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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Uncertainty estimation;gaussian processes;deep learning;variational inference
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.25 |
2;5;5;5
| null | null |
Variational Deterministic Uncertainty Quantification
| 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 | null | null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 2.25 |
1;2;2;4
| null | null |
Illuminating Dark Knowledge via Random Matrix Ensembles
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null |
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3169; None
| null | 0 | null | null | null | null | null |
Yujia Liu, Stefano D'Aronco, Konrad Schindler, Jan D Wegner
|
https://iclr.cc/virtual/2021/poster/3169
|
deep neural network;3d point cloud;wireframe model
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3169
|
PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds
| null | null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Episodic Memory;Time Perception;Active Inference;Model-based Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Episodic Memory for Learning Subjective-Timescale Models
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;generalization;stochastic gradient descent;large-batch training
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Improved generalization by noise enhancement
| 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 |
Interpretable Machine Learning;Counterfactuals;Computer Vision;Human Evaluation;User Study
| null | 0 | null | null |
iclr
| 0 | 0 |
https://anonymous.4open.science/r/ae263acc-aad1-42f8-a639-aec20ff31fc3/
|
main
| 5.6 |
5;5;6;6;6
| null | null |
Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces
| null | null | 0 | 4 |
Reject
|
5;3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deel learning;equilibrium model;neural tangent kernel
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
On the Neural Tangent Kernel of Equilibrium Models
| 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 |
time frequency representation;time series;wigner ville;cohen class;wavelet transform;scalogram;bird;speech
| null | 0 | null | null |
iclr
| 0.229416 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Interpretable Super-Resolution via a Learned Time-Series Representation
| null | null | 0 | 3.75 |
Withdraw
|
2;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
Tencent AI Lab; Department of Automation, Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3343; None
| null | 0 | null | null | null | null | null |
Lanqing Li, Rui Yang, Dijun Luo
|
https://iclr.cc/virtual/2021/poster/3343
|
offline/batch reinforcement learning;meta-reinforcement learning;multi-task reinforcement learning;distance metric learning;contrastive learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null |
https://iclr.cc/virtual/2021/poster/3343
|
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
|
https://github.com/FOCAL-ICLR/FOCAL-ICLR/
| null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Facebook AI Research (FAIR)
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3071; None
| null | 0 | null | null | null | null | null |
Duy-Kien Nguyen, Vedanuj Goswami, Xinlei Chen
|
https://iclr.cc/virtual/2021/poster/3071
|
visual counting;visual question answering;common object counting;visual reasoning;modulated convolution
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3071
|
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
| null | null | 0 | 3.75 |
Poster
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Model Based RL;Continuous Control;Search;Planning;MCTS
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Dream and Search to Control: Latent Space Planning for Continuous Control
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph convolutional neural networks;protease specificity;proteins;Rosetta energy function
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Prediction of Enzyme Specificity using Protein Graph Convolutional Neural Networks
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Augmentation;Binary Classification;Autoencoder;Tabular Data;Imbalanced Data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
AE-SMOTE: A Multi-Modal Minority Oversampling Framework
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explainability of deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Neural networks behave as hash encoders: An empirical study
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null |
Facebook AI Research, Menlo Park, CA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2799; None
| null | 0 | null | null | null | null | null |
Matthew Leavitt, Ari Morcos
|
https://iclr.cc/virtual/2021/poster/2799
|
interpretability;explainability;empirical analysis;deep learning;selectivity
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2799
|
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse neural network;Lottery Ticket Hypothesis;network pruning;generalization analysis;optimization landscape;sample complexity
| null | 0 | null | null |
iclr
| 0.454545 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
| null | null | 0 | 3.25 |
Reject
|
2;4;3;4
| null |
null |
The Hospital for Sick Children; University of Toronto & Vector Institute, The Hospital for Sick Children
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2897; None
| null | 0 | null | null | null | null | null |
Sana Tonekaboni, Danny Eytan, Anna Goldenberg
|
https://iclr.cc/virtual/2021/poster/2897
| null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2897
|
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
| 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 |
Graph Neural Networks;Link Prediction
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Revisiting Graph Neural Networks for Link Prediction
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
