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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Lifelong learning;continual learning;feature hashing
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Continual learning using hash-routed convolutional neural 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 |
out-of-distribution detection;transductive;predictive uncertainty;ensembles;ensemble diversity;outlier detection
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;6;6;8
| null | null |
Learn what you can't learn: Regularized Ensembles for Transductive out-of-distribution detection
| null | null | 0 | 3 |
Reject
|
4;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model Compression;Non-uniform Quantization;Post-training Quantization;Language Model
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Post-Training Weighted Quantization of Neural Networks for Language Models
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;6;8
| null | null |
Information Condensing Active Learning
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Non-Linear Rewards For Successor Features
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
matrix factorization;honey bees;explainable;social networks;implicit bias;dataset
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Individuality in the hive - Learning to embed lifetime social behaviour of honey bees
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT;Training speedup;Multi-stage training;Natural language processing
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup
| 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 |
medical imaging;generative modeling;privacy;de-identification
| null | 0 | null | null |
iclr
| -0.597614 | 0 | null |
main
| 5 |
3;3;6;6;7
| null | null |
Adversarial Privacy Preservation in MRI Scans of the Brain
| null | null | 0 | 3.8 |
Reject
|
4;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
| 0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
AUL is a better optimization metric in PU learning
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Euler GAN;GAN;time series;Wasserstein;Sinkhorn divergence;transfer learning
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 3.8 |
3;3;3;5;5
| null | null |
An Euler-based GAN for time series
| null | null | 0 | 3.8 |
Withdraw
|
4;3;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Lookahead optimizer;game dynamics;smooth game
| null | 0 | null | null |
iclr
| 0.544331 | 0 | null |
main
| 6 |
4;4;7;9
| null | null |
Characterizing Lookahead Dynamics of Smooth Games
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
Tencent YouTu Lab; University of California, Santa Cruz
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2755; None
| null | 0 | null | null | null | null | null |
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, Yang Liu
|
https://iclr.cc/virtual/2021/poster/2755
|
Learning with noisy labels;instance-based label noise;deep neural networks.
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2755
|
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
|
https://github.com/UCSC-REAL/cores
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hybrid Perceptual Systems;Foveation;Visual Crowding;Texture;Two-stage models
| null | 0 | null | null |
iclr
| -0.612372 | 0 | null |
main
| 5.8 |
3;5;7;7;7
| null | null |
Emergent Properties of Foveated Perceptual Systems
| null | null | 0 | 3.6 |
Reject
|
4;4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Channel Optimization;Channel Pruning;Neural Architecture Search;Convolutional Neural Network;Image Classification
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Width transfer: on the (in)variance of width optimization
| 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 |
Point cloud;adversarial defense;implicit function
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
| null | null | 0 | 3.75 |
Withdraw
|
3;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;regularization;hyperparameter optimization;benchmarks.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Regularization Cocktails for Tabular Datasets
| 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 |
Contrastive Learning;On-device Training;Data Selection
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection
| null | null | 0 | 3.333333 |
Withdraw
|
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 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Deep Positive Unlabeled Learning with a Sequential Bias
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Wasserstein barycenters;Optimal Transport
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Learning to generate Wasserstein barycenters
|
https://github.com/iclr2021-anonymous-author/learning-to-generate-wasserstein-barycenters
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GCN;graph spectrum;stability;graph Laplacian
| null | 0 | null | null |
iclr
| -0.789474 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
A Spectral Perspective on Deep Supervised Community Detection
| null | null | 0 | 3.75 |
Reject
|
4;4;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Nicholas Teague
| null |
tabular;feature engineering;preprocessing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.5 |
2;2;3;3
| null | null |
Numeric Encoding Options with Automunge
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Graduate School of Information Science and Technology, The University of Tokyo, Japan; Graduate School of Information Science and Technology, The University of Tokyo, Japan; Center for Advanced Intelligence Project, RIKEN, Japan
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2725; None
| null | 0 | null | null | null | null | null |
Taiji Suzuki, Akiyama Shunta
|
https://iclr.cc/virtual/2021/poster/2725
|
Excess risk;minimax optimal rate;local Rademacher complexity;fast learning rate;kernel method;linear estimator
| null | 0 | null | null |
iclr
| 0.636364 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2725
|
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
| null | null | 0 | 3.25 |
Spotlight
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph neural networks;universal node embeddings;node classification;link prediction;unsupervised learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
PanRep: Universal node embeddings for heterogeneous graphs
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Raven's Progressive Matrices;visual analogical reasoning.
