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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learned optimizers;optimization;recurrent neural networks;RNNs;interpretability
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;5;5;8
| null | null |
Reverse engineering learned optimizers reveals known and novel mechanisms
| 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 |
Similarity learning;kernel methods;constrained clustering;transformer analysis;spectral clustering;supervised learning;deep learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Attention-Based Clustering: Learning a Kernel from Context
| 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 |
localized meta-learning;PAC-Bayes;meta-learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior
| 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 |
uncertainty;prior networks;regression;ensemble distribution distillation;depth estimation.
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Regression Prior Networks
| 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 |
HPO;NAS;AutoML
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
AutoHAS: Efficient Hyperparameter and Architecture Search
| null | null | 0 | 4 |
Withdraw
|
5;5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Time Series;Symmetry;Homology;Augmentation;Machine Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Symmetry-Augmented Representation for Time Series
| null | null | 0 | 2.25 |
Withdraw
|
3;2;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
novelty detection;variational autoencoding;robustness;Wasserstein metric;one-class classification;semi-supervised anomaly detection
| null | 0 | null | null |
iclr
| -0.845154 | 0 | null |
main
| 5.75 |
4;5;6;8
| null | null |
Novelty Detection via Robust Variational Autoencoding
| 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 |
Planning;Reinforcement Learning;Representation Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
TOMA: Topological Map Abstraction for Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
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
| 0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
University of Southern California, Los Angeles, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3113; None
| null | 0 | null | null | null | null | null |
Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan
|
https://iclr.cc/virtual/2021/poster/3113
|
representation learning;hyperbolic deep learning;persistent homology;persistence diagrams
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3113
|
Learning Hyperbolic Representations of Topological Features
| null | null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null |
National University of Singapore; University of California, Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2916; None
| null | 0 | null | null | null | null | null |
Zhuang Liu, Xuanlin Li, Bingyi Kang, trevor darrell
|
https://iclr.cc/virtual/2021/poster/2916
|
Policy Optimization;Regularization;Continuous Control;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/2916
|
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
|
https://github.com/xuanlinli17/iclr2021_rlreg
| null | 0 | 4 |
Spotlight
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
algorithmic fairness
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.4 |
5;5;5;6;6
| null | null |
Attainability and Optimality: The Equalized-Odds Fairness Revisited
| null | null | 0 | 3.6 |
Reject
|
4;4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
wave equation;wave acoustics;geometric deep learning;sound simulation;shape laplacian
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Fast 3D Acoustic Scattering via Discrete Laplacian Based Implicit Function Encoders
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;generalization;overparameterization
| null | 0 | null | null |
iclr
| 0.406181 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Is deeper better? It depends on locality of relevant features
| null | null | 0 | 3.25 |
Reject
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Clustering;Manifold Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Deep Clustering and Representation Learning that Preserves Geometric Structures
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GNNs;Expressive power;Diverse sampling;Injective
| null | 0 | null | null |
iclr
| 0.801784 | 0 | null |
main
| 3.8 |
3;4;4;4;4
| null | null |
Towards Powerful Graph Neural Networks: Diversity Matters
| null | null | 0 | 4.2 |
Reject
|
3;5;4;4;5
| null |
null |
Max Planck Institute for Informatics; University of Bonn; Bosch Center for Artificial Intelligence
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2686; None
| null | 0 | null | null | null | null | null |
Edgar Schoenfeld, Vadim Sushko, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
|
https://iclr.cc/virtual/2021/poster/2686
|
Semantic Image Synthesis;GANs;Image Generation;Deep Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2686
|
You Only Need Adversarial Supervision for Semantic Image Synthesis
| null | null | 0 | 3.666667 |
Poster
|
3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial example;robustness;data manifold;adversarial training
| null | 0 | null | null |
iclr
| -0.406181 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Improving Model Robustness with Latent Distribution Locally and Globally
| null | null | 0 | 3.25 |
Reject
