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 |
self-supervised learning;time series;deep learning;relational reasoning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning theory;meta-learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
How Important is the Train-Validation Split in Meta-Learning?
| 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 | null | null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Information distance for neural network functions
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Department of Justice (Executive Office of Immigration Review)
| null |
NLP;Textual Style Transfer;Fair Classification;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Learning to Disentangle Textual Representations and Attributes via Mutual Information
| null | null | 0 | 2.666667 |
Withdraw
|
3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed optimization;gradient compression;error-feedback
| null | 0 | null | null |
iclr
| -0.87831 | 0 | null |
main
| 4.25 |
2;4;5;6
| null | null |
Compressing gradients in distributed SGD by exploiting their temporal correlation
| 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 |
Distributionally Robust Learning;Domain Adaptation;Self-training;Density Ratio Estimation
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Distributionally Robust Learning for Unsupervised Domain Adaptation
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic gradient descent;streaming algorithm;stochastic optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Face perception;fMRI;Generative Adversarial Networks;Neural decoding
| null | 0 | null | null |
iclr
| 0.065795 | 0 | null |
main
| 4.6 |
2;4;5;5;7
| null | null |
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space
| null | null | 0 | 4.2 |
Reject
|
5;3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Safe Reinforcement Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Lyapunov Barrier Policy Optimization
| null | null | 0 | 4 |
Withdraw
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive Stochastic Optimization;Deep Convolution Neural Network;Low-Rank Factorization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
AdaS: Adaptive Scheduling of Stochastic Gradients
| null | null | 0 | 3.75 |
Withdraw
|
5;3;4;3
| null |
null |
Google DeepMind; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3225; None
| null | 0 | null | null | null | null | null |
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler
|
https://iclr.cc/virtual/2021/poster/3225
|
Transformers;Attention;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3225
|
Long Range Arena : A Benchmark for Efficient Transformers
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
Bosch Center for Artificial Intelligence, University of Tübingen; Bosch Center for Artificial Intelligence; Karlsruhe Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2985; None
| null | 0 | null | null | null | null | null |
Fabian Otto, Philipp Becker, Vien A Ngo, Hanna Ziesche, Gerhard Neumann
|
https://iclr.cc/virtual/2021/poster/2985
|
reinforcement learning;trust region;policy gradient;projection;Wasserstein distance;Kullback-Leibler divergence;Frobenius norm
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2985
|
Differentiable Trust Region Layers for Deep Reinforcement Learning
|
https://git.io/Jthb0
| null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null |
Cognizant AI Labs; UT Austin & Cognizant AI Labs
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3007; None
| null | 0 | null | null | null | null | null |
Elliot Meyerson, Risto Miikkulainen
|
https://iclr.cc/virtual/2021/poster/3007
|
Multi-task;Many-task;Multi-domain;Cross-domain;Variable Embeddings;Task Embeddings;Tabular;Analogies
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7.5 |
6;6;9;9
| null |
https://iclr.cc/virtual/2021/poster/3007
|
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
| null | null | 0 | 3.75 |
Spotlight
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
user
| null |
Data augmentation;Image recognition;Computer vision
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering
| null | null | 0 | 4.25 |
Withdraw
|
4;5;5;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3030; None
| null | 0 | null | null | null | null | null |
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang
|
https://iclr.cc/virtual/2021/poster/3030
|
Robust Overfitting;Adversarial Training;Adversarial Robustness
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3030
|
Robust Overfitting may be mitigated by properly learned smoothening
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;out-of-distribution detection
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Communications and Information Theory Chair, Technical University of Berlin; Data Science in Earth Observation, Technical University of Munich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3179; None
| null | 0 | null | null | null | null | null |
Freya Behrens, Jonathan Sauder, Peter Jung
|
https://iclr.cc/virtual/2021/poster/3179
|
compressed sensing;sparse reconstruction;unrolled algorithms;learned ISTA
| null | 0 | null | null |
iclr
| -0.288675 | 0 | null |
main
| 7 |
5;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3179
|
Neurally Augmented ALISTA
| null | null | 0 | 4 |
Poster
|
4;5;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
| 5 |
4;5;5;6
| null | null |
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation
| null | null | 0 | 3 |
Reject
|
4;3;2;3
| null |
