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 |
Department of Computer Science, University of Oxford; Department of Computer Science & Technology, University of Cambridge; Department of Computer Science and Technology, University of Cambridge & Samsung AI Center
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2990; None
| null | 0 | null | null | null | null | null |
Shyam Tailor, Javier Fernandez-Marques, Nicholas Lane
|
https://iclr.cc/virtual/2021/poster/2990
|
Graph neural networks;quantization;benchmark
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2990
|
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
| null | null | 0 | 2.666667 |
Poster
|
2;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Source code embedding;Unsupervised learning
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Analysing Features Learned Using Unsupervised Models on Program Embeddings
| null | null | 0 | 4 |
Withdraw
|
4;4;5;3
| null |
null |
Facebook AI Research; Facebook AI Research, Ecole normale supérieure, PSL University, Inria
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2774; None
| null | 0 | null | null | null | null | null |
Gautier Izacard, Edouard Grave
|
https://iclr.cc/virtual/2021/poster/2774
|
question answering;information retrieval
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2774
|
Distilling Knowledge from Reader to Retriever for Question Answering
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
safe reinforcement learning;formal languages;constrained Markov decision process;safety gym;safety
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Formal Language Constrained Markov Decision Processes
| null | null | 0 | 4 |
Reject
|
5;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Empirical Studies on the Convergence of Feature Spaces in Deep Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Inria, Scool team; Inria, Scool team, Univ. Lille, CRIStAL, CNRS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3319; None
| null | 0 | null | null | null | null | null |
Yannis Flet-Berliac, reda ouhamma, odalric-ambrym maillard, philippe preux
|
https://iclr.cc/virtual/2021/poster/3319
| null | null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null |
https://iclr.cc/virtual/2021/poster/3319
|
Learning Value Functions in Deep Policy Gradients using Residual Variance
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Model compression;Neural Network Pruning;High Performance Computation
| null | 0 | null | null |
iclr
| -0.818182 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Intragroup sparsity for efficient inference
| null | null | 0 | 3.75 |
Withdraw
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian;Calibration
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Improving Neural Network Accuracy and Calibration Under Distributional Shift with Prior Augmented Data
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null |
Salesforce Research; Cornell University; Google
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2825; None
| null | 0 | null | null | null | null | null |
Junwen Bai, Weiran Wang, Yingbo Zhou, Caiming Xiong
|
https://iclr.cc/virtual/2021/poster/2825
|
Mutual Information;Unsupervised Learning;Sequence Data;Masked Reconstruction
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2825
|
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
|
https://github.com/JunwenBai/DAPC
| null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null |
Autonomous Learning Group, Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Engineering Cybernetics, NTNU, Trondheim, Norway; Autonomous Learning Group, Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Computer Science, ETH Zurich and Max Planck ETH Center for Learning Systems; Autonomous Learning Group, Max Planck Institute for Intelligent Systems, Tübingen, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3209; None
| null | 0 | null | null | null | null | null |
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Georg Martius
|
https://iclr.cc/virtual/2021/poster/3209
|
reinforcement learning;zero-order optimization;policy learning;model-based learning;robotics;model predictive control
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3209
|
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers
| null | null | 0 | 2.75 |
Poster
|
3;3;3;2
| null |
null |
Department of Computer Science, Ryerson University, Canada; Vector Institute for AI, Canada; Department of Computer Science, Ryerson University, Canada; Samsung AI Centre Toronto, Canada; Vector Institute for AI, Canada; School of Computer Science, University of Guelph, Canada; Vector Institute for AI, Canada; University of Waterloo, Canada; IWR, HCI, Heidelberg University, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3180; None
| null | 0 | null | null | null | null | null |
Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Björn Ommer, Kosta Derpanis, Neil Bruce
|
https://iclr.cc/virtual/2021/poster/3180
|
Shape;Texture;Shape Bias;Texture Bias;Shape Encoding;Mutual Information
| null | 0 | null | null |
iclr
| -0.727607 | 0 | null |
main
| 5.75 |
