pdf
stringlengths 49
199
⌀ | aff
stringlengths 1
1.36k
⌀ | year
stringclasses 19
values | technical_novelty_avg
float64 0
4
⌀ | video
stringlengths 21
47
⌀ | doi
stringlengths 31
63
⌀ | presentation_avg
float64 0
4
⌀ | proceeding
stringlengths 43
129
⌀ | presentation
stringclasses 796
values | sess
stringclasses 576
values | technical_novelty
stringclasses 700
values | arxiv
stringlengths 10
16
⌀ | author
stringlengths 1
1.96k
⌀ | site
stringlengths 37
191
⌀ | keywords
stringlengths 2
582
⌀ | oa
stringlengths 86
198
⌀ | empirical_novelty_avg
float64 0
4
⌀ | poster
stringlengths 57
95
⌀ | openreview
stringlengths 41
45
⌀ | conference
stringclasses 11
values | corr_rating_confidence
float64 -1
1
⌀ | corr_rating_correctness
float64 -1
1
⌀ | project
stringlengths 1
162
⌀ | track
stringclasses 3
values | rating_avg
float64 0
10
⌀ | rating
stringlengths 1
17
⌀ | correctness
stringclasses 809
values | slides
stringlengths 32
41
⌀ | title
stringlengths 2
192
⌀ | github
stringlengths 3
165
⌀ | authors
stringlengths 7
161
⌀ | correctness_avg
float64 0
5
⌀ | confidence_avg
float64 0
5
⌀ | status
stringclasses 22
values | confidence
stringlengths 1
17
⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2688; None
| null | 0 | null | null | null | null | null |
Benjamin Haeffele, Chong You, Rene Vidal
|
https://iclr.cc/virtual/2021/poster/2688
|
Subspace clustering;Manifold clustering;Theory of deep learning;Autoencoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2688
|
A Critique of Self-Expressive Deep Subspace Clustering
| 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 |
Hyper-parameter Optimization;Transfer Learning;Meta-learning
| null | 0 | null | null |
iclr
| -0.918559 | 0 | null |
main
| 5.8 |
4;6;6;6;7
| null | null |
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization
| null | null | 0 | 3.2 |
Reject
|
4;3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Learning;Few-Shot Learning;Meta-initialization;Task-specific Adaptation
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML
| 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 |
Federated Learning;GAN;Deep Learning
| null | 0 | null | null |
iclr
| 0.942809 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
| 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 |
checkpoint selection;transfer learning;task transferability;network generalization prediction
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Ranking Neural Checkpoints
| null | null | 0 | 4 |
Withdraw
|
3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph;neural networks;deep learning;spectral theory;directional aggregation;over-smoothing
| null | 0 | null | null |
iclr
| -0.102598 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Directional graph networks
| null | null | 0 | 3.5 |
Reject
|
5;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Inductive (symmetry) Bias;Predictive Models;Hamiltonian Dynamics;Physics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Symmetry Control Neural Networks
| null | null | 0 | 4 |
Reject
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
submodularity;text generation;attention
| null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
Resurrecting Submodularity for Neural Text Generation
| null | null | 0 | 3.5 |
Withdraw
|
4;4;3;3
| null |
null |
University of Tsukuba, Research Institute for Computational Science Co. Ltd.; Research Institute for Computational Science Co. Ltd.; University of Tsukuba
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3192; None
| null | 0 | null | null | null | null | null |
Masanobu Horie, Naoki Morita, Toshiaki Hishinuma, Yu Ihara, Naoto Mitsume
|
https://iclr.cc/virtual/2021/poster/3192
|
Machine Learning;Graph Neural Network;Invariance;Equivariance;Simulation;Mesh
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3192
|
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
School of Computer Science and Engineering, University of Electronic Science and Technology of China
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2644; None
| null | 0 | null | null | null | null | null |
Shikuang Deng, Shi Gu
|
https://iclr.cc/virtual/2021/poster/2644
|
spiking neural network;weight balance;second-order approximation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/2644
|
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
|
https://github.com/Jackn0/snn_optimal_conversion_pipeline
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Online Limited Memory Neural-Linear Bandits
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
First Order Optimization;Zeroth Order Optimization
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Joint Descent: Training and Tuning Simultaneously
| null | null | 0 | 3.5 |
Withdraw
|
3;5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
emergent communication;multi-agent communication;multi-agent reinforcement learning
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
| null | null | 0 | 2.75 |
Reject
|
4;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation Learning;Disentanglement;Unsupervised Learning;Semantic Representations;VAE;Causal Representations;PCA
| null | 0 | null | null |
iclr
| 0.174078 | 0 |
https://sites.google.com/view/sem-rep-learning
|
main
| 4.25 |
3;4;5;5
| null | null |
Clearing the Path for Truly Semantic Representation Learning
| null | null | 0 | 3.25 |
Reject
|
2;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
object detection;image recognition;computer vision
| null | 0 | null | null |
iclr
| 0.471405 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Learning a unified label space
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null |
