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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Salesforce Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, richard socher
|
https://iclr.cc/virtual/2019/poster/780
|
Domain adaptation;generative adversarial network;cyclic adversarial learning;speech
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null |
Samsung-HSE Laboratory, National Research University Higher School of Economics, Samsung AI Center Moscow; Samsung AI Center Moscow
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
|
https://iclr.cc/virtual/2019/poster/884
|
deep learning;variational inference;variational dropout
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Variance Networks: When Expectation Does Not Meet Your Expectations
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hypernetworks;generative adversarial networks;anomaly detection
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
HyperGAN: Exploring the Manifold of Neural Networks
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Efficient CNN;Seed convolutional filter
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
PolyCNN: Learning Seed Convolutional Filters
| null | null | 0 | 3 |
Withdraw
|
4;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Exploration;goal-directed;deep reinforcement learning;explicit memory
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Explicit Recall for Efficient Exploration
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image-to-image;Translation;Unsupervised;Generation;Adversarial;Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Unsupervised one-to-many image translation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
QUV A Lab, University of Amsterdam, Amsterdam, Netherlands
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Peter OConnor, Efstratios Gavves, Max Welling
|
https://iclr.cc/virtual/2019/poster/968
|
credit assignment;energy-based models;biologically plausible learning
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Initialized Equilibrium Propagation for Backprop-Free Training
|
https://github.com/QUVA-Lab/init-eqprop
| null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4 |
2;4;6
| null | null |
NA
| null | null | 0 | 3.333333 |
Withdraw
|
5;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep neural networks;memorizing;data-dependent regularization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gradient Acceleration;Saturation Areas;Dropout;Coadaptation
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Gradient Acceleration in Activation Functions
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
linear regions;approximate model counting;mixed-integer linear programming
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Empirical Bounds on Linear Regions of Deep Rectifier Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;on-policy learning;trust region policy optimisation;replay buffer
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
On-Policy Trust Region Policy Optimisation with Replay Buffers
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;optimization;variance reduction
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Online Learning;Supervised Dimension Reduction;Incremental Sliced Inverse Regression;Effective Dimension Reduction Space
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 4.333333 |
2;5;6
| null | null |
Online Learning for Supervised Dimension Reduction
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial;equivariance;universal;rotation;translation;CNN;GCNN
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Universal Attacks on Equivariant Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
University of Toronto, Vector Institute
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
|
https://iclr.cc/virtual/2019/poster/1104
|
Explainability;Interpretability;Generative Models;Saliency Map;Machine Learning;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Explaining Image Classifiers by Counterfactual Generation
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequence model;switching linear dynamical systems;variational bayes;filter;variational inference;stochastic recurrent neural network
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Switching Linear Dynamics for Variational Bayes Filtering
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Boltzmann Softmax Operator;Value Function Estimation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continuous Control;Reinforcement Learning;Policy Optimization;Policy Gradient;Evolution Strategies;CMA-ES;PPO
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;4;9
| null | null |
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
| null | null | 0 | 3 |
Reject
|
2;4;3
| null |
null |
University of Oxford
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Namhoon Lee, Thalaiyasingam Ajanthan, Philip H.S Torr
|
https://iclr.cc/virtual/2019/poster/902
|
neural network pruning;connection sensitivity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null | null |
SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Second order pooling
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Efficient Codebook and Factorization for Second Order Representation Learning
| null | null | 0 | 3.666667 |
Reject
|
5;2;4
| null |
null |
Microsoft Research, Beijing, China; Institute for Advanced Study, Tsinghua University, Beijing, China
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bin Dai, David Wipf
|
https://iclr.cc/virtual/2019/poster/899
|
variational autoencoder;generative models
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Diagnosing and Enhancing VAE Models
|
https://github.com/daib13/TwoStageVAE
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;semantic representations;local vs global information;latent variable modelling;generative modelling;semi-supervised learning;variational autoencoders.