4;4;7;8
| null |
https://iclr.cc/virtual/2021/poster/3180
|
Shape or Texture: Understanding Discriminative Features in CNNs
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
Yandex, HSE University; Yandex; Marchuk Institute of Numerical Mathematics RAS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3329; None
| null | 0 | null | null | null | null | null |
Stanislav Morozov, Andrey Voynov, Artem Babenko
|
https://iclr.cc/virtual/2021/poster/3329
|
GAN;evaluation;embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3329
|
On Self-Supervised Image Representations for GAN Evaluation
| null | null | 0 | 4 |
Spotlight
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Procedurally Generated Environments;Curriculum Learning;Procgen Benchmark
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Prioritized Level Replay
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
calibration error;uncertainty estimation;statistical bias
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Mitigating bias in calibration error estimation
| 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 |
Distributed Machine Learning;Federated Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Faster Federated Learning with Decaying Number of Local SGD Steps
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
MCTS;planning;goal-directed planning;divide and conquer
| null | 0 | null | null |
iclr
| 0.19245 | 0 | null |
main
| 6.25 |
5;5;7;8
| null | null |
Divide-and-Conquer Monte Carlo Tree Search
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Bar-Ilan University, Israel; NVIDIA, Israel; Bar-Ilan University, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2593; None
| null | 0 | null | null | null | null | null |
Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik
|
https://iclr.cc/virtual/2021/poster/2593
|
Multi-objective optimization;multi-task learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2593
|
Learning the Pareto Front with Hypernetworks
| 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 |
GAN;large margin;SVM
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Gradient penalty from a maximum margin perspective
| null | null | 0 | 3.25 |
Withdraw
|
4;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
memory augmented neural network;distributed memory;memorization;relational reasoning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.6 |
4;5;5;6;8
| null | null |
Distributed Associative Memory Network with Association Reinforcing Loss
| null | null | 0 | 4 |
Reject
|
4;5;3;4;4
| null |
null |
Department of Statistics, University of Oxford
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2855; None
| null | 0 | null | null | null | null | null |
Adam Foster, Rattana Pukdee, Tom Rainforth
|
https://iclr.cc/virtual/2021/poster/2855
|
contrastive learning;representation learning;transformation invariance
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2855
|
Improving Transformation Invariance in Contrastive Representation Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep networks;activation functions;reproducibility
| null | 0 | null | null |
iclr
| -0.789474 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Smooth Activations and Reproducibility in Deep Networks
| null | null | 0 | 3.25 |
Reject
|
5;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial;robustness;trading;finance;security
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fine-tuning;AutoML;NAS
| null | 0 | null | null |
iclr
| 0.440225 | 0 | null |
main
| 5.75 |
3;6;7;7
| null | null |
NASOA: Towards Faster Task-oriented Online Fine-tuning
|
https://github.com/NAS-OA/NASOA
| null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;Graph Structure Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Differentiable Graph Optimization for Neural Architecture Search
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
selection;automatic;reward;shaping;reinforcement learning
| null | 0 | null | null |
iclr
| -0.973329 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Learning to Dynamically Select Between Reward Shaping Signals
| null | null | 0 | 4 |
Reject
|
5;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
| 5.333333 |
4;4;8
| null | null |
Matrix Shuffle-Exchange Networks for Hard 2D Tasks
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Johns Hopkins University, Center for Language and Speech Processing; Google Research, Brain Team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3220; None
| null | 0 | null | null | null | null | null |
Nanxin Chen, Yu Zhang, Heiga Zen, Ron Weiss, Mohammad Norouzi, William Chan
|
https://iclr.cc/virtual/2021/poster/3220
|
vocoder;diffusion;score matching;text-to-speech;gradient estimation;waveform generation
| null | 0 | null | null |
iclr
| -0.316228 | 0 |
https://wavegrad.github.io/
|
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3220
|
WaveGrad: Estimating Gradients for Waveform Generation
|
https://github.com/wavegrad
| null | 0 | 4 |
Poster
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
anomaly detection
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