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.324443 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Dual Averaging is Surprisingly Effective for Deep Learning Optimization
| null | null | 0 | 4 |
Withdraw
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Learning;Natural Language Processing;BERT;News Recommendation;Attention
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Fighting Filterbubbles with Adversarial BERT-Training for News-Recommendation
| 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 |
Deep Learning;Feature Learning;Compatible Learning
| null | 0 | null | null |
iclr
| -0.666667 | 0 | null |
main
| 4.5 |
2;5;5;6
| null | null |
Non-Inherent Feature Compatible Learning
| 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 |
Deep ensembles;deep learning;computer vision;density estimation;uncertainty
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Parametric Density Estimation with Uncertainty using Deep Ensembles
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Carnegie Mellon University; University of Pennsylvania and Amazon; University of Washington, Seattle and Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2830; None
| null | 0 | null | null | null | null | null |
Ruosong Wang, Dean Foster, Sham M Kakade
|
https://iclr.cc/virtual/2021/poster/2830
|
batch reinforcement learning;function approximation;lower bound;representation
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2830
|
What are the Statistical Limits of Offline RL with Linear Function Approximation?
| null | null | 0 | 3.25 |
Spotlight
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Causal Relation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Deep Reinforcement Learning with Causality-based Intrinsic Reward
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty evaluation;sampling-free method;variance propagation;LSTM;out-of-distribution
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation
| 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 |
Neural Architecture Search;Pareto Frontier Learning;Resource Constraint
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Pareto-Frontier-aware Neural Architecture Search
| null | null | 0 | 4.5 |
Withdraw
|
3;5;5;5
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;robustness;adversarial defense;adversarial example
| null | 0 | null | null |
iclr
| -1 | 0 |
Not provided
|
main
| 5.5 |
5;5;6;6
| null | null |
How to compare adversarial robustness of classifiers from a global perspective
|
Not provided
| null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
The Chinese University of Hong Kong, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China; University of Munich, Germany; Corporate Technology, Siemens AG, Munich, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2711; None
| null | 0 | null | null | null | null | null |
Jindong Gu, Baoyuan Wu, Volker Tresp
|
https://iclr.cc/virtual/2021/poster/2711
|
Capsule Networks;Adversarial Attacks;Adversarial Example Detection
| null | 0 | null | null |
iclr
| -0.648886 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2711
|
Effective and Efficient Vote Attack on Capsule Networks
| null | null | 0 | 3 |
Poster
|
3;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
speech recognition;multilingual;long-tail;adapter;logit adjustments
| null | 0 | null | null |
iclr
| -0.790569 | 0 | null |
main
| 5 |
4;5;5;5;6
| null | null |
Adapt-and-Adjust: Overcoming the Long-tail Problem of Multilingual Speech Recognition
| null | null | 0 | 3.8 |
Reject
|
4;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
| 4 |
3;4;5
| null | null |
On the Discovery of Feature Importance Distribution: An Overlooked Area
|
https://github.com/paper-github-repository
| null | 0 | 3.666667 |
Withdraw
|
3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning theory;stochastic gradient descent;deep learning;neural networks;dynamical systems;chaos theory;Lyapunov exponents
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Chaos Theory Approach to Understand Neural Network Optimization
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null |
Google Research, New York, NY
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2675; None
| null | 0 | null | null | null | null | null |
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar
|
https://iclr.cc/virtual/2021/poster/2675
|
long-tail learning;class imbalance
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2675
|
Long-tail learning via logit adjustment
| null | null | 0 | 3.75 |
Spotlight
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Labeling;Empirical Evaluation;Active Machine Learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null |
School of Electrical and Computer Engineering, Purdue University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3289; None
| null | 0 | null | null | null | null | null |
Gobinda Saha, Isha Garg, Kaushik Roy
|
https://iclr.cc/virtual/2021/poster/3289
|
Continual Learning;Representation Learning;Computer Vision;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
6;8;8;8
| null |
https://iclr.cc/virtual/2021/poster/3289
|
Gradient Projection Memory for Continual Learning
|
https://github.com/sahagobinda/GPM
| null | 0 | 5 |
Oral
|
5;5;5;5
| null |
null |
