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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2014
| 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 |
Rate-Distortion Auto-Encoders
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Generative Product-of-Filters Model of Audio
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Improving Deep Neural Networks with Probabilistic Maxout Units
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
On Fast Dropout and its Applicability to Recurrent Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Neuronal Synchrony in Complex-Valued Deep Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Distinction between features extracted using deep belief networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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, complex-valued representations of natural sounds learned with phase and amplitude continuity priors
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
k-Sparse Autoencoders
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 does the unsupervised pretraining encourage moderate-sparseness?
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 for neuroimaging: a validation study
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Graph Architectures
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Understanding Deep Architectures using a Recursive Convolutional Network
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Large-scale Multi-label Text Classification - Revisiting Neural Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Factorial Hidden Markov Models for Learning Representations of Natural Language
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Multimodal Transitions for Generative Stochastic Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 empirical analysis of dropout in piecewise linear networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 to encode motion using spatio-temporal synchrony
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
On the number of inference regions of deep feed forward networks with piece-wise linear activations
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Bounding the Test Log-Likelihood of Generative Models
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Paired-associate Images with An Unsupervised Deep Learning Architecture
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
EXMOVES: Classifier-based Features for Scalable Action Recognition
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Intriguing properties of neural networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Human Pose Estimation Features with Convolutional Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Generative NeuroEvolution for Deep Learning
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Non-Linear Feature Maps, With An Application To Representation Learning
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Sequentially Generated Instance-Dependent Image Representations for Classification
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Zero-Shot Learning and Clustering for Semantic Utterance Classification
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Nonparametric Weight Initialization of Neural Networks via Integral Representation
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 Convolutional Ranking for Multilabel Image Annotation
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Correlation-based construction of neighborhood and edge features
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
One-Shot Adaptation of Supervised Deep Convolutional Models
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 similarity-preserving hashing
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Some Improvements on Deep Convolutional Neural Network Based Image Classification
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Revisiting Natural Gradient for Deep Networks
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Group-sparse Embeddings in Collective Matrix Factorization
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Network In Network
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2014
| 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 |
Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines
| null | null | 0 | 0 |
Poster
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null |
Princeton Neuroscience Institute; Department of Psychology, Princeton University; Department of Computer Science, Princeton University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3064; None
| null | 0 | null | null | null | null | null |
Sreejan Kumar, Ishita Dasgupta, Jonathan Cohen, Nathaniel Daw, Thomas L Griffiths
|
https://iclr.cc/virtual/2021/poster/3064
|
meta-learning;human cognition;reinforcement learning;compositionality
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3064
|
Meta-Learning of Structured Task Distributions in Humans and Machines
| null | null | 0 | 3.75 |
Poster
|
3;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Object detection;weakly supervised;transfer leaning
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
CROSS-SUPERVISED OBJECT DETECTION
| null | null | 0 | 4.25 |
Reject
|
5;4;3;5
| null |
null |
McGill University; University of California, Berkeley; OATML group, University of Oxford; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2863; None
| null | 0 | null | null | null | null | null |
Amy Zhang, Rowan T McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
|
https://iclr.cc/virtual/2021/poster/2863
|
rich observations;bisimulation metrics;representation learning;state abstractions
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.666667 |
7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2863
|
Learning Invariant Representations for Reinforcement Learning without Reconstruction
| null | null | 0 | 4 |
Oral
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Vector embedding;Logs;Search;Causal Analysis;Anomaly Detection
| null | 0 | null | null |
iclr
| 0.169031 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Log representation as an interface for log processing applications
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Technion; Intel Labs; Intel Israel
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2625; None
| null | 0 | null | null | null | null | null |
Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar
|
https://iclr.cc/virtual/2021/poster/2625
|
Reinforcement Learning;Memory Mapping;Device Placement;Evolutionary Algorithms
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2625
|
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null |
s00504852
| null |
Nueral Architecture Search;Deep Learning;Generative Adversarial Network;Graph Neural Network;Computer Vision
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Generative Adversarial Neural Architecture Search with Importance Sampling
| null | null | 0 | 3.25 |
Reject
|
4;2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
2;5;5;5
| null | null |
Can Kernel Transfer Operators Help Flow based Generative Models?