Department of Electrical Engineering and Computer Science, University of California, Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2793; None
| null | 0 | null | null | null | null | null |
Justin Fu, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2793
|
model-based optimization;normalized maximum likelihood
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2793
|
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
DeepMind, USA; DeepMind, UK; Washington State University, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2876; None
| null | 0 | null | null | null | null | null |
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu, Hassan Ghasemzadeh
|
https://iclr.cc/virtual/2021/poster/2876
|
continual learning;catastrophic forgetting;mode connectivity;multitask learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2876
|
Linear Mode Connectivity in Multitask and Continual Learning
|
https://github.com/imirzadeh/MC-SGD
| null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Training;Contrastive Divergence
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Adversarial Training using Contrastive Divergence
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Evolutionary Learning;Fragment-Based Drug Design;Deep Generative Model;Drug Design;Multi-objective Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Deep Evolutionary Learning for Molecular Design
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;task-agnostic;agent evaluation;exploration;information gain;empowerment;curiosity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Evaluating Agents Without Rewards
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning curve;deep network;analysis;asymptotic error;learning efficiency;power law
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null | null |
Learning Curves for Analysis of Deep Networks
| null | null | 0 | 3.5 |
Reject
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Forward prediction;physical reasoning
| null | 0 | null | null |
iclr
| 0.801784 | 0 | null |
main
| 5.2 |
5;5;5;5;6
| null | null |
Forward Prediction for Physical Reasoning
| null | null | 0 | 3.8 |
Reject
|
4;3;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
out-of-distribution generalization;extrapolation
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Out-of-Distribution Generalization with Maximal Invariant Predictor
| null | null | 0 | 3 |
Withdraw
|
4;3;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;DNF;read-once;inductive bias;reconstruction;alignment
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
On Learning Read-once DNFs With Neural Networks
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Inter-class correlation;Human cognitive system;Weak supervision;Calibration;Regularization
| null | 0 | null | null |
iclr
| -0.87831 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction
| null | null | 0 | 3.5 |
Withdraw
|
4;4;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active learning;active search
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Similarity Search for Efficient Active Learning and Search of Rare Concepts
| null | null | 0 | 4 |
Reject
|
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
| 6.2 |
4;6;6;6;9
| null | null |
Deep Networks from the Principle of Rate Reduction
| null | null | 0 | 3 |
Reject
|
3;3;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pruning;Paths;Neural Networks;Neural Tangent Kernel
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
A Unified Paths Perspective for Pruning at Initialization
| 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 | null | null | 0 | null | null |
iclr
| 0.565916 | 0 | null |
main
| 5.75 |
4;4;7;8
| null | null |
Conditional Coverage Estimation for High-quality Prediction Intervals
| 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 |
Compressive sensing;nonuniform subsampling;machine learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction
|
https://figshare.com/s/519a923fae8f386d7f5b
| null | 0 | 4.75 |
Reject
|
5;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
structure learning;deep learning;continuous;optimization
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Dependency Structure Discovery from Interventions
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
CMU and Google; Google; MBZUAI & CMU
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2670; None
| null | 0 | null | null | null | null | null |
Maruan Al-Shedivat, Jennifer Gillenwater, Eric P Xing, Afshin Rostamizadeh
|
https://iclr.cc/virtual/2021/poster/2670
|
federated learning;posterior inference;MCMC
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2670
|
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
Department of Neuroscience, Washington University in St Louis, St Louis, MO, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3366; None
| null | 0 | null | null | null | null | null |
Binxu Wang, Carlos Ponce
|
https://iclr.cc/virtual/2021/poster/3366
|
Deep generative model;Interpretability;GAN;Differential Geometry;Optimization;Model Inversion;Feature Visualization
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null |
https://iclr.cc/virtual/2021/poster/3366
|
A Geometric Analysis of Deep Generative Image Models and Its Applications
| 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 |
Compositionality
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 3 |
2;3;3;4
| null | null |
Transferability of Compositionality
| null | null | 0 | 4 |
Reject
|
5;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Graph Sampling;Network Embedding
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Deep Graph Neural Networks with Shallow Subgraph Samplers
| 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 |