Double Blind Submission
| null |
embedding;visualization;prior;tsne;umap
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Factoring out Prior Knowledge from Low-Dimensional Embeddings
| 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 |
deep probabilistic subsampling;sparse deep learning;structured pruning;hardware-oriented pruning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
2;4;5;5
| null | null |
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking
| null | null | 0 | 4 |
Withdraw
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multiagent;social dilemma;reinforcement learning
| null | 0 | null | null |
iclr
| -0.19245 | 0 | null |
main
| 5.5 |
3;6;6;7
| null | null |
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
| null | null | 0 | 2.75 |
Reject
|
3;2;3;3
| null |
null |
University of Southern California
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3059; None
| null | 0 | null | null | null | null | null |
Yunhao Ge, Sami Abu-El-Haija, Gan Xin, Laurent Itti
|
https://iclr.cc/virtual/2021/poster/3059
|
Disentangled representation learning;Group-supervised learning;Zero-shot synthesis;Knowledge factorization
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3059
|
Zero-shot Synthesis with Group-Supervised Learning
| null | null | 0 | 3.5 |
Poster
|
4;3;4;3
| null |
null |
Stanford University; UT Austin & Nvidia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3094; None
| null | 0 | null | null | null | null | null |
Kuan Fang, Yuke Zhu, Silvio Savarese, Li Fei-Fei
|
https://iclr.cc/virtual/2021/poster/3094
|
reinforcement learning;curriculum learning;procedural generation;task generation
| null | 0 | null | null |
iclr
| -0.662266 | 0 |
https://kuanfang.github.io/apt-gen/
|
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3094
|
Adaptive Procedural Task Generation for Hard-Exploration Problems
| null | null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2851; None
| null | 0 | null | null | null | null | null |
Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F Grewe
|
https://iclr.cc/virtual/2021/poster/2851
|
Recurrent Neural Networks;Continual Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2851
|
Continual learning in recurrent neural networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Stanford University; Harvard University; Rutgers University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2734; None
| null | 0 | null | null | null | null | null |
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou
|
https://iclr.cc/virtual/2021/poster/2734
|
Mixup;adversarial robustness;generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2734
|
How Does Mixup Help With Robustness and Generalization?
| null | null | 0 | 3.5 |
Spotlight
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dictionary learning;unrolled algorithm;convolutional sparse coding;interpretable deep learning;inverse problems;blind denoising;LISTA
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
University of Massachusetts Amherst, Amherst, MA, USA; Bielefeld University, Bielefeld, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3009; None
| null | 0 | null | null | null | null | null |
André Hottung, Bhanu Bhandari, Kevin Tierney
|
https://iclr.cc/virtual/2021/poster/3009
|
heuristic search;variational autoencoders;learning to optimize;routing problems;traveling salesperson problem;vehicle routing problem;combinatorial optimization
| null | 0 | null | null |
iclr
| -0.454545 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3009
|
Learning a Latent Search Space for Routing Problems using Variational Autoencoders
| null | null | 0 | 3.25 |
Poster
|
4;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;bayesian learning
| null | 0 | null | null |
iclr
| 0.478091 | 0 | null |
main
| 6.25 |
4;6;7;8
| null | null |
A Unified Bayesian Framework for Discriminative and Generative Continual Learning
| null | null | 0 | 4 |
Reject
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.320256 | 0 | null |
main
| 4.4 |
3;4;4;5;6
| null | null |
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning
| null | null | 0 | 3.4 |
Reject
|
4;3;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-play;policy optimization;two-player zero-sum game;multiagent
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Efficient Competitive Self-Play Policy Optimization
| 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 |
Representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Fundamental Limits and Tradeoffs in Invariant Representation Learning
| null | null | 0 | 2 |
Reject
|
1;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
subspace indexing;locality preserving projection;Stiefel and Grassmann manifolds
| null | 0 | null | null |
iclr
| 0.632456 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds
| null | null | 0 | 3.5 |
Reject
|
2;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge Transfer;Deep Learning;Medical Image Segmentation;Pseudo Annotation
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Network-Agnostic Knowledge Transfer for Medical Image Segmentation
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
NEC Laboratories Europe
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2658; None
| null | 0 | null | null | null | null | null |
Cheng Wang, Carolin Lawrence, Mathias Niepert
|
https://iclr.cc/virtual/2021/poster/2658
|
uncertainty estimation;calibration;RNN
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2658
|
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
|
https://github.com/nec-research/st_tau
| null | 0 | 2.75 |
Poster
|
4;2;3;2
| null |
null |
Washington University in St. Louis; Los Alamos National Laboratory
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3077; None
| null | 0 | null | null | null | null | null |
Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek Kamilov
|
https://iclr.cc/virtual/2021/poster/3077
|