| 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 deep learning;neural network verification;interval analysis
| null | 0 | null | null |
iclr
| -0.894427 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Generalized Universal Approximation for Certified Networks
| null | null | 0 | 3.5 |
Reject
|
5;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;language modeling;sequence modeling;temperature scaling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Context-Aware Temperature for Language Modeling
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
University of Amsterdam, QUV A lab; University of Amsterdam
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2730; None
| null | 0 | null | null | null | null | null |
Phillip Lippe, Efstratios Gavves
|
https://iclr.cc/virtual/2021/poster/2730
|
Normalizing Flows;Density Estimation;Graph Generation
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2730
|
Categorical Normalizing Flows via Continuous Transformations
| 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 | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
AggMask: Exploring locally aggregated learning of mask representations for instance segmentation
|
https://github.com/advdfacd/AggMask
| null | 0 | 4.25 |
Withdraw
|
4;5;4;4
| null |
null |
University of Oxford; University of Oxford, Alan Turing Institute, London
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2585; None
| null | 0 | null | null | null | null | null |
Matthew Willetts, Alexander Camuto, Tom Rainforth, S Roberts, Christopher Holmes
|
https://iclr.cc/virtual/2021/poster/2585
|
deep generative models;variational autoencoders;robustness;adversarial attack
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2585
|
Improving VAEs' Robustness to Adversarial Attack
| null | null | 0 | 3.25 |
Poster
|
5;3;2;3
| null |
null |
Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada; Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA 90095
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3010; None
| null | 0 | null | null | null | null | null |
Paulo Tabuada, Bahman Gharesifard
|
https://iclr.cc/virtual/2021/poster/3010
|
Deep residual neural networks;universal approximation;nonlinear control theory
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3010
|
Universal approximation power of deep residual neural networks via nonlinear control theory
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Learning;Initialization;Few-shot classification
| null | 0 | null | null |
iclr
| -0.906327 | 0 | null |
main
| 4.4 |
3;3;4;6;6
| null | null |
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
| null | null | 0 | 3.8 |
Reject
|
5;4;4;3;3
| null |
null |
Mila, University of Montreal; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3303; None
| null | 0 | null | null | null | null | null |
Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam Harley, Katerina Fragkiadaki
|
https://iclr.cc/virtual/2021/poster/3303
|
Disentanglement;Few Shot Learning;3D Vision;VQA
| null | 0 | null | null |
iclr
| 0.707107 | 0 |
https://mihirp1998.github.io/project_pages/d3dp/
|
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3303
|
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
Department of Computer Science & Engineering, Kyung Hee University, South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2986; None
| null | 0 | null | null | null | null | null |
A F M Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae
|
https://iclr.cc/virtual/2021/poster/2986
|
SaliencyMix;Saliency Guided Data Augmentation;Data Augmentation;Regularization
| null | 0 | null | null |
iclr
| 0.790569 | 0 | null |
main
| 5.8 |
3;3;7;7;9
| null |
https://iclr.cc/virtual/2021/poster/2986
|
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
|
https://github.com/SaliencyMix/SaliencyMix
| null | 0 | 4 |
Poster
|
3;4;4;4;5
| null |
null |
Ben-Gurion University; ICSI and UC Berkeley; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3112; None
| null | 0 | null | null | null | null | null |
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W Mahoney
|
https://iclr.cc/virtual/2021/poster/3112
|
recurrent neural networks;dynamical systems;differential equations
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3112
|
Lipschitz Recurrent Neural Networks
| 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 |
neural networks;generative models;log-likelihood;diffusion models;denoising diffusion probabilistic models;image generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Improved Denoising Diffusion Probabilistic Models
| null | null | 0 | 3 |
Reject
|
2;3;4;3
| null |
null |
Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3255; None
| null | 0 | null | null | null | null | null |
