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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.447214 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Trojans and Adversarial Examples: A Lethal Combination
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null |
Nanyang Technological University; UC Berkeley / ICSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3018; None
| null | 0 | null | null | null | null | null |
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella Yu
|
https://iclr.cc/virtual/2021/poster/3018
|
Long-tailed Recognition;Bias-variance Decomposition
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3018
|
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
|
https://github.com/frank-xwang/RIDE-LongTailRecognition
| null | 0 | 4.25 |
Spotlight
|
4;4;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
| 6 |
5;6;6;7
| null | null |
Implicit Acceleration of Gradient Flow in Overparameterized Linear 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 |
Self-supervised Representation Learning;Graph Representation Learning;Hierarchical Semantic Learning
| null | 0 | null | null |
iclr
| -0.288675 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
Self-supervised Graph-level Representation Learning with Local and Global Structure
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
algorithmic fairness;domain generalization;representation learning;invariance
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
| null | null | 0 | 3 |
Reject
|
3;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;model-based;exploration;on-line planning;imperfect environment model
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4 |
2;4;4;6
| null | null |
Trust, but verify: model-based exploration in sparse reward environments
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
Department of Computer Science, ETH Zürich, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2605; None
| null | 0 | null | null | null | null | null |
Ričards Marcinkevičs, Julia E Vogt
|
https://iclr.cc/virtual/2021/poster/2605
|
time series;Granger causality;interpretability;inference;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2605
|
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
assistance;reward learning;preference learning;active learning
| null | 0 | null | null |
iclr
| 0.615385 | 0 | null |
main
| 5.4 |
4;5;5;6;7
| null | null |
Benefits of Assistance over Reward Learning
| null | null | 0 | 3.4 |
Reject
|
3;2;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Chess;Transformers;Language Modeling;World State
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
Learning Chess Blindfolded
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;unsupervised interpretable directions;controllable text generation
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs
| 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 |
Lipschitz constant;self-attention;theory
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
The Lipschitz Constant of Self-Attention
| null | null | 0 | 3.25 |
Reject
|
3;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
weakly supervised learning;loss function;risk consistency
| null | 0 | null | null |
iclr
| 0.190693 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Leveraged Weighted Loss For Partial Label Learning
| null | null | 0 | 3.25 |
Withdraw
|
4;2;3;4
| null |
null |
Australian National University; University of Technology Sydney
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3287; None
| null | 0 | null | null | null | null | null |
Heming Du, Xin Yu, Liang Zheng
|
https://iclr.cc/virtual/2021/poster/3287
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3287
|
VTNet: Visual Transformer Network for Object Goal Navigation
| 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 |
Quantization;Compression;Efficient Inference;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pre-trained Language Model;Multi-Grained Tokenization
| null | 0 | null | null |
iclr
| -0.476731 | 0 | null |
main
| 4.8 |
3;4;5;5;7
| null | null |
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization
| null | null | 0 | 4 |
Reject
|
4;4;4;5;3
| null |
null |
CSAIL, MIT; IBM Research AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2612; None
| null | 0 | null | null | null | null | null |
Ching-Yao Chuang, Youssef Mroueh
|
https://iclr.cc/virtual/2021/poster/2612
|
fairness;data augmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2612
|
Fair Mixup: Fairness via Interpolation
|
https://github.com/chingyaoc/fair-mixup
| null | 0 | 4 |
Poster
|
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
| 0.333333 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Model information as an analysis tool in deep learning
| null | null | 0 | 2.75 |
Reject
|
3;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot learning;cross-modal;image classification
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
cross-modal knowledge enhancement mechanism for few-shot learning
| null | null | 0 | 4.75 |
Withdraw
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.428571 | 0 | null |
main
| 4.2 |
3;4;4;5;5
| null | null |
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron
| null | null | 0 | 4.2 |
Withdraw
|
5;3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Graph Pooling;Spectral Similarity on Graph