AI21 Labs, Tel Aviv, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2783; None
| null | 0 | null | null | null | null | null |
Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham
|
https://iclr.cc/virtual/2021/poster/2783
|
Language modeling;BERT;pointwise mutual information
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2783
|
PMI-Masking: Principled masking of correlated spans
| null | null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Association
| null | 0 | null | null |
iclr
| -0.207514 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Learning Online Data Association
| null | null | 0 | 3.25 |
Reject
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
supervised learning;sample distribution;statistical methods;sample weighting;approximation theory;Taylor expansion
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.5 |
3;6;6;7
| null | null |
Variance Based Sample Weighting for Supervised Deep Learning
| 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
| -0.688247 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry
| 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 |
distributional shift;social impact of AI;content recommendation;incentives;meta-learning
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Hidden Incentives for Auto-Induced Distributional Shift
| null | null | 0 | 2.75 |
Reject
|
3;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble;Diversity Metrics;Hierarchical Pruning;Ensemble Accuracy;Deep Neural Networks
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Deep Ensembles with Hierarchical Diversity Pruning
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
video generation;vqvae;transformers;gpt
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/videogen
|
main
| 4 |
4;4;4;4
| null | null |
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;Genetic Algorithm;SVD
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Neural Network Surgery: Combining Training with Topology Optimization
| 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 |
Bayesian optimization;uncertainty quantification;Gaussian process
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Uncertainty Quantification for Bayesian Optimization
| null | null | 0 | 3.75 |
Withdraw
|
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
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Structural Knowledge Distillation
| null | null | 0 | 3.75 |
Withdraw
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Mutual information estimation;discriminative classification
| null | 0 | null | null |
iclr
| -0.8 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
DEMI: Discriminative Estimator of Mutual Information
| null | null | 0 | 3.5 |
Reject
|
4;5;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
content generation;spatial data representation;tree-based network
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.75 |
2;3;5;5
| null | null |
AETree: Areal Spatial Data Generation
| null | null | 0 | 4.25 |
Withdraw
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multitasking;attention;deep learning;natural language processing
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Temporal Attention Modules for Memory-Augmented Neural Networks
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
debugging;interpretability;explainability
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
MOLOCO; KAIST; University of Michigan; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2718; None
| null | 0 | null | null | null | null | null |
Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas E Huang, Hyungsuk Yoon, Honglak Lee, Seunghoon Hong
|
https://iclr.cc/virtual/2021/poster/2718
|
Video prediction;generative model;long-term prediction
| null | 0 | null | null |
iclr
| 0 | 0 |
https://1konny.github.io/HVP/
|
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2718
|
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
|
https://github.com/1konny/HVP
| null | 0 | 4.5 |
Poster
|
5;4;4;5
| null |
null |
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2948; None
| null | 0 | null | null | null | null | null |
Kuilin Chen, Chi-Guhn Lee
|
https://iclr.cc/virtual/2021/poster/2948
|
incremental learning;few-shot;vector quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null |
https://iclr.cc/virtual/2021/poster/2948
|
Incremental few-shot learning via vector quantization in deep embedded space
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
ETH Zurich; University of Maryland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2533; None
| null | 0 | null | null | null | null | null |
Renkun Ni, Hong-Min Chu, Oscar Castaneda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein
|
https://iclr.cc/virtual/2021/poster/2533
|
quantization;efficient inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
5;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2533
|
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
| null | null | 0 | 4 |
Poster
|
4;3;5;4
| null |
null |
Department of Electrical and Computer Engineering, Rice University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2590; None
| null | 0 | null | null | null | null | null |
Sina Alemohammad, Jack Wang, Randall Balestriero, Richard Baraniuk
|
https://iclr.cc/virtual/2021/poster/2590
|