|
4;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph attention networks;Joint attention mechanism;Graph transductive learning
| null | 0 | null | null |
iclr
| -0.324443 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Graph Joint Attention Networks
| null | null | 0 | 4 |
Reject
|
5;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Disentanglement;Intervention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Unsupervised Disentanglement Learning by intervention
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequential testing;A/B testing;qualitative treatment effects;bootstrap
| null | 0 | null | null |
iclr
| -0.141421 | 0 | null |
main
| 5 |
3;4;6;7
| null | null |
AN ONLINE SEQUENTIAL TEST FOR QUALITATIVE TREATMENT EFFECTS
| null | null | 0 | 2.5 |
Reject
|
4;1;2;3
| null |
null |
Harvard University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3286; None
| null | 0 | null | null | null | null | null |
Abdul Wasay, Stratos Idreos
|
https://iclr.cc/virtual/2021/poster/3286
|
ensemble learning;empirical study;machine learning systems;computer vision
| null | 0 | null | null |
iclr
| -0.471405 | 0 | null |
main
| 7 |
5;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/3286
|
More or Less: When and How to Build Convolutional Neural Network Ensembles
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null |
Department of Statistics, University of Michigan; IBM Research, MIT-IBM Watson AI lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2966; None
| null | 0 | null | null | null | null | null |
Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
|
https://iclr.cc/virtual/2021/poster/2966
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2966
|
Statistical inference for individual fairness
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hyper-Parameter Optimization;Response Surface Modeling;Convolution Neural Network;Low-Rank Factorization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Response Modeling of Hyper-Parameters for Deep Convolutional Neural Networks
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Technion – Israel Institute of Technology; Habana Labs - Intel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2885; None
| null | 0 | null | null | null | null | null |
Nurit Spingarn Eliezer, Ron Banner, Tomer Michaeli
|
https://iclr.cc/virtual/2021/poster/2885
|
Generative Adversarial Network;semantic directions in latent space;nonlinear walk
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7 |
6;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/2885
|
GAN "Steerability" without optimization
| null | null | 0 | 4.25 |
Spotlight
|
5;4;4;4
| null |
null |
University of Alberta
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2951; None
| null | 0 | null | null | null | null | null |
Yangchen Pan, Kirby Banman, Martha White
|
https://iclr.cc/virtual/2021/poster/2951
|
Reinforcement learning;natural sparsity;sparse representation;fuzzy tiling activation function
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2951
|
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online
|
https://github.com/yannickycpan/reproduceRL.git
| null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Topological Learning;GNN;VAE
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
On the Importance of Looking at the Manifold
| 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;feature selection;semantic correspondence
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Feature-Robust Optimal Transport for High-Dimensional Data
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feature Attribution;Convolutional Neural Networks;Explanation Methods
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
| null | null | 0 | 3 |
Withdraw
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;hierarchical modeling;neural network analysis
| null | 0 | null | null |
iclr
| -0.547723 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Embedding semantic relationships in hidden representations via label smoothing
| null | null | 0 | 4 |
Withdraw
|
5;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Perception;Adversarial Training;Bioinspired Architectures;Filling-in;Blind-spot;Deep Neural Networks;Robust Deep Neural Networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Perceptual Deep Neural Networks: Adversarial Robustness Through Input Recreation
| null | null | 0 | 4 |
Withdraw
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
attention network;pixel-wise prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Variational Structured Attention Networks for Dense Pixel-Wise Prediction
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative modeling;deep learning;deep autoencoders
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Self-Supervised Variational Auto-Encoders
| null | null | 0 | 3.5 |
Reject
|
4;5;1;4
| null |
null |
Tsinghua Berkeley Shenzhen Institute, Tsinghua University, China; School of Information Technology, Deakin University, Geelong, VIC, Australia; PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen, China; Key Lab. of Machine Perception (MoE), School of EECS, Peking University, Beijing, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2827; None
| null | 0 | null | null | null | null | null |
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang
|
https://iclr.cc/virtual/2021/poster/2827
|
Adversarial robustness;channel suppressing;activation strategy.
