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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
School of Informatics, University of Edinburgh
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3178; None
| null | 0 | null | null | null | null | null |
Yordan Hristov, Subramanian Ramamoorthy
|
https://iclr.cc/virtual/2021/poster/3178
|
representation learning for robotics;physical symbol grounding;semi-supervised learning
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://sites.google.com/view/weak-label-lfd
|
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3178
|
Learning from Demonstration with Weakly Supervised Disentanglement
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recurrent Neural Networks;Mutli Arm-Bandits
| null | 0 | null | null |
iclr
| -0.94388 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Solving Non-Stationary Bandit Problems with an RNN and an Energy Minimization Loss
| null | null | 0 | 3.75 |
Withdraw
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Complementary Labels;Robustness;Machine Learning
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 5.5 |
3;5;7;7
| null | null |
Robust Loss Functions for Complementary Labels Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null |
Google Brain, Mountain View, CA; Blueshift, Alphabet, Mountain View, CA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2907; None
| null | 0 | null | null | null | null | null |
Vinay Ramasesh, Ethan Dyer, Maithra Raghu
|
https://iclr.cc/virtual/2021/poster/2907
|
Catastrophic forgetting;continual learning;representation analysis;representation learning
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2907
|
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
| null | null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null |
University of California, Berkeley; University of California, San Francisco
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3231; None
| null | 0 | null | null | null | null | null |
Jensen Gao, Siddharth Reddy, Glen Berseth, Nick Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca Dragan, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/3231
|
reinforcement learning;human-computer interaction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
4;7;8
| null |
https://iclr.cc/virtual/2021/poster/3231
|
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian neural networks;Bayesian fine-tuning;uncertainty estimation;OOD detection
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning
| 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 |
Parkinson's disease;drawing tests;data augmentation;CNN;diagnostics support
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics
| null | null | 0 | 4 |
Withdraw
|
5;4;2;5
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2691; None
| null | 0 | null | null | null | null | null |
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan
|
https://iclr.cc/virtual/2021/poster/2691
|
Federated learning;optimization;adaptive optimization;distributed optimization
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2691
|
Adaptive Federated Optimization
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null |
Graduate School of AI, KAIST, Daejeon, Republic of Korea; Graduate School of AI, KAIST, Daejeon, Republic of Korea and School of Computing, KAIST, Daejeon, Republic of Korea; School of Computing, KAIST, Daejeon, Republic of Korea and Gauss Labs Inc., Seoul, Republic of Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3003; None
| null | 0 | null | null | null | null | null |
Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-Eung Kim
|
https://iclr.cc/virtual/2021/poster/3003
|
power grid management;deep reinforcement learning;graph neural network
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 7.5 |
7;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3003
|
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
| null | null | 0 | 3.25 |
Spotlight
|
4;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-shot edge detection;Few-shot learning;Semantic edge detection
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
CAFENet: Class-Agnostic Few-Shot Edge Detection Network
| null | null | 0 | 4.5 |
Reject
|
5;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Learning-Augmented Sketches for Hessians
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep clustering;variational auto encoder;VAE
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
A Mixture of Variational Autoencoders for Deep Clustering
| 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 |
blackbox optimization;evolutionary strategies
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 3.666667 |
2;3;6
| null | null |
CoNES: Convex Natural Evolutionary Strategies
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Complex Deep Learning;Invariance;Equivariance;Manifold;SAR Imaging
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
HyperReal: Complex-Valued Layer Functions For Complex-Valued Scaling Invariance
| null | null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Sample Efficiency;Gaussian Mixture Models;Mixture-of-Experts
| null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Georgia Institute of Technology, Atlanta GA, USA; University of Michigan, Ann Arbor MI, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2696; None
| null | 0 | null | null | null | null | null |
Sanjay Kariyappa, Atul Prakash, Moinuddin K Qureshi
|
https://iclr.cc/virtual/2021/poster/2696
|
