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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
batch normalization;adversarial robustness;feature perspective
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features
| null | null | 0 | 4.25 |
Withdraw
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;catastrophic forgetting;Bayesian neural network
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Continual Learning Without Knowing Task Identities: Do Simple Models Work?
| null | null | 0 | 4.5 |
Withdraw
|
4;5;4;5
| null |
null |
Department of Automation, BNRist, Tsinghua University, Beijing, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3135; None
| null | 0 | null | null | null | null | null |
Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, Gao Huang
|
https://iclr.cc/virtual/2021/poster/3135
|
Locally supervised training;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3135
|
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
|
https://github.com/blackfeather-wang/InfoPro-Pytorch
| null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Odometry Learning;Information Bottleneck;Generalization Bound
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Information-Theoretic Odometry Learning
| null | null | 0 | 2.666667 |
Reject
|
2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
marginalized importance sampling;off-policy evaluation;deep reinforcement learning;successor representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Practical Marginalized Importance Sampling with the Successor Representation
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Department of Electrical Engineering, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3254; None
| null | 0 | null | null | null | null | null |
Arda Sahiner, Tolga Ergen, John M Pauly, Mert Pilanci
|
https://iclr.cc/virtual/2021/poster/3254
|
neural networks;theory;convex optimization;copositive programming;convex duality;nonnegative PCA;semi-nonnegative matrix factorization;computational complexity;global optima;semi-infinite duality;convolutional neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3254
|
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms
| null | null | 0 | 3.5 |
Poster
|
4;5;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimization;sketching;linear programming;central path method;running time complexity
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Oblivious Sketching-based Central Path Method for Solving Linear Programming Problems
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Causal Mechanisms;Identifiability;Disentangled Representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Blueshift, Alphabet; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2659; None
| null | 0 | null | null | null | null | null |
Vaishnavh Nagarajan, Anders J Andreassen, Behnam Neyshabur
|
https://iclr.cc/virtual/2021/poster/2659
|
out-of-distribution generalization;spurious correlations;empirical risk minimization;theoretical study
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2659
|
Understanding the failure modes of out-of-distribution generalization
|
https://github.com/google-research/OOD-failures
| null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Decentralized;Distributed Deep Learning;Large Batch
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Calibration;Uncertainty Estimates;Adversarial Robustness
| null | 0 | null | null |
iclr
| -0.805823 | 0 | null |
main
| 5 |
2;5;6;7
| null | null |
Improving Calibration through the Relationship with Adversarial Robustness
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Simulation of Human and Artificial Emotion (SHArE)
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Automatic Data Cleansing;Incorrect Labels;Multiple Objects
| null | 0 | null | null |
iclr
| -0.94388 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
AutoCleansing: Unbiased Estimation of Deep Learning with Mislabeled Data
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.454545 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
A Reduction Approach to Constrained Reinforcement Learning
| null | null | 0 | 2.75 |
Reject
|
2;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Non-decreasing Quantile Function;Distributional Reinforcement Learning;Distributional Prediction Error;Exploration
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-shot learning;representation learning
| null | 0 | null | null |
iclr
| -0.646997 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Exploring representation learning for flexible few-shot tasks
| null | null | 0 | 4 |
Withdraw
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed Machine Learning;Federated Learning;Distributed Averaging Consensus
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 2.25 |
1;2;3;3
| null | null |
Consensus Driven Learning
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;bayesian optimization;probabilistic modelling
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Probabilistic Meta-Learning for Bayesian Optimization
| 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 |
Generative Adversarial Networks;StyleGAN;learning theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null | null |
On Noise Injection in Generative Adversarial Networks
| null | null | 0 | 3 |
Reject
|
3;3;4;2
| null |
null |
Department of Computer Science, University of California, Los Angeles
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2621; None
| null | 0 | null | null | null | null | null |
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
|
https://iclr.cc/virtual/2021/poster/2621