Deep Reinforcement Learning;Education;Automated Grading;Program Testing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;Contrastive Learning;Textual Representation Learning;Natural Language Processing
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Towards Robust Textual Representations with Disentangled Contrastive Learning
| 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 |
Min-Max Optimization;Riemannian Manifold;Robust Training
| null | 0 | null | null |
iclr
| 0.471405 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Gradient Descent Ascent for Min-Max Problems on Riemannian Manifolds
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
The Swiss AI Lab IDSIA, USI, SUPSI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3023; None
| null | 0 | null | null | null | null | null |
Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber
|
https://iclr.cc/virtual/2021/poster/3023
|
unsupervised representation learning;object-keypoint representations;visual saliency
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3023
|
Unsupervised Object Keypoint Learning using Local Spatial Predictability
| null | null | 0 | 2.666667 |
Spotlight
|
1;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
online A/B testing;subgroup treatment effects testing;continuous monitoring;supervised representation learning;classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
3;5;7;7
| null | null |
Online Testing of Subgroup Treatment Effects Based on Value Difference
| null | null | 0 | 4 |
Reject
|
4;4;3;5
| null |
null |
The University of Texas at Austin; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2808; None
| null | 0 | null | null | null | null | null |
Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang
|
https://iclr.cc/virtual/2021/poster/2808
| null | null | 0 | null | null |
iclr
| 0.87831 | 0 | null |
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2808
|
A Design Space Study for LISTA and Beyond
| 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 |
routing algorithms;adversarial learning;congestion functions
| null | 0 | null | null |
iclr
| -0.375823 | 0 | null |
main
| 5.75 |
3;5;7;8
| null | null |
Robust Learning for Congestion-Aware Routing
| 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 |
Data Recovery;Data Separability;Distributed Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
token-level contrastive loss;video and language alignment;video retrieval;multi-modal representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Token-Level Contrast for Video and Language Alignment
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive gradient methods;Over-parameterization;Stochastic line-search;Momentum
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Microsoft Corporation
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2753; None
| null | 0 | null | null | null | null | null |
Minjia Zhang, Menghao Li, Chi Wang, Mingqin Li
|
https://iclr.cc/virtual/2021/poster/2753
|
Efficient Deep Learning Inference;Scalability;Code Compilation;Bayesian Inference
| null | 0 | null | null |
iclr
| -0.730297 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2753
|
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation
| null | null | 0 | 3 |
Poster
|
5;4;1;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic Optimization;Deep Learning;Proximal Gradient Descent
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
A Unified Framework for Proximal Methods
| 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 |
multi-agent reinforcement leanring;ad hoc team play
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Multi-Agent Collaboration via Reward Attribution Decomposition
| null | null | 0 | 3 |
Reject
|
2;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive Step Size;Large Batch Optimization;Transformers
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Adaptive Learning Rates with Maximum Variation Averaging
| null | null | 0 | 4.25 |
Withdraw
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Simplifying Models with Unlabeled Output Data
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
New York University; New York University & Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3188; None
| null | 0 | null | null | null | null | null |
Denis Yarats, Ilya Kostrikov, Rob Fergus
|
https://iclr.cc/virtual/2021/poster/3188
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3188
|
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
| null | null | 0 | 4.25 |
Spotlight
|
4;3;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bias amplification;fairness;societal considerations
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
A Technical and Normative Investigation of Social Bias Amplification
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;deep learning;clustering;embedding latent space
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.5 |
2;4;4;4
| null | null |
Semi-Supervised Learning via Clustering Representation Space
| null | null | 0 | 4.75 |
Reject
|
5;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
joint pre-training;multimodal representation learning;spoken language understanding;speech representation learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Semi-Supervised Speech-Language Joint Pre-Training for Spoken Language Understanding
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Safety constraints;Constrained Markov Decision Process
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Reconnaissance for reinforcement learning with safety constraints
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.899229 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.