PANDA - Adapting Pretrained Features for Anomaly Detection
| null | null | 0 | 3.5 |
Withdraw
|
4;3;3;4
| null |
null |
DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3125; None
| null | 0 | null | null | null | null | null |
Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
|
https://iclr.cc/virtual/2021/poster/3125
|
pca;principal components analysis;nash;games;eigendecomposition;svd;singular value decomposition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3125
|
EigenGame: PCA as a Nash Equilibrium
| null | null | 0 | 3 |
Oral
|
3;3;3
| null |
null |
Harvard University; Columbia University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2530; None
| null | 0 | null | null | null | null | null |
Sam Buchanan, Dar Gilboa, John Wright
|
https://iclr.cc/virtual/2021/poster/2530
|
deep learning;overparameterized neural networks;low-dimensional structure
| null | 0 | null | null |
iclr
| 0.512989 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2530
|
Deep Networks and the Multiple Manifold Problem
| null | null | 0 | 2.25 |
Poster
|
1;2;4;2
| null |
null |
KAIST AI; MIT EECS; KAIST EE
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2920; None
| null | 0 | null | null | null | null | null |
Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin
|
https://iclr.cc/virtual/2021/poster/2920
|
universal approximation;neural networks
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2920
|
Minimum Width for Universal Approximation
| null | null | 0 | 3.75 |
Spotlight
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Point cloud;Generation;Generative model
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
A Point Cloud Generative Model Based on Nonequilibrium Thermodynamics
| null | null | 0 | 4.333333 |
Withdraw
|
5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
entity extraction;medical entity extraction;named entity recognition;named entity normalization;electronic health records;unsupervised learning;distant supervision.
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality
| 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 |
Code Synthesis;Neural Code Synthesis;Genetic Programming;Programming By Example
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Neurally Guided Genetic Programming for Turing Complete Programming by Example
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Department of Computer Science, Cornell University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3168; None
| null | 0 | null | null | null | null | null |
Cheng Perng Phoo, Bharath Hariharan
|
https://iclr.cc/virtual/2021/poster/3168
|
few-shot learning;self-training;cross-domain few-shot learning
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3168
|
Self-training For Few-shot Transfer Across Extreme Task Differences
|
https://github.com/cpphoo/STARTUP
| null | 0 | 4.5 |
Oral
|
4;4;5;5
| null |
null |
Baidu Research; School of Computing, National University of Singapore; ReLER, University of Technology Sydney
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3101; None
| null | 0 | null | null | null | null | null |
Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang, Mohan Kankanhalli
|
https://iclr.cc/virtual/2021/poster/3101
|
Point cloud;spatio-temporal modeling;video analysis;action recognition;semantic segmentation;convolutional neural network
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3101
|
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
University of Toronto & Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2689; None
| null | 0 | null | null | null | null | null |
Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger Grosse
|
https://iclr.cc/virtual/2021/poster/2689
|
Theorem proving;Synthetic benchmark dataset;Generalization;Transformers;Graph neural networks;Monte Carlo Tree Search
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2689
|
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
| null | null | 0 | 3 |
Poster
|
2;4;2;4
| null |
null |
Rutgers University; PCG, Tencent; NEC Laboratories America; Texas A&M University; Alexa AI, Amazon
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3257; None
| null | 0 | null | null | null | null | null |
Jun Han, Martin Min, Ligong Han, Erran Li, Xuan Zhang
|
https://iclr.cc/virtual/2021/poster/3257
|
Sequential Representation Learning;Disentanglement;Recurrent Generative Model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3257
|
Disentangled Recurrent Wasserstein Autoencoder
| null | null | 0 | 3.666667 |
Spotlight
|
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
| 4.25 |
4;4;4;5
| null | null |
Knapsack Pruning with Inner Distillation
| null | null | 0 | 4.5 |
Withdraw
|
4;5;5;4
| null |
null |
Google Research, MILA, Université de Montréal; UC Berkeley, Google Research; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3301; None
| null | 0 | null | null | null | null | null |
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/3301
|
deep Q-learning;data-efficient RL;rank-collapse;offline RL
| null | 0 | null | null |
iclr
| 0.13484 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3301
|