|
deep ReLU networks;neural tangent kernel;(stochastic) gradient descent;generalization error;classification
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2621
|
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
| null | null | 0 | 3.25 |
Poster
|
2;4;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture search;systems;hardware
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
NAHAS: Neural Architecture and Hardware Accelerator Search
| null | null | 0 | 3.25 |
Reject
|
4;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Network;Node Proximity;Permutation-equivariant
| null | 0 | null | null |
iclr
| -0.367884 | 0 | null |
main
| 6 |
3;6;7;8
| null | null |
A Simple and General Graph Neural Network with Stochastic Message Passing
| null | null | 0 | 3.75 |
Reject
|
5;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
class imbalance;over-sampling;genetic algorithm
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
ROGA: Random Over-sampling Based on Genetic Algorithm
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;sentiment analysis;cross-domain data representation;distribution alignment
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
McGill University; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2847; None
| null | 0 | null | null | null | null | null |
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau
|
https://iclr.cc/virtual/2021/poster/2847
|
multi-task reinforcement learning;bisimulation;hidden-parameter mdp;block mdp
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2847
|
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
| null | null | 0 | 3 |
Poster
|
2;3;4;3
| null |
null |
Key Laboratory of Machine Perception, MOE, School of EECS, Peking University, Center for Data Science, Peking University; Key Laboratory of Machine Perception, MOE, School of EECS, Peking University; Amazon
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2756; None
| null | 0 | null | null | null | null | null |
Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang
|
https://iclr.cc/virtual/2021/poster/2756
|
reinforcement learning;factored MDP;constrained RL;learning theory
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2756
|
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
| 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 |
out of distribution;domain generalization;invariant risk minimization;robust optimization;invariant causal prediction;spurious features;generalization
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Out-of-Distribution Generalization via Risk Extrapolation (REx)
| null | null | 0 | 3.75 |
Reject
|
5;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;human in the loop learning;atypical examples
| null | 0 | null | null |
iclr
| 0.790569 | 0 | null |
main
| 5 |
3;4;6;6;6
| null | null |
Estimating Example Difficulty using Variance of Gradients
| null | null | 0 | 3.8 |
Reject
|
3;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Benchmark;Real-world;Framework;Few-shot Learning;Sequential Learning;Continual Learning;Long tail;Open-world;Deep Learning
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
In the Wild: From ML Models to Pragmatic ML Systems
| null | null | 0 | 3.75 |
Withdraw
|
3;4;4;4
| null |
null |
Stanford University; Rice University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3284; None
| null | 0 | null | null | null | null | null |
Tharun Kumar Reddy Medini, Beidi Chen, Anshumali Shrivastava
|
https://iclr.cc/virtual/2021/poster/3284
|
Sparse Embedding;Inverted Index;Learning to Hash;Embedding Models
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6 |
3;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3284
|
SOLAR: Sparse Orthogonal Learned and Random Embeddings
| null | null | 0 | 4.25 |
Poster
|
5;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Knowledge Graph;Entity Alignment;Knowledge Graph Completion;Knowledge Graph Embedding
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Decentralized Knowledge Graph Representation Learning
| 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 |
Hybrid Models;Contrastive Learning;Energy-Based Models;Discriminative-Generative Models
| null | 0 | null | null |
iclr
| -0.738549 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Hybrid Discriminative-Generative Training via Contrastive Learning
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null |
Facebook AI Research; Harvard University; Facebook AI Research & New York University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3016; None
| null | 0 | null | null | null | null | null |
Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives
|
https://iclr.cc/virtual/2021/poster/3016
|
proteins;language modeling;structure prediction;unsupervised learning;explainable
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3016
|
Transformer protein language models are unsupervised structure learners
|
https://github.com/facebookresearch/esm
| null | 0 | 4.5 |
Poster
|
4;5;4;5
| null |
null |
Department of Computer Science, University of Oxford, Oxford, UK; The Alan Turing Institute, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Five AI Limited; Department of Engineering Science, University of Oxford, Oxford, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2955; None
| null | 0 | null | null | null | null | null |
Amartya Sanyal, Puneet Dokania, Varun Kanade, Philip Torr
|
https://iclr.cc/virtual/2021/poster/2955
|
benign overfitting;adversarial robustness;memorization;generalization
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2955
|
How Benign is Benign Overfitting ?