Regularization by denoising;Computational imaging;asynchronous parallel algorithm;Deep denoising priors
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3077
|
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
| null | null | 0 | 3 |
Spotlight
|
2;3;5;2
| null |
null |
Department of Computer Science, UCLA; DiDi AI Labs
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3124; None
| null | 0 | null | null | null | null | null |
Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh
|
https://iclr.cc/virtual/2021/poster/3124
| null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3124
|
DrNAS: Dirichlet Neural Architecture Search
| null | null | 0 | 3 |
Poster
|
3;3;2;4
| null |
null |
University of Chinese Academy of Sciences; Academy of Mathematics and Systems Science, Chinese Academy of Sciences; Huawei Noah's Ark Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3131; None
| null | 0 | null | null | null | null | null |
Mingyang Yi, LU HOU, Lifeng Shang, Xin Jiang, Qun Liu, Zhi-Ming Ma
|
https://iclr.cc/virtual/2021/poster/3131
|
data augmentation;sample reweighting
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3131
|
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
King Abdullah University of Science and Technology; Udacity; Alexa AI, Amazon and Columbia University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2883; None
| null | 0 | null | null | null | null | null |
Deyao Zhu, Mohamed Zahran, Erran Li, Mohamed Elhoseiny
|
https://iclr.cc/virtual/2021/poster/2883
| null | null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2883
|
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
| null | null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reduction;Compression;Regularization;Theory;Pruning;Deep;Interpretability;Generalization
| null | 0 | null | null |
iclr
| -0.612372 | 0 | null |
main
| 2.6 |
2;2;2;3;4
| null | null |
Reducing the number of neurons of Deep ReLU Networks based on the current theory of Regularization
| null | null | 0 | 4.4 |
Reject
|
5;4;5;4;4
| null |
null |
University of Oxford & Five AI Limited; University of Oxford; University of Oxford & NAVER LABS Europe; NAVER LABS Europe; University of Oxford & The Alan Turing Institute, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2957; None
| null | 0 | null | null | null | null | null |
Pau de Jorge Aranda, Amartya Sanyal, Harkirat Singh Behl, Philip Torr, Grégory Rogez, Puneet Dokania
|
https://iclr.cc/virtual/2021/poster/2957
|
Pruning;Pruning at initialization;Sparsity
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2957
|
Progressive Skeletonization: Trimming more fat from a network at initialization
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Depts. of Computer Science & Neuroscience, The University of Texas at Austin, Austin, TX, USA; Department of Computer Science, Brain-Inspired Computing Lab, The University of Texas at Austin; Intel Labs, Hillsboro, OR, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3095; None
| null | 0 | null | null | null | null | null |
Shivangi Mahto, Vy Vo, Javier Turek, Alexander Huth
|
https://iclr.cc/virtual/2021/poster/3095
|
Language Model;LSTM;timescales
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3095
|
Multi-timescale Representation Learning in LSTM Language Models
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameter search;population based training;differential evolution;hyperparameter optimization;online optimization;deep learning
| null | 0 | null | null |
iclr
| 0.927173 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
ROMUL: Scale Adaptative Population Based Training
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA; Department of Mathematics, Duke University, Durham, NC, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2849; None
| null | 0 | null | null | null | null | null |
Xiuyuan Cheng, Zichen Miao, Qiang Qiu
|
https://iclr.cc/virtual/2021/poster/2849
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2849
|
Graph Convolution with Low-rank Learnable Local Filters
| null | null | 0 | 3.5 |
Spotlight
|
3;3;3;5
| null |
null |
SenseTime Research; University of California, San Diego; State Key Lab of Software Development Environment, Beihang University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3065; None
| null | 0 | null | null | null | null | null |
Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Liu, Hao Su
|
https://iclr.cc/virtual/2021/poster/3065
|
point clouds;efficient deep learning;binary neural networks
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 6.5 |
4;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3065
|
BiPointNet: Binary Neural Network for Point Clouds
| null | null | 0 | 3.5 |
Poster
|
5;3;3;3
| null |
null |
UC Berkeley; Institute for Interdisciplinary Information Sciences, Tsinghua University; UCSD; Shanghai Qi Zhi Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3368; None
| null | 0 | null | null | null | null | null |
Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu
|
https://iclr.cc/virtual/2021/poster/3368
|
compositional task;sparse reward;reinforcement learning;task reduction;imitation learning
| null | 0 | null | null |
iclr
| 0.169031 | 0 |
https://sites.google.com/view/sir-compositional
|
main
| 5.25 |
3;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3368
|
Solving Compositional Reinforcement Learning Problems via Task Reduction
| null | null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Classification;Interpretability;Disentangled Representations;Uncertainty Estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Identifying the Sources of Uncertainty in Object Classification
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimization;binary trees
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
Learning Binary Trees via Sparse Relaxation
| 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 |
Reinforcement learning theory;Markov games;model-based RL;task-agnostic RL;multi-agent RL