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Invariant-equivariant representation learning for multi-class data
| null | null | 0 | 3.333333 |
Reject
|
3;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep RL;Exploration Exploitation;DQN;Bayesian Regret;Thompson Sampling
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4 |
2;4;4;6
| null | null |
Efficient Exploration through Bayesian Deep Q-Networks
| null | null | 0 | 3.25 |
Reject
|
5;4;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Uncertainty;Out-of-Distribution;Generative Models
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Generative Ensembles for Robust Anomaly Detection
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pretend to share;Gradient Communication
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Multi-task Learning with Gradient Communication
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Embedding;Generalization Analysis;Matrix Factorization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble classification;random subspace;data sketching
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;continuous action space RL
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;RNA-Seq;gene expression;bioinformatics;computational biology;transcriptomics;deep learning;genomics
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
Towards the Latent Transcriptome
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Guider Network for Multi-Dual Learning
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary weight networks;neural network quantization;reinforcement learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;optimization;batch normalization;mean field theory;Fisher information
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
MEAN-FIELD ANALYSIS OF BATCH NORMALIZATION
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Open-Ended Content-Style Recombination Via Leakage Filtering
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic parsing;non-deterministic oracles;natural language to SQL;incremental parsing;sequence prediction
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian deep learning;Bayesian neural networks;adversarial examples
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;information theory;robust neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
predicted variables;machine learning;programming;computing systems;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Predicted Variables in Programming
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer Vision;Deep Learning;Autoencoder;GAN;Image Modification;Social Traits;Social Psychology
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Look Ma, No GANs! Image Transformation with ModifAE
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dpp;submodularity;determinant
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
DppNet: Approximating Determinantal Point Processes with Deep Networks
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
imitation learning;reinforcement learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Georgia Institute of Technology; Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh
|
https://iclr.cc/virtual/2019/poster/810
|
cross-modal matching;voices;faces
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
|
https://github.com/ydwen/DIMNet
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Machine Translation;Natural Language Generation;Graph Embedding;LSTM
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Riemannian Stochastic Gradient Descent;Tensor-Train;Recurrent Neural Networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
memory;visual attention;image classification;image reconstruction;latent representations
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 6 |
4;5;9
| null | null |
A Biologically Inspired Visual Working Memory for Deep Networks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Electrical Engineering and Computer Science, University of California, Berkeley, USA; Electrical Engineering and Computer Science, University of California, Berkeley; Psychology and Cognitive Science, Princeton University, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L Griffiths
|
https://iclr.cc/virtual/2019/poster/864
|
compositionality;deep learning;metareasoning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
Automatically Composing Representation Transformations as a Means for Generalization
|
https://github.com/mbchang/crl
| null | 0 | 3 |
Poster
|
3;2;4
| null |
null |
Department of Computer Science, University of Toronto; Vector Institute, Canada; Department of Computer Science, University of Toronto; Vector Institute, Canada; NVIDIA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Seung Wook Kim, Makarand Tapaswi, Sanja Fidler
|
https://iclr.cc/virtual/2019/poster/647
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Visual Reasoning by Progressive Module Networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
binary neural Network;efficient deep learning;stochastic training;discrete neural network;efficient inference
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Probabilistic Binary Neural Networks
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;scaling with data;computational complexity;learning curves;speech recognition;image recognition;machine translation;language modeling
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
A Proposed Hierarchy of Deep Learning Tasks
| null | null | 0 | 3.333333 |
Reject
|
2;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model-based reinforcement learning;mbrl;reinforcement learning;predictive models;predictive learning;forward models;deep learning
| null | 0 | null | null |
iclr
| -0.169031 | 0 | null |
main
| 4.25 |
2;4;5;6
| null | null |
Understanding the Asymptotic Performance of Model-Based RL Methods
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null |
Department of Engineering, University of Cambridge; Microsoft Research AI; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
|
https://iclr.cc/virtual/2019/poster/703
|
Deep Convolutional Neural Networks;Gaussian Processes;Bayesian
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 6.75 |
6;7;7;7
| null | null |
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
| null | null | 0 | 3.5 |
Poster
|
4;5;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-supervised robotics;object understanding;object representations;metric learning;unsupervised vision
| null | 0 | null | null |
iclr
| -1 | 0 |
sites.google.com/view/object-contrastive-networks
|
main
| 3.666667 |
3;3;5
| null | null |
Object-Contrastive Networks: Unsupervised Object Representations
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial attack;zero-confidence attack
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture search;evolutionary algorithms