| null | null | 0 | 3.25 |
Spotlight
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated learning;Personalization;Optimization
| null | 0 | null | null |
iclr
| -0.845154 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Adaptive Personalized Federated Learning
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3282; None
| null | 0 | null | null | null | null | null |
Yeming Wen, Ghassen Jerfel, Rafael Müller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
|
https://iclr.cc/virtual/2021/poster/3282
|
Ensembles;Uncertainty estimates;Calibration
| null | 0 | null | null |
iclr
| 0.19245 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3282
|
Combining Ensembles and Data Augmentation Can Harm Your Calibration
| null | null | 0 | 4.25 |
Poster
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformer;memory augmented networks
| null | 0 | null | null |
iclr
| 0.133631 | 0 | null |
main
| 3.8 |
3;3;4;4;5
| null | null |
Memory Representation in Transformer
| null | null | 0 | 4.2 |
Reject
|
4;4;4;5;4
| null |
null |
MIT CSAIL/MIT-IBM Waston AI Lab; MIT-IBM Waston AI Lab; Boston University; Microsoft; MIT CSAIL
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2971; None
| null | 0 | null | null | null | null | null |
Bowen Pan, Rameswar Panda, Camilo L Fosco, Chung-Ching Lin, Alex J Andonian, Yue Meng, Kate Saenko, Aude Oliva, Rogerio Feris
|
https://iclr.cc/virtual/2021/poster/2971
| null | null | 0 | null | null |
iclr
| 0 | 0 |
http://people.csail.mit.edu/bpan/va-red/
|
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2971
|
VA-RED$^2$: Video Adaptive Redundancy Reduction
| null | null | 0 | 3 |
Poster
|
1;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NAS;efficiency;search;fast;cheap;convnets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Neural Architecture Search without Training
| 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 | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Implicit Regularization Effects of Unbiased Random Label Noises with SGD
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Privileged Experts;Imitation Learning;Reinforcement Learning;Actor-Critic;Behavior Cloning;MiniGrid;Knowledge Distillation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Bridging the Imitation Gap by Adaptive Insubordination
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Fairness;Privacy
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
WAFFLe: Weight Anonymized Factorization for Federated Learning
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Online Learning;Learning Theory;Bandits;Robustness;Adversarial Corruptions
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Online Learning under Adversarial Corruptions
| 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 |
Butterfly Network;Matrix approximation;Encoder-Decoder Network;Optimization Landscape
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice
| null | null | 0 | 4 |
Reject
|
3;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent deep reinforcement learning;graph neural networks;value function factorization;attention mechanisms
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning
| null | null | 0 | 4.25 |
Withdraw
|
4;4;4;5
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Causal Discovery;Principle of Independent Causal Mechanisms;Normalizing Flows;Domain Generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Learning Robust Models using the Principle of Independent Causal Mechanisms
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain generalization;variational invariant learning;Bayesian inference
| null | 0 | null | null |
iclr
| 0.927173 | 0 | null |
main
| 6.25 |
5;6;6;8
| null | null |
Variational Invariant Learning for Bayesian Domain Generalization
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null |
Horizon Robotics; University at Buffalo; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2807; None
| null | 0 | null | null | null | null | null |
Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, Qian Zhang
|
https://iclr.cc/virtual/2021/poster/2807
|
Knowledge distillation;soft labels;teacher-student model
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2807
|
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective
|
https://github.com/bellymonster/Weighted-Soft-Label-Distillation
| null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
University of California, Berkeley; Korea Advanced Institute of Science and Technology; Samsung Advanced Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3138; None
| null | 0 | null | null | null | null | null |
Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, Sung Ju Hwang
|
https://iclr.cc/virtual/2021/poster/3138
|
reinforcement learning;experience replay buffer;off-policy RL
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3138
|
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
|
https://github.com/youngmin0oh/NERS
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
University of Edinburgh, UK and Samsung AI Centre, Cambridge, UK; University of Edinburgh, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2656; None
| null | 0 | null | null | null | null | null |
Carl Allen, Ivana Balazevic, Timothy Hospedales
|
https://iclr.cc/virtual/2021/poster/2656
|
knowledge graphs;word embedding;representation learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2656
|
Interpreting Knowledge Graph Relation Representation from Word Embeddings
| null | null | 0 | 3.25 |
Poster
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Few-Round Learning for Federated Learning
| 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 |
Disentanglement;Identifiability;Nonlinear ICA;Self-supervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Self-supervised Disentangled Representation Learning
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Backdoor attack;Autoencoders;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models
| null | null | 0 | 4.5 |
Withdraw
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Privacy;Security;Reconstruction Attack;Federated Learning
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
What can we learn from gradients?