null |
Computational Neuroscience Unit, Department of Computer Science, University of Bristol, Bristol, United Kingdom; School of Psychological Science, University of Bristol, Bristol, United Kingdom; School of Psychological Science and Computational Neuroscience Unit, Department of Computer Science, University of Bristol, Bristol, United Kingdom
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3173; None
| null | 0 | null | null | null | null | null |
Milton Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey Bowers
|
https://iclr.cc/virtual/2021/poster/3173
|
disentanglement;compositionality;compositional generalization;generalisation;generative models;variational autoencoders
| null | 0 | null | null |
iclr
| 0.774597 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3173
|
The role of Disentanglement in Generalisation
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;thoerem proving;exploration;mathematics
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 5.6 |
4;6;6;6;6
| null | null |
Learning to Reason in Large Theories without Imitation
| null | null | 0 | 3.6 |
Reject
|
4;3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural network;self-supervised learning;contrastive learning;graph motif learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Motif-Driven Contrastive Learning of Graph Representations
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null |
Google Research, Brain Team; Now at DeepMind; Google Research, Brain Team; Also at Mila, Université de Montréal; Google Research, Brain Team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2609; None
| null | 0 | null | null | null | null | null |
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare
|
https://iclr.cc/virtual/2021/poster/2609
|
Reinforcement;Generalization;Contrastive learning;Bisimulation;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2609
|
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
| null | null | 0 | 3 |
Spotlight
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Node representation;VECoDeR;Graph Neural Networks;Variational Embeddings
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
VECoDeR - Variational Embeddings for Community Detection and Node Representation
| 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 |
Scalable Transformers;Neural Machine Translation;Parameter Sharing;Self-distillation
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Scalable Transformers for Neural Machine Translation
| null | null | 0 | 4.25 |
Withdraw
|
5;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;generative adversarial network;WGAN;GNN;graph neural network;generative model;graph
| null | 0 | null | null |
iclr
| -0.128624 | 0 | null |
main
| 5.6 |
5;5;5;6;7
| null | null |
GG-GAN: A Geometric Graph Generative Adversarial Network
| null | null | 0 | 3.2 |
Reject
|
4;4;3;1;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Program Synthesis;Singular Learning Theory;Bayesian Inference;MCMC
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Geometry of Program Synthesis
| null | null | 0 | 1.333333 |
Reject
|
1;1;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;AutoML;Computer Vision
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space
|
https://github.com/IcLr2020SuBmIsSiOn/EPS
| null | 0 | 3.25 |
Reject
|
3;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LMU;LSTM;RNN;NLP;Transformers;Feedforward Training
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Feedforward Legendre Memory Unit
| null | null | 0 | 3.5 |
Withdraw
|
5;3;3;3
| null |
null |
Mila, Universit ´e de Montr ´eal, SAIT AI Lab, Montreal; Mila, Universit ´e de Montr ´eal; Samsung Advanced Institute of Technology (SAIT), South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3250; None
| null | 0 | null | null | null | null | null |
Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien
|
https://iclr.cc/virtual/2021/poster/3250
|
Meta-learning;Few-shot learning;Out-of-domain;Uncertainty;Ensemble;Adversarial training;Stepsize optimization
| null | 0 | null | null |
iclr
| 0.760886 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3250
|
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
| null | null | 0 | 3.75 |
Poster
|
4;2;4;5
| 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 |
4;6;6
| null | null |
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Prediction Intervals;Uncertainty Estimation;Regression
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
| 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 |
neural tangent kernel;mean field limit
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Dynamically Stable Infinite-Width Limits of Neural Classifiers
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null |
School of Electrical Engineering, KAIST, Daejeon, South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3363; None
| null | 0 | null | null | null | null | null |
WOOJUN KIM, Jongeui Park, Youngchul Sung
|
https://iclr.cc/virtual/2021/poster/3363
|
Multi-agent reinforcement learning;communication;intention;attention
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 5.25 |
4;5;6;6
| null |
https://iclr.cc/virtual/2021/poster/3363
|
Communication in Multi-Agent Reinforcement Learning: Intention Sharing
| null | null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null |
Google
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3196; None
| null | 0 | null | null | null | null | null |
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
|
