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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;deep learning;generalization error;scaling;scalability;pruning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
On the Predictability of Pruning Across Scales
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null |
Stanford University; Technical University of Munich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2838; None
| null | 0 | null | null | null | null | null |
Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
|
https://iclr.cc/virtual/2021/poster/2838
|
machine learning for code;code summarization
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2838
|
Language-Agnostic Representation Learning of Source Code from Structure and Context
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null |
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2589; None
| null | 0 | null | null | null | null | null |
Tsz Him Cheung, Dit-Yan Yeung
|
https://iclr.cc/virtual/2021/poster/2589
|
deep learning;data augmentation;automated data augmentation;latent space
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2589
|
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
|
https://github.com/jamestszhim/modals
| null | 0 | 4 |
Poster
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;pruning;understanding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Uncovering the impact of hyperparameters for global magnitude pruning
| null | null | 0 | 4.5 |
Reject
|
5;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural module networks;machine reading comprehension;numerical reasoning over text
| null | 0 | null | null |
iclr
| -0.632456 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
| null | null | 0 | 3 |
Reject
|
4;3;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
exploration;curiosity-driven reinforcement learning;Bayesian optimisation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Learning to Explore with Pleasure
| null | null | 0 | 3 |
Withdraw
|
3;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Semantics-Preserving Attacks;Logic Relations;Normalization
| null | 0 | null | null |
iclr
| -0.374634 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Robustness against Relational Adversary
| null | null | 0 | 4 |
Reject
|
5;2;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;privacy;parameter selection
| null | 0 | null | null |
iclr
| -0.090909 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Fast Estimation for Privacy and Utility in Differentially Private Machine Learning
| null | null | 0 | 4.25 |
Withdraw
|
4;5;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
performance estimation;neural architecture search
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Microsoft Research Montréal; Imperial College London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2731; None
| null | 0 | null | null | null | null | null |
Arash Tavakoli, Mehdi Fatemi, Petar Kormushev
|
https://iclr.cc/virtual/2021/poster/2731
|
reinforcement learning;structural credit assignment;structural inductive bias;multi-dimensional discrete action spaces;learning action representations
| null | 0 | null | null |
iclr
| 0.771744 | 0 | null |
main
| 6.8 |
5;6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2731
|
Learning to Represent Action Values as a Hypergraph on the Action Vertices
| null | null | 0 | 3.4 |
Poster
|
2;2;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3D Vision;Point Cloud Processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.25 |
4;7;7;7
| null | null |
Revisiting Point Cloud Classification with a Simple and Effective Baseline
| 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 |
neural network;pruning;coreset;approximation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
When Are Neural Pruning Approximation Bounds Useful?
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;exploration;sample efficient reinforcement learning;sparse rewards
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
| null | null | 0 | 3.75 |
Reject
|
3;5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Fast Training of Contrastive Learning with Intermediate Contrastive Loss
| null | null | 0 | 4 |
Reject
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph convolutional neural network;superpixel;FAUST;differential operators
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multiscale covariance map (MCM);least square loss;hyperspectral anomaly detection;generative adversarial network (GAN)
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neurosymbolic;sequence;program synthesis;generative;constraint;music;poetry
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2677; None
| null | 0 | null | null | null | null | null |
Valerie Chen, Abhinav Gupta, Kenneth Marino
|
https://iclr.cc/virtual/2021/poster/2677
| null | null | 0 | null | null |
iclr
| -0.324443 | 0 | null |
main
| 6.75 |
5;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2677
|
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
|
https://github.com/valeriechen/ask-your-humans
| null | 0 | 4 |
Poster
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Department of Statistics and Data Science, The Hebrew University of Jerusalem, Jerusalem, Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2595; None