| null | null | 0 | 3.25 |
Withdraw
|
2;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning rate adaptation;hyper-gradient descent;meta learning;optimisation;hierarchical system
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Adaptive Hierarchical Hyper-gradient Descent
| null | null | 0 | 3 |
Reject
|
2;4;3;3
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
PAC-Bayes Bounds;Large Deviation Theory;Concentration Inequalities;Generalisation Error
| null | 0 | null | null |
iclr
| 0.218218 | 0 | null |
main
| 4.4 |
4;4;4;5;5
| null | null |
Non-Asymptotic PAC-Bayes Bounds on Generalisation Error
| null | null | 0 | 2.8 |
Withdraw
|
2;2;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
constraint satisfaction problem;graph attention;transformers
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
Transformers satisfy
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent network;representation learning;collective decision making;type-preserving data augmentation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Mixture Representation Learning with Coupled Autoencoding Agents
| null | null | 0 | 3.5 |
Reject
|
3;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active learning;consistency-training;cardiac signals;healthcare
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals
| 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 |
matrix exponential;tensor methods;supervised learning;domain extrapolation;certified robustness
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Intelligent Matrix Exponentiation
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
MIT; Universit ´e de Montr ´eal
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2959; None
| null | 0 | null | null | null | null | null |
Dmitriy Smirnov, Mikhail Bessmeltsev, Justin Solomon
|
https://iclr.cc/virtual/2021/poster/2959
|
3D shape representations;CAD modeling;sketch-based modeling;computer graphics;computer vision;deep learning
| null | 0 | null | null |
iclr
| -0.492366 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2959
|
Learning Manifold Patch-Based Representations of Man-Made Shapes
| null | null | 0 | 4.25 |
Poster
|
5;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised approach;image-to-image translation;representation learning
| null | 0 | null | null |
iclr
| 0.870388 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Rethinking the Truly Unsupervised Image-to-Image Translation
| null | null | 0 | 4.25 |
Reject
|
3;4;5;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
scene understanding;representation learning;multi-object scene decomposition;pose estimation;shape and appearance estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Semi-Supervised Learning of Multi-Object 3D Scene Representations
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gromov-Wasserstein;Non-convex optimization;Optimal Transport;Partial matching
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
| 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 |
Unsupervised Representation Learning;Robot Control;Quality-Diversity
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
On the Importance of Distraction-Robust Representations for Robot Learning
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep neural network;Volterra–Fredholm–Hammerstein integral equations;Legendre orthogonal polynomials;Gaussian quadrature method;Collocation method
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra–Fredholm–Hammerstein integral equations
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Network;Self-Attention;Generalization of GNNs;Weisfeiler-Lehman
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Global Attention Improves Graph Networks Generalization
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transformers;Deep Learning;Attention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
4;5;7;7
| null | null |
Synthesizer: Rethinking Self-Attention for Transformer Models
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Samsung SAIT AI Lab & Mila, Montreal, Canada; Google Research; Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3051; None
| null | 0 | null | null | null | null | null |
Carles Domingo i Enrich, Fabian Pedregosa, Damien Scieur
|
https://iclr.cc/virtual/2021/poster/3051
|
Smooth games;First-order Methods;Acceleration;Bilinear games;Average-case Analysis;Orthogonal Polynomials
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3051
|
Average-case Acceleration for Bilinear Games and Normal Matrices
| null | null | 0 | 3 |
Poster
|
3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Networks;Graph Node Classification;Label Noise
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Towards Robust Graph Neural Networks against Label Noise
| 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 |
Robotics;Reinforcement Learning;Learning from Demonstration
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/gaddpg
|
main
| 6 |
5;6;7
| null | null |
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
KAIST EE; KAIST AIM; MBZUAI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3108; None
| null | 0 | null | null | null | null | null |
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin
|
https://iclr.cc/virtual/2021/poster/3108
|
network pruning;layerwise sparsity;magnitude-based pruning
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3108
|
Layer-adaptive Sparsity for the Magnitude-based Pruning
|
https://github.com/jaeho-lee/layer-adaptive-sparsity
| null | 0 | 4 |
Poster
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
binarized neural network;binary;quantized;1-bit;low precision
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Fast Binarized Neural Network Training with Partial Pre-training
| null | null | 0 | 4.25 |
Reject
|
5;5;3;4
| null |
null |
´Ecole Polytechnique, France; ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2636; None
| null | 0 | null | null | null | null | null |
El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault
|
https://iclr.cc/virtual/2021/poster/2636
|
Byzantine SGD;Distributed ML;Momentum
| null | 0 | null | null |
iclr
| 0.272166 | 0 | null |
main
| 5.25 |
4;4;6;7
| null |
https://iclr.cc/virtual/2021/poster/2636
|
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent
| null | null | 0 | 3 |
Poster
|
3;3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recalibration;normalizing flows;uncertainty
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Normalizing Flows for Calibration and Recalibration
| null | null | 0 | 3.25 |
Reject
|
