pdf
stringlengths 49
199
⌀ | aff
stringlengths 1
1.36k
⌀ | year
stringclasses 19
values | technical_novelty_avg
float64 0
4
⌀ | video
stringlengths 21
47
⌀ | doi
stringlengths 31
63
⌀ | presentation_avg
float64 0
4
⌀ | proceeding
stringlengths 43
129
⌀ | presentation
stringclasses 796
values | sess
stringclasses 576
values | technical_novelty
stringclasses 700
values | arxiv
stringlengths 10
16
⌀ | author
stringlengths 1
1.96k
⌀ | site
stringlengths 37
191
⌀ | keywords
stringlengths 2
582
⌀ | oa
stringlengths 86
198
⌀ | empirical_novelty_avg
float64 0
4
⌀ | poster
stringlengths 57
95
⌀ | openreview
stringlengths 41
45
⌀ | conference
stringclasses 11
values | corr_rating_confidence
float64 -1
1
⌀ | corr_rating_correctness
float64 -1
1
⌀ | project
stringlengths 1
162
⌀ | track
stringclasses 3
values | rating_avg
float64 0
10
⌀ | rating
stringlengths 1
17
⌀ | correctness
stringclasses 809
values | slides
stringlengths 32
41
⌀ | title
stringlengths 2
192
⌀ | github
stringlengths 3
165
⌀ | authors
stringlengths 7
161
⌀ | correctness_avg
float64 0
5
⌀ | confidence_avg
float64 0
5
⌀ | status
stringclasses 22
values | confidence
stringlengths 1
17
⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Non-convex optimization;Complex-valued neural networks;Optimization landscape;Wirtinger calculus
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Complex neural networks have no spurious local minima
| null | null | 0 | 3 |
Withdraw
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unconstrained optimization;Step-size policy;L-BFGS;Learned optimizers
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null |
Duke University; Princeton University; MIT; Columbia University, Princeton University / IAS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2969; None
| null | 0 | null | null | null | null | null |
Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
|
https://iclr.cc/virtual/2021/poster/2969
|
Distributed learning;InstaHide;phase retrieval;matrix factorization
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2969
|
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Multivariate Time Series;General Representations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
GenAD: General Representations of Multivariate Time Series for Anomaly Detection
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Effective Training of Sparse Neural Networks under Global Sparsity Constraint
| 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 |
Knowledge Distillation;Transformer Compression;BERT
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Contextual Knowledge Distillation for Transformer Compression
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
networks;number of communities;Bethe-Hessian;sparse networks;stochastic block model
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Estimation of Number of Communities in Assortative Sparse Networks
| 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 |
Audio Curiosity;RL exploration
| null | 0 | null | null |
iclr
| -0.094491 | 0 | null |
main
| 4.333333 |
2;4;4;5;5;6
| null | null |
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
| null | null | 0 | 3.666667 |
Reject
|
4;4;3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Signal Filtering;Recurrent Neural Network;Time Series;Denoising;Temporal Gradients
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
Active Tuning
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
federated learning;mixture of experts
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.75 |
3;3;3;6
| null | null |
Federated learning using mixture of experts
| null | null | 0 | 4.5 |
Reject
|
4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image classification;GNN;superpixel;SLIC;wavelet
| null | 0 | null | null |
iclr
| -0.080845 | 0 | null |
main
| 4.75 |
2;5;5;7
| null | null |
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling
| 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 |
GRU;LSTM;Sequence-level;Features;N-grams
| null | 0 | null | null |
iclr
| 0.539319 | 0 | null |
main
| 4.4 |
3;4;4;5;6
| null | null |
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS
| null | null | 0 | 3.4 |
Reject
|
3;2;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
knowledge graph;logical rules;logical query
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Differentiable Learning of Graph-like Logical Rules from Knowledge Graphs
| 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 |
Natural Language Processing;Visual Representation;Multimodal Language Representation;Natural Language Understanding
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Accurate Word Representations with Universal Visual Guidance
| null | null | 0 | 4.25 |
Withdraw
|
4;4;5;4
| null |
null |
ByteDance AI Lab, Beijing, China; Dartmouth College, Hanover, NH, United States
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3136; None
| null | 0 | null | null | null | null | null |
Shiying Xiong, Yunjin Tong, Xingzhe He, Shuqi Yang, Cheng Yang, Bo Zhu
|
https://iclr.cc/virtual/2021/poster/3136
|
Data-driven modeling;nonseparable Hailtonian system;symplectic networks
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3136
|
Nonseparable Symplectic Neural Networks
| null | null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Experience replay;prioritized sampling;model-based reinforcement learning;Dyna architecture