| null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Spectrally Similar Graph Pooling
| null | null | 0 | 3.25 |
Withdraw
|
4;3;3;3
| null |
null |
National Institutes of Health & med εrrata; National Institutes of Health; medεrrata; Walter Reed Army Institute of Research & medεrrata
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3262; None
| null | 0 | null | null | null | null | null |
Joshua Chang, Patrick A Fletcher, Jungmin Han, Ted Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson Chow
|
https://iclr.cc/virtual/2021/poster/3262
|
poisson matrix factorization;generalized additive model;probabilistic matrix factorization;bayesian;sparse coding;interpretability;factor analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3262
|
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of California, Santa Cruz; Shanghai Jiaotong University; Johns Hopkins University; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2870; None
| null | 0 | null | null | null | null | null |
Yinigwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie
|
https://iclr.cc/virtual/2021/poster/2870
|
data augmentation;representation learning;debiased training
| null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2870
|
Shape-Texture Debiased Neural Network Training
|
https://github.com/LiYingwei/ShapeTextureDebiasedTraining
| null | 0 | 3.5 |
Poster
|
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
| -0.19245 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
Dropout's Dream Land: Generalization from Learned Simulators to Reality
| 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 |
Learning from label proportions;Optimal transport;Weakly supervised learning;Classification
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
OT-LLP: Optimal Transport for Learning from Label Proportions
| null | null | 0 | 3.25 |
Withdraw
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;Adversarial Examples;Natural Perturbations;General Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Adversarial and Natural Perturbations for General Robustness
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Nicholas Teague
| null |
tabular;feature engineering
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Parsed Categoric Encodings with Automunge
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null |
VinAI Research, Vietnam; University of Texas, Austin; VinAI Research, Vietnam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2980; None
| null | 0 | null | null | null | null | null |
Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui
|
https://iclr.cc/virtual/2021/poster/2980
|
Relational regularized autoencoder;deep generative model;sliced fused Gromov Wasserstein;spherical distributions
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2980
|
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
maximum mean discrepancy;RKHS;two-sample test;empirical estimator;discrete distributions
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 2 |
1;2;2;3
| null | null |
A generalized probability kernel on discrete distributions and its application in two-sample test
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null |
Department of Statistics, University of Michigan; IBM Research, MIT-IBM Watson AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2777; None
| null | 0 | null | null | null | null | null |
Mikhail Yurochkin, Yuekai Sun
|
https://iclr.cc/virtual/2021/poster/2777
|
Algorithmic fairness;invariance
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2777
|
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
| null | null | 0 | 3 |
Oral
|
3;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural ODE;Partial Differential Equations;Image Classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
PDE-regularized Neural Networks for Image Classification
| 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 |
disentanglement;model pruning;representation learning;transformers
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Disentangling Representations of Text by Masking Transformers
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
Microsoft Research Asia, Microsoft Azure Speech
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3164; None
| null | 0 | null | null | null | null | null |
Mingjian Chen, Xu Tan, Bohan Li, Eric Liu, Tao Qin, sheng zhao, Tie-Yan Liu
|
https://iclr.cc/virtual/2021/poster/3164
|
Text to speech;adaptation;fine-tuning;custom voice;acoustic condition modeling;conditional layer normalization
| null | 0 | null | null |
iclr
| 0.09759 | 0 |
https://speechresearch.github.io/adaspeech/
|
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3164
|
AdaSpeech: Adaptive Text to Speech for Custom Voice
| null | null | 0 | 4.25 |
Poster
|
5;2;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;GOLD-NAS
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
GOLD-NAS: Gradual, One-Level, Differentiable
| null | null | 0 | 4.5 |
Withdraw
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;A/B testing;causal inference;sequential testing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING
| null | null | 0 | 2.666667 |
Reject
|
2;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Weak Supervision;Policy Learning;Correlated Agreement
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Policy Learning Using Weak Supervision