| null | 0 | null | null | null | null | null |
Yuli Slavutsky, Yuval Benjamini
|
https://iclr.cc/virtual/2021/poster/2595
|
multiclass classification;classification;extrapolation;accuracy;ROC
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null |
https://iclr.cc/virtual/2021/poster/2595
|
Predicting Classification Accuracy When Adding New Unobserved Classes
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dialog;intent prediction;pre-training
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Example-Driven Intent Prediction with Observers
| null | null | 0 | 3.75 |
Withdraw
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Networks;Capsule networks;Compositionality
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning compositional structures for deep learning: why routing-by-agreement is necessary
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Department of Mathematics, Halıcıoğlu Data Science Institute, University of California San Diego
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3243; None
| null | 0 | null | null | null | null | null |
Jinjie Zhang, Rayan Saab
|
https://iclr.cc/virtual/2021/poster/3243
|
Binary Embeddings;Johnson-Lindenstrauss Transforms;Sigma Delta Quantization
| null | 0 | null | null |
iclr
| -0.641689 | 0 | null |
main
| 6.2 |
5;6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3243
|
Faster Binary Embeddings for Preserving Euclidean Distances
|
https://github.com/jayzhang0727/Faster-Binary-Embeddings-for-Preserving-Euclidean-Distances.git
| null | 0 | 3.8 |
Poster
|
5;3;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;predictive uncertainty;explicit regularization
| null | 0 | null | null |
iclr
| 0.102062 | 0 | null |
main
| 4.4 |
3;4;5;5;5
| null | null |
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability
| null | null | 0 | 3.4 |
Reject
|
3;4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image Manipulation;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Text as Neural Operator: Image Manipulation by Text Instruction
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null |
Princeton University, Princeton, NJ 08540, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2623; None
| null | 0 | null | null | null | null | null |
Jad Rahme, Samy Jelassi, S. M Weinberg
|
https://iclr.cc/virtual/2021/poster/2623
|
Mechanism Design;Auction Theory;Game Theory;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.2 |
6;6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2623
|
Auction Learning as a Two-Player Game
| null | null | 0 | 3 |
Poster
|
3;3;2;4;3
| null |
null |
Institute for Aerospace Studies, University of Toronto
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2594; None
| null | 0 | null | null | null | null | null |
Ali Harakeh, Steven L Waslander
|
https://iclr.cc/virtual/2021/poster/2594
|
Object Detection;Predictive Uncertainty Estimation;Proper Scoring Rules;Variance Networks;Energy Score;Computer Vision
| null | 0 | null | null |
iclr
| 0.774597 | 0 | null |
main
| 6.75 |
6;6;6;9
| null |
https://iclr.cc/virtual/2021/poster/2594
|
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
|
https://github.com/asharakeh/probdet.git
| null | 0 | 3.5 |
Poster
|
4;3;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Nash Equilibrium;Games;CFR
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
The Advantage Regret-Matching Actor-Critic
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
University of Wisconsin-Madison; KAIST
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2652; None
| null | 0 | null | null | null | null | null |
Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
|
https://iclr.cc/virtual/2021/poster/2652
|
model fairness;bilevel optimization;batch selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2652
|
FairBatch: Batch Selection for Model Fairness
| null | null | 0 | 4 |
Poster
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural network;signed graph analysis;representation learning;graph diffusion;random walk;link sign prediction
| null | 0 | null | null |
iclr
| -0.19245 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Signed Graph Diffusion Network
| 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 |
Transformer;Convolution;Attention Map
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
2;6;6;6
| null | null |
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3021; None
| null | 0 | null | null | null | null | null |
Florian Tramer, Dan Boneh
|
https://iclr.cc/virtual/2021/poster/3021
|
Differential Privacy;Privacy;Deep Learning
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3021
|
Differentially Private Learning Needs Better Features (or Much More Data)
| null | null | 0 | 3.5 |
Spotlight
|
5;3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Network Sparsity;Machine Learning;Initialization Pruning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.5 |
1;2;3;4
| null | null |
What to Prune and What Not to Prune at Initialization
| null | null | 0 | 4.5 |
Reject
|
4;5;5;4
| null |
null |
Department of Computer Science, University of Maryland, College Park, MD 20742, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2867; None
| null | 0 | null | null | null | null | null |
Alexander Levine, Soheil Feizi