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Attention;RL;Top-Down;Interpretability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
wake-sleep;variational;amortised inference;hierarchical bayes;program learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
From Amortised to Memoised Inference: Combining Wake-Sleep and Variational-Bayes for Unsupervised Few-Shot Program Learning
| null | null | 0 | 4.666667 |
Withdraw
|
4;5;5
| null |
null |
Linguistics and Computer Science Departments, Stanford NLP Group, Stanford University; IBM Research, Yorktown Heights, NY; Department of Brain and Cognitive Sciences, MIT
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Matt Riemer, Juan Ignacio Cases Martin, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro
|
https://iclr.cc/virtual/2019/poster/1085
| null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;abstraction;mdp homomorphism;deep learning;robotics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;5
| null | null |
Online abstraction with MDP homomorphisms for Deep Learning
| null | null | 0 | 3 |
Withdraw
|
3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Exploration;Policy Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Exploration in Policy Mirror Descent
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
School of Computer Science, Simon Fraser University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chengzhou Tang, Ping Tan
|
https://iclr.cc/virtual/2019/poster/944
|
Structure-from-Motion;Bundle Adjustment;Dense Depth Estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null | null |
BA-Net: Dense Bundle Adjustment Networks
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
clustering;deep learning;application;chemistry;natural products
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Using Deep Siamese Neural Networks to Speed up Natural Products Research
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null |
Intel Corporation, Santa Clara, CA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tsung-Han Lin, Ping Tak P Tang
|
https://iclr.cc/virtual/2019/poster/846
|
dynamical neural networks;spiking neural networks;dynamical system;hardware friendly learning;feedback;contrastive learning;dictionary learning;sparse coding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
Sparse Dictionary Learning by Dynamical Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Google AI Berlin; Department of Engineering, University of Cambridge; Microsoft Research, Cambridge; Princeton Neuroscience Institute, Princeton University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Anqi Wu, Sebastian Nowozin, Ted Meeds, Richard E Turner, José Miguel Hernández Lobato, Alexander Gaunt
|
https://iclr.cc/virtual/2019/poster/1019
|
Bayesian neural network;variational inference;variational bayes;variance reduction;empirical bayes
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Deterministic Variational Inference for Robust Bayesian Neural Networks
| null | null | 0 | 3.666667 |
Oral
|
5;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representations;VAE;generative models;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Unsupervised Disentangling Structure and Appearance
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
A Walk with SGD: How SGD Explores Regions of Deep Network Loss?
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Mila/Université de Montréal, Montréal, Canada; Microsoft Research, Montréal, Canada; Mila/Université de Montréal and Microsoft Research, Montréal, Canada
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville
|
https://iclr.cc/virtual/2019/poster/660
|
Deep Learning;Natural Language Processing;Recurrent Neural Networks;Language Modeling
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
|
https://github.com/yikangshen/Ordered-Neurons
| null | 0 | 3.666667 |
Oral
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data selection;non-convex optimization;learning theory;active learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Learning and Data Selection in Big Datasets
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic optimization;multi-scale data analysis;non-decomposable loss;generalization;one-shot learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Backdrop: Stochastic Backpropagation
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
mental fatigue;brain dynamics preference;brain dynamics ranking;channel reliability;channel Selection
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 4.333333 |
2;4;7
| null | null |
Mental Fatigue Monitoring using Brain Dynamics Preferences
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent reinforcement learning;causal inference;game theory;social dilemma;intrinsic motivation;counterfactual reasoning;empowerment;communication
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Google AI; University of California, Merced; Stanford University; Tsinghua University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei
|
https://iclr.cc/virtual/2019/poster/997
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Eidetic 3D LSTM: A Model for Video Prediction and Beyond
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;overfitting;generalization;memorization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Overfitting Detection of Deep Neural Networks without a Hold Out Set
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
tactile sensing;multimodal representations;vision;object identification
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Classification in the dark using tactile exploration
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NLP;Reading Comprehension;Memory Networks;Multi-hop Reasoning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
How to learn (and how not to learn) multi-hop reasoning with memory networks
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multitask learning;natural language processing;question answering;machine translation;relation extraction;semantic parsing;commensense reasoning;summarization;entailment;sentiment;dialog
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
The Natural Language Decathlon: Multitask Learning as Question Answering
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Self-attention;Transformer;generative adversarial networks;GAN;neural text generation;NTG;generative models
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
SALSA-TEXT : SELF ATTENTIVE LATENT SPACE BASED ADVERSARIAL TEXT GENERATION
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoder;interpretable;graph signal processing;graph spectrum;graph filter;capsule
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Graph Spectral Regularization For Neural Network Interpretability
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Department of