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
| null | null | 0 | 3.25 |
Poster
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pretraining;Natural Language Generation;GPT-2;QA;Knowledge Graph
| null | 0 | null | null |
iclr
| -0.40452 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Pretrain Knowledge-Aware Language Models
| null | null | 0 | 2.75 |
Reject
|
4;2;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed optimization;lower bounds;upper bounds;communication complexity
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Improved Communication Lower Bounds for Distributed Optimisation
| null | null | 0 | 2.666667 |
Reject
|
3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Addressing Extrapolation Error in Deep Offline Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Addressing Extrapolation Error in Deep Offline Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feedback;Memory;Transformers
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Addressing Some Limitations of Transformers with Feedback Memory
| null | null | 0 | 4.5 |
Reject
|
5;3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical learning;unsupervised learning;unsupervised hierarchical learning;video representation learning;learning from demonstrations
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Unsupervised Hierarchical Concept 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 |
unsupervised domain adaptation;EM;generative model;density estimation;deep learning;transfer learning
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
EMTL: A Generative Domain Adaptation Approach
| null | null | 0 | 4.25 |
Reject
|
5;5;3;4
| null |
null |
Stanford University; Department of Engineering, University of Cambridge; Google Research & Harvard University; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3062; None
| null | 0 | null | null | null | null | null |
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Dai, Dustin Tran
|
https://iclr.cc/virtual/2021/poster/3062
|
Efficient ensembles;robustness
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3062
|
Training independent subnetworks for robust prediction
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
Gatsby Unit, UCL and University of Cambridge; Gatsby Unit, UCL
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2903; None
| null | 0 | null | null | null | null | null |
Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
|
https://iclr.cc/virtual/2021/poster/2903
|
reinforcement learning;optimization
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null |
https://iclr.cc/virtual/2021/poster/2903
|
Efficient Wasserstein Natural Gradients for Reinforcement Learning
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spherical cnn;GNN;graph convolution;rotation equivariance;3D
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Concentric Spherical GNN for 3D Representation Learning
| 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 |
reinforcement learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
3;4;6;6;6
| null | null |
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
| null | null | 0 | 4 |
Reject
|
4;5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture search;network hardware co-design
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Triple-Search: Differentiable Joint-Search of Networks, Precision, and Accelerators
| null | null | 0 | 3.5 |
Reject
|
5;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial machine learning;data poisoning attack;convergence
| null | 0 | null | null |
iclr
| 0.239046 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Model-Targeted Poisoning Attacks with Provable Convergence
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3240; None
| null | 0 | null | null | null | null | null |
Arthur Argenson, Gabriel Dulac-Arnold
|
https://iclr.cc/virtual/2021/poster/3240
|
off-line reinforcement learning;model-based reinforcement learning;model-based control;reinforcement learning;model predictive control;robotics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;5;7;8
| null |
https://iclr.cc/virtual/2021/poster/3240
|
Model-Based Offline Planning
| null | null | 0 | 4 |
Poster
|
5;3;4;4
| null |
null |
Microsoft Research, Redmond, WA, USA; Microsoft, Redmond, WA, USA; Sun Yat-sen University, Guangzhou, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2900; None
| null | 0 | null | null | null | null | null |
Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song
|
https://iclr.cc/virtual/2021/poster/2900
|
self-supervised learning;contrastive representation learning;active learning;audio-visual representation;video recognition
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2900
|
Active Contrastive Learning of Audio-Visual Video Representations
|
https://github.com/yunyikristy/CM-ACC
| null | 0 | 3.75 |
Poster
|
4;3;5;3
| null |
null |
DeepMind, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3210; None
| null | 0 | null | null | null | null | null |
Will Dabney, Georg Ostrovski, Andre Barreto
|
https://iclr.cc/virtual/2021/poster/3210
|
reinforcement learning;exploration
| null | 0 | null | null |
iclr