|
https://iclr.cc/virtual/2021/poster/2867
|
bagging;ensemble;robustness;certificate;poisoning;smoothing
| null | 0 | null | null |
iclr
| 0.09759 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2867
|
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks
|
https://github.com/alevine0/DPA
| null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null |
Salesforce Research, University of Illinois at Urbana-Champaign; Salesforce Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3057; None
| null | 0 | null | null | null | null | null |
Jesse Vig, Ali Madani, Lav R Varshney, Caiming Xiong, Richard Socher, Nazneen Rajani
|
https://iclr.cc/virtual/2021/poster/3057
|
interpretability;black box;computational biology;representation learning;attention;transformers;visualization;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.6 |
6;6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3057
|
BERTology Meets Biology: Interpreting Attention in Protein Language Models
|
https://github.com/salesforce/provis
| null | 0 | 4 |
Poster
|
4;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised learning;unsupervised learning;deep learning;neural networks;good practices
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Towards Good Practices in Self-Supervised Representation Learning
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Disentanglement;Group Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Quantifying and Learning Disentangled Representations with Limited Supervision
| null | null | 0 | 3 |
Reject
|
3;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 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency
|
https://github.com/paper-submissions/mixsize
| null | 0 | 4.25 |
Reject
|
3;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Scientific Machine Learning;Deep Learning;Physics;Surrogate Modeling;Koopman;Transformers;Attention
| null | 0 | null | null |
iclr
| 0.301511 | 0 |
https://sites.google.com/view/transformersphysx
|
main
| 6.5 |
6;6;7;7
| null | null |
Transformers for Modeling Physical Systems
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semantic similarities;metric learning;prototypical classifiers;adversarial robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Learning Semantic Similarities for Prototypical Classifiers
| null | null | 0 | 4 |
Withdraw
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Automated machine Learning;Reinforcement learning;Macro action ensemble
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Toward Synergism in Macro Action Ensembles
| 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 |
Federated Learning;personalized models;non-i.i.d data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Federated Mixture of Experts
| 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
| 0.333333 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
Google Research, Brain Team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3013; None
| null | 0 | null | null | null | null | null |
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
|
https://iclr.cc/virtual/2021/poster/3013
|
computer vision;image recognition;self-attention;transformer;large-scale training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3013
|
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
|
https://github.com/google-research/vision_transformer
| null | 0 | 4 |
Oral
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Two steps at a time --- taking GAN training in stride with Tseng's method
| 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 |
Natural Language Processing;Representation Learning
| null | 0 | null | null |
iclr
| -0.084215 | 0 | null |
main
| 5.8 |
4;4;5;7;9
| null | null |
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation
| null | null | 0 | 4.6 |
Reject
|
5;5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequence learning;simultaneous machine translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Learning to Use Future Information in Simultaneous Translation
|
https://github.com/P2F-research/simulNMT
| null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Facebook AI; Facebook AI / ENS Ulm
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3006; None
| null | 0 | null | null | null | null | null |
Kharitonov Eugene, Rahma Chaabouni
|
https://iclr.cc/virtual/2021/poster/3006
|
inductive biases;description length;sequence-to-sequence models
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3006
|
What they do when in doubt: a study of inductive biases in seq2seq learners
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3055; None
| null | 0 | null | null | null | null | null |
Markus Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy
|
https://iclr.cc/virtual/2021/poster/3055
|
self-supervised learning;mathematics;reasoning;theorem proving;language modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3055
|
Mathematical Reasoning via Self-supervised Skip-tree Training
| null | null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null |
University of California, Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2862; None
| null | 0 | null | null | null | null | null |
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2862
|
reinforcement learning;imitation learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/parrot-rl