| null | null | 0 | 3.25 |
Withdraw
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent imitation learning;dependence modeling;copula
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Multi-Agent Imitation Learning with Copulas
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian process;deep learning theory;finite DNNs;statistical mechanics
| null | 0 | null | null |
iclr
| 0.632456 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Predicting the Outputs of Finite Networks Trained with Noisy Gradients
| null | null | 0 | 3.5 |
Reject
|
2;3;5;4
| null |
null |
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing, 100084 China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2882; None
| null | 0 | null | null | null | null | null |
Tsung Wei Tsai, Chongxuan Li, Jun Zhu
|
https://iclr.cc/virtual/2021/poster/2882
|
unsupervised learning;clustering;self supervised learning;mixture of experts
| null | 0 | null | null |
iclr
| -0.622543 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2882
|
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
|
https://github.com/TsungWeiTsai/MiCE
| null | 0 | 4.25 |
Poster
|
4;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
| null | null | 0 | 4 |
Reject
|
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
| 1 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Powers of layers for image-to-image translation
| 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 |
automated reasoning;reinforcement learning;reasoning by analogy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Towards Finding Longer Proofs
| null | null | 0 | 2.666667 |
Reject
|
3;2;3
| null |
null |
University of Science and Technology of China; The Chinese University of Hong Kong; SenseTime Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3105; None
| null | 0 | null | null | null | null | null |
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai
|
https://iclr.cc/virtual/2021/poster/3105
|
Efficient Attention Mechanism;Deformation Modeling;Multi-scale Representation;End-to-End Object Detection
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 8 |
7;8;8;9
| null |
https://iclr.cc/virtual/2021/poster/3105
|
Deformable DETR: Deformable Transformers for End-to-End Object Detection
|
https://github.com/fundamentalvision/Deformable-DETR
| null | 0 | 4.5 |
Oral
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;carbon footprint
| null | 0 | null | null |
iclr
| 0.288675 | 0 | null |
main
| 4 |
3;3;4;6
| null | null |
A first look into the carbon footprint of federated learning
| null | null | 0 | 4 |
Withdraw
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causal discovery;structure learning;low rank graphs;directed acyclic graphs
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
| 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 |
Self-normalizing Neural Network;Activation Function
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Redefining The Self-Normalization Property
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.948683 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Frequency Decomposition in Neural Processes
|
https://github.com/***
| null | 0 | 3 |
Reject
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Language Models;NLP;Knowledge Graphs;Probe tasks;Word2Vec;GloVe;ELMo;BERT;RoBERTa;XLNet;ALBERT;T5;GPT2
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs
| null | null | 0 | 3.5 |
Reject
|
2;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning with Small Datasets;Self-Pretraining
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Self-Pretraining for Small Datasets by Exploiting Patch Information
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Artificial and Natural Intelligence Toulouse Institute, Universite de Toulouse, France; Centre de Recherche Cerveau & Cognition CNRS, Universite de Toulouse; Artificial and Natural Intelligence Toulouse Institute, Universite de Toulouse, France; Carney Institute for Brain Science, Dpt. of Cognitive Linguistic & Psychological Sciences, Brown University, Providence, RI 02912; Data Science Initiative, Brown University, Providence, RI 02912
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3217; None
| null | 0 | null | null | null | null | null |
Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen, Thomas Serre
|
https://iclr.cc/virtual/2021/poster/3217
|
Neural ODEs;Optimal Control Theory;Hypernetworks;Normalizing flows
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3217
|
Go with the flow: Adaptive control for Neural ODEs
| null | null | 0 | 3.75 |
Poster
|
4;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dimensionality Reduction;Manifold Learning;Visualization;Topological Data Analysis;UMAP
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Uniform Manifold Approximation with Two-phase Optimization
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
MIT
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2857; None
| null | 0 | null | null | null | null | null |
Kai Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
|
https://iclr.cc/virtual/2021/poster/2857
|
Backgrounds;Model Biases;Robustness;Computer Vision
| null | 0 | null | null |
iclr
| 0.6742 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2857
|
Noise or Signal: The Role of Image Backgrounds in Object Recognition
| null | null | 0 | 3.75 |
Poster
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Recurrent Neural Networks