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah
|
https://iclr.cc/virtual/2021/poster/3255
|
Semi-Supervised Learning;Pseudo-Labeling;Uncertainty;Calibration;Deep Learning
| null | 0 | null | null |
iclr
| 0.96225 | 0 | null |
main
| 6.5 |
5;6;6;9
| null |
https://iclr.cc/virtual/2021/poster/3255
|
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Spiking Neural Networks;Input Encoding;Low Latency;Discrete Cosine Transform;Temporal Information;Frequency Domain
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
| null | null | 0 | 4 |
Reject
|
4;3;4;5
| null |
null |
Dept of Computer Science, Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2607; None
| null | 0 | null | null | null | null | null |
Allan Zhou, Tom Knowles, Chelsea Finn
|
https://iclr.cc/virtual/2021/poster/2607
|
meta-learning;equivariance;convolution;symmetry
| null | 0 | null | null |
iclr
| 0.730297 | 0 | null |
main
| 7 |
5;6;8;9
| null |
https://iclr.cc/virtual/2021/poster/2607
|
Meta-learning Symmetries by Reparameterization
|
https://github.com/AllanYangZhou/metalearning-symmetries
| null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-reinforcement learning;actor-critic;deep learning;interpretable
| null | 0 | null | null |
iclr
| -0.642857 | 0 | null |
main
| 3.2 |
2;3;3;4;4
| null | null |
Interpretable Meta-Reinforcement Learning with Actor-Critic Method
| null | null | 0 | 3.8 |
Reject
|
5;4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Neural network quantization;Information geometry
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 4 |
3;3;5;5
| null | null |
Optimizing Quantized Neural Networks in a Weak Curvature Manifold
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Virginia Tech; Google Cloud AI; Google Brain
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3033; None
| null | 0 | null | null | null | null | null |
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister
|
https://iclr.cc/virtual/2021/poster/3033
|
pseudo-labeling;semi-supervised;semantic-segmentation
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3033
|
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
|
https://github.com/googleinterns/wss
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep neural networks;mean-field theory;global convergence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Modeling from Features: a Mean-field Frameworkfor Over-parameterized Deep Neural Networks
| null | null | 0 | 0 |
Desk Reject
| null | null |
null |
Paper under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-supervised Learning;graph neural network;vision and language;question generation
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
| null | null | 0 | 4.5 |
Reject
|
5;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
physics learning;symbolic regression;intuitive physics
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3362; None
| null | 0 | null | null | null | null | null |
Hideaki Hayashi, Seiichi Uchida
|
https://iclr.cc/virtual/2021/poster/3362
|
classification;sparse Bayesian learning;Gaussian mixture model
| null | 0 | null | null |
iclr
| 0.948683 | 0 | null |
main
| 6.5 |
5;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3362
|
A Discriminative Gaussian Mixture Model with Sparsity
| null | null | 0 | 4 |
Poster
|
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 | 0 | null |
main
| 3 |
2;2;3;4;4
| null | null |
A Theory of Self-Supervised Framework for Few-Shot Learning
| null | null | 0 | 3.4 |
Reject
|
3;4;3;3;4
| null |
null |
Machine Learning Department, Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2613; None
| null | 0 | null | null | null | null | null |
Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency
|
https://iclr.cc/virtual/2021/poster/2613
|
Self-supervised Learning;Unsupervised Learning;Multi-view Representation Learning
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 6.25 |
6;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2613
|
Self-supervised Learning from a Multi-view Perspective
| null | null | 0 | 4 |
Poster
|
3;4;4;5
| null |
null |
Division of Statistics and Machine Learning, Linköping University, Sweden; Department of Information Technology, Uppsala University, Sweden
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2682; None
| null | 0 | null | null | null | null | null |
David Widmann, Fredrik Lindsten, Dave Zachariah
|
https://iclr.cc/virtual/2021/poster/2682
|
calibration;uncertainty quantification;framework;integral probability metric;maximum mean discrepancy
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
5;7;9
| null |
https://iclr.cc/virtual/2021/poster/2682
|
Calibration tests beyond classification
|
https://github.com/devmotion/Calibration_ICLR2021