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null |
Boston University, Boston, MA; Arm ML Research Lab, Boston, MA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2748; None
| null | 0 | null | null | null | null | null |
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, Venkatesh Saligrama
|
https://iclr.cc/virtual/2021/poster/2748
|
Federated Learning;Deep Neural Networks;Distributed Optimization
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2748
|
Federated Learning Based on Dynamic Regularization
| null | null | 0 | 3.75 |
Oral
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Offline Reinforcement Learning;Off-Policy Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
| 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 | null | null | 0 | null | null |
iclr
| -0.473684 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks
| 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 |
Unified Message Passing Model;Graph Neural Network;Label Propagation Algorithm;Semi-Supervised Classification.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
mean-variance reinforcement learning;finance
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Detecting Adversarial Examples by Additional Evidence from Noise Domain
| 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
| -0.522233 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Efficiently labelling sequences using semi-supervised active learning
| null | null | 0 | 3.75 |
Withdraw
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-learning;Imitation Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Differential Privacy;Representation Learning;Variational Inference;Generative Modelling
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy
| null | null | 0 | 3.5 |
Reject
|
4;3;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
latent optimization;Variational Autoencoder;molecular generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
LATENT OPTIMIZATION VARIATIONAL AUTOENCODER FOR CONDITIONAL MOLECULAR GENERATION
| 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 |
RNA splicing;Computational Biology;RNA
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
RNA Alternative Splicing Prediction with Discrete Compositional Energy Network
| null | null | 0 | 4.5 |
Withdraw
|
4;5;5;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2976; None
| null | 0 | null | null | null | null | null |
Jiayi Shen, Haotao Wang, Shupeng Gui, Jianchao Tan, Zhangyang Wang, Ji Liu
|
https://iclr.cc/virtual/2021/poster/2976
|
recommendation system;model compression;ADMM;resource constrained
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2976
|
UMEC: Unified model and embedding compression for efficient recommendation systems
| null | null | 0 | 4.25 |
Poster
|
5;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Median-of-means;divide-and-conquer;privacy;sign recovery
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LASSO;Bootstrapping;Bagging;sparsity;group sparsity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Sparse Recovery via Bootstrapping: Collaborative or Independent?
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
capsule network;self-attention
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Trans-Caps: Transformer Capsule Networks with Self-attention Routing
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
Department of Computer Science, University of Virginia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2690; None
| null | 0 | null | null | null | null | null |
Jack Prescott, Xiao Zhang, David Evans
|
https://iclr.cc/virtual/2021/poster/2690
|
Adversarial Examples;Concentration of Measure;Gaussian Isoperimetric Inequality
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2690
|
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces
| null | null | 0 | 3.25 |
Poster
|
4;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;GANs
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Assisting the Adversary to Improve GAN Training
| null | null | 0 | 3.75 |
Reject
|
5;4;4;2
| null |
null |
Yale University; University of North Carolina at Chapel Hill
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2877; None
| null | 0 | null | null | null | null | null |
Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, Marc Niethammer
|
https://iclr.cc/virtual/2021/poster/2877
|
domain generalization;robustness;representation learning;data augmentation
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2877
|
Robust and Generalizable Visual Representation Learning via Random Convolutions
|
https://github.com/wildphoton/RandConv
| null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian Mixture Models;Stochastic Gradient Descent;Unsupervised Representation Learning;Continual Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5;5
| null | null |
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data
| null | null | 0 | 3.2 |
Reject
|
3;4;3;4;2
| null |
null |
1Max Planck Institute for Intelligent Systems, Tübingen2University of Tübingen
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2892; None
| null | 0 | null | null | null | null | null |
Axel Sauer, Andreas Geiger
|
https://iclr.cc/virtual/2021/poster/2892
|