https://iclr.cc/virtual/2021/poster/3196
| null | null | 0 | null | null |
iclr
| -0.36823 | 0 | null |
main
| 6.25 |
4;5;7;9
| null |
https://iclr.cc/virtual/2021/poster/3196
|
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
| null | null | 0 | 4 |
Poster
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNNs;computer vision
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
New York University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3058; None
| null | 0 | null | null | null | null | null |
Reuben Feinman, Brenden Lake
|
https://iclr.cc/virtual/2021/poster/3058
|
few-shot concept learning;neuro-symbolic models;probabilistic programs;generative models
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3058
|
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation
| null | null | 0 | 4.5 |
Withdraw
|
5;5;4;4
| null |
null |
Horizon Robotics; University of Southern California
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2805; None
| null | 0 | null | null | null | null | null |
Jesse Zhang, Haonan Yu, Wei Xu
|
https://iclr.cc/virtual/2021/poster/2805
|
hierarchical reinforcement learning;reinforcement learning;options;unsupervised skill discovery;exploration
| null | 0 | null | null |
iclr
| 0.792118 | 0 | null |
main
| 5.75 |
4;4;7;8
| null |
https://iclr.cc/virtual/2021/poster/2805
|
Hierarchical Reinforcement Learning by Discovering Intrinsic Options
|
https://www.github.com/jesbu1/hidio
| null | 0 | 3 |
Poster
|
3;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary classification;pairwise comparisons;unbiased risk estimator
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Pointwise Binary Classification with Pairwise Confidence Comparisons
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Learning;Adversarial Learning;Bandit Algorithms;Meta-Learning
| null | 0 | null | null |
iclr
| 0.050965 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Learning to Actively Learn: A Robust Approach
| null | null | 0 | 3.25 |
Reject
|
4;3;2;4
| null |
null |
Department of Mathematics and Statistics, State University of New York at Albany, USA; School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom; Department of Computer Science, TU Kaiserslautern, Kaiserslautern 67653, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3141; None
| null | 0 | null | null | null | null | null |
Yunwen Lei, Yiming Ying
|
https://iclr.cc/virtual/2021/poster/3141
|
generalization bounds;non-convex learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3141
|
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions
| 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 |
Compositionality
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.25 |
3;3;3;4
| null | null |
Necessary and Sufficient Conditions for Compositional Representations
| 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 |
dice enterprise;partial rejection control;sequential Monte-Carlo;Bernoulli factory;variational Inference;Rejection Sampling
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Partial Rejection Control for Robust Variational Inference in Sequential Latent Variable Models
| null | null | 0 | 4 |
Reject
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
one-shot object detection;false positives;focus on classification
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
FOC OSOD: Focus on Classification One-Shot Object Detection
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null |
Department of Electrical and Computer Engineering, Seoul National University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2914; None
| null | 0 | null | null | null | null | null |
Dongsu Zhang, Changwoon Choi, Jeonghwan Kim, Young Min Kim
|
https://iclr.cc/virtual/2021/poster/2914
|
3D generation;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2914
|
Learning to Generate 3D Shapes with Generative Cellular Automata
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
off-policy learning;multi-step reinforcement learning;Q learning
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Greedy Multi-Step Off-Policy Reinforcement Learning
| null | null | 0 | 3.75 |
Withdraw
|
4;4;4;3
| null |
null |
Carnegie Mellon University; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2574; None
| null | 0 | null | null | null | null | null |
Dibya Ghosh, Abhishek Gupta, Ashwin D Reddy, Justin Fu, Coline M Devin, Benjamin Eysenbach, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2574
|
goal reaching;reinforcement learning;behavior cloning;goal-conditioned RL
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2574
|
Learning to Reach Goals via Iterated Supervised Learning
| null | null | 0 | 3.75 |
Oral
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;GANs;MMD GANs;bayesian learning;bayesian inference
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Using MMD GANs to correct physics models and improve Bayesian parameter estimation
| null | null | 0 | 3.25 |
Withdraw
|
3;4;3;3
| null |
null |
Department of Computer Science, Stanford University, Stanford, CA 94305, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2547; None
| null | 0 | null | null | null | null | null |
Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma
|
https://iclr.cc/virtual/2021/poster/2547
|
deep learning theory;domain adaptation theory;unsupervised learning theory;semi-supervised learning theory
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 8 |
7;7;9;9
| null |
https://iclr.cc/virtual/2021/poster/2547
|
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
