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 | null | null | 0 | null | null |
iclr
| -0.109109 | 0 | null |
main
| 6 |
3;6;7;8
| null | null |
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
5;2;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Retrosynthesis;Energy-based Model
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;5;5;8
| null | null |
Energy-based View of Retrosynthesis
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null |
University of Stuttgart, Faculty of Mathematics and Physics, Institute for Stochastics and Applications
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2611; None
| null | 0 | null | null | null | null | null |
David Holzmüller
|
https://iclr.cc/virtual/2021/poster/2611
|
Double Descent;Interpolation Peak;Linear Regression;Random Features;Random Weights Neural Networks
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2611
|
On the Universality of the Double Descent Peak in Ridgeless Regression
| null | null | 0 | 2.75 |
Poster
|
3;2;3;3
| null |
null |
Microsoft Research, Montréal; University of Washington; Carnegie Mellon University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2973; None
| null | 0 | null | null | null | null | null |
Mohit Shridhar, Eric Yuan, Marc-Alexandre Cote, Yonatan Bisk, Adam Trischler, Matthew Hausknecht
|
https://iclr.cc/virtual/2021/poster/2973
|
Textworld;Text-based Games;Embodied Agents;Language Grounding;Generalization;Imitation Learning;ALFRED
| null | 0 | null | null |
iclr
| -0.981981 | 0 |
http://alfworld.github.io/
|
main
| 5.666667 |
4;6;7
| null |
https://iclr.cc/virtual/2021/poster/2973
|
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
|
https://github.com/ALFWorld
| null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Differential Privacy;Optimization Algorithms
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
FAST DIFFERENTIALLY PRIVATE-SGD VIA JL PROJECTIONS
| null | null | 0 | 2.666667 |
Withdraw
|
3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Comm-POSG;Imaginary Rewards
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Truthful Self-Play
| 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 |
few-shot classification;few-shot learning;episodic training;meta-learning
| null | 0 | null | null |
iclr
| 0.152894 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training
| null | null | 0 | 3.25 |
Reject
|
4;1;3;5
| null |
null |
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3039; None
| null | 0 | null | null | null | null | null |
Tolga Ergen, Mert Pilanci
|
https://iclr.cc/virtual/2021/poster/3039
|
Convex optimization;non-convex optimization;group sparsity;$\ell_1$ norm;convex duality;polynomial time;deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3039
|
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time
| null | null | 0 | 3 |
Spotlight
|
4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Compressed Sensing;subsampling;active acquisition;accelerated MRI
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Active Deep Probabilistic Subsampling
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of Computer Science, Durham University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3137; None
| null | 0 | null | null | null | null | null |
Sam Bond-Taylor, Chris G. Willcocks
|
https://iclr.cc/virtual/2021/poster/3137
|
Deep Learning;Generative Models;Implicit Representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3137
|
Gradient Origin Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
DaSciM, LIX, Ecole Polytechnique, Institute Polytechnique de Paris, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2932; None
| null | 0 | null | null | null | null | null |
George Dasoulas, Johannes Lutzeyer, Michalis Vazirgiannis
|
https://iclr.cc/virtual/2021/poster/2932
|
graph neural networks;graph shift operators;graph classification;node classification;graph representation learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2932
|
Learning Parametrised Graph Shift Operators
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Gatsby Computational Neuroscience Unit, University College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3223; None
| null | 0 | null | null | null | null | null |
Michael Arbel, Liang Zhou, Arthur Gretton
|
https://iclr.cc/virtual/2021/poster/3223
|
Sampling;MCMC;Generative Models;Adversarial training;Optimization;Density estimation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null |
https://iclr.cc/virtual/2021/poster/3223
|
Generalized Energy Based Models
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-supervised depth estimation;semantic-guided depth;multitask learning;semantic-guided attention mechanism
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
| 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 |
Bayesian estimation;Full probability distribution;MCMC;Hybrid non-Gaussian system
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
ARMCMC: Online Model Parameters full probability Estimation in Bayesian Paradigm
| 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 | 0 | null |
main
| 4.5 |
3;4;4;7
| null | null |
Adaptive Gradient Methods Can Be Provably Faster than SGD with Random Shuffling
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Research at Google, Mountain View, CA 94043, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3056; None
| null | 0 | null | null | null | null | null |
John Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust
|
https://iclr.cc/virtual/2021/poster/3056
|
reinforcement learning;evolutionary algorithms;meta-learning;genetic programming
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 7.333333 |
6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3056
|
Evolving Reinforcement Learning Algorithms
| null | null | 0 | 3.333333 |
Oral
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
open-set recognition;anomaly detection;statistical models;Gaussian Mixture Models;open-world image classification;open-world semantic segmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
3;3;4;7
| null | null |
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain generalization;Invariant learning;Batch Normalization