Causality;Counterfactuals;Generative Models;Robustness;Image Classification;Data Augmentation
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.25 |
5;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/2892
|
Counterfactual Generative Networks
| null | null | 0 | 3.75 |
Poster
|
3;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Adaptation;Class-Invariant Features;Adversarial Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.25 |
2;3;3;5
| null | null |
Dual Adversarial Training for Unsupervised Domain Adaptation
| null | null | 0 | 5 |
Withdraw
|
5;5;5;5
| null |
null |
Machine Learning Department, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2752; None
| null | 0 | null | null | null | null | null |
Elan Rosenfeld, Pradeep K Ravikumar, Andrej Risteski
|
https://iclr.cc/virtual/2021/poster/2752
|
out-of-distribution generalization;causality;representation learning;deep learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2752
|
The Risks of Invariant Risk Minimization
| null | null | 0 | 2.75 |
Poster
|
4;2;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
inverse problems;phase retrieval;generative priors;holography;coherent diffraction imaging
| null | 0 | null | null |
iclr
| -0.2 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Practical Phase Retrieval: Low-Photon Holography with Untrained Priors
| null | null | 0 | 3.25 |
Withdraw
|
5;1;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Memorization;Long Tail
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Catching the Long Tail in Deep Neural Networks
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative;probabilistic;sketch;drawing;few-shot learning;classification;embedding learning
| null | 0 | null | null |
iclr
| 0.70014 | 0 | null |
main
| 6.25 |
4;6;6;9
| null | null |
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
LANIT, Moscow, Russia; Space Research Institute Russian Academy of Science, Russia
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
time series analysis;neural networks;variational autoencoders;anomaly detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Anomaly detection and regime searching in fitness-tracker data
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Noisy Label;Corrupted Supervision;Robustness;Optimization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Provable Robust Learning for Deep Neural Networks under Agnostic Corrupted Supervision
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Theoretical Reinforcement Learning;Drug Discovery;Molecule Generation;de novo drug design
| null | 0 | null | null |
iclr
| -0.784465 | 0 | null |
main
| 4.6 |
3;4;5;5;6
| null | null |
Maximum Reward Formulation In Reinforcement Learning
| 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 |
Deterministic Policy Gradient;Deterministic Exploration;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Truly Deterministic Policy Optimization
| 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 |
state-dependent noise;power-law dynamic;stochastic gradient descent;generalization;deep neural network;heavy-tailed;escape time
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Dynamic of Stochastic Gradient Descent with State-dependent Noise
| 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 |
Interpretability
| null | 0 | null | null |
iclr
| 0.738549 | 0 | null |
main
| 4 |
3;3;4;6
| null | null |
Disentanglement, Visualization and Analysis of Complex Features in DNNs
| null | null | 0 | 3.25 |
Withdraw
|
3;2;4;4
| null |
null |
Massachusetts Institute of Technology; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3242; None
| null | 0 | null | null | null | null | null |
Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
|
https://iclr.cc/virtual/2021/poster/3242
|
Label shift;distributional robust optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null |
https://iclr.cc/virtual/2021/poster/3242
|
Coping with Label Shift via Distributionally Robust Optimisation
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;gradient descent;optimization
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties
| null | null | 0 | 3.75 |
Reject
|
4;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Spatiotemporal Feature Learning;Video and Text Pair Discrimination;Self-/Weakly Supervised Learning
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Learning Spatiotemporal Features via Video and Text Pair Discrimination
| null | null | 0 | 4.25 |
Reject
|
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
| -0.301511 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
DyHCN: Dynamic Hypergraph Convolutional Networks
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2930; None
| null | 0 | null | null | null | null | null |
Johannes von Oswald, Seijin Kobayashi, Joao Sacramento, Alexander Meulemans, Christian Henning, Benjamin F Grewe
|
https://iclr.cc/virtual/2021/poster/2930
| null | null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2930
|
Neural networks with late-phase weights
|
https://github.com/seijin-kobayashi/late-phase-weights
| null | 0 | 3.5 |
Poster
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed Training;Distillation;Neural Networks;Deep Learning;Large-scale Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
A Closer Look at Codistillation for Distributed Training
