 0 | 3.5 |
Reject
|
2;4;4;4
| null |
null |
Corporate R&D Center, Toshiba Corporation
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2836; None
| null | 0 | null | null | null | null | null |
Yaling Tao, Kentaro Takagi, Kouta Nakata
|
https://iclr.cc/virtual/2021/poster/2836
|
clustering;representation learning;deep embedding
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2836
|
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Min-max optimization;Lyapunov functions;Stability Analysis;Generative Adversarial Networks;Non-convex optimization
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
| 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 |
alignment;finite width network;teacher student model;angular distance function
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Analysis of Alignment Phenomenon in Simple Teacher-student Networks with Finite Width
| 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 Networks;Attribution Methods;Generative Adversarial Networks;Interpretability;Explainability
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
GANMEX: Class-Targeted One-vs-One Attributions using GAN-based Model Explainability
| null | null | 0 | 3.25 |
Reject
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
point clouds;instance segmentation;uncertainty estimation;probabilistic embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Point Cloud Instance Segmentation using Probabilistic Embeddings
| 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;4
| null | null |
WordsWorth Scores for Attacking CNNs and LSTMs for Text Classification
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
RIKEN AIP, Tokyo, Japan; The University of Tokyo, RIKEN AIP, Tokyo, Japan; The University of Tokyo, Tokyo, Japan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3103; None
| null | 0 | null | null | null | null | null |
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo
|
https://iclr.cc/virtual/2021/poster/3103
|
Neural Processes;Conditional Neural Processes;Stochastic Processes;Regression;Group Equivariance;Symmetry
| null | 0 | null | null |
iclr
| -0.13484 | 0 | null |
main
| 5.5 |
4;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3103
|
Group Equivariant Conditional Neural Processes
| 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 |
effective dimension;hessian;generalization;double descent
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Rethinking Parameter Counting: Effective Dimensionality Revisited
| 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 |
Efficient Training;Multi-Output Gaussian Process;Gaussian Process;Bayesian;Single-shot network pruning;Dynamic Sparse Reparameterization;Lottery Ticket Hypothesis
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Balancing training time vs. performance with Bayesian Early Pruning
| null | null | 0 | 3.75 |
Reject
|
5;5;3;2
| null |
null |
VinUniversity, VinAI Research; VinAI Research, Hanoi University of Science and Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3087; None
| null | 0 | null | null | null | null | null |
Tuan Anh Nguyen, Anh T Tran
|
https://iclr.cc/virtual/2021/poster/3087
|
backdoor attack;image warping;wanet
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3087
|
WaNet - Imperceptible Warping-based Backdoor Attack
|
https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release
| null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent learning;reinforcement learning;empirical game-theoretic analysis
| null | 0 | null | null |
iclr
| -0.09759 | 0 | null |
main
| 5.75 |
4;5;6;8
| null | null |
Multi-Agent Trust Region 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 |
protein design;conditional generative adversarial networks;gene ontology;hierarchical multi-label;GO;GAN
| null | 0 | null | null |
iclr
| -0.862662 | 0 | null |
main
| 4.25 |
3;3;4;7
| null | null |
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Electrical and Computer Engineering, AIIS, ASRI, INMC, and Institute of Engineering Research, Seoul National University, Seoul 08826, South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2608; None
| null | 0 | null | null | null | null | null |
Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon
|
https://iclr.cc/virtual/2021/poster/2608
|
adversarial training;adversarial robustness;generalization;out-of-distribution
| null | 0 | null | null |
iclr
| 0.666667 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2608
|
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
| null | null | 0 | 3.5 |
Poster
|
4;1;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Intelligent Tutoring Systems;Adaptive policy;Instructional Sequencing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.666667 |
2;2;4
| null | null |
Using Deep Reinforcement Learning to Train and Evaluate Instructional Sequencing Policies for an Intelligent Tutoring System
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Department of Computer Science, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2737; None
| null | 0 | null | null | null | null | null |
Kaidi Cao, Maria Brbic, Jure Leskovec
|
https://iclr.cc/virtual/2021/poster/2737
|
few-shot learning;meta learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2737
|
Concept Learners for Few-Shot Learning
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3155; None
| null | 0 | null | null | null | null | null |
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Combes
|
https://iclr.cc/virtual/2021/poster/3155
|
adversarial;score matching;Langevin dynamics;GAN;generative model