|
main
| 7.5 |
6;7;8;9
| null |
https://iclr.cc/virtual/2021/poster/2862
|
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
| null | null | 0 | 3.5 |
Oral
|
4;3;3;4
| null |
null |
School of Computing Science, University of Glasgow, Glasgow, UK; Amazon, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2666; None
| null | 0 | null | null | null | null | null |
Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou, Roderick Murray-Smith
|
https://iclr.cc/virtual/2021/poster/2666
|
Missing value imputation;variational inference;variational auto-encoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2666
|
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
| null | null | 0 | 3.5 |
Poster
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Orthogonal Weights Householder Reflections Normalizing Flows
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
One Reflection Suffice
| null | null | 0 | 2.75 |
Reject
|
5;2;2;2
| null |
null |
Department of Electrical and Computer Engineering, University of California San Diego; Microsoft
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2520; None
| null | 0 | null | null | null | null | null |
Yunsheng Li, Yinpeng Chen, Xiyang Dai, mengchen liu, Dongdong Chen, Ye Yu, Lu Yuan, Zicheng Liu, Mei Chen, Nuno Vasconcelos
|
https://iclr.cc/virtual/2021/poster/2520
|
supervised representation learning;efficient network;dynamic network;matrix decomposition
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2520
|
Revisiting Dynamic Convolution via Matrix Decomposition
|
https://github.com/liyunsheng13/dcd
| null | 0 | 2.75 |
Poster
|
3;3;2;3
| null |
null |
School of Electrical Engineering, Korea University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3134; None
| null | 0 | null | null | null | null | null |
Seon-Ho Lee, Chang-Su Kim
|
https://iclr.cc/virtual/2021/poster/3134
|
Clustering;order learning;age estimation;aesthetic assessment;historical color image classification
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3134
|
Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition
| null | null | 0 | 3.5 |
Poster
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Weight Decay;Regularization;Optimization;Deep Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Stable Weight Decay Regularization
| 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 |
quantum embedding;knowledge graph embedding;knowledge graph completion;logical rules mining;knowledge base
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Quantum and Translation Embedding for Knowledge Graph Completion
| null | null | 0 | 4.25 |
Withdraw
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;deep-rl;exploration
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.5 |
5;5;5;7
| null | null |
Temporal Difference Uncertainties as a Signal for Exploration
| null | null | 0 | 3.25 |
Reject
|
3;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;generalization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Boston University; Blueshift, Alphabet; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3091; None
| null | 0 | null | null | null | null | null |
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
|
https://iclr.cc/virtual/2021/poster/3091
|
Scale of initialization;Memorization;Overfitting;Generalization;Generalization Measure;Understanding Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;7;9
| null |
https://iclr.cc/virtual/2021/poster/3091
|
Extreme Memorization via Scale of Initialization
|
https://github.com/google-research/google-research/tree/master/extreme_memorization
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feature upsampling;semantically-adaptive;layout-to-image translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Semantically-Adaptive Upsampling for Layout-to-Image Translation
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph-level representation learning;knowledge distillation
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Iterative Graph Self-Distillation
| 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 |
object detection;object recognition;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
EMPIRICAL UPPER BOUND IN OBJECT DETECTION
|
[TBA]
| null | 0 | 4 |
Withdraw
|
4;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Double Descent;Neural Networks;Generalization
| null | 0 | null | null |
iclr
| 0.801784 | 0 | null |
main
| 5.4 |
5;5;5;5;7
| null | null |
Optimization Variance: Exploring Generalization Properties of DNNs
| null | null | 0 | 3.8 |
Reject
|
4;3;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-Object Detection;Density Estimation;Mixture Model;Ground Truth Assignment
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Density-Based Object Detection: Learning Bounding Boxes without Ground Truth Assignment
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pruning Criteria;Similarity;Convolution;Weight Distribution
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Rethinking the Pruning Criteria for Convolutional Neural Network
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Computer Science Department, Stanford University; School of Computer Science, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3201; None
| null | 0 | null | null | null | null | null |