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null |
University of Southern California, Los Angeles, CA; University of Maryland, College Park, MD
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3216; None
| null | 0 | null | null | null | null | null |
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
|
https://iclr.cc/virtual/2021/poster/3216
|
GAN;Over-parameterization;min-max optimization
| null | 0 | null | null |
iclr
| -0.789474 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3216
|
Understanding Over-parameterization in Generative Adversarial Networks
| null | null | 0 | 3.25 |
Poster
|
5;2;3;3
| 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
| 5.5 |
5;5;6;6
| null | null |
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference
| 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 |
Multiagent reinforcement learning
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
A Coach-Player Framework for Dynamic Team Composition
| null | null | 0 | 2.75 |
Reject
|
3;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Eric Platon
| null |
perception;resilience;robotics;synesthesia
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Sensory Resilience based on Synesthesia
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, [email protected]; Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, [email protected]; Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, [email protected]
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3195; None
| null | 0 | null | null | null | null | null |
Timothy Castiglia, Anirban Das, Stacy Patterson
|
https://iclr.cc/virtual/2021/poster/3195
|
Machine Learning;Stochastic Gradient Descent;Federated Learning;Hierarchical Networks;Distributed;Heterogeneous;Convergence Analysis
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3195
|
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks
| null | null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;policy gradient;incremental;online;eligibility traces
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Incremental Policy Gradients for Online Reinforcement Learning Control
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
University of Washington; University of Florida; Carnegie Mellon University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2563; None
| null | 0 | null | null | null | null | null |
Yining Wang, Ruosong Wang, Simon Du, Akshay Krishnamurthy
|
https://iclr.cc/virtual/2021/poster/2563
|
reinforcement learning;optimism;exploration;function approximation;theory;regret analysis;provable sample efficiency
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2563
|
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
| null | null | 0 | 3.25 |
Poster
|
3;4;3;3
| null |
null |
University of Illinois at Urbana-Champaign, Champaign, IL, USA; University of Southern California, Los Angeles, CA, USA; Shanghai Jiao Tong University, Shanghai, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3080; None
| null | 0 | null | null | null | null | null |
Chao Pan, Siheng Chen, Antonio Ortega
|
https://iclr.cc/virtual/2021/poster/3080
|
scattering transform;spatio-temporal graph;graph neural networks;skeleton-based action recognition
| null | 0 | null | null |
iclr
| -0.912871 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3080
|
Spatio-Temporal Graph Scattering Transform
| null | null | 0 | 3.5 |
Poster
|
5;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
tts;text-to-speech
| null | 0 | null | null |
iclr
| 0.050965 | 0 | null |
main
| 5.75 |
4;5;6;8
| null | null |
Non-Attentive Tacotron: Robust and controllable neural TTS synthesis including unsupervised duration modeling
| null | null | 0 | 3.25 |
Reject
|
4;3;2;4
| null |
null |
New York University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2987; None
| null | 0 | null | null | null | null | null |
Donald Hejna III, Pieter Abbeel, Lerrel Pinto
|
https://iclr.cc/virtual/2021/poster/2987
|
morphology;unsupervised;evolution;information theory;empowerment
| null | 0 | null | null |
iclr
| 0.57735 | 0 |
https://sites.google.com/view/task-agnostic-evolution
|
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2987
|
Task-Agnostic Morphology Evolution
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3024; None
| null | 0 | null | null | null | null | null |
Thomas Fischbacher, Luciano Sbaiz
|
https://iclr.cc/virtual/2021/poster/3024
|
quantum mechanics;image classification;quantum machine learning;theoretical limits
| null | 0 | null | null |
iclr
| 0.92582 | 0 | null |
main
| 6 |
3;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3024
|
Single-Photon Image Classification
| null | null | 0 | 2.5 |
Poster
|
1;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning;Causal Learning;Invariant Risk Minimization;Continual Learning
| null | 0 | null | null |
iclr
| -0.374634 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Continual Invariant Risk Minimization
| null | null | 0 | 3.75 |
Reject
|
4;5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
| 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 |
Box embeddings;Representation Learning;Joint Hierarchy;transitive relations;knowledge graph embedding;relational learning.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;4;6;8
| null | null |