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Invariant Batch Normalization for Multi-source Domain Generalization
| null | null | 0 | 4.25 |
Withdraw
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph neural network;Combinatorial optimization;Reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Cogent Labs; Bocconi University; University of Torino; University of Torino, Collegio Carlo Alberto
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2635; None
| null | 0 | null | null | null | null | null |
Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti
|
https://iclr.cc/virtual/2021/poster/2635
|
deep learning theory;infinitely wide neural network;Gaussian process;stochastic process
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2635
|
Large-width functional asymptotics for deep Gaussian neural networks
| 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 |
spike trains;signal encoding;reconstruction;kernel;representer theorem;compression;convolutional matching pursuit;COMP
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.5 |
3;3;5;7
| null | null |
Signal Coding and Reconstruction using Spike Trains
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Samsung AI Center, Cambridge; Samsung AI Center, Cambridge · University of Cambridge
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2861; None
| null | 0 | null | null | null | null | null |
Mohamed Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, Nicholas Lane
|
https://iclr.cc/virtual/2021/poster/2861
|
NAS;AutoML;proxy;pruning;efficient
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2861
|
Zero-Cost Proxies for Lightweight NAS
|
https://github.com/mohsaied/zero-cost-nas
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-supervised Learning;Multi-View Learning
| null | 0 | null | null |
iclr
| -0.473684 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
| 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 |
image classification;interpretability;feature attribution;saliency;ablation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Ablation Path Saliency
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Privacy;Graphs;Secure Aggregation;Communication-Efficient;Computation-Efficient
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Communication-Computation Efficient Secure Aggregation for Federated Learning
| 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 |
reinforcement learning;actor critic algorithms;policy gradient methods;stochastic approximation;PPO;RUDDER
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
| null | null | 0 | 3.5 |
Withdraw
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theoretical guarantees;Unsupervised learning;Contrastive learning;Overparametrized models
| null | 0 | null | null |
iclr
| -0.923381 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
About contrastive unsupervised representation learning for classification and its convergence
| null | null | 0 | 3.25 |
Withdraw
|
5;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Learning;Corpus Linguistics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Integrating linguistic knowledge into DNNs: Application to online grooming detection
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Directed acyclic graph;reinforcement learning;Q Learning;Graph Auto-Encoder
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs
| 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 |
proximal policy optimization;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
A Strong On-Policy Competitor To PPO
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robustness;uncertainty;discretization;data augmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness
| 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
| -0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
University of Toronto; DeepMind; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2742; None
| null | 0 | null | null | null | null | null |
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba
|
https://iclr.cc/virtual/2021/poster/2742
|
Atari;world models;model-based reinforcement learning;reinforcement learning;planning;actor critic
| null | 0 | null | null |
iclr
| 0.970143 | 0 | null |
main
| 6.5 |
4;5;8;9
| null |
https://iclr.cc/virtual/2021/poster/2742
|
Mastering Atari with Discrete World Models
| null | null | 0 | 4.5 |
Poster
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Adaptation;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Cross-Modal Domain Adaptation for Reinforcement 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 |
multitask learning;meta-optimization;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
$\alpha$VIL: Learning to Leverage Auxiliary Tasks for Multitask Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2667; None
| null | 0 | null | null | null | null | null |
William Bakst, Nobuyuki Morioka, Erez Louidor
|
https://iclr.cc/virtual/2021/poster/2667
|
Theory;Regularization;Algorithms;Classification;Regression;Matrix and Tensor Factorization;Fairness;Evaluation;Efficiency;Machine Learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2667
|
Monotonic Kronecker-Factored Lattice
| null | null | 0 | 2.25 |
Poster
|
2;1;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-supervised clustering;clustering;deep learning;autoencoder
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Latent Space Semi-Supervised Time Series Data Clustering
| null | null | 0 | 3.25 |
Reject
|
3;5;2;3
| null |
null |
Department of Computer Science, Duke University, Durham, NC 27708
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hessian;neural network;Kronecker factorization;PAC-Bayes bound;eigenspace;eigenvalue
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta Learning;Information Bottleneck;Gaussian Processes;Few-shot learning;Variational Inference
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Information Theoretic Meta Learning with Gaussian Processes
| 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 |
meta-learning;few-shot learning;adversarial attacks;poisoning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Attacking Few-Shot Classifiers with Adversarial Support Sets