| 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 |
attention;hard attention;variational inference;bayesian optimal experimental design
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks
| null | null | 0 | 3 |
Reject
|
4;4;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.94388 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Provable Acceleration of Wide Neural Net Training via Polyak's Momentum
| null | null | 0 | 2.75 |
Withdraw
|
4;3;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
invariance;causality;spurious correlation;out-of-distribution generalization;interpretability;variational auto-encoder
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Latent Causal Invariant Model
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2655; None
| null | 0 | null | null | null | null | null |
Jacob Buckman, Carles Gelada, Marc G Bellemare
|
https://iclr.cc/virtual/2021/poster/2655
|
deep learning;reinforcement learning;offline reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2655
|
The Importance of Pessimism in Fixed-Dataset Policy Optimization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;parameter efficiency
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;5;6;8
| null | null |
Parameter-Efficient Transfer Learning with Diff Pruning
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.288675 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Defective Convolutional Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep generative modeling;GAN;super-resolution;wavelet transformation;energy efficient
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 4.333333 |
2;5;6
| null | null |
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Network;Hyperspherical Energy;Inductive Bias;Orthogonality
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Orthogonal Over-Parameterized Training
| null | null | 0 | 3 |
Withdraw
|
4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.229416 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active learning;ensembles
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
3;4;4;7
| null | null |
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
MPI for Intelligent Systems, Tübingen; MPI for Intelligent Systems, Tübingen, ETH, Zürich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3008; None
| null | 0 | null | null | null | null | null |
Alexander Neitz, Giambattista Parascandolo, Bernhard Schoelkopf
|
https://iclr.cc/virtual/2021/poster/3008
|
meta-learning;privileged information
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3008
|
A teacher-student framework to distill future trajectories
| null | null | 0 | 3 |
Poster
|
4;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multitask-learning;multitasking;parallel processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Navigating the Trade-Off between Learning Efficacy and Processing Efficiency in Deep Neural Networks
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Learning not to learn: Nature versus nurture in silico
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
University of Illinois, Urbana-Champaign; Columbia University, New York City
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3261; None
| null | 0 | null | null | null | null | null |
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
|
https://iclr.cc/virtual/2021/poster/3261
|
Generalization;statistical learning theory;theory;distillation
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3261
|
Generalization bounds via distillation
| null | null | 0 | 2.75 |
Spotlight
|
2;2;2;5
| null |
null |
LIP6, Sorbonne Université, France; Facebook Artificial Intelligence Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3040; None
| null | 0 | null | null | null | null | null |
Tom Veniat, Ludovic Denoyer, Marc'Aurelio Ranzato
|
https://iclr.cc/virtual/2021/poster/3040
|
Continual learning;Lifelong learning;Benchmark;Modular network;Neural Network
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3040
|
Efficient Continual Learning with Modular Networks and Task-Driven Priors
|
https://github.com/facebookresearch/CTrLBenchmark
| null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual learning;physiological signals;healthcare
| null | 0 | null | null |
iclr
| -0.37998 | 0 | null |
main
| 5.25 |
3;4;7;7
| null | null |
CLOPS: Continual Learning of Physiological Signals
| null | null | 0 | 3.25 |
Reject
|
4;3;2;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
knowledge graph completion;bread first search
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
|
https://github.com/1173710224/link-prediction-with-rule-based-reasoning
| null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
label noise;hard confident examples
| null | 0 | null | null |
iclr
| 0.565916 | 0 | null |
main
| 5.75 |
4;4;7;8
| null | null |
ME-MOMENTUM: EXTRACTING HARD CONFIDENT EXAMPLES FROM NOISILY LABELED DATA
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
University of Massachusetts Amherst; University College London; University of California, Santa Barbara; Facebook AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2806; None
| null | 0 | null | null | null | null | null |
Wenhan Xiong, Xiang Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Scott Yih, Sebastian Riedel, Douwe Kiela, Barlas Oguz
|
https://iclr.cc/virtual/2021/poster/2806
|