Paul Michel, Tatsunori Hashimoto, Graham Neubig
|
https://iclr.cc/virtual/2021/poster/3201
|
distributionally robust optimization;deep learning;robustness;adversarial learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3201
|
Modeling the Second Player in Distributionally Robust Optimization
|
https://github.com/pmichel31415/P-DRO
| null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Wasserstein barycenter;graph learning;diffusion;missing features;matrix completion
| null | 0 | null | null |
iclr
| -0.845154 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Wasserstein diffusion on graphs with missing attributes
| 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 |
16-bit training;Low precision training;Deep learning hardware
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.25 |
3;3;5;6
| null | null |
Revisiting BFfloat16 Training
| null | null | 0 | 4.25 |
Reject
|
5;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributed optimization;asynchronous Byzantine learning
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
BASGD: Buffered Asynchronous SGD for Byzantine 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 |
Contrastive learning;dataset distillation;patient-similarity;physiological signals;healthcare
| null | 0 | null | null |
iclr
| -0.993399 | 0 | null |
main
| 4.666667 |
2;5;7
| null | null |
PCPs: Patient Cardiac Prototypes
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
†Inria,‡CNRS-ENS; ‡CNRS-ENS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2853; None
| null | 0 | null | null | null | null | null |
Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal
|
https://iclr.cc/virtual/2021/poster/2853
|
bioinformatics;optimal transport;kernel methods;attention;transformers
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2853
|
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
|
https://github.com/claying/OTK
| null | 0 | 2.75 |
Poster
|
2;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical vine copula model;copula;gaussian process;mutual information;neuroscience;neuronal activity;calcium imaging;visual cortex
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships
| 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 |
Reinforcement Learning;Interpretability;Visualization
| null | 0 | null | null |
iclr
| 0.40452 | 0 |
https://vizarel-demo.herokuapp.com
|
main
| 4.5 |
3;4;5;6
| null | null |
Interactive Visualization for Debugging RL
| null | null | 0 | 3.75 |
Reject
|
4;3;3;5
| null |
null |
Stony Brook University; City University of New York; Rutgers University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2708; None
| null | 0 | null | null | null | null | null |
Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen
|
https://iclr.cc/virtual/2021/poster/2708
|
Noisy Label;Deep Learning;Classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2708
|
Learning with Feature-Dependent Label Noise: A Progressive Approach
| 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 |
Graph Representation Learning;Disentangled Representation Learning
| null | 0 | null | null |
iclr
| 0.920575 | 0 | null |
main
| 5.25 |
3;4;6;8
| null | null |
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null |
The University of Sydney; University of Macau; Tencent AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3147; None
| null | 0 | null | null | null | null | null |
Liang Ding, Longyue Wang, Xuebo Liu, Derek Wong, Dacheng Tao, Zhaopeng Tu
|
https://iclr.cc/virtual/2021/poster/3147
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3147
|
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian process;doubly stochastic variational inference;variational Inference;Bayesian Inference
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Deep Kernel Processes
| null | null | 0 | 2.75 |
Reject
|
3;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
expire;long attention;memory;transformers
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Not All Memories are Created Equal: Learning to Expire
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Capsule Networks;Object Detection
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Deformable Capsules for Object Detection
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
EPFL; University of Geneva
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3358; None
| null | 0 | null | null | null | null | null |
Tatjana Chavdarova, Matteo Pagliardini, Sebastian Stich, François Fleuret, Martin Jaggi
|
https://iclr.cc/virtual/2021/poster/3358
|
Minmax;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| -0.182574 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3358
|
Taming GANs with Lookahead-Minmax
|
https://github.com/Chavdarova/LAGAN-Lookahead_Minimax
| null | 0 | 3.5 |
Poster
|
4;3;5;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-learning;Adversarial manner;Adversarial Meta-Learner;Adversarial samples
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 4.4 |
3;4;4;5;6
| null | null |
Adversarial Meta-Learning
| null | null | 0 | 4.4 |
Reject
|
5;4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial transferability;knowledge transferability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Does Adversarial Transferability Indicate Knowledge Transferability?