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Recurrently Controlling a Recurrent Network with Recurrent Networks Controlled by More Recurrent Networks
| null | null | 0 | 4 |
Withdraw
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causal induction;model based RL
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Systematic Evaluation of Causal Discovery in Visual Model Based 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 |
overestimation;continuous control;deep reinforcement learning;policy improvement
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.5 |
3;3;3;5
| null | null |
Deep Reinforcement Learning With Adaptive Combined Critics
| 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 |
Sparsity;Pruning;Efficiency;Mathematics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
AlgebraNets
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2584; None
| null | 0 | null | null | null | null | null |
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi
|
https://iclr.cc/virtual/2021/poster/2584
|
generalization;optimization;online learning;understanding deep learning;empirical investigation
| null | 0 | null | null |
iclr
| 0.94388 | 0 | null |
main
| 5.5 |
4;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2584
|
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
| null | null | 0 | 3.75 |
Poster
|
3;3;4;5
| null |
null |
Department of Computer Science and Engineering, Seoul National University; Neural Processing Research Center
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2766; None
| null | 0 | null | null | null | null | null |
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
|
https://iclr.cc/virtual/2021/poster/2766
|
Data Augmentation;Deep Learning;Supervised Learning;Discrete Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2766
|
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
|
https://github.com/snu-mllab/Co-Mixup
| null | 0 | 3.333333 |
Oral
|
3;4;3
| null |
null |
Department of Computer Science, University of California, Los Angeles; Department of Statistics and Data Science, University of Central Florida; Department of Statistics, University of California, Los Angeles
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3228; None
| null | 0 | null | null | null | null | null |
Mitchell Hill, Jonathan Mitchell, Song-Chun Zhu
|
https://iclr.cc/virtual/2021/poster/3228
|
adversarial defense;adversarial robustness;energy-based model;Markov chain Monte Carlo;Langevin sampling;adversarial attack
| null | 0 | null | null |
iclr
| 0.291111 | 0 | null |
main
| 6.25 |
4;5;7;9
| null |
https://iclr.cc/virtual/2021/poster/3228
|
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models
|
https://github.com/point0bar1/ebm-defense
| null | 0 | 3.5 |
Poster
|
2;4;5;3
| null |
null |
University of Maryland, College Park; Facebook AI Research, Georgia Institute of Technology; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2736; None
| null | 0 | null | null | null | null | null |
Songwei Ge, Vedanuj Goswami, Larry Zitnick, Devi Parikh
|
https://iclr.cc/virtual/2021/poster/2736
|
creativity;sketches;part-based;GAN;dataset;generative art
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2736
|
Creative Sketch Generation
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
University of Cambridge, Cambridge, UK; Cambridge Center for AI in Medicine, UK; University of California, Los Angeles, USA; The Alan Turing Institute, London, UK; University of Cambridge, Cambridge, UK; University of Oxford, Oxford, UK; The Alan Turing Institute, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2854; None
| null | 0 | null | null | null | null | null |
Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar
|
https://iclr.cc/virtual/2021/poster/2854
|
counterfactuals;explaining decision-making;preference learning
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2854
|
Learning "What-if" Explanations for Sequential Decision-Making
| null | null | 0 | 3 |
Poster
|
3;2;4;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2786; None
| null | 0 | null | null | null | null | null |
Christopher Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
|
https://iclr.cc/virtual/2021/poster/2786
|
machine learning;deep learning;privacy;confidentiality;security;homomorphic encryption;mpc;differential privacy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2786
|
CaPC Learning: Confidential and Private Collaborative Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Conditional Computation;Generalization
| null | 0 | null | null |
iclr
| -0.324443 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Conditional Networks
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimal transport;sinkhorn divergences;robustness;neural networks;lipschitz;spectral norm
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.5 |
2;4;4;4
| null | null |
Efficient estimates of optimal transport via low-dimensional embeddings
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;NLP;ImageNet;generative;diversity;VAE;CelebA;language model
| null | 0 | null | null |
iclr
| -0.102062 | 0 | null |
main
| 4.6 |
4;4;4;5;6
| null | null |
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
| null | null | 0 | 3.4 |
Reject
|
4;3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
systems neuroscience;cerebellum;neocortex;decoupled neural interfaces;deep learning;decorrelation;inverse models;forward models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Cortico-cerebellar networks as decoupled neural interfaces