| 0.589768 | 0 | null |
main
| 6.4 |
5;5;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3210
|
Temporally-Extended ε-Greedy Exploration
| null | null | 0 | 4.2 |
Poster
|
4;4;4;5;4
| null |
null |
Inception Institute of Artificial Intelligence, Abu Dhabi, UAE; Institute of High Performance Computing, A*STAR, Singapore; College of Intelligence and Computing, Tianjin University, Tianjin, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2908; None
| null | 0 | null | null | null | null | null |
Zongbo Han, Changqing Zhang, Huazhu FU, Joey T Zhou
|
https://iclr.cc/virtual/2021/poster/2908
|
Multi-Modal Learning;Multi-View Learning;Uncertainty Machine Learning
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 6.333333 |
4;7;8
| null |
https://iclr.cc/virtual/2021/poster/2908
|
Trusted Multi-View Classification
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null |
Faculty, 54 Welbeck Street, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2856; None
| null | 0 | null | null | null | null | null |
Christopher Frye, Damien De Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, Ilya Feige
|
https://iclr.cc/virtual/2021/poster/2856
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2856
|
Shapley explainability on the data manifold
| null | null | 0 | 3.75 |
Poster
|
3;5;4;3
| null |
null |
Facebook
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3061; None
| null | 0 | null | null | null | null | null |
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, Sonal Gupta
|
https://iclr.cc/virtual/2021/poster/3061
|
finetuning;nlp;representational learning;glue
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3061
|
Better Fine-Tuning by Reducing Representational Collapse
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Finite-time optimization;dynamical systems;deep neural networks optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
First-Order Optimization Algorithms via Discretization of Finite-Time Convergent Flows
| 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 |
Reinforcement Learning;Goal Reaching;Bayesian Classification;Reward Inference
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Moitreya Chatterjee
| null |
Blank
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Blank
| null | null | 0 | 4.25 |
Withdraw
|
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
| 5 |
4;5;5;6
| null | null |
Model Compression via Hyper-Structure Network
| null | null | 0 | 4.5 |
Reject
|
5;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Representation Learning;Graph Neural Networks;Random Walks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Are Graph Convolutional Networks Fully Exploiting the Graph Structure?
| 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 examples;robustness;multiple perturbation types
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Perturbation Type Categorization for Multiple $\ell_p$ Bounded Adversarial Robustness
| 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 |
meta-learning;life-long learning;transfer;bayesian neural networks;prior;few-shot learning;pac-bayes;generalization bound
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory
| null | null | 0 | 3.5 |
Reject
|
4;4;2;4
| null |
null |
Affiliation not provided
|
2021
| 0 | null | null | 0 | null | null | null | null | null |
-
| null |
anomaly detection;trajectory;variational auto-encoder;data transformation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Data Transformer for Anomalous Trajectory Detection
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;gradient descent;sgd;adversarial;robustness;features
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
SGD on Neural Networks learns Robust Features before Non-Robust
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Korea Advanced Institute of Science and Technology (KAIST)
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3145; None
| null | 0 | null | null | null | null | null |
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang
|
https://iclr.cc/virtual/2021/poster/3145
|
federated learning;mixup
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3145
|
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;neural architecture search;tensor decomposition
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Generalizing and Tensorizing Subgraph Search in the Supernet
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
manifold learning;dimensionality reduction;visualization;data generation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness
| null | null | 0 | 3.75 |
Withdraw
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
SelfNorm and CrossNorm for Out-of-Distribution Robustness
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multinomial variational autoencoders;variational autoencoders;ILR transform;compositional PCA;probabilistic PCA;multinomial logistic normal
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Inferring Principal Components in the Simplex with Multinomial Variational Autoencoders
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Out-of-Distribution Generalization;Out-of-Distribution Classification;Out-of-Distribution Clustering;Class Overfitting