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3155
|
Adversarial score matching and improved sampling for image generation
| null | null | 0 | 2.75 |
Poster
|
3;2;3;3
| null |
null |
Oregon State University, Corvallis, OR 97330, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3092; None
| null | 0 | null | null | null | null | null |
aayam shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern
|
https://iclr.cc/virtual/2021/poster/3092
|
Offline Reinforcement Learning;Planning
| null | 0 | null | null |
iclr
| -0.612372 | 0 | null |
main
| 6.8 |
6;7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3092
|
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs
| null | null | 0 | 3.4 |
Spotlight
|
4;4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Privacy;Residual Perturbation;Deep Learning
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Deep Learning with Data Privacy via Residual Perturbation
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Departments of Electrical Engineering and Computer Sciences and Statistics, University of California, Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3246; None
| null | 0 | null | null | null | null | null |
Anastasios Angelopoulos, Stephen Bates, Michael Jordan, Jitendra Malik
|
https://iclr.cc/virtual/2021/poster/3246
|
classification;predictive uncertainty;conformal inference;computer vision;imagenet
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3246
|
Uncertainty Sets for Image Classifiers using Conformal Prediction
|
https://people.eecs.berkeley.edu/~angelopoulos/blog/posts/conformal-classification
| null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
activation function;parametric;evolution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Discovering Parametric Activation Functions
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Australian National University, Google Research; Australian National University; Australian National University, Data61, CSIRO; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2978; None
| null | 0 | null | null | null | null | null |
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley
|
https://iclr.cc/virtual/2021/poster/2978
|
neural network calibration;uncertainty;calibration measure
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
5;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2978
|
Calibration of Neural Networks using Splines
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
UCL; University of Cambridge; Université de Montréal, MILA; McGill University, MILA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3014; None
| null | 0 | null | null | null | null | null |
Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette, Dimitri Kullmann, Blake A Richards
|
https://iclr.cc/virtual/2021/poster/3014
| null | null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.75 |
6;6;6;9
| null |
https://iclr.cc/virtual/2021/poster/3014
|
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units
| null | null | 0 | 4 |
Poster
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
text-based games;reinforcement learning;exploration;intrinsic motivation;knowledge graphs;question answering;natural language processing
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
| null | null | 0 | 3.5 |
Reject
|
4;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph neural networks;random architectures
| null | 0 | null | null |
iclr
| 0.19245 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Don't stack layers in graph neural networks, wire them randomly
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
Criteo AI Lab; Skoltech, Paris, France; Yandex; HSE University; MIPT, Moscow, Russia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2989; None
| null | 0 | null | null | null | null | null |
Sergei Ivanov, Liudmila Prokhorenkova
|
https://iclr.cc/virtual/2021/poster/2989
|
GNN;GBDT;graphs;tabular data;heterogeneous data
| null | 0 | null | null |
iclr
| 0.29277 | 0 | null |
main
| 6.75 |
5;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2989
|
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
|
https://github.com/nd7141/bgnn
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Princeton University; University of Washington; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2588; None
| null | 0 | null | null | null | null | null |
Jiaqi Yang, Wei Hu, Jason Lee, Simon Du
|
https://iclr.cc/virtual/2021/poster/2588
|
linear bandits;representation learning;multi-task learning
| null | 0 | null | null |
iclr
| -0.763763 | 0 | null |
main
| 6.6 |
6;6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2588
|
Impact of Representation Learning in Linear Bandits
| null | null | 0 | 3.8 |
Poster
|
4;5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
MCMC;Adaptive MCMC;Neural MCMC;Normalizing Flow;Entropy based speed Measure;HMC;Energy-based Model;Sampling
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
A Neural Network MCMC sampler that maximizes Proposal Entropy
| null | null | 0 | 2.75 |
Reject
|
2;3;3;3
| null |
null |
Tel-Aviv University, University of Washington; University of Washington; The Allen Institute for AI, Tel-Aviv University; Tel-Aviv University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2538; None
| null | 0 | null | null | null | null | null |
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, Jonathan Berant
|
https://iclr.cc/virtual/2021/poster/2538
|
NLP;Question Answering;Dataset;Multi-Modal;Multi-Hop
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2538
|
MultiModalQA: complex question answering over text, tables and images