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 7.25 |
7;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2827
|
Improving Adversarial Robustness via Channel-wise Activation Suppressing
|
https://github.com/bymavis/CAS_ICLR2021
| null | 0 | 4.25 |
Spotlight
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Representation Learning;Graph Convolution;Graph Signal Processing;Oversmoothing
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
A Unified Framework for Convolution-based Graph Neural Networks
| null | null | 0 | 3.5 |
Reject
|
3;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feature Selection;Mutual Information;Unique Relevance
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 4.5 |
2;4;4;8
| null | null |
Improving Mutual Information based Feature Selection by Boosting Unique Relevance
| null | null | 0 | 4.5 |
Reject
|
5;5;5;3
| null |
null |
Carnegie Mellon University, Pittsburgh, PA, USA; Carnegie Mellon University and NASA Ames, Moffett Field, CA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3325; None
| null | 0 | null | null | null | null | null |
Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, Corina Pasareanu
|
https://iclr.cc/virtual/2021/poster/3325
|
verification;robustness;safety
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3325
|
Fast Geometric Projections for Local Robustness Certification
|
https://github.com/klasleino/fast-geometric-projections
| null | 0 | 4 |
Spotlight
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
label noise;noise transition matrix;entropy;information theory;local intrinsic dimensionality
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
An information-theoretic framework for learning models of instance-independent label noise
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-learning;Few-shot learning;Transductive learning;Semi-supervised learning
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Meta-Learned Confidence for Transductive Few-shot Learning
| null | null | 0 | 4.25 |
Withdraw
|
3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
corruption robustness;data augmentation;perceptual similarity;deep learning
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
On interaction between augmentations and corruptions in natural corruption robustness
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Learning;Semi-Supervised Learning;Neural Tangent Kernel;Deep Learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control
| 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 |
Multiagent reinforcement learning;Meta-learning;Non-stationarity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null |
Microsoft Corporation
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2673; None
| null | 0 | null | null | null | null | null |
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N Bennett, Junaid Ahmed, Arnold Overwijk
|
https://iclr.cc/virtual/2021/poster/2673
|
Dense Retrieval;Text Retrieval;Text Representation;Neural IR
| null | 0 | null | null |
iclr
| 0.738549 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2673
|
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
| null | null | 0 | 3.75 |
Poster
|
3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational autoencoder;VAE;disentanglement;global features;sequential models;representation learning;mutual information
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Information Theoretic Regularization for Learning Global Features by Sequential VAE
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial network;generative adversarial privacy;information-theoretic privacy;compression;private information retrieval;data-driven framework
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval
| null | null | 0 | 2.666667 |
Reject
|
4;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Feedback Alignment;Backpropagation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning the Connections in Direct Feedback Alignment
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Differential Privacy;Generative Learning;GAN;Optimal Transport
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Differentially Private Generative Models Through Optimal Transport
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
OOD;out of distribution;trust;model confidence;DNN;deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
| null | null | 0 | 4 |
Reject
|
5;5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bioinformatics;CRISPR-Cas9;Off-target mutations;Feedforward Neural Networks;Convolutional Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Novel Encoding of sgRNA-DNA Sequences for Accurate Deep Learning Off-Target Predictions
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
user representations;representation learning;self-supervised learning;pretraining;transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
UserBERT: Self-supervised User Representation Learning
| null | null | 0 | 4.25 |
Reject
|
5;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.565916 | 0 | null |
main
| 5.25 |
3;4;7;7
| null | null |
Latent Programmer: Discrete Latent Codes for Program Synthesis
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null |
Ricoh Company, Ltd.