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantization;activation function;unbounded;full-precision
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
LEARNING BILATERAL CLIPPING PARAMETRIC ACTIVATION FUNCTION FOR LOW-BIT NEURAL NETWORKS
| null | null | 0 | 3.5 |
Withdraw
|
3;4;3;4
| null |
null |
Department of Computer Science, University of Maryland; Department of Computer Science, US Naval Academy; Department of Mathematics, University of Maryland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3088; None
| null | 0 | null | null | null | null | null |
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P Dickerson, Gavin Taylor, Tom Goldstein
|
https://iclr.cc/virtual/2021/poster/3088
|
facial recognition;adversarial attacks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3088
|
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial defense;Loss function;Neural networks robustness
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Improving robustness of softmax corss-entropy loss via inference information
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.324443 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning
| null | null | 0 | 3.75 |
Reject
|
5;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
localization;dynamic environment;deep reinforcement learning;benchmark
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Mobile Construction Benchmark
| null | null | 0 | 4.25 |
Withdraw
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;variational inference
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Boltzman Tuning of Generative Models
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty quantification;deep learning;deep sequence models;spatiotemporal forecasting;bayesian uncertainty estimation;frequentist uncertainty estimation;traffic;COVID-19
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Salesforce Research Asia; Singapore Management University; A*STAR, Institute for Infocomm Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3106; None
| null | 0 | null | null | null | null | null |
Hung Le, Nancy F Chen, Steven Hoi
|
https://iclr.cc/virtual/2021/poster/3106
|
video-grounded dialogues;reasoning paths;semantic graphs
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3106
|
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
German Cancer Research Center, Heidelberg, Germany; Department of Computer Science, University of Freiburg, Germany; Bosch Center for Artificial Intelligence, Renningen, Germany; Department of Computer Science, University of Freiburg, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3206; None
| null | 0 | null | null | null | null | null |
Jörg Franke, Gregor Koehler, André Biedenkapp, Frank Hutter
|
https://iclr.cc/virtual/2021/poster/3206
|
AutoRL;Deep Reinforcement Learning;Hyperparameter Optimization;Neuroevolution
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3206
|
Sample-Efficient Automated Deep Reinforcement Learning
| null | null | 0 | 3.75 |
Poster
|
3;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Disentangled Representation;Generative Adversarial Network;Computer Vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
ADIS-GAN: Affine Disentangled GAN
| null | null | 0 | 2 |
Reject
|
2;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
classification;multi-label learning;extreme multi-label classification;tail label
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Improving Tail Label Prediction for Extreme Multi-label Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Qualcomm AI Research∗
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3001; None
| null | 0 | null | null | null | null | null |
Juntae Lee, Mihir Jain, Hyoungwoo Park, Sungrack Yun
|
https://iclr.cc/virtual/2021/poster/3001
|
Audio-Visual;Multimodal Attention;Action localization;Event localization;Weak-supervision
| null | 0 | null | null |
iclr
| 0.555556 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3001
|
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization
| null | null | 0 | 3.75 |
Poster
|
5;2;3;5
| null |
null |
MPI for Intelligent Systems, Tübingen; ETH, Zürich; MPI for Biological Cybernetics, Tübingen
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2721; None
| null | 0 | null | null | null | null | null |
Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schoelkopf
|
https://iclr.cc/virtual/2021/poster/2721
|
invariances;consistency;gradient alignment
| null | 0 | null | null |
iclr
| 0.483368 | 0 | null |
main
| 5.75 |
2;5;7;9
| null |
https://iclr.cc/virtual/2021/poster/2721
|
Learning explanations that are hard to vary
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchy;Manifold;Classification
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Connecting Sphere Manifolds Hierarchically for Regularization
| 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 |
adversarial robustness;adversarial learning;convolutional neural networks
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Adversarial Feature Desensitization
| null | null | 0 | 4.75 |
Reject
|
4;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distant Supervision;Relation Extraction;Federated Learning
| null | 0 | null | null |
iclr
| -0.534522 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
Distantly Supervised Relation Extraction in Federated Settings
| null | null | 0 | 3.8 |
Reject
|
4;4;4;4;3
| null |
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