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adam;decentralized optimization;adaptive gradient methods
| null | 0 | null | null |
iclr
| -0.445941 | 0 | null |
main
| 5 |
3;3;4;7;8
| null | null |
Convergent Adaptive Gradient Methods in Decentralized Optimization
| null | null | 0 | 3.6 |
Reject
|
3;5;5;1;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural machine translation;translation memory;pre-train language model
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.5 |
2;4;4;4
| null | null |
Translation Memory Guided Neural Machine Translation
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null |
Facebook AI Research; Ecole des Ponts Paristech, Rutgers University - Camden
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3219; None
| null | 0 | null | null | null | null | null |
François Charton, Amaury Hayat, Guillaume Lample
|
https://iclr.cc/virtual/2021/poster/3219
|
differential equations;computation;transformers;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
3;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3219
|
Learning advanced mathematical computations from examples
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Monte-Carlo Tree Search;Entropy regularization;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.680414 | 0 | null |
main
| 5.5 |
4;5;5;8
| null | null |
Convex Regularization in Monte-Carlo Tree Search
| null | null | 0 | 3 |
Reject
|
1;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
video generation;GANs;scalable methods
| null | 0 | null | null |
iclr
| 0.534522 | 0 | null |
main
| 5 |
3;3;6;6;7
| null | null |
SSW-GAN: Scalable Stage-wise Training of Video GANs
| null | null | 0 | 4 |
Reject
|
4;3;5;3;5
| null |
null |
University of Washington; University of Technology Sydney
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2709; None
| null | 0 | null | null | null | null | null |
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang
|
https://iclr.cc/virtual/2021/poster/2709
|
Zero-shot learning;isometric;prototype propagation;alignment of semantic and visual space
| null | 0 | null | null |
iclr
| 0.288675 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2709
|
Isometric Propagation Network for Generalized Zero-shot Learning
| null | null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Rethinking Convolution: Towards an Optimal Efficiency
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;orthogonal multi-path
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
Learn Robust Features via Orthogonal Multi-Path
| 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 |
spoken question answering;natural language processing;speech and language processing;knowledge distillation
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null |
Normandy University, LITIS Lab, University of Rouen Normandy, INSA Rouen Normandie, Rouen, 76000, France
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3158; None
| null | 0 | null | null | null | null | null |
Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, Paul Honeine
|
https://iclr.cc/virtual/2021/poster/3158
|
Graph Neural Networks;Spectral Graph Filter;Spectral Analysis
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7 |
6;6;8;8
| null |
https://iclr.cc/virtual/2021/poster/3158
|
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective
|
https://github.com/balcilar/gnn-spectral-expressive-power
| null | 0 | 3.5 |
Poster
|
2;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Graph Structured Reinforcement Learning;Exploration
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Explore with Dynamic Map: Graph Structured Reinforcement Learning
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic Optimization;Non-convex Optimization;Deep Learning;Adaptive methods;Newton methods;Second-order optimization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Adaptive norms for deep learning with regularized Newton methods
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null |
Salesforce Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2567; None
| null | 0 | null | null | null | null | null |
Junnan Li, Caiming Xiong, Steven Hoi
|
https://iclr.cc/virtual/2021/poster/2567
|
webly-supervised learning;weakly-supervised learning;contrastive learning;representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2567
|
MoPro: Webly Supervised Learning with Momentum Prototypes
|
https://github.com/salesforce/MoPro
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2843; None
| null | 0 | null | null | null | null | null |
Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown
|
https://iclr.cc/virtual/2021/poster/2843
|
multi-agent systems;regret minimization;no-regret learning;game theory;reinforcement learning