Model stealing;machine learning security
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2696
|
Protecting DNNs from Theft using an Ensemble of Diverse Models
| null | null | 0 | 3.75 |
Poster
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
compositional learning;meta-learning;systematicity;reasoning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hamiltonian Monte Carlo;HMC;MCMC;Variational Inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Information Theory;Regularization
| null | 0 | null | null |
iclr
| -0.982708 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Regularized Mutual Information Neural Estimation
| null | null | 0 | 3.5 |
Reject
|
5;4;3;2
| null |
null |
OpenAI; DeepMind; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3348; None
| null | 0 | null | null | null | null | null |
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike
|
https://iclr.cc/virtual/2021/poster/3348
|
rl;irl;reward learning;distance;benchmarks
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3348
|
Quantifying Differences in Reward Functions
|
https://github.com/HumanCompatibleAI/evaluating-rewards
| null | 0 | 3.25 |
Spotlight
|
2;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Empirical Optimization;Expected Loss;Line Search
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;deep learning;interpretability
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Conformational Space
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Translation;Data augmentation
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Switching-Aligned-Words Data Augmentation for Neural Machine Translation
| 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 |
Reinforcement learning with constraints;Safe reinforcement learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 |
https://sites.google.com/view/spacealgo
|
main
| 5.75 |
5;5;6;7
| null | null |
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies
| 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 |
Boundary Effects;Absolute Position Information;Padding;Canvas color;Location Dependent Task
| null | 0 | null | null |
iclr
| -0.697097 | 0 | null |
main
| 5.25 |
3;3;7;8
| null | null |
Boundary Effects in CNNs: Feature or Bug?
| 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 |
Bootstrapping;Uncertainty Estimation;Deep Learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Neural Bootstrapper
| null | null | 0 | 3.75 |
Withdraw
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model Interpretability;Model Pruning;Attribution;Model Visualization
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Extract Local Inference Chains of Deep Neural Nets
| null | null | 0 | 3 |
Reject
|
2;3;3;4
| null |
null |
University of California, Davis
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3300; None
| null | 0 | null | null | null | null | null |
Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee
|
https://iclr.cc/virtual/2021/poster/3300
|
multi-domain disentanglement;generative adversarial networks;appearance transfer
| null | 0 | null | null |
iclr
| -0.57735 | 0 |
http://utkarshojha.github.io/inter-domain-gan/
|
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3300
|
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian neural networks;uncertainty estimation;memory efficiency
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
| 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 |
meta-learning;distribution shift;distributional robustness;test time adaptation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Semi-supervised Learning;Data Efficiency;Slowness Principle
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Unsupervised Learning of Slow Features for Data Efficient Regression
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hyperparameter Optimization;Neural Architecture Search
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Efficient Model Performance Estimation via Feature Histories
| null | null | 0 | 3.75 |
Withdraw
|
5;4;4;2
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2779; None
| null | 0 | null | null | null | null | null |
Rianne van den Berg, Alexey Gritsenko, Mostafa Dehghani, Casper Sønderby, Tim Salimans
|
https://iclr.cc/virtual/2021/poster/2779
|
normalizing flows;lossless source compression;generative modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2779
|
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Imitation learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
Department of Electrical and Computer Engineering, Duke University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2591; None
| null | 0 | null | null | null | null | null |
Chris Cannella, Mohammadreza Soltani, VAHID TAROKH
|
https://iclr.cc/virtual/2021/poster/2591
|
Conditional Sampling;Normalizing Flows;Markov Chain Monte Carlo;Missing Data Inference
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2591
|
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learned optimizers;meta-learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Overcoming barriers to the training of effective learned optimizers
| null | null | 0 | 2.666667 |
Reject
|
3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Graph Signal Denoising;Smoothness
| null | 0 | null | null |
iclr
| -0.638877 | 0 | null |
main
| 5 |
3;3;6;6;7
| null | null |
A Unified View on Graph Neural Networks as Graph Signal Denoising
| null | null | 0 | 3.8 |
Reject
|
4;5;3;3;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
| 4.5 |
4;4;4;6