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3110; None
| null | 0 | null | null | null | null | null |
Fumihiro Sasaki, Ryota Yamashina
|
https://iclr.cc/virtual/2021/poster/3110
|
Imitation Learning;Inverse Reinforcement Learning;Noisy Demonstrations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3110
|
Behavioral Cloning from Noisy Demonstrations
| null | null | 0 | 3.666667 |
Spotlight
|
4;3;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Backdoor Attacks;Denoised Smoothing;Perceptually-Aligned Gradients
| null | 0 | null | null |
iclr
| -0.54886 | 0 | null |
main
| 4.75 |
2;5;5;7
| null | null |
Poisoned classifiers are not only backdoored, they are fundamentally broken
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-Modal;VQA;Retrieval
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph representation learning;graph neural networks;expressive power
| null | 0 | null | null |
iclr
| 0.133631 | 0 | null |
main
| 5.8 |
5;5;6;6;7
| null | null |
Breaking the Expressive Bottlenecks of Graph Neural Networks
| null | null | 0 | 3.2 |
Reject
|
3;3;3;4;3
| null |
null |
Rensselaer Polytechnic Institute; Nanyang Technology University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3330; None
| null | 0 | null | null | null | null | null |
Shangqing Liu, Yu Chen, Xiaofei Xie, Siow Jing Kai, Yang Liu
|
https://iclr.cc/virtual/2021/poster/3330
|
Code Summarization;Graph Neural Network;Retrieval;Generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3330
|
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
| null | null | 0 | 3 |
Spotlight
|
3;3;3
| null |
null |
Salesforce Research Asia; Singapore Management University, Salesforce Research Asia; Singapore Management University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2887; None
| null | 0 | null | null | null | null | null |
Quang Pham, Chenghao Liu, Doyen Sahoo, Steven HOI
|
https://iclr.cc/virtual/2021/poster/2887
|
Continual Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2887
|
Contextual Transformation Networks for Online Continual Learning
|
https://github.com/phquang/Contextual-Transformation-Network
| null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
What Regularized Auto-Encoders Learn from the Data Generating Distribution
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Information Theoretic Learning with Infinitely Divisible Kernels
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Joint Training Deep Boltzmann Machines for Classification
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Factorized Topic Models
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Herded Gibbs Sampling
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
A Geometric Descriptor for Cell-Division Detection
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
The Expressive Power of Word Embeddings
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Feature grouping from spatially constrained multiplicative interaction
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Hierarchical Data Representation Model - Multi-layer NMF
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
An Efficient Sufficient Dimension Reduction Method for Identifying Genetic Variants of Clinical Significance
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Behavior Pattern Recognition using A New Representation Model
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Cutting Recursive Autoencoder Trees
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Latent Relation Representations for Universal Schemas
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
The Neural Representation Benchmark and its Evaluation on Brain and Machine
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Affinity Weighted Embedding
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Efficient Learning of Domain-invariant Image Representations
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
A Semantic Matching Energy Function for Learning with Multi-relational Data
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Why Size Matters: Feature Coding as Nystrom Sampling
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Block Coordinate Descent for Sparse NMF
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Joint Space Neural Probabilistic Language Model for Statistical Machine Translation
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Deep learning and the renormalization group
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Boltzmann Machines and Denoising Autoencoders for Image Denoising
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
A Nested HDP for Hierarchical Topic Models
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Visual Objects Classification with Sliding Spatial Pyramid Matching
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Unsupervised Feature Learning for low-level Local Image Descriptors
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Matrix Approximation under Local Low-Rank Assumption
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Knowledge Matters: Importance of Prior Information for Optimization
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Switched linear encoding with rectified linear autoencoders
| null | null | 0 | 0 |
Reject
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Big Neural Networks Waste Capacity
| null | null | 0 | 0 |
Oral Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
When Does a Mixture of Products Contain a Product of Mixtures?
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
The Manifold of Human Emotions
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Two SVDs produce more focal deep learning representations
| null | null | 0 | 0 |
Poster Workshop
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Complexity of Representation and Inference in Compositional Models with Part Sharing
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Discriminative Recurrent Sparse Auto-Encoders
| null | null | 0 | 0 |
Oral
| null | null |
null | null |
2013
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning Stable Group Invariant Representations with Convolutional Networks
| null | null | 0 | 0 |
Poster Workshop
| null | null |
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