Box-To-Box Transformation for Modeling Joint Hierarchies
| 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
| -0.688247 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
| 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 |
Normalization;Weight decay;SGD;Momentum
| null | 0 | null | null |
iclr
| -0.13484 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD
| 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 |
Tropical Geometry;Decision Boundaries;Neural Networks
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective
| null | null | 0 | 2.75 |
Reject
|
4;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
imitation learning;reinforcement learning;inverse reinforcement learning
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Robust Imitation via Decision-Time Planning
| null | null | 0 | 4.5 |
Reject
|
5;5;5;3
| null |
null |
Massachusetts Institute of Technology, Cambridge, MA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2801; None
| null | 0 | null | null | null | null | null |
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
|
https://iclr.cc/virtual/2021/poster/2801
|
contrastive learning;unsupervised representation learning;hard negative sampling
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2801
|
Contrastive Learning with Hard Negative Samples
|
https://github.com/joshr17/HCL
| null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
Samsung AI Center, Cambridge ⋅ University of Cambridge; Samsung AI Center, Cambridge
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3034; None
| null | 0 | null | null | null | null | null |
Abhinav Mehrotra, Alberto Gil Couto Pimentel Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla, Thomas C Chau, Mohamed Abdelfattah, Samin Ishtiaq, Nicholas Lane
|
https://iclr.cc/virtual/2021/poster/3034
|
NAS;ASR;Benchmark
| null | 0 | null | null |
iclr
| -0.943456 | 0 | null |
main
| 5.6 |
4;5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3034
|
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition
| null | null | 0 | 4.2 |
Poster
|
5;5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Differentiable Programming;piecewise polynomial regression;generative models;segmentation
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Differentiable Programming for Piecewise Polynomial Functions
| null | null | 0 | 3.25 |
Withdraw
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta learning;few-shot learning;continual learning;recommender systems;deep learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Contextual HyperNetworks for Novel Feature Adaptation
| null | null | 0 | 3 |
Reject
|
3;3;4;2
| null |
null |
Australian National University, Canberra, Australia; Data61/CSIRO, Canberra, Australia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3377; None
| null | 0 | null | null | null | null | null |
Hao Zhu, Piotr Koniusz
|
https://iclr.cc/virtual/2021/poster/3377
|
Graph Convolutional Network;Oversmoothing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3377
|
Simple Spectral Graph Convolution
|
https://github.com/allenhaozhu/SSGC
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
UC San Diego; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2809; None
| null | 0 | null | null | null | null | null |
Tete Xiao, Xiaolong Wang, Alexei Efros, trevor darrell
|
https://iclr.cc/virtual/2021/poster/2809
|
Self-supervised learning;Contrastive learning;Representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2809
|
What Should Not Be Contrastive in Contrastive Learning
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Inference;Free Energy Principle;Reinforcement Learning;Reward Design
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Prior Preference Learning From Experts: Designing A Reward with Active Inference
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
linear programming;nondecomposable functions;differentiable;AUC;Fscore
| null | 0 | null | null |
iclr
| -0.749269 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs
| null | null | 0 | 4 |
Reject
|
5;5;4;2
| null |
null |
VinAI Research, Vietnam; VinAI Research and VinUniversity, Vietnam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2991; None
| null | 0 | null | null | null | null | null |
Duong Le, Binh-Son Hua
|
https://iclr.cc/virtual/2021/poster/2991
|
Network Pruning
| null | 0 | null | null |
iclr
| 0.4842 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2991
|
Network Pruning That Matters: A Case Study on Retraining Variants
| null | null | 0 | 4.25 |
Poster
|
4;3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Active Feature Acquisition;Feature Selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Active Feature Acquisition with Generative Surrogate Models
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Conference Review;OpenReview;Gender;Bias;Reproducibility;Fairness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2816; None
| null | 0 | null | null | null | null | null |
Wuyang Chen, Xinyu Gong, Zhangyang Wang
|
https://iclr.cc/virtual/2021/poster/2816
|
Neural Architecture Search;neural tangent kernel;number of linear regions
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
4;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2816
|
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
|
https://github.com/VITA-Group/TENAS