Neural Tangent Kernel;Recurrent Neural Network;Gaussian Process;Overparameterization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2590
|
The Recurrent Neural Tangent Kernel
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
IOE, University of Michigan, [email protected]; ECE, University of Minnesota - Twin Cities, [email protected]; ISE, University of Illinois at Urbana-Champaign, [email protected]; University of Illinois at Urbana-Champaign, [email protected]
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3374; None
| null | 0 | null | null | null | null | null |
Naichen Shi, Dawei Li, Mingyi Hong, Ruoyu Sun
|
https://iclr.cc/virtual/2021/poster/3374
|
RMSprop;convergence;hyperparameter
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3374
|
RMSprop converges with proper hyper-parameter
| null | null | 0 | 3 |
Spotlight
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;AutoML;irregularly sampled time series;anomaly detection;clustering
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;unsupervised learning;contrastive loss;triplet loss;whitening
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Whitening for Self-Supervised 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 |
Automated Hyperparameter Optimization;Budgets;Efficiency;Optimal Initial Design;Robustness
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA; Computer Science Department, Johns Hopkins University, Baltimore, MD 21218, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2821; None
| null | 0 | null | null | null | null | null |
Jingfeng Wu, Difan Zou, vladimir braverman, Quanquan Gu
|
https://iclr.cc/virtual/2021/poster/2821
|
SGD;regularization;implicit bias
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2821
|
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data augmentation;stochastic policy;multi-stage augmentation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
NOSE Augment: Fast and Effective Data Augmentation Without Searching
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unconditional Image Synthesis;Complex Scene;GAN;Semantic Bottleneck
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;6;8
| null | null |
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck
| 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 |
survival analysis;time-to-event;counterfactual inference;causal survival analysis
| null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Enabling counterfactual survival analysis with balanced representations
| 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 |
computer generated holography;inverse problems;deep learning
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Learned residual Gerchberg-Saxton network for computer generated holography
| null | null | 0 | 3.75 |
Withdraw
|
5;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Spectral Synthesis for Satellite-to-Satellite Translation
|
https://github.com/anonymous-ai-for-earth/satellite-to-satellite-translation
| null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
training-free;uncertainty estimation;dense regression;super resolution;depth estimation;deep learning
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
| null | null | 0 | 4 |
Withdraw
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Loss-surface;sharpness;learning rate;generalization
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
SALR: Sharpness-aware Learning Rates for Improved Generalization
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null |
The University of Tokyo; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3081; None
| null | 0 | null | null | null | null | null |
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu
|
https://iclr.cc/virtual/2021/poster/3081
|
Reinforcement Learning;deployment-efficiency;offline RL;Model-based RL
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3081
|
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
|
https://github.com/matsuolab/BREMEN
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Uncertainty Estimation;Calibration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Amortized Conditional Normalized Maximum Likelihood
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2541; None
| null | 0 | null | null | null | null | null |
Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig
|
https://iclr.cc/virtual/2021/poster/2541
|
meta learning;machine translation;back translation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2541
|
Meta Back-Translation
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null |
University of Southern California; Beihang University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3272; None
| null | 0 | null | null | null | null | null |
Wangchunshu Zhou, Dong-Ho Lee, Ravi Kiran Selvam, Seyeon Lee, Xiang Ren
|
https://iclr.cc/virtual/2021/poster/3272
|
Language Model Pre-training;Commonsense Reasoning;Self-supervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
4;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3272
|
Pre-training Text-to-Text Transformers for Concept-centric Common Sense
|
https://github.com/INK-USC/CALM
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Backdoor attack;Machine learning security
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Reward Calibration;Empirical Sufficiency;Overfitting.