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent reinforcement learning;deep Q-learning;universal value functions;successor features;relative overgeneralization
| null | 0 | null | null |
iclr
| -0.187317 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
5;2;4;5
| null |
null |
Yale University; University of Texas at Austin; Texas A&M University; University of California, Irvine
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3114; None
| null | 0 | null | null | null | null | null |
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang
|
https://iclr.cc/virtual/2021/poster/3114
|
knowledge distillation;avoid knowledge leaking
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3114
|
Undistillable: Making A Nasty Teacher That CANNOT teach students
|
https://github.com/VITA-Group/Nasty-Teacher
| null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Joint Embedding Learning;Generative Model;Transformer Autoencoder;Inverse Protein Folding;Sequence Design
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
| 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 |
adversarial attack;robustness;domain adaptation;privacy-preserving machine learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data efficient RL;$Q$-Learning;Hamiltonian Monte Carlo
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Hamiltonian Q-Learning: Leveraging Importance-sampling for Data Efficient RL
| 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 |
calcium imaging;calcium traces;generative adversarial networks;spike train analysis
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Synthesising Realistic Calcium Traces of Neuronal Populations Using GAN
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial Life;AI Generating Algorithms
| null | 0 | null | null |
iclr
| -0.872872 | 0 | null |
main
| 5 |
3;4;5;8
| null | null |
Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
| null | null | 0 | 3 |
Reject
|
4;3;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep Reinforcement Learning;Exploration;Temporal Difference Error;Variance
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
Leveraging the Variance of Return Sequences for Exploration Policy
| null | null | 0 | 4 |
Withdraw
|
5;4;4;3
| null |
null |
Alibaba Group; Beijing Jiaotong University; University of Edinburgh
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2905; None
| null | 0 | null | null | null | null | null |
Boli Chen, Yao Fu, Guangwei Xu, Pengjun Xie, Chuanqi Tan, Mosha Chen, Liping Jing
|
https://iclr.cc/virtual/2021/poster/2905
|
Hyperbolic;BERT;Probe;Syntax;Sentiment
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2905
|
Probing BERT in Hyperbolic Spaces
|
https://github.com/FranxYao/PoincareProbe
| null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null |
University of Oxford & Five AI Limited; University of Oxford; CVIT, IIIT Hyderabad, India
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3041; None
| null | 0 | null | null | null | null | null |
Shyamgopal Karthik, Ameya Prabhu, Puneet Dokania, Vineet Gandhi
|
https://iclr.cc/virtual/2021/poster/3041
|
Hierarchy-Aware Classification;Conditional Risk Minimization;Post-Hoc Correction
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3041
|
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
| null | null | 0 | 3.25 |
Poster
|
3;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Distributional Reinforcement Learning for Risk-Sensitive Policies
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null |
University of Science and Technology of China; University of Texas at Austin
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2934; None
| null | 0 | null | null | null | null | null |
Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen
|
https://iclr.cc/virtual/2021/poster/2934
|
lottery tickets;GAN compression;generative adversarial networks
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2934
|
GANs Can Play Lottery Tickets Too
|
https://github.com/VITA-Group/GAN-LTH
| null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
certificates;adversarial;robustness;defenses;smoothing;curvature
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Tight Second-Order Certificates for Randomized Smoothing
|
https://github.com/alevine0/smoothing_second_order
| null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Risk-sensitive learning;cooperative multi-agent reinforcement learning;reinforcement learning
| null | 0 | null | null |
iclr
| -0.207514 | 0 |
https://sites.google.com/view/rmix
|
main
| 5.75 |
4;6;6;7
| null | null |
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning
| null | null | 0 | 3.25 |
Reject
|
4;2;3;4
| null |
null |
Playform - Artrendex Inc., USA and Department of Computer Science, Rutgers University; Department of Computer Science, Rutgers University and Playform - Artrendex Inc., USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3048; None
| null | 0 | null | null | null | null | null |
Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal
|
https://iclr.cc/virtual/2021/poster/3048
|
deep learning;generative model;image synthesis;few-shot learning;generative adversarial network;self-supervised learning;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3048
|
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
|
https://github.com/odegeasslbc/FastGAN-pytorch
| null | 0 | 4 |
Poster
|
5;4;3;4
| null |
null |
Zhejiang Lab; Key Lab. of Machine Perception (MoE), School of EECS, Peking University; Key Lab. of Machine Perception (MoE), School of EECS, Peking University; Key Lab. of Machine Perception (MoE), School of EECS, Peking University; Pazhou Lab; Tsinghua University; School of Data Science, Fudan University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3364; None
| null | 0 | null | null | null | null | null |
Zhengyang Geng, Meng-Hao Guo, Hongxu None Chen, Xia Li, Ke Wei, Zhouchen Lin
|
https://iclr.cc/virtual/2021/poster/3364
|
attention models;matrix decomposition;computer vision
| null | 0 | null | null |
iclr
| 0.870388 | 0 |
Not provided
|
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3364
|
Is Attention Better Than Matrix Decomposition?