| null | null |
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
University of Illinois at Urbana-Champaign; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3161; None
| null | 0 | null | null | null | null | null |
Efthymios Tzinis, Scott Wisdom, Aren Jansen, Shawn Hershey, Tal Remez, Dan Ellis, John Hershey
|
https://iclr.cc/virtual/2021/poster/3161
|
Audio-visual sound separation;in-the-wild data;unsupervised learning;self-supervised learning;universal sound separation
| null | 0 | null | null |
iclr
| 0 | 0 |
https://audioscope.github.io
|
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3161
|
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
|
https://github.com/audioscope
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Adaptation;Data Selection
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Domain Adaptation via Anaomaly Detection
| null | null | 0 | 4 |
Withdraw
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Few-shot Learning;Behavioral Biometrics;Biometric Authentication
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 6 |
4;5;9
| null | null |
A Siamese Neural Network for Behavioral Biometrics Authentication
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Computer Science Department, Carnegie Mellon University and Bosch Center for Artificial Intelligence, Pittsburgh, PA 15213, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2570; None
| null | 0 | null | null | null | null | null |
Eric Wong, Zico Kolter
|
https://iclr.cc/virtual/2021/poster/2570
|
adversarial examples;perturbation sets;robust machine learning;conditional variational autoencoder
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2570
|
Learning perturbation sets for robust machine learning
|
https://github.com/locuslab/perturbation_learning
| null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null |
SenseTime Research; The Chinese University of Hong Kong and SenseTime Research; SenseTime Research and Qing Yuan Research Institute, Shanghai Jiao Tong University; Tsinghua University; The Chinese University of Hong Kong
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2992; None
| null | 0 | null | null | null | null | null |
Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai
|
https://iclr.cc/virtual/2021/poster/2992
|
Loss Function Search;Metric Surrogate;Semantic Segmentation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6 |
5;5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2992
|
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
|
https://github.com/fundamentalvision/Auto-Seg-Loss
| null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null |
Facebook Inc.; Georgia Tech
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2757; None
| null | 0 | null | null | null | null | null |
Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda
|
https://iclr.cc/virtual/2021/poster/2757
|
Object Detection
| null | 0 | null | null |
iclr
| 0.324443 | 0 | null |
main
| 7.25 |
6;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2757
|
Unbiased Teacher for Semi-Supervised Object Detection
|
https://github.com/facebookresearch/unbiased-teacher
| null | 0 | 4 |
Poster
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated learning;Federated optimization;Adaptive optimization;Adam;Variance Reduction;Distributed optimization;Decentralized optimization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null |
Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2898; None
| null | 0 | null | null | null | null | null |
Omer Yair, Tomer Michaeli
|
https://iclr.cc/virtual/2021/poster/2898
|
Unsupervised learning;energy based model;adversarial learning;contrastive divergence;noise contrastive estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2898
|
Contrastive Divergence Learning is a Time Reversal Adversarial Game
| null | null | 0 | 3.5 |
Spotlight
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fairness;fairness in machine learning;fairness without demographics;robustness;subgroup robustness;blind fairness;pareto fairness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Blind Pareto Fairness and Subgroup Robustness
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Analogical Reasoning for Visually Grounded Compositional Generalization
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent RL;Deep RL;Exploration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Coordinated Multi-Agent Exploration Using Shared Goals
| 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 |
unsupervised;self-supervised;image clustering;visual representation learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning Representations by Contrasting Clusters While Bootstrapping Instances
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
complex knowledge graph embeddings;convolutions
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Convolutional Complex Knowledge Graph Embeddings
|
https://github.com/conex-kge/ConEx
| null | 0 | 4.5 |
Withdraw
|
5;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Multi-agent Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.5 |
3;3;5;7
| null | null |
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
| null | null | 0 | 4.5 |
Withdraw
|
4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Membership Inference Attack;Image translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Membership Attacks on Conditional Generative Models Using Image Difficulty
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;natural language processing;transformer;BERT