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Empirical Sufficiency Featuring Reward Delay Calibration
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
DeepMind, London; Google, Dublin
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3150; None
| null | 0 | null | null | null | null | null |
David Barrett, Benoit Dherin
|
https://iclr.cc/virtual/2021/poster/3150
|
implicit regularization;deep learning;deep learning theory;theoretical issues in deep learning;theory;regularization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3150
|
Implicit Gradient Regularization
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;fine-tuning;pre-training
| null | 0 | null | null |
iclr
| -0.215666 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Bi-tuning of Pre-trained Representations
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null |
Massachusetts Institute of Technology, Geometric Data Processing Group, Cambridge, Massachusetts, USA; Skolkovo Institute of Science and Technology, Advanced Data Analytics in Science and Engineering Group, Moscow, Russia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2713; None
| null | 0 | null | null | null | null | null |
Alexander Korotin, Lingxiao Li, Justin Solomon, Evgeny Burnaev
|
https://iclr.cc/virtual/2021/poster/2713
|
wasserstein-2 barycenters;non-minimax optimization;cycle-consistency regularizer;input convex neural networks;continuous case
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2713
|
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
| null | null | 0 | 4 |
Poster
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
exposure bias;natural language generation;autoregressive
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;3;6;6
| null | null |
Quantifying Exposure Bias for Open-ended Language Generation
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
molecule;retrosynthesis;contrastive learning;graph representation learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
| null | null | 0 | 4.75 |
Reject
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Cold Start;Semi-supervised Learning
| null | 0 | null | null |
iclr
| 0.703526 | 0 | null |
main
| 6.5 |
5;6;6;9
| null | null |
ColdExpand: Semi-Supervised Graph Learning in Cold Start
| null | null | 0 | 4.25 |
Reject
|
3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Deep Learning;Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
| 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
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Learning and Generalization in Univariate Overparameterized Normalizing Flows
| 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 |
Weakly Supervised Learning;Scene Graph Grounding;Visual Relation;Computer Vision
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Weakly Supervised Scene Graph Grounding
| null | null | 0 | 4.75 |
Reject
|
5;5;4;5
| null |
null |
Department of Physics, University of Washington, Seattle, WA; Blueshift, Alphabet, Mountain View, CA; Google Research, Mountain View, CA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2576; None
| null | 0 | null | null | null | null | null |
Kyle Aitken, Vinay Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan
|
https://iclr.cc/virtual/2021/poster/2576
|
Recurrent neural networks;dynamical systems;interpretability;document classification;reverse engineering
| null | 0 | null | null |
iclr
| 0.102062 | 0 | null |
main
| 6.8 |
5;7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2576
|
The geometry of integration in text classification RNNs
| null | null | 0 | 4.2 |
Poster
|
4;5;4;4;4
| null |
null |
Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2705; None
| null | 0 | null | null | null | null | null |
Kangle Deng, Aayush Bansal, Deva Ramanan
|
https://iclr.cc/virtual/2021/poster/2705
|
unsupervised learning;autoencoders;speech-impaired;assistive technology;audiovisual synthesis;voice conversion
| null | 0 | null | null |
iclr
| 1 | 0 |
https://dunbar12138.github.io/projectpage/Audiovisual/
|
main
| 7 |
6;6;9
| null |
https://iclr.cc/virtual/2021/poster/2705
|
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Seoul National University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3130; None
| null | 0 | null | null | null | null | null |
Seungjun Lee, Haesang Yang, Woojae Seong
|
https://iclr.cc/virtual/2021/poster/3130
|
Learning physical laws;meta-learning;Hamiltonian systems
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3130
|
Identifying Physical Law of Hamiltonian Systems via Meta-Learning
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
AIP, RIKEN, PRESTO, JST; Courant Institute, New York University, Center for Data Science, New York University, CIFAR Fellow
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2660; None
| null | 0 | null | null | null | null | null |
Shuhei Kurita, Kyunghyun Cho
|
https://iclr.cc/virtual/2021/poster/2660
|
vision-and-language-navigation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
4;5;8;8
| null |
https://iclr.cc/virtual/2021/poster/2660
|
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule
| null | null | 0 | 4 |
Poster
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Metric Learning;Gastronomy;Memory Network;Knowledge Graph;Interpretable
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Interpretable Relational Representations for Food Ingredient Recommendation Systems
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient Robust Training;Backward Smoothing;Robustness
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Efficient Robust Training via Backward Smoothing
| 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 |
Reinforcement Learning;Policy Optimization;Amortization;Variational Inference
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Iterative Amortized Policy Optimization
| null | null | 0 | 3 |
Reject
|
2;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Recovering Geometric Information with Learned Texture Perturbations
| null | null | 0 | 3.25 |
Reject
|
2;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;video representation learning;spatiotemporal jigsaw
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;class-wise properties;robustness;adversarial example
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Intriguing class-wise properties of 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 |
Reinforcement Learning;Intrinsic Rewards;Symbolic Regression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
black-box optimization;mujoco;wizard;benchmarking;BBOB;LSGO
| null | 0 | null | null |
iclr
| 0.560612 | 0 |
https://optimsuite.com/ (assumed from the text 'open source in OptimSuite')
|
main
| 6.75 |
5;6;7;9
| null | null |
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;adversarial attack;generation-based attack;adversarial generative model;non-constrained adversarial examples
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
BasisNet: Two-stage Model Synthesis for Efficient Inference
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
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