|
Not provided
| null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null |
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA; Google Research
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
pre-training;multitask learning;deeplearning;gradient decomposition
| null | 0 | null | null |
iclr
| 0.662266 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
AUXILIARY TASK UPDATE DECOMPOSITION: THE GOOD, THE BAD AND THE NEUTRAL
| null | null | 0 | 3.25 |
Poster
|
2;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Yandex; HSE University; Moscow Institute of Physics and Technology, Moscow, Russia; Yandex; HSE University, Moscow, Russia; Yandex, Moscow, Russia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2928; None
| null | 0 | null | null | null | null | null |
Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko
|
https://iclr.cc/virtual/2021/poster/2928
|
uncertainty;ensembles;gradient boosting;decision trees;knowledge uncertainty
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2928
|
Uncertainty in Gradient Boosting via Ensembles
| 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 |
multitask learning;deep learning;gradnorm
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Rotograd: Dynamic Gradient Homogenization for Multitask Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-modal;multitask;machine learning in healthcare;benchmark
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
A Multi-Modal and Multitask Benchmark in the Clinical Domain
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Planning
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
World Model as a Graph: Learning Latent Landmarks for Planning
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, K1N 6N5, Canada
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2692; None
| null | 0 | null | null | null | null | null |
Haoye Lu, Yongyi Mao, Amiya Nayak
|
https://iclr.cc/virtual/2021/poster/2692
| null | null | 0 | null | null |
iclr
| -0.29277 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2692
|
On the Dynamics of Training Attention Models
| null | null | 0 | 2.75 |
Poster
|
3;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain adaptation;Disentanglement
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Disentangled cyclic reconstruction for domain adaptation
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null | null |
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function
| 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 |
reinforcement learning;real-time strategy games;sparse rewards;shaped rewards;policy gradient;sample-efficiency
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
| 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 |
Generalization;Pruning;Generalization Measures
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Robustness to Pruning Predicts Generalization in Deep Neural Networks
| 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 |
reproducibility;churn;confidence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
On the Reproducibility of Neural Network Predictions
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformer;mechanism;modularity;modules;independence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Transformers with Competitive Ensembles of Independent Mechanisms
| 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 |
Neural Network Classifier;Error Detection;AI safety
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model
| null | null | 0 | 2.75 |
Reject
|
3;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;pre-trained language models;contextualized language models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models
| null | null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3102; None
| null | 0 | null | null | null | null | null |
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J Townshend, Ron Dror
|
https://iclr.cc/virtual/2021/poster/3102
|
structural biology;graph neural networks;proteins;geometric deep learning
| null | 0 | null | null |
iclr
| 0.088045 | 0 | null |
main
| 7.25 |
6;6;7;10
| null |
https://iclr.cc/virtual/2021/poster/3102
|
Learning from Protein Structure with Geometric Vector Perceptrons
|
https://github.com/drorlab/gvp
| null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Nonlinear Dynamical Systems;Global Optimization;Deep Neural Networks;Ensemble.