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
ABSTRACTING INFLUENCE PATHS FOR EXPLAINING (CONTEXTUALIZATION OF) BERT MODELS
| 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 |
Mixed Integer Programming;Branching and Bound;Strong Branching;Reinforcement Learning;Evolution Strategy;Novelty Search
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 6.5 |
4;7;7;8
| null | null |
Improving Learning to Branch via Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural network;approximation;universality;Slater determinant;Vandermonde matrix;equivariance;symmetry;anti-symmetry;symmetric polynomials;polarized basis;multilayer perceptron;continuity;smoothness
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
On Representing (Anti)Symmetric Functions
| 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 |
Interpretable Reinforcement Learning;Neural Symbolic Logic
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Interpretable Reinforcement Learning With Neural Symbolic Logic
| null | null | 0 | 3.5 |
Withdraw
|
2;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
grid cells;path integration;representational model;Lie algebras;error correction
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null |
NEC Laboratories America, Inc., San Jose, CA, USA; Department of Computer Science, Rutgers University, Piscataway, NJ, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3260; None
| null | 0 | null | null | null | null | null |
Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Min, Mubbasir Kapadia, Hans P Graf
|
https://iclr.cc/virtual/2021/poster/3260
|
Multi-hop Reasoning;Object Permanence;Spatiotemporal Understanding;Video Recognition;Transformer
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3260
|
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
|
https://github.com/necla-ml/cater-h
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Metric learning;sequence processing;siamese recurrent neural network;dynamical systems
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Sequence Metric Learning as Synchronization of Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;reinforcement learning;information theory
| null | 0 | null | null |
iclr
| -0.375 | 0 | null |
main
| 5.6 |
5;5;5;6;7
| null | null |
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
| null | null | 0 | 3.2 |
Reject
|
4;3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Latent Domain Learning;CNN Architectures
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Reducing Implicit Bias in Latent Domain Learning
| null | null | 0 | 3.25 |
Reject
|
4;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient Deep Learning;Quantization;Compression
| null | 0 | null | null |
iclr
| 0.342997 | 0 | null |
main
| 5.4 |
4;4;6;6;7
| null | null |
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
|
https://github.com/papers-submission/CalibTIP
| null | 0 | 3.8 |
Reject
|
2;5;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;GNNs;Geometric Scattering;Radial Basis Network;Graph Signal Processing;Wavelet
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 6 |
4;6;6;8
| null | null |
Data-driven Learning of Geometric Scattering Networks
| null | null | 0 | 3.75 |
Reject
|
3;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Contribution Evaluation;Multi-party Participation
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
A Real-time Contribution Measurement Method for Participants in Federated Learning
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null |
Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2865; None
| null | 0 | null | null | null | null | null |
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
|
https://iclr.cc/virtual/2021/poster/2865
|
Deep Learning;Uncertainty;Infinite-Width Limit;Neural Network Gaussian Process;Bayesian Neural Networks;Gaussian Process
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2865
|
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
ETH Zürich; ETH Zürich, MPI for Intelligent Systems, Tübingen; University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2724; None
| null | 0 | null | null | null | null | null |
Max B Paulus, Chris Maddison, Andreas Krause
|
https://iclr.cc/virtual/2021/poster/2724
|
gumbel;softmax;gumbel-softmax;straight-through;straightthrough;rao;rao-blackwell
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2724
|
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
| null | null | 0 | 3.333333 |
Oral
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
anomaly detection;out-of-distribution detection;OOD detection;outlier detection;density estimation
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Perfect density models cannot guarantee anomaly detection
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
University of California, Santa Barbara; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2617; None
| null | 0 | null | null | null | null | null |
wenhu chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William Cohen
|
https://iclr.cc/virtual/2021/poster/2617
|
Question Answering;Tabular Data;Open-domain;Retrieval
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2617
|
Open Question Answering over Tables and Text
|
https://github.com/wenhuchen/OTT-QA
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised reinforcement learning;goal-conditioned policy;intrinsic reward
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null |