multi-hop question answering;recursive dense retrieval;open domain complex question answering
| null | 0 | null | null |
iclr
| 0.239046 | 0 | null |
main
| 6.75 |
5;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2806
|
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
|
https://github.com/facebookresearch/multihop_dense_retrieval
| null | 0 | 4 |
Poster
|
3;5;4;4
| null |
null |
CSAIL, MIT; Robotics & Big Data Labs, Department of Computer Science, University of Haifa
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3076; None
| null | 0 | null | null | null | null | null |
Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman
|
https://iclr.cc/virtual/2021/poster/3076
|
Compressing Deep Networks;NLP;Matrix Factorization;SVD
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null |
https://iclr.cc/virtual/2021/poster/3076
|
Deep Learning meets Projective Clustering
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Stochastic Proximal Point Algorithm for Large-scale Nonconvex Optimization: Convergence, Implementation, and Application to Neural Networks
| 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 |
Multimodal Machine Learning;Representation Learning;AutoEncoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3;3
| null | null |
Robust Multi-view Representation Learning
| 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 |
generalization;deep learning;hardness of examples
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale
| null | null | 0 | 4.25 |
Reject
|
3;4;5;5
| null |
null |
University of Illinois at Urbana-Champaign; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3357; None
| null | 0 | null | null | null | null | null |
Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
|
https://iclr.cc/virtual/2021/poster/3357
|
computer vision;object recognition;few-shot learning;generative models;adversarial training
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3357
|
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
|
https://github.com/zpbao/bowtie_networks
| null | 0 | 3.75 |
Poster
|
3;3;5;4
| null |
null |
UC San Diego; National University of Singapore; Adobe Research
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spatiotemporal point process;deep sequence models;time series
| null | 0 | null | null |
iclr
| -0.440225 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Neural Point Process for Forecasting Spatiotemporal Events
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null |
New York University, New York, USA; Facebook AI Research, London, UK; University College London & Facebook AI Research, London, UK; Brain and Cognitive Sciences, MIT, Cambridge, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2781; None
| null | 0 | null | null | null | null | null |
Andres Campero, Roberta Raileanu, Heinrich Kuttler, Joshua B Tenenbaum, Tim Rocktaeschel, Edward Grefenstette
|
https://iclr.cc/virtual/2021/poster/2781
|
reinforcement learning;exploration;meta-learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2781
|
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
| 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 |
active learning;imitating learning;ensembles
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
IALE: Imitating Active Learner Ensembles
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine reading comprehension;BERT;linguistic verifiers;hierarchical attention networks
| null | 0 | null | null |
iclr
| 0.818182 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Machine Reading Comprehension with Enhanced Linguistic Verifiers
| null | null | 0 | 4.25 |
Reject
|
3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Provable More Data Hurt in High Dimensional Least Squares Estimator
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.408248 | 0 | null |
main
| 4.6 |
4;4;4;5;6
| null | null |
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks
| 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 |
Delay-tolerant;communication-efficient;distributed learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Delay-Tolerant Local SGD for Efficient Distributed Training
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
University of Tübingen
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3144; None
| null | 0 | null | null | null | null | null |
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan Paiton
|
https://iclr.cc/virtual/2021/poster/3144
|
disentanglement;independent component analysis;natural scene statistics
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 8.25 |
7;8;9;9
| null |
https://iclr.cc/virtual/2021/poster/3144
|
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
|
https://github.com/bethgelab/slow_disentanglement
| null | 0 | 3.5 |
Oral
|
3;4;3;4
| null |
null |
The University of Tokyo, RIKEN AIP; The University of Tokyo
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3337; None
| null | 0 | null | null | null | null | null |
Ryohei Shimizu, YUSUKE Mukuta, Tatsuya Harada
|
https://iclr.cc/virtual/2021/poster/3337
|
Hyperbolic Geometry;Poincaré Ball Model;Parameter-Reduced MLR;Geodesic-Aware FC Layer;Convolutional Layer;Attention Mechanism
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3337
|
Hyperbolic Neural Networks++
| 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 |