| null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
FSPN;Probabilistic Graphical Model;Bayesian Network;Sum-Product Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
FSPN: A New Class of Probabilistic Graphical Model
| null | null | 0 | 4.75 |
Withdraw
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explainability;uncertainty;adversarial example detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Laboratoire des Signaux et Systèmes, Université Paris-Sud, Orsay, France; Huawei Technologies Research and Development (UK), London, UK; Gipsa Lab, Université Grenoble-Alpes, Saint Martin d’Hères, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2634; None
| null | 0 | null | null | null | null | null |
Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet
|
https://iclr.cc/virtual/2021/poster/2634
|
Transfer Learning;Multi Task Learning;Random Matrix Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2634
|
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
| null | null | 0 | 3 |
Spotlight
|
3;3;3;3
| 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
| 5.5 |
5;5;6;6
| null | null |
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
| 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 |
Continual Learning;Regularization
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Regularization Shortcomings for Continual Learning
| null | null | 0 | 3.75 |
Reject
|
5;4;4;2
| null |
null |
Indiana University Bloomington; Samsung Research America
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3278; None
| null | 0 | null | null | null | null | null |
Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang
|
https://iclr.cc/virtual/2021/poster/3278
|
Cryptographic inference;Channel-Wise Approximated Activation;Hyper-Parameter Optimization;Garbled Circuits
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3278
|
SAFENet: A Secure, Accurate and Fast Neural Network Inference
| null | null | 0 | 3.25 |
Poster
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
irrationality;reward learning;irl
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
The impacts of known and unknown demonstrator irrationality on reward inference
| null | null | 0 | 3.5 |
Reject
|
3;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.68313 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
A frequency domain analysis of gradient-based adversarial examples
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
label noise;out-of-distribution noise;contrastive learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
Learning from Noisy Data with Robust Representation 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 |
covariance;covariate shift;distance metrics;divergence;generative adversarial networks;interpretable approaches;kernel methods;probability metric;RKHS
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Max-sliced Bures Distance for Interpreting Discrepancies
| null | null | 0 | 2.666667 |
Reject
|
3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
theory;self-supervised learning;representation learning;unsupervised learning;conditional independence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6;6
| null | null |
Predicting What You Already Know Helps: Provable Self-Supervised Learning
| null | null | 0 | 3.4 |
Reject
|
3;3;5;3;3
| null |
null |
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China; Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China; University of Surrey, Guildford, Surrey, UK; Alibaba DAMO Academy, Hangzhou, China; School of Information, Renmin University of China, Beijing, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3256; None
| null | 0 | null | null | null | null | null |
Nanyi Fei, Zhiwu Lu, Tao Xiang, Songfang Huang
|
https://iclr.cc/virtual/2021/poster/3256
|
few-shot learning;episodic training;cross-episode attention
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3256
|
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
| null | null | 0 | 4.25 |
Poster
|
3;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
VAE;variational autoencoder;Representation Learning;treatment effects;causal inference;Unobserved Confounding;identifiability;CATE;ATE
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning
| 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 |
neural PDE;functional convolution;adjoint method
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Neural Partial Differential Equations with Functional Convolution
| 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 | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
R-LAtte: Attention Module for Visual Control via Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Visual Concept Development;Rapid Problem Solving;Abstract Reasoning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
| null | null | 0 | 2.5 |
Reject
|
2;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
commercial recommendation;maximizing platform benefits;uncertainty-aware;influence of display policy;non-convex optimization
| null | 0 | null | null |
iclr
| 0.254824 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
PURE: An Uncertainty-aware Recommendation Framework for Maximizing Expected Posterior Utility of Platform
| null | null | 0 | 3.25 |
Reject
|
4;1;5;3
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.