| null | null | 0 | 3.75 |
Oral
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-attention;object representations;visual reasoning;dynamics;visual question answering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Neural spatio-temporal reasoning with object-centric self-supervised learning
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;logic programming;neural network;relational learning
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
NeuralLog: a Neural Logic Language
| 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 |
safe reinforcement learning;constrained markov decision process;policy optimization
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis
| null | null | 0 | 3 |
Reject
|
3;2;3;4
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3276; None
| null | 0 | null | null | null | null | null |
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
|
https://iclr.cc/virtual/2021/poster/3276
|
generative models;autoregressive models
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3276
|
Improved Autoregressive Modeling with Distribution Smoothing
| null | null | 0 | 3.25 |
Oral
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;variable-length input;distribution embedding
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Distribution Embedding Network for Meta-Learning with Variable-Length Input
| null | null | 0 | 3 |
Reject
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image classification;computer vision;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
2;4;4;5;5
| null | null |
Exploring Target Driven Image Classification
| null | null | 0 | 3.8 |
Withdraw
|
4;3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;undiscounted return;success rate
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Department of Computing, Imperial College London; Dyson Robotics Lab, Imperial College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3268; None
| null | 0 | null | null | null | null | null |
Daniel Lenton, Stephen James, Ronald Clark, Andrew Davison
|
https://iclr.cc/virtual/2021/poster/3268
|
egocentric;differentiable memory;spatial awareness;mapping;image-to-action learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null |
https://iclr.cc/virtual/2021/poster/3268
|
End-to-End Egospheric Spatial Memory
|
https://github.com/ivy-dl/memory
| null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-reinforcement learning;reinforcement learning;exploration
| null | 0 | null | null |
iclr
| -0.94388 | 0 |
https://anonymouspapersubmission.github.io/dream/
|
main
| 5.5 |
4;5;6;7
| null | null |
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;nonconvex optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Nonconvex Continual Learning with Episodic Memory
| null | null | 0 | 4 |
Reject
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed optimization;communication efficiency;SVRG;importance sampling;Internet-of-Things
| null | 0 | null | null |
iclr
| 0.801784 | 0 | null |
main
| 3.8 |
3;4;4;4;4
| null | null |
Cost-efficient SVRG with Arbitrary Sampling
| null | null | 0 | 4.2 |
Withdraw
|
3;4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model-based reinforcement learning;perception modeling;object-based reinforcement learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Joint Perception and Control as Inference with an Object-based Implementation
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
Kagenova Limited, Guildford GU5 9LD, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2722; None
| null | 0 | null | null | null | null | null |
Oliver Cobb, Christopher Wallis, Augustine Mavor-Parker, Augustin Marignier, Matthew Price, Mayeul d'Avezac, Jason McEwen
|
https://iclr.cc/virtual/2021/poster/2722
| null | null | 0 | null | null |
iclr
| 0.98644 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2722
|
Efficient Generalized Spherical CNNs
| null | null | 0 | 3.25 |
Poster
|
2;2;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Depth;Over-parameterization;Neural Networks
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Do Deeper Convolutional Networks Perform Better?
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null |
NTU, Singapore; Mila, Universite de Montreal; NTU, Singapore, VinAI; Amazon Web Services AI; Google Research; ETH Zurich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3068; None
| null | 0 | null | null | null | null | null |
Aston Zhang, Yi Tay, Shuai Zhang, Alvin Chan, Anh Tuan Luu, Siu Hui, Jie Fu
|
https://iclr.cc/virtual/2021/poster/3068
|
hypercomplex representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null |
https://iclr.cc/virtual/2021/poster/3068
|
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
| null | null | 0 | 4 |
Spotlight
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;rl;offline rl;continuous control;atari;sample efficiency
| null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Q-Value Weighted Regression: Reinforcement Learning with Limited Data
| 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 |
Graph Neural Network;Normalization
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
| 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 |
neural program synthesis;spreadsheet formula prediction
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
SpreadsheetCoder: Formula Prediction from Semi-structured Context
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null |
New York University; Data Science & Scientific Informatics, Merck & Co., Inc.