Self-supervised Learning;Data Mixing;Contrastive Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Center-wise Local Image Mixture For Contrastive Representation 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 |
epidemiology;covid19;reinforcement learning;evolutionary algorithms;multi-objective optimization;decision-making;toolbox
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;robustness;ODE
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Layer-wise Adversarial Defense: An ODE Perspective
| 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 |
graph reasoning;context-aware representation;long-range dependencies;semantic segmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Contextual Graph Reasoning Networks
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.781661 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Central Server Free Federated Learning over Single-sided Trust Social Networks
| null | null | 0 | 3.75 |
Reject
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural network architecture optimization;unsupervised inference;deep feedforward neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Unsupervised inference for optimizing deep feedforward neural network architecture
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Habana Labs – An Intel company, Caesarea, Israel; Department of Electrical Engineering - Technion, Haifa, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2716; None
| null | 0 | null | null | null | null | null |
Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry
|
https://iclr.cc/virtual/2021/poster/2716
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2716
|
Neural gradients are near-lognormal: improved quantized and sparse training
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
The Emergence of Individuality in Multi-Agent Reinforcement Learning
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null |
Department of Computer Science, University of Freiburg; Department of Computer Science, University of Freiburg; Bosch Center for Artificial Intelligence
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;Automated Machine Learning;Deep Learning;Open-Source;Software;Python;PyTorch
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
NASLib: A Modular and Flexible Neural Architecture Search Library
|
https://github.com/automl/NASLib
| null | 0 | 4 |
Withdraw
|
4;4;5;3
| null |
null |
School of Artificial Intelligence, Jilin University; Trustworthy Machine Learning Lab, School of Computer Science, The University of Sydney; Department of Computer Science, Hong Kong Baptist University; Medical AI Group, Faculty of Engineering, Monash University; School of Computer Science and Engineering, Nanjing University of Science and Technology; ISN State Key Laboratory, School of Telecommunications Engineering, Xidian University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3129; None
| null | 0 | null | null | null | null | null |
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang
|
https://iclr.cc/virtual/2021/poster/3129
| null | null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3129
|
Robust early-learning: Hindering the memorization of noisy labels
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null |
Facebook AI; Visual Geometry Group, University of Oxford; Language Technologies Institute, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2999; None
| null | 0 | null | null | null | null | null |
Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander G Hauptmann, Joao F. Henriques, Andrea Vedaldi
|
https://iclr.cc/virtual/2021/poster/2999
|
video representation learning;multi-modal learning;video-text learning;contrastive learning
| null | 0 | null | null |
iclr
| -0.648886 | 0 | null |
main
| 7.25 |
6;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2999
|
Support-set bottlenecks for video-text representation learning
| null | null | 0 | 4 |
Spotlight
|
4;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Byzantine-Robust Federated Learning;Secure Aggregation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
F^2ed-Learning: Good Fences Make Good Neighbors
| null | null | 0 | 3 |
Reject
|
2;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
heavy tails;stochastic gradient descent;deep learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
The Heavy-Tail Phenomenon in SGD
| 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 reinforcement learning;adversarial
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents
| 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 |
Active Learning;Object Detection
| null | 0 | null | null |
iclr
| -0.512989 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Deep Active Learning for Object Detection with Mixture Density Networks
| null | null | 0 | 3.5 |
Withdraw
|
4;3;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Clustering;Differentiable EM
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Streamlining EM into Auto-Encoder Networks
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null |
DeepMind, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3211; None
| null | 0 | null | null | null | null | null |
Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew Botvinick, David Raposo
|
https://iclr.cc/virtual/2021/poster/3211
|
deep reinforcement learning;meta learning;deep learning;exploration;planning
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3211