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Out-of-Distribution Classification and Clustering
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
Bosch Center for Artificial Intelligence, Pittsburgh, PA; Bosch Center for Artificial Intelligence, Carnegie Mellon University, Pittsburgh, PA; Amazon Alexa AI, Seattle, WA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3198; None
| null | 0 | null | null | null | null | null |
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, Zico Kolter
|
https://iclr.cc/virtual/2021/poster/3198
|
Deep Architectures;Implicit Neural Representations;Fourier Features
| null | 0 | null | null |
iclr
| 0.19245 | 0 | null |
main
| 7.25 |
6;6;8;9
| null |
https://iclr.cc/virtual/2021/poster/3198
|
Multiplicative Filter Networks
| 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 |
value iteration;graph neural networks;reinforcement learning
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
XLVIN: eXecuted Latent Value Iteration Nets
| null | null | 0 | 3 |
Reject
|
2;3;3;4
| null |
null |
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Australia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3340; None
| null | 0 | null | null | null | null | null |
He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine
|
https://iclr.cc/virtual/2021/poster/3340
|
topic modelling;optimal transport;document analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3340
|
Neural Topic Model via Optimal Transport
| null | null | 0 | 3.5 |
Spotlight
|
4;3;3;4
| null |
null |
Huawei Noah’s Ark Lab, China; University of Stuttgart, Germany; University of Stuttgart, Germany and University of Southampton, United Kingdom
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Automated Hyperparameter Optimization;Factorial Analysis;Model-Free;Sample Efficiency;Orthogonal Latin Hypercubes
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
MOFA: Modular Factorial Design for Hyperparameter Optimization
| null | null | 0 | 4.25 |
Withdraw
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph neural network;large-scale machine learning;convergence analysis
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
On the Importance of Sampling in Training GCNs: Convergence Analysis and Variance Reduction
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
National University of Singapore; Institute of Computing Technology, CAS; ByteDance AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2977; None
| null | 0 | null | null | null | null | null |
Bingyi Kang, Yu Li, Sain Xie, Zehuan Yuan, Jiashi Feng
|
https://iclr.cc/virtual/2021/poster/2977
|
Representation Learning;Contrastive Learning;Long-Tailed Recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null |
https://iclr.cc/virtual/2021/poster/2977
|
Exploring Balanced Feature Spaces for Representation Learning
|
https://github.com/bingykang/BalFeat
| null | 0 | 5 |
Poster
|
5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Hierachical Reinforcement Learning;Exploration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Diverse Exploration via InfoMax Options
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null |
Ludwig Maximilian University of Munich, Siemens AG; Horizon Robotics; University of California, Berkeley; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2723; None
| null | 0 | null | null | null | null | null |
Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu
|
https://iclr.cc/virtual/2021/poster/2723
|
Intrinsically Motivated Reinforcement Learning;Intrinsic Reward;Intrinsic Motivation;Deep Reinforcement Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 |
https://youtu.be/AUCwc9RThpk
|
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2723
|
Mutual Information State Intrinsic Control
| null | null | 0 | 4 |
Spotlight
|
5;3;3;5
| null |
null |
RISE - NLU Group
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2937; None
| null | 0 | null | null | null | null | null |
Fredrik Carlsson, Amaru C Gyllensten, Evangelia Gogoulou, Erik Y Hellqvist, Magnus Sahlgren
|
https://iclr.cc/virtual/2021/poster/2937
|
Semantic Textual Similarity;Transformers;Language Modelling;Sentence Embeddings;Sentence Representations;Pre-training;Fine-tuning
| null | 0 | null | null |
iclr
| 0.845154 | 0 | null |
main
| 6.75 |
5;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2937
|
Semantic Re-tuning with Contrastive Tension
| null | null | 0 | 4.5 |
Poster
|
4;4;5;5
| null |
null |
Department of Computer Science, Stanford University; Samsung Research America
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2546; None
| null | 0 | null | null | null | null | null |
Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
|
https://iclr.cc/virtual/2021/poster/2546
|
generative models;self-supervised learning;data augmentation;anomaly detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
5;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2546
|
Negative Data Augmentation
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
decoding;error correcting codes;belief propagation;deep learning