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2843
|
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
| null | null | 0 | 4.25 |
Oral
|
4;5;4;4
| null |
null |
Department of Computer Science, University of Maryland; Dep. of Electr. Eng. and Computer Science, University of Siegen; Computer Science, US Naval Academy; Department of Mathematics, University of Maryland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2561; None
| null | 0 | null | null | null | null | null |
Jonas Geiping, Liam H Fowl, Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein
|
https://iclr.cc/virtual/2021/poster/2561
|
Data Poisoning;ImageNet;Large-scale;Gradient Alignment;Security;Backdoor Attacks;from-scratch;clean-label
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2561
|
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
| null | null | 0 | 4 |
Poster
|
5;4;4;3
| null |
null |
University of Amsterdam; TNO
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2835; None
| null | 0 | null | null | null | null | null |
David Zhang, Gertjan J Burghouts, Cees G Snoek
|
https://iclr.cc/virtual/2021/poster/2835
|
set prediction;energy based models
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2835
|
Set Prediction without Imposing Structure as Conditional Density Estimation
| null | null | 0 | 2.75 |
Poster
|
2;3;3;3
| null |
null |
Microsoft Research India; Carnegie Mellon University; Indian Institute of Science
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2553; None
| null | 0 | null | null | null | null | null |
Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David Woodruff, Samson Zhou
|
https://iclr.cc/virtual/2021/poster/2553
|
Latent Simplex;numerical linear algebra;low-rank approximation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null |
https://iclr.cc/virtual/2021/poster/2553
|
Learning a Latent Simplex in Input Sparsity Time
| null | null | 0 | 3.666667 |
Spotlight
|
4;3;4
| null |
null |
Mila, Google Research, Brain Team; Mila, Australian AI Institute, UTS; Mila, McGill University; Australian AI Institute, UTS
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2568; None
| null | 0 | null | null | null | null | null |
Lu Liu, William Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle
|
https://iclr.cc/virtual/2021/poster/2568
| null | null | 0 | null | null |
iclr
| 0.534522 | 0 | null |
main
| 6.8 |
6;6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2568
|
A Universal Representation Transformer Layer for Few-Shot Image Classification
|
https://github.com/liulu112601/URT
| null | 0 | 4.8 |
Poster
|
4;5;5;5;5
| null |
null |
Carnegie Mellon University; D.E. Shaw & Co.
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2792; None
| null | 0 | null | null | null | null | null |
Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Han Zhao, Louis-Philippe Morency, Ruslan Salakhutdinov
|
https://iclr.cc/virtual/2021/poster/2792
|
self-supervised learning;contrastive learning;dependency based method
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.75 |
6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2792
|
Self-supervised Representation Learning with Relative Predictive Coding
|
https://github.com/martinmamql/relative_predictive_coding
| null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Partial updating;communication constraints;server-to-edge;deep neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Deep Partial Updating
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Microsoft Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2982; None
| null | 0 | null | null | null | null | null |
Guolin Ke, Di He, Tie-Yan Liu
|
https://iclr.cc/virtual/2021/poster/2982
|
Natural Language Processing;Pre-training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2982
|
Rethinking Positional Encoding in Language Pre-training
|
https://github.com/guolinke/TUPE
| null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
min-max optimization;GANs
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
A Provably Convergent and Practical Algorithm for Min-Max Optimization with Applications to GANs
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
accelerated gradient method;matrix completion;first-order methods;differential equation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
A new accelerated gradient method inspired by continuous-time perspective
| 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 |
OOD detection;adversarial samples detection;deep learning;classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Understanding Classifiers with Generative Models
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
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