| null | 0 | null | null |
iclr
| -0.580381 | 0 | null |
main
| 6 |
4;5;7;8
| null | null |
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
| null | null | 0 | 3.75 |
Reject
|
5;4;2;4
| null |
null |
Department of Computer Science, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2873; None
| null | 0 | null | null | null | null | null |
Karan Goel, Albert Gu, Yixuan Li, Christopher Re
|
https://iclr.cc/virtual/2021/poster/2873
|
Robust Machine Learning;Data Augmentation;Consistency Training;Invariant Representations
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2873
|
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
| null | null | 0 | 3 |
Poster
|
3;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;exploration;latent variable models
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5.5 |
4;4;7;7
| null | null |
Deep Coherent Exploration For Continuous Control
| null | null | 0 | 3 |
Reject
|
4;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised learning;node representations;mutual information
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
| null | null | 0 | 3.25 |
Reject
|
4;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;offline reinforcement learning;control;distribution shift
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null |
University of Illinois at Urbana–Champaign; The University of Melbourne; Xidian University; Deakin University, Geelong; Ant Group
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2760; None
| null | 0 | null | null | null | null | null |
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma
|
https://iclr.cc/virtual/2021/poster/2760
|
Backdoor Defense;Deep Neural Networks;Neural Attention Distillation
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2760
|
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
|
https://github.com/bboylyg/NAD
| null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;generalization;data augmentation
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Automatic Data Augmentation for Generalization in Reinforcement Learning
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Neural Random Projection: From the Initial Task To the Input Similarity Problem
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;gnn;generalization;Weisfeiler-Lehman
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
On Size Generalization in Graph Neural Networks
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null |
DeepMind, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3350; None
| null | 0 | null | null | null | null | null |
Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Buesing, Charles Blundell
|
https://iclr.cc/virtual/2021/poster/3350
|
Representation Learning;Self-supervised Learning;Contrastive Methods;Causality
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3350
|
Representation Learning via Invariant Causal Mechanisms
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2665; None
| null | 0 | null | null | null | null | null |
Yanchao Sun, Da Huo, Furong Huang
|
https://iclr.cc/virtual/2021/poster/2665
|
poisoning attack;policy gradient;vulnerability of RL;deep RL
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2665
|
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics
| null | null | 0 | 3.75 |
Poster
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
8-bit floating-point format;accuracy loss minimization;numerics;memory-efficient inference;deep learning
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep Learning;Stationarity;Adaptive
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.5 |
3;5;7;7
| null | null |
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
RIKEN Center for Advanced Intelligence Project1, Tohoku University2; Osaka University3; Tohoku University2, RIKEN Center for Advanced Intelligence Project1
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2619; None
| null | 0 | null | null | null | null | null |
Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui
|
https://iclr.cc/virtual/2021/poster/2619
|
Interpretability;Explainability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2619
|
Evaluation of Similarity-based Explanations
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.454545 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Iterated graph neural network system
| null | null | 0 | 3.25 |
Reject
|
4;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multiagent learning;transfer learning;reinforcement learning
| null | 0 | null | null |
iclr
| -0.25 | 0 | null |
main
| 5.6 |
4;6;6;6;6
| null | null |
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework
| null | null | 0 | 3.6 |
Reject
|
4;3;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary neural networks;network quantization;network compression
| null | 0 | null | null |
iclr
| -0.912871 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond
| null | null | 0 | 4 |
Reject
|
5;5;4;2
| null |
null |
†Alexandru Ioan Cuza University, Ias ,i, Romania; University of Maryland, College Park, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3283; None
| null | 0 | null | null | null | null | null |
Sanghyun Hong, Yigitcan Kaya, Ionut-Vlad Modoranu, Tudor Dumitras
|
https://iclr.cc/virtual/2021/poster/3283
|
Slowdown attacks;efficient inference;input-adaptive multi-exit neural networks;adversarial examples
| null | 0 | null | null |
iclr
| 0.805823 | 0 | null |
main
| 6 |
3;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3283
|
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
|
https://github.com/sanghyun-hong/deepsloth
| null | 0 | 3.75 |
Spotlight
|
3;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neurolinguistics;natural language processing;computational neuroscience
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Does injecting linguistic structure into language models lead to better alignment with brain recordings?