| null | 0 | null | null |
iclr
| 0.218218 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
| null | null | 0 | 3.6 |
Reject
|
3;4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
low resource;BERT;clustering
| null | 0 | null | null |
iclr
| -0.70014 | 0 | null |
main
| 5.75 |
3;6;6;8
| null | null |
Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
University of Illinois, Urbana-Champaign; Microsoft Dynamics 365 AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2549; None
| null | 0 | null | null | null | null | null |
Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Weizhu Chen, Jiawei Han
|
https://iclr.cc/virtual/2021/poster/2549
|
data augmentation;natural language understanding;consistency training;contrastive learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null |
https://iclr.cc/virtual/2021/poster/2549
|
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
NC
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3149; None
| null | 0 | null | null | null | null | null |
Taehwan Kwon
|
https://iclr.cc/virtual/2021/poster/3149
|
Unsupervised reinforcement learning;Information theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3149
|
Variational Intrinsic Control Revisited
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;offline batch RL;off-policy;policy optimization;variance regularization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Offline Policy Optimization with Variance Regularization
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational optimization;variational autoencoders;denoising;evolutionary algorithms
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents
| 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;Information Geometry;Data Manifold;Fisher matrix
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Model-centric data manifold: the data through the eyes of the model
| 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 | null | null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward
| null | null | 0 | 4.25 |
Reject
|
5;3;4;5
| null |
null |
University of Illinois Urbana-Champaign; Massachusetts Institute of Technology; California Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2820; None
| null | 0 | null | null | null | null | null |
Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan
|
https://iclr.cc/virtual/2021/poster/2820
|
Multi-agent;safe;control barrier function;reinforcement learning
| null | 0 | null | null |
iclr
| -0.464286 | 0 |
https://realm.mit.edu/blog/learning-safe-multi-agent-control-decentralized-neural-barrier-certificates
|
main
| 6.6 |
4;6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2820
|
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates
| null | null | 0 | 3.2 |
Poster
|
4;3;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;transfer learning;actor-critic RL;representation transfer;instance transfer;task similarity;MuJoCo;DeepRacer
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement 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 |
Reinforcement Learning;Continuous Control;Action Space Discretization;Policy Gradient
| null | 0 | null | null |
iclr
| -0.132453 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Adaptive Discretization for Continuous Control using Particle Filtering Policy Network
| 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 |
Imbalanced data classification;Evaluation metrics;Log parsing;Sentiment analysis
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 4 |
3;3;4;6
| null | null |
Class-Weighted Evaluation Metrics for Imbalanced Data Classification
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.560612 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Understanding Adversarial Attacks on Autoencoders
| null | null | 0 | 3.25 |
Withdraw
|
4;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
equivariance;object tracking;equivariant neural networks;deep learning;point cloud;lie group;lie algebra;lorentz group;poincaré group
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Irreducible Representations of Noncommutative Lie Groups
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
density ratio estimation;bregman divergence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
| null | null | 0 | 3 |
Reject
|
3;4;3;2
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3346; None
| null | 0 | null | null | null | null | null |
Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, Una-May O'Reilly
|
https://iclr.cc/virtual/2021/poster/3346
|
Machine Learning (ML) for Programming Languages (PL)/Software Engineering (SE);Adversarial computer programs;Program obfuscation;Combinatorial optimization;Differentiable program generator;Models for code
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3346
|
Generating Adversarial Computer Programs using Optimized Obfuscations
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian Mixture Model;Deep Learning;Unsupervised Representation Learning;Sampling
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Image Modeling with Deep Convolutional Gaussian Mixture Models
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.