Vrije Universiteit Amsterdam, The Netherlands; Discovery Lab, Elsevier, The Netherlands; UCL Centre for Artificial Intelligence, University College London, United Kingdom
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3140; None
| null | 0 | null | null | null | null | null |
Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez
|
https://iclr.cc/virtual/2021/poster/3140
|
neural link prediction;complex query answering
| null | 0 | null | null |
iclr
| -0.749269 | 0 | null |
main
| 8 |
6;8;9;9
| null |
https://iclr.cc/virtual/2021/poster/3140
|
Complex Query Answering with Neural Link Predictors
|
https://github.com/uclnlp/cqd
| null | 0 | 3.75 |
Oral
|
5;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
weighted automata;automatic differentiation;sequence models
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Differentiable Weighted Finite-State Transducers
| 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 |
Knowledge distillation;Federated learning
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5 |
3;4;8
| null | null |
Asynchronous Edge Learning using Cloned Knowledge Distillation
| null | null | 0 | 3 |
Withdraw
|
4;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-supervised learning;Meta-learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning
| null | null | 0 | 3.333333 |
Withdraw
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Robustness
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;7;7
| null | null |
SOAR: Second-Order Adversarial Regularization
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
AI Platform, Kwai Inc.; Department of Computer Science and Engineering, Texas A&M University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2671; None
| null | 0 | null | null | null | null | null |
Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu
|
https://iclr.cc/virtual/2021/poster/2671
|
Reinforcement Learning;Exploration;Generalization of Reinforcement Learning;Self-Imitation
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2671
|
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
|
https://github.com/daochenzha/rapid
| null | 0 | 3.25 |
Poster
|
3;3;4;3
| null |
null |
Department of Electrical & Computer Engineering, Texas A&M University; Department of Electrical & Computer Engineering, Texas A&M University; Computational Science Initiative, Brookhaven National Laboratory; Department of Electrical & Computer Engineering, Texas A&M University; Department of Computer Science & Engineering, Texas A&M University; Computational Science Initiative, Brookhaven National Laboratory
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3026; None
| null | 0 | null | null | null | null | null |
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian
|
https://iclr.cc/virtual/2021/poster/3026
|
Active learning;Bayesian classification
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3026
|
Uncertainty-aware Active Learning for Optimal Bayesian Classifier
| null | null | 0 | 3.25 |
Poster
|
2;4;4;3
| null |
null |
Determined AI; Carnegie Mellon University; Determined AI, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2618; None
| null | 0 | null | null | null | null | null |
Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar
|
https://iclr.cc/virtual/2021/poster/2618
|
neural architecture search;automated machine learning;weight-sharing;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2618
|
Geometry-Aware Gradient Algorithms for Neural Architecture Search
| null | null | 0 | 3.666667 |
Spotlight
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
Identifying Informative Latent Variables Learned by GIN via Mutual Information
| null | null | 0 | 3 |
Reject
|
3;3;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised learning;representation learning;flow-based generative model;renormalization group;sparse encoding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Double Q-learning;Finite-time analysis;Convergence rate;Stochastic approximation
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Double Q-learning: New Analysis and Sharper Finite-time Bound
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null |
Samsung AI Cambridge, Queen Mary University of London, UK; Samsung AI Cambridge
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2953; None
| null | 0 | null | null | null | null | null |
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
|
https://iclr.cc/virtual/2021/poster/2953
| null | null | 0 | null | null |
iclr
| -0.4 | 0 | null |
main
| 5.5 |
4;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2953
|
High-Capacity Expert Binary Networks
| null | null | 0 | 3.5 |
Poster
|
5;2;4;3
| null |
null |
Columbia University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3226; None
| null | 0 | null | null | null | null | null |
Boyuan Chen, Yu Li, Sunand Raghupathi, Hod Lipson
|
https://iclr.cc/virtual/2021/poster/3226
|
Label Representation;Image Classification;Representation Learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 |
https://www.creativemachineslab.com/label-representation.html
|
main
| 6.25 |
4;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3226
|
Beyond Categorical Label Representations for Image Classification
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sign activation neural network;gradient-free training;stochastic coordinate descent;black box adversarial attack;hopskipjump;transferability;image distortion
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Defending against black-box adversarial attacks with gradient-free trained sign activation neural networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3060; None