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3005; None
| null | 0 | null | null | null | null | null |
Neel Dey, Antong Chen, Soheil Ghafurian
|
https://iclr.cc/virtual/2021/poster/3005
|
Group Equivariance;Geometric Deep Learning;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3005
|
Group Equivariant Generative Adversarial Networks
| null | null | 0 | 3.5 |
Poster
|
2;4;4;4
| null |
null |
KAIST; KAIST, AITRICS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3142; None
| null | 0 | null | null | null | null | null |
Hayeon Lee, Eunyoung Hyung, Sung Ju Hwang
|
https://iclr.cc/virtual/2021/poster/3142
|
Machine Learning;Neural Architecture Search;Meta-learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null |
https://iclr.cc/virtual/2021/poster/3142
|
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
|
https://github.com/HayeonLee/MetaD2A
| null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dialogue State Tracking;Domain-slot Relationship Modeling;Pre-trained Language Encoder
| null | 0 | null | null |
iclr
| -0.560612 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking
| null | null | 0 | 4.25 |
Reject
|
5;5;3;4
| null |
null |
Deepmind, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2837; None
| null | 0 | null | null | null | null | null |
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez Gonzalez, Peter Battaglia
|
https://iclr.cc/virtual/2021/poster/2837
|
graph networks;simulation;mesh;physics
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/meshgraphnets
|
main
| 7.75 |
6;6;9;10
| null |
https://iclr.cc/virtual/2021/poster/2837
|
Learning Mesh-Based Simulation with Graph Networks
| null | null | 0 | 4 |
Spotlight
|
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 |
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
| null | null | 0 | 4.25 |
Withdraw
|
5;4;4;4
| null |
null |
Google; DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3157; None
| null | 0 | null | null | null | null | null |
Samuel Smith, Benoit Dherin, David Barrett, Soham De
|
https://iclr.cc/virtual/2021/poster/3157
|
SGD;learning rate;batch size;optimization;generalization;implicit regularization;backward error analysis;SDE;stochastic differential equation;ODE;ordinary differential equation
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3157
|
On the Origin of Implicit Regularization in Stochastic Gradient Descent
| 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 | null | null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active 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 |
3D ConvNets;Network Training;Video Recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Optimization Planning for 3D ConvNets
| null | null | 0 | 3.75 |
Reject
|
5;1;4;5
| null |
null |
DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2778; None
| null | 0 | null | null | null | null | null |
Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan
|
https://iclr.cc/virtual/2021/poster/2778
|
text-to-speech;speech synthesis;adversarial;GAN;end-to-end;feed-forward;generative model
| null | 0 | null | null |
iclr
| 0.57735 | 0 |
https://deepmind.com/research/publications/End-to-End-Adversarial-Text-to-Speech
|
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2778
|
End-to-end Adversarial Text-to-Speech
| null | null | 0 | 3.75 |
Oral
|
4;3;4;4
| null |
null |
EPFL; Australian National University; University of Oxford
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3069; None
| null | 0 | null | null | null | null | null |
Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr, Martin Jaggi
|
https://iclr.cc/virtual/2021/poster/3069
|
data parallelism;sparsity;neural network training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3069
|
Understanding the effects of data parallelism and sparsity on neural network training
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
EECS Department, University of Michigan, Ann Arbor, MI 48105, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3215; None
| null | 0 | null | null | null | null | null |
Ekdeep Singh Lubana, Robert Dick
|
https://iclr.cc/virtual/2021/poster/3215
|
Network pruning;Gradient flow;Early pruning
| null | 0 | null | null |
iclr
| 0.246183 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3215
|
A Gradient Flow Framework For Analyzing Network Pruning
| null | null | 0 | 4.25 |
Spotlight
|
5;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
music arrangement;generative adversarial networks;music generation
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
Automatic Music Production Using Generative Adversarial Networks
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model compression;Network pruning;Iterative pruning;Dead connections
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Waste not, Want not: All-Alive Pruning for Extremely Sparse Networks
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
Unknown Affiliation
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hybrid Recommendation;Customer Relationship Management;Semantic Embedding;Approximate Nearest Neighbor
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
SEMANTIC APPROACH TO AGENT ROUTING USING A HYBRID ATTRIBUTE-BASED RECOMMENDER SYSTEM
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantum machine learning;Binary neural networks;Bayesian deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;6;6
| null | null |
Quantum Deformed Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explaniable AI;explanation methods;robust explanations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
Better sampling in explanation methods can prevent dieselgate-like deception
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine reading comprehension;uncertainty-based sampling;adaptive loss minimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Uncertainty-Based Adaptive Learning for Reading Comprehension