| null | null | 0 | 3.25 |
Reject
|
3;3;4;3
| null |
null |
Inception Institute of Artificial Intelligence; AIM Lab, University of Amsterdam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3313; None
| null | 0 | null | null | null | null | null |
Yingjun Du, Xiantong Zhen, Ling Shao, Cees G Snoek
|
https://iclr.cc/virtual/2021/poster/3313
|
Meta-learning;batch normalization;few-shot domain generalization
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3313
|
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
| null | null | 0 | 3.75 |
Poster
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
normalization
| null | 0 | null | null |
iclr
| 0.688247 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Sandwich Batch Normalization
| null | null | 0 | 4.5 |
Reject
|
4;4;5;5
| null |
null |
Stanford University; Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2958; None
| null | 0 | null | null | null | null | null |
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
|
https://iclr.cc/virtual/2021/poster/2958
|
multilingual representations;word embeddings;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2958
|
Filtered Inner Product Projection for Crosslingual Embedding Alignment
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
ML group, Technical University of Berlin, Germany; ML group, Technical University of Kaiserslautern, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2521; None
| null | 0 | null | null | null | null | null |
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy J Franks, Marius Kloft, Klaus R Muller
|
https://iclr.cc/virtual/2021/poster/2521
|
anomaly-detection;deep-learning;explanations;interpretability;xai;one-class-classification;deep-anomaly-detection;novelty-detection;outlier-detection
| null | 0 | null | null |
iclr
| 0.970725 | 0 | null |
main
| 6.333333 |
4;7;8
| null |
https://iclr.cc/virtual/2021/poster/2521
|
Explainable Deep One-Class Classification
|
https://github.com/liznerski/fcdd
| null | 0 | 3 |
Poster
|
1;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neuroscience;fMRI;syntactic representations;graph embeddings
| null | 0 | null | null |
iclr
| 0.68313 | 0 | null |
main
| 5.75 |
4;5;6;8
| null | null |
Syntactic representations in the human brain: beyond effort-based metrics
| 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 |
Visual Reasoning;Relational Reasoning;Generalisation
| null | 0 | null | null |
iclr
| -0.422577 | 0 | null |
main
| 4.8 |
4;4;5;5;6
| null | null |
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
| null | null | 0 | 4 |
Reject
|
4;5;3;4;4
| null |
null |
HRL Laboratories, LLC.; University of Virginia
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3182; None
| null | 0 | null | null | null | null | null |
Soheil Kolouri, Navid Naderializadeh, Gustavo K Rohde, Heiko Hoffmann
|
https://iclr.cc/virtual/2021/poster/3182
|
Wasserstein;graph embedding;graph-level prediction
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3182
|
Wasserstein Embedding for Graph Learning
|
https://github.com/navid-naderi/WEGL
| null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
privacy;differential privacy;generative adversarial networks;gan;security;synthetic data;evaluation;benchmarking;ensemble
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Differentially Private Synthetic Data: Applied Evaluations and Enhancements
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of Computer and Information Science, University of Pennsylvania
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2733; None
| null | 0 | null | null | null | null | null |
Jorge Mendez, ERIC EATON
|
https://iclr.cc/virtual/2021/poster/2733
|
lifelong learning;continual learning;compositional learning;modular networks
| null | 0 | null | null |
iclr
| 0.490098 | 0 | null |
main
| 6.8 |
6;6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2733
|
Lifelong Learning of Compositional Structures
| null | null | 0 | 3.4 |
Poster
|
3;4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
nonnegative tensor decompositions;topic modeling;hierarchical model;CP decomposition;neural network;backpropagation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Explicit Learning Topology for Differentiable Neural Architecture Search
| null | null | 0 | 4.25 |
Withdraw
|
5;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual learning;memory replay;regularization;lifelong learning;multi-task learning
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
The Effectiveness of Memory Replay in Large Scale Continual Learning
| null | null | 0 | 4.25 |
Withdraw
|
4;5;4;4
| null |
null |
Princeton University; Tsinghua University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3291; None
| null | 0 | null | null | null | null | null |
Zhiyuan Li, Yuping Luo, Kaifeng Lyu
|
https://iclr.cc/virtual/2021/poster/3291
|
matrix factorization;gradient descent;implicit regularization;implicit bias
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3291
|
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
| null | null | 0 | 3.25 |
Poster
|
4;3;3;3
| null |
null |
Microsoft Corporation; Arizona State University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2707; None
| null | 0 | null | null | null | null | null |
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, 'YZ' Yezhou Yang, Zicheng Liu
|
https://iclr.cc/virtual/2021/poster/2707
|