| null | 0 | null | null | null | null | null |
Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, Percy Liang
|
https://iclr.cc/virtual/2021/poster/3060
|
selective classification;group disparities;log-concavity;robustness
| null | 0 | null | null |
iclr
| 0.688247 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3060
|
Selective Classification Can Magnify Disparities Across Groups
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
UC Berkeley / ICSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3167; None
| null | 0 | null | null | null | null | null |
Tsung-Wei Ke, Jyh-Jing Hwang, Stella Yu
|
https://iclr.cc/virtual/2021/poster/3167
|
weakly supervised representation learning;representation learning for computer vision;metric learning;semantic segmentation
| null | 0 | null | null |
iclr
| -0.071429 | 0 | null |
main
| 6.2 |
5;6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3167
|
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
|
https://github.com/twke18/SPML
| null | 0 | 4.2 |
Poster
|
4;5;4;3;5
| null |
null |
Department of Software Technology, Delft University of Technology, Delft, Netherlands; Department of Computer Science, University College London, London, United Kingdom; Department of Computer Science, University of Oxford, Oxford, United Kingdom
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2942; None
| null | 0 | null | null | null | null | null |
Vitaly Kurin, Maximilian Igl, Tim Rocktaeschel, Wendelin Boehmer, Shimon Whiteson
|
https://iclr.cc/virtual/2021/poster/2942
|
Deep Reinforcement Learning;Multitask Reinforcement Learning;Graph Neural Networks;Continuous Control;Incompatible Environments
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2942
|
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
| null | null | 0 | 3.5 |
Poster
|
4;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimal control;mpc;lyapunov neural networks;safe-learning;safety
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Neural Lyapunov Model Predictive Control
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Electrical and Computer Engineering, Duke University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2555; None
| null | 0 | null | null | null | null | null |
Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, Lawrence Carin
|
https://iclr.cc/virtual/2021/poster/2555
|
Fairness;Contrastive Learning;Mutual Information;Pretrained Text Encoders
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2555
|
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Networks;CNN;explaining;interpretable;Rules;black box
| null | 0 | null | null |
iclr
| -0.5547 | 0 | null |
main
| 5.5 |
3;5;6;8
| null | null |
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
| 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 |
Algorithmic Fairness;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Understanding and Mitigating Accuracy Disparity in Regression
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Tel Aviv University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3327; None
| null | 0 | null | null | null | null | null |
Eli Ovits, Lior Wolf
|
https://iclr.cc/virtual/2021/poster/3327
| null | null | 0 | null | null |
iclr
| 0.777778 | 0 | null |
main
| 7.25 |
6;6;8;9
| null |
https://iclr.cc/virtual/2021/poster/3327
|
Fidelity-based Deep Adiabatic Scheduling
| null | null | 0 | 4.25 |
Spotlight
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fast adversarial training;adversarial examples
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Towards Understanding Fast Adversarial Training
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Single Image Depth Estimation;Stereo Matching;View Prediction;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Single Image Depth Estimation Based on Spectral Consistency and Predicted View
| null | null | 0 | 4.333333 |
Desk Reject
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
shape representation;single image 3d reconstruction;few-shot learning;meta learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Training Data Generating Networks: Linking 3D Shapes and Few-Shot Classification
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Generative Models;Adversarial Networks;Game Theory
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6;6
| null | null |
Acoustic Neighbor Embeddings
| null | null | 0 | 3.8 |
Reject
|
4;4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Voting-based Approaches For Differentially Private Federated Learning
| null | null | 0 | 3 |
Reject
|
4;4;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
theory;sparse network;landscape
| null | 0 | null | null |
iclr
| 0.942809 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
On the Landscape of Sparse Linear Networks
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
aletcher.github.io
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2794; None
| null | 0 | null | null | null | null | null |
Alistair Letcher
|
https://iclr.cc/virtual/2021/poster/2794
|
impossibility;global;convergence;optimization;multi-loss;multi-player;multi-agent;gradient;descent
| null | 0 | null | null |
iclr
| 0.68313 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2794
|
On the Impossibility of Global Convergence in Multi-Loss Optimization
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
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