| null | null | 0 | 4 |
Reject
|
3;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pre-trained Neural Networks;Over-parameterization;Perturbations
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
LLBoost: Last Layer Perturbation to Boost Pre-trained Neural Networks
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic semantic segmentation;conditional generative models;adversarial training;calibration;uncertainty
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Calibrated Adversarial Refinement for Stochastic Semantic Segmentation
| 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 |
deep anomaly detection;bias;PAC guarantee
| null | 0 | null | null |
iclr
| -0.374634 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Understanding the Effect of Bias in Deep Anomaly Detection
| null | null | 0 | 3.75 |
Reject
|
5;2;4;4
| null |
null |
Department of Computer Science, UCLA; Department of EECS, MIT
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2624; None
| null | 0 | null | null | null | null | null |
Huan Zhang, Hongge Chen, Duane S Boning, Cho-Jui Hsieh
|
https://iclr.cc/virtual/2021/poster/2624
|
reinforcement learning;robustness;adversarial attacks;adversarial defense
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2624
|
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
|
https://github.com/huanzhang12/ATLA_robust_RL
| null | 0 | 3.75 |
Poster
|
5;5;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semantic Hashing;Approximate Nearest Neighbor
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Semantic Hashing with Locality Sensitive Embeddings
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image processing;Image recognition;Transferability;Decision Boundary
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Transferable Recognition-Aware Image Processing
|
https://github.com/anonymous20202020/Transferable_RA
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Robustness;Provable Adversarial Defense;Randomized Smoothing;Image Denoising;Score Estimation
| null | 0 | null | null |
iclr
| -0.87831 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
Efficient randomized smoothing by denoising with learned score function
| null | null | 0 | 2.75 |
Reject
|
5;1;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bilevel Optimization;Computational Complexity;Meta-Learning;Hyper-Parameter Optimization
| null | 0 | null | null |
iclr
| 0.254824 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning
| null | null | 0 | 2.75 |
Reject
|
2;4;2;3
| null |
null |
Bosch Center for Artificial Intelligence, Pittsburgh, PA; Bosch Center for Artificial Intelligence, Carnegie Mellon University, Pittsburgh, PA; The University of Texas at Austin, Austin, TX
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3279; None
| null | 0 | null | null | null | null | null |
Fatemeh Sheikholeslami, Ali Lotfi, Zico Kolter
|
https://iclr.cc/virtual/2021/poster/3279
|
Adversarial robustness;robust deep learning
| null | 0 | null | null |
iclr
| 0.636364 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3279
|
Provably robust classification of adversarial examples with detection
| null | null | 0 | 3.75 |
Poster
|
3;3;5;4
| null |
null |
IIIS, Tsinghua University; Vacaville Christian Schools; IIIS, Tsinghua University and Microsoft Research; The High School Affiliated to Renmin University of China; Microsoft Research; School of Biological Science and Medical Engineering and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2759; None
| null | 0 | null | null | null | null | null |
Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric Chang, Yan Xu
|
https://iclr.cc/virtual/2021/poster/2759
|
image completion;generative adversarial networks;co-modulation
| null | 0 | null | null |
iclr
| 0.367484 | 0 | null |
main
| 6.6 |
4;6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2759
|
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
|
https://github.com/zsyzzsoft/co-mod-gan
| null | 0 | 3.6 |
Spotlight
|
3;4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge Graphs;Graph Inference;Transformers;NLP;Wisdom of Crowds;Attention Mechanism
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
An Automated Domain Understanding Technique for Knowledge Graph Generation
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null |
Department of Computer Science, University College London; Independent Researcher
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2946; None
| null | 0 | null | null | null | null | null |
Thomas Bird, Friso Kingma, David Barber
|
https://iclr.cc/virtual/2021/poster/2946
|
binary;generative;optimization;compression
| null | 0 | null | null |
iclr
| 0.845154 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2946
|
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks
| 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 |
misclassification detection;augmentation;uncertainty;confidence
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Misclassification Detection via Class Augmentation
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Network;Adversarial Attack;Influence Maximization
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Near-Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem
| 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 | null | null | 0 | null | null |
iclr
| -0.353553 | 0 | null |
main
| 5 |
3;4;5;6;7
| null | null |
AriEL: Volume Coding for Sentence Generation Comparisons
| null | null | 0 | 3.4 |
Reject
|
4;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 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Natural Compression for Distributed Deep Learning
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.