|
Rapid Task-Solving in Novel Environments
| null | null | 0 | 4 |
Poster
|
5;3;4;4
| null |
null |
Google AI; Carnegie Mellon University; Carnegie Mellon University, Google AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2550; None
| null | 0 | null | null | null | null | null |
Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao
|
https://iclr.cc/virtual/2021/poster/2550
|
Multi-task Learning;Multilingual Modeling
| null | 0 | null | null |
iclr
| 0.904534 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2550
|
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
| null | null | 0 | 3.5 |
Spotlight
|
3;3;4;4
| null |
null |
Harvard University; Seoul National University; Sungkyunkwan University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3353; None
| null | 0 | null | null | null | null | null |
Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio Calmon, Taesup Moon
|
https://iclr.cc/virtual/2021/poster/3353
|
continual learning;regularization;wide local minima
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3353
|
CPR: Classifier-Projection Regularization for Continual Learning
|
https://github.com/csm9493/CPR_CL
| null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null |
Seminar for Applied Mathematics (SAM), Department of Mathematics, ETH Zürich, Zürich, 8092, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3335; None
| null | 0 | null | null | null | null | null |
T. Konstantin Rusch, Siddhartha Mishra
|
https://iclr.cc/virtual/2021/poster/3335
|
RNNs;Oscillators;Gradient stability;Long-term dependencies
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3335
|
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
| null | null | 0 | 3.5 |
Oral
|
3;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
nmt;nlp;neural machine translation;natural language processing;deep learning;machine learning;machine translation;mt
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Crowd-sourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The case of Fon Language
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null |
Czech Technical University in Prague; Samsung-HSE Laboratory National Research University Higher School of Economics, Moscow
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
straight-through;binary;stochastic binary;mirror descent
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
| null | null | 0 | 2.5 |
Reject
|
3;3;2;2
| null |
null |
NVIDIA; NVIDIA, California Institute of Technology; The University of Texas at Austin
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2949; None
| null | 0 | null | null | null | null | null |
Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, Jose M. Alvarez, Zhangyang Wang, Anima Anandkumar
|
https://iclr.cc/virtual/2021/poster/2949
|
synthetic-to-real generalization;domain generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2949
|
Contrastive Syn-to-Real Generalization
|
https://github.com/NVlabs/CSG
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
credit assignment;model-free RL;causality;hindsight
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Model-Free Counterfactual Credit Assignment
| 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 |
machine translation;discourse;evaluation;benchmark;testsets;leaderboard
| null | 0 | null | null |
iclr
| -0.777778 | 0 |
http://dipbenchmark1.github.io
|
main
| 5.25 |
4;4;6;7
| null | null |
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT
|
https://github.com/dipbenchmark1
| null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
The University of Hong Kong; S-Lab, Nanyang Technological University; The Chinese University of Hong Kong
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2881; None
| null | 0 | null | null | null | null | null |
Xingang Pan, Bo DAI, Ziwei Liu, Chen Change Loy, Ping Luo
|
https://iclr.cc/virtual/2021/poster/2881
|
Generative Adversarial Network;3D Reconstruction
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.666667 |
7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2881
|
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
|
https://github.com/XingangPan/GAN2Shape
| null | 0 | 4 |
Oral
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Adaptation;Morphologic Segmentation;Image-to-image Translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.8 |
3;3;4;4;5
| null | null |
Domain Adaptation with Morphologic Segmentation
| null | null | 0 | 5 |
Withdraw
|
5;5;5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;Q-learning;Estimation bias
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
On the Estimation Bias in Double Q-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 |
Matrix decomposition;Local Low Rank matrix detection;Representation learning;Subspace learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep neural networks;verification;interpretation;AI safety;ACAS
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Computing Preimages of Deep Neural Networks with Applications to Safety
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;representation evaluation;unsupervised learning;self-supervised learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Evaluating representations by the complexity of learning low-loss predictors
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
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