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;optimization;benchmarking
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
| 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 |
metric learning;context encoding;context discovery;image parsing;panoptic segmentation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Contextual Image Parsing via Panoptic Segment Sorting
| 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 |
Vocabulary Construction;NLP
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Information-theoretic Vocabularization via Optimal Transport for Machine Translation
| null | null | 0 | 4 |
Withdraw
|
4;5;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;Benchmarking;Performance Prediction;Deep Learning
| null | 0 | null | null |
iclr
| -0.130189 | 0 | null |
main
| 5.75 |
3;5;7;8
| null | null |
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null |
Under Review at ICLR 2021
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
temporal action proposal;transformer;video analysis
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Feature Integration and Group Transformers for Action Proposal Generation
| null | null | 0 | 3.75 |
Reject
|
3;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;disentanglement
| null | 0 | null | null |
iclr
| -0.240192 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
On Disentangled Representations Learned From Correlated Data
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.763763 | 0 | null |
main
| 3.8 |
3;3;4;4;5
| null | null |
Graph View-Consistent Learning Network
| null | null | 0 | 4.4 |
Reject
|
4;4;5;4;5
| null |
null |
Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3127; None
| null | 0 | null | null | null | null | null |
Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim
|
https://iclr.cc/virtual/2021/poster/3127
|
Reinforcement learning;Information bottleneck
| null | 0 | null | null |
iclr
| 0.57735 | 0 |
http://vision.snu.ac.kr/projects/db
|
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3127
|
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
| 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 | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
FILTRA: Rethinking Steerable CNN by Filter Transform
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;kernel methods
| null | 0 | null | null |
iclr
| -0.648886 | 0 | null |
main
| 6 |
4;6;6;8
| null | null |
Deep Learning Is Composite Kernel Learning
| null | null | 0 | 2.25 |
Reject
|
4;1;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;AutoML
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Neural Architecture Search of SPD Manifold Networks
| null | null | 0 | 4.25 |
Reject
|
3;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Networks;Biological Plausibility;Backprop
| null | 0 | null | null |
iclr
| -0.727607 | 0 | null |
main
| 5.75 |
4;4;7;8
| null | null |
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
| 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 |
resource-rich machine translation;neural machine translation;pre-training;self-supervised learning;joint training
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Preconditioning;Condition Number
| null | 0 | null | null |
iclr
| 0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Precondition Layer and Its Use for GANs
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial networks;multi-agent systems.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
MAS-GAN: Adversarial Calibration of Multi-Agent Market Simulators.
| null | null | 0 | 4.333333 |
Reject
|
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
| 4 |
3;4;4;5
| null | null |
Robust Learning via Golden Symmetric Loss of (un)Trusted Labels
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Counterfactual fairness;data preprocessing;fairness test;discrimination detection;affirmative action
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Counterfactual Fairness through Data Preprocessing
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
knowledge distillation;neural architecture search;nas;automl;knowledge trasfer;model compression
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Don't be picky, all students in the right family can learn from good teachers
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
ETH Zurich; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2545; None
| null | 0 | null | null | null | null | null |
Joan Puigcerver i Perez, Carlos Riquelme, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
|
https://iclr.cc/virtual/2021/poster/2545
|
Transfer Learning;Expert Models;Few Shot
| null | 0 | null | null |
iclr
| -0.454545 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2545
|
Scalable Transfer Learning with Expert Models
| null | null | 0 | 3.25 |
Poster
|
4;3;4;2
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
copy number variation;deep learning;convolutional neural network;computational biology;DNA sequencing
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;2;3;5
| null | null |
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;few-shot learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
| 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 |
Reinforcement Learning;Representation Learning
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Measuring and mitigating interference in reinforcement learning
| null | null | 0 | 3.25 |
Reject
|
3;4;4;2
| null |
null |
Department of Computer Science, University of Cambridge, Cambridge, United Kingdom; Department of Computer Science, University College London, London, United Kingdom
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2925; None
| null | 0 | null | null | null | null | null |
Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò
|
https://iclr.cc/virtual/2021/poster/2925
|
differential equations;neural processes;dynamics;deep learning;neural ode
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2925
|
Neural ODE Processes
| null | null | 0 | 3 |
Poster
|
4;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Label noise;Neural network robustness;Regularization methods;Spectral normalization;Fourier analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
A Spectral Perspective of Neural Networks Robustness to Label Noise
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
Department of Electrical & Computer Engineering, University of California, Santa Barbara, CA 93106; Department of Mathematics, National University of Singapore, Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2963; None
| null | 0 | null | null | null | null | null |
Zhuotong Chen, Qianxiao Li, Zheng Zhang
|
https://iclr.cc/virtual/2021/poster/2963
|
neural network robustness;optimal control;dynamical system
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2963
|
Towards Robust Neural Networks via Close-loop Control
|
https://github.com/zhuotongchen/Towards-Robust-Neural-Networks-via-Close-loop-Control.git
| null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null |
InterDigital Inc., Cesson-Sévigné, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3160; None
| null | 0 | null | null | null | null | null |
Tsiry MAYET, Anne Lambert, Pascal Le Guyadec, Francoise Le Bolzer, François Schnitzler
|
https://iclr.cc/virtual/2021/poster/3160
|
Recurrent neural networks;Flexibility;Computational resources
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3160
|
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit
| null | null | 0 | 3.75 |
Poster
|
5;5;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural network;neural network;language modeling;minimum description length;genetic algorithm;semantics;syntax
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Minimum Description Length Recurrent Neural Networks
| null | null | 0 | 3.5 |
Reject
|
3;3;5;3
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.