Self Supervised Learning;Knowledge Distillation;Representation Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2707
|
SEED: Self-supervised Distillation For Visual Representation
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null |
Bosch Center for Artificial Intelligence, Renningen, Germany; Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2923; None
| null | 0 | null | null | null | null | null |
Kanil Patel, William H Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang
|
https://iclr.cc/virtual/2021/poster/2923
|
uncertainty calibration;post-hoc calibration;histogram binning;mutual information;deep neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2923
|
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
|
https://github.com/boschresearch/imax-calibration
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hyper-parameter Learning;AutoML;Computer Vision
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
AUTOSAMPLING: SEARCH FOR EFFECTIVE DATA SAMPLING SCHEDULES
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causal inference;treatment effects;healthcare
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;6;8
| null | null |
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
clustering;variational inference
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Deep Goal-Oriented Clustering
| 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 |
Optimal transport;sinkhorn distance;locality sensitive hashing;nyström method;graph neural networks;embedding alignment
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
Warpspeed Computation of Optimal Transport, Graph Distances, and Embedding Alignment
| 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 |
Uncertainty Quantification;Uncertainty Prediction;Deep Learning;Regression;Meta Modeling
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Uncertainty Prediction for Deep Sequential Regression Using Meta Models
| 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 |
Indirect supervision;Perturbation;Downstream models;Image enhancement
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Indirect Supervision to Mitigate Perturbations
| null | null | 0 | 4.25 |
Withdraw
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-training;Neural Sequence Labeling;Meta Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Adaptive Self-training for Neural Sequence Labeling with Few Labels
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;bio-inspired;brain-like;unsupervised learning;structural plasticity
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Brain-like approaches to unsupervised learning of hidden representations - a comparative study
| 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 | null | null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
| null | null | 0 | 4.5 |
Withdraw
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fairness;Classification;Statistical Parity;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
A Near-Optimal Recipe for Debiasing Trained Machine Learning Models
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Reinforcement Learning;Meta Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
| null | null | 0 | 4 |
Reject
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
systematic generalization;category-viewpoint classification;multi-task learning
| null | 0 | null | null |
iclr
| 0.207514 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null |
Department of Computer Science, Indian Institute of Technology Delhi, INDIA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2941; None
| null | 0 | null | null | null | null | null |
Yatin Nandwani, Deepanshu Jindal, Mausam ., Parag Singla
|
https://iclr.cc/virtual/2021/poster/2941
|
Neuro symbolic;constraint satisfaction;reasoning
| null | 0 | null | null |
iclr
| 0.942809 | 0 | null |
main
| 6 |
5;5;6;8
| null |
https://iclr.cc/virtual/2021/poster/2941
|
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces
| null | null | 0 | 3.25 |
Poster
|
3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transformer models;attention models;kernel methods;reproducing kernel Banach spaces
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3036; None
| null | 0 | null | null | null | null | null |
Tanner Fiez, Lillian J Ratliff
|
https://iclr.cc/virtual/2021/poster/3036
|
game theory;continuous games;generative adversarial networks;theory;gradient descent-ascent;equilibrium;convergence
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3036
|
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null |
New York University Shanghai and New York University; New York University Shanghai
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3320; None
| null | 0 | null | null | null | null | null |
Xinyue Chen, Che Wang, Zijian Zhou, Keith Ross
|
https://iclr.cc/virtual/2021/poster/3320
|
Artificial Integlligence;Machine Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3320
|
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Programming;Machine Learning;Code Similarity;Code Representation
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative model;GAN;VAE;Recursive Neural Network;self-play
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3 |
2;2;4;4
| null | null |
Generative modeling with one recursive network
| null | null | 0 | 4 |
Withdraw
|
5;4;3;4
| null |
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