| 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 | null | null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Non-iterative Parallel Text Generation via Glancing Transformer
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null |
Department of Computer Science, School of Computing, National University of Singapore
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3109; None
| null | 0 | null | null | null | null | null |
Abdul Fatir Ansari, Ming Liang Ang, Harold Soh
|
https://iclr.cc/virtual/2021/poster/3109
|
gradient flows;generative models;GAN;VAE;Normalizing Flow
| null | 0 | null | null |
iclr
| 0.133631 | 0 | null |
main
| 6.8 |
6;7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3109
|
Refining Deep Generative Models via Discriminator Gradient Flow
| null | null | 0 | 3.2 |
Poster
|
3;2;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;transfer learning;fewshot learning;image generation
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Data Instance Prior for Transfer Learning in GANs
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null |
Blueshift, Alphabet; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3212; None
| null | 0 | null | null | null | null | null |
Raphael Gontijo Lopes, Sylvia Smullin, Ekin Cubuk, Ethan Dyer
|
https://iclr.cc/virtual/2021/poster/3212
|
Generalization;Interpretability;Understanding Data Augmentation
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 6.25 |
5;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3212
|
Tradeoffs in Data Augmentation: An Empirical Study
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperbolic learning;hyperbolic neural network;Poincare embedding
| null | 0 | null | null |
iclr
| 0.298142 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Laplacian Eigenspaces, Horocycles and Neuron Models on Hyperbolic Spaces
| null | null | 0 | 3.5 |
Reject
|
3;2;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sentence;Embeddings;Structure;Contrastive;Multi-views
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Contrasting distinct structured views to learn sentence embeddings
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Neural Networks;Out-of-Core Execution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Out-of-Core Training for Extremely Large-Scale Neural Networks with Adaptive Window-Based Scheduling
| null | null | 0 | 3.75 |
Withdraw
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robustness;adversarial examples;adversarial training;physical-world adversarial attacks;adversarial patch;universal perturbation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Meta Adversarial Training
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Causality;Robust Optimization;Domain Generalization
| null | 0 | null | null |
iclr
| -0.512989 | 0 | null |
main
| 6 |
3;5;7;9
| null | null |
Accounting for Unobserved Confounding in Domain Generalization
| null | null | 0 | 3.75 |
Reject
|
5;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Drug-Target Binding Affinity;Transformers;Graph Neural networks
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network
| null | null | 0 | 3.75 |
Withdraw
|
4;3;4;4
| null |
null |
MIT; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2826; None
| null | 0 | null | null | null | null | null |
Chulhee Yun, Shankar Krishnan, Hossein Mobahi
|
https://iclr.cc/virtual/2021/poster/2826
|
implicit bias;implicit regularization;convergence;gradient flow;gradient descent
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2826
|
A unifying view on implicit bias in training linear neural networks
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Machine Learning;Learning Theory
| null | 0 | null | null |
iclr
| 0.207514 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness
| null | null | 0 | 3.25 |
Reject
|
2;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Momentum;Reinforcement Learning;Temporal Difference;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Correcting Momentum in Temporal Difference Learning
| 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 |
Alignment;Linear Neural Networks;Implicit Regularization
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.75 |
4;4;4;7
| null | null |
On Alignment in Deep Linear Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null |
Samsung AI Center, Cambridge, UK; University of Nottingham, Nottingham, UK; Samsung AI Center, Cambridge, UK; Queen Mary University of London, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2531; None
| null | 0 | null | null | null | null | null |
Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos
|
https://iclr.cc/virtual/2021/poster/2531
| null | null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2531
|
Knowledge distillation via softmax regression representation learning
|
https://github.com/jingyang2017/KD_SRRL
| null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Auto-encoder Out-of-distribution Detection Deep Generative Model Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
Bigeminal Priors Variational Auto-encoder
| null | null | 0 | 4 |
Withdraw
|
4;5;3;4
| null |
null |
Department of Electrical and Computer Engineering, Rice University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3199; None
| null | 0 | null | null | null | null | null |
Chaojian Li, Zhongzhi Yu, Yonggan Fu, Yongan Zhang, Yang Zhao, Haoran You, Qixuan Yu, Yue Wang, Cong Hao, Yingyan Lin
|
https://iclr.cc/virtual/2021/poster/3199
|
Hardware-Aware Neural Architecture Search;AutoML;Benchmark
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3199
|
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
|
https://github.com/RICE-EIC/HW-NAS-Bench
| null | 0 | 3.75 |
Spotlight
|
3;5;4;3
| null |
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