5;4;3;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Treatment Effects;Regularization;Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;5;7
| null | null |
Estimating Treatment Effects via Orthogonal 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 |
adversarial examples;deep learning;robustness
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Composite Adversarial Training for Multiple Adversarial Perturbations and Beyond
| 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 |
Applications;side channel attacks;supervised disentangled learning;video domain adaptation
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks
| null | null | 0 | 2.75 |
Reject
|
2;4;3;2
| null |
null |
Google Cloud AI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3244; None
| null | 0 | null | null | null | null | null |
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister
|
https://iclr.cc/virtual/2021/poster/3244
|
deep one-class classification;self-supervised learning
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3244
|
Learning and Evaluating Representations for Deep One-Class Classification
|
https://github.com/google-research/deep_representation_one_class
| null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep generative models;generative adversarial networks;density estimation
| null | 0 | null | null |
iclr
| -0.622543 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Mutual Calibration between Explicit and Implicit Deep Generative Models
| 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 |
Network pruning;Spline theory
| null | 0 | null | null |
iclr
| 0.688247 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Max-Affine Spline Insights Into Deep Network Pruning
| null | null | 0 | 4.5 |
Withdraw
|
4;4;5;5
| null |
null |
Department of Statistics and Data Science, Cornell University, Ithaca, NY 14850
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2524; None
| null | 0 | null | null | null | null | null |
Binh Tang, David S Matteson
|
https://iclr.cc/virtual/2021/poster/2524
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2524
|
Graph-Based Continual Learning
| null | null | 0 | 3.75 |
Spotlight
|
4;4;3;4
| null |
null |
Carnegie Mellon University, Pittsburgh, PA, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3111; None
| null | 0 | null | null | null | null | null |
Divyansh Kaushik, Amrith Setlur, Eduard H Hovy, Zachary Lipton
|
https://iclr.cc/virtual/2021/poster/3111
|
humans in the loop;annotation artifacts;text classification;sentiment analysis;natural language inference
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 7 |
6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/3111
|
Explaining the Efficacy of Counterfactually Augmented 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 |
neural networks;sparsity;gradient flow;sparse network optimization
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Keep the Gradients Flowing: Using Gradient Flow to study Sparse Network Optimization
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Google Brain; UC Berkeley
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3326; None
| null | 0 | null | null | null | null | null |
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
|
https://iclr.cc/virtual/2021/poster/3326
|
reset-free;lifelong;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/berkeley.edu/reset-free-lifelong-learning
|
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3326
|
Reset-Free Lifelong Learning with Skill-Space Planning
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;multitask learning;attention mechanism;electrocardiography
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Attention Based Joint Learning for Supervised Electrocardiogram Arrhythmia Differentiation with Unsupervised Abnormal Beat Segmentation
| 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
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Later Span Adaptation for Language Understanding
| 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 |
shape completion;Meta-learning;Few-shot;3D reconstruction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Bayesian Meta-Learning for Few-Shot 3D Shape Completion
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial defense;adversarial machine learning;activation function;adversarial training;neural network architecture
| null | 0 | null | null |
iclr
| -0.555556 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Smooth Adversarial Training
| 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 |
Layout Representation;Pre-training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
data augmentation;image classification;calibration;distributional shifts;adversarial robustness
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
What are effective labels for augmented data? Improving robustness with AutoLabel
| 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 |
long-tail recognition;classifier composition
| null | 0 | null | null |
iclr
| -0.650945 | 0 | null |
main
| 4.75 |
3;4;4;8
| null | null |
Alpha Net: Adaptation with Composition in Classifier Space
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null |
University of Illinois at Urbana-Champaign
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2858; None
| null | 0 | null | null | null | null | null |
Peiye Zhuang, Sanmi Koyejo, Alex Schwing
|
https://iclr.cc/virtual/2021/poster/2858
|
Image manipulation;GANs;latent space of GANs
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/2858
|
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
| 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 |
graph matching;maximum common subgraph;graph neural network;reinforcement learning;search
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Learning to Search for Fast Maximum Common Subgraph Detection
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Contrastive learning;information retrieval;clustering;physiological signals;healthcare
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoder;outlier detection;novelty detection;energy-based model
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection
| 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 | null | null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search
| null | null | 0 | 4.25 |
Withdraw
|
4;5;3;5
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
abstractive summarization;scientific papers
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
What's new? Summarizing Contributions in Scientific Literature
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.