| null | null | 0 | 2.5 |
Poster
|
1;3;3;3
| null |
null |
Stanford University; NVIDIA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3315; None
| null | 0 | null | null | null | null | null |
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, Jose M. Alvarez
|
https://iclr.cc/virtual/2021/poster/3315
|
Federated learning;personalized learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3315
|
Personalized Federated Learning with First Order Model Optimization
| null | null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Vehicle Routing Problem;Multiple Traveling Salesmen Problem;Capacitated Vehicle Routing Problem;Reinforcement Learning;Graph Neural Network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning
| null | null | 0 | 4.333333 |
Reject
|
5;3;5
| null |
null |
Department of Computer Science, Stanford University, Stanford, CA 94305, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2544; None
| null | 0 | null | null | null | null | null |
Alex Tamkin, Mike Wu, Noah Goodman
|
https://iclr.cc/virtual/2021/poster/2544
|
unsupervised learning;self-supervised;representation learning;contrastive learning;views;data augmentation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2544
|
Viewmaker Networks: Learning Views for Unsupervised Representation Learning
|
https://github.com/alextamkin/viewmaker
| null | 0 | 3.25 |
Poster
|
3;3;3;4
| null |
null |
Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST); School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2840; None
| null | 0 | null | null | null | null | null |
Jongheon Jeong, Jinwoo Shin
|
https://iclr.cc/virtual/2021/poster/2840
|
generative adversarial networks;contrastive learning;data augmentation;visual representation learning;unsupervised learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2840
|
Training GANs with Stronger Augmentations via Contrastive Discriminator
|
https://github.com/jh-jeong/ContraD
| null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
federated;learning;sparsity;expectation;maximization;efficient;FedAvg;FedSparse;EM
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Federated Averaging as Expectation Maximization
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null |
Salesforce Research; Salesforce Research†University of California, Santa Barbara; University of California, Santa Barbara†Salesforce Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3227; None
| null | 0 | null | null | null | null | null |
Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Rajani, Xifeng Yan, Yingbo Zhou, Caiming Xiong
|
https://iclr.cc/virtual/2021/poster/3227
|
task-oriented dialogue;dialogue state tracking;robustness;dst;evaluation
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.666667 |
4;6;7
| null |
https://iclr.cc/virtual/2021/poster/3227
|
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
|
https://github.com/salesforce/coco-dst
| null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative model;contrastive learning;autoencoder;Wasserstein autoencoder
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Momentum Contrastive Autoencoder
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
UC Berkeley, Google Brain; CMU, Google Brain; CMU; University of Pittsburgh
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2818; None
| null | 0 | null | null | null | null | null |
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov
|
https://iclr.cc/virtual/2021/poster/2818
|
reinforcement learning;transfer learning;domain adaptation
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2818
|
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
Institute for Theoretical Computer Science, Shanghai University of Finance and Economics; Department of Mathematics (MSCS), University of Illinois at Chicago
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3328; None
| null | 0 | null | null | null | null | null |
Yu Cheng, Honghao Lin
|
https://iclr.cc/virtual/2021/poster/3328
|
Bayesian networks;robust statistics;learning theory
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 5.75 |
4;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3328
|
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
| 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 |
variational autoencoders;disentangling;mixture;clustering
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
CIGMO: Learning categorical invariant deep generative models from grouped data
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Mixture-of-Experts;Neural Machine Translation;Multilingual;Multi-Task Learning;Conditional Computation;Natural Language Processing
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Exploring Routing Strategies for Multilingual Mixture-of-Experts Models
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Langevin dynamics;amortized inference;deep generative model
| null | 0 | null | null |
iclr
| 0.5 | 0 |
https://bit.ly/2Shmsq3
|
main
| 5.666667 |
5;6;6
| null | null |
Learning Deep Latent Variable Models via Amortized Langevin Dynamics
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
saliency maps;interpretability;explainable AI;image recognition;image masking;adversarial training
| null | 0 | null | null |
iclr
| -0.718185 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
| null | null | 0 | 3.5 |
Reject
|
5;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Practical Locally Private Federated Learning with Communication Efficiency
| null | null | 0 | 3.25 |
Reject
|
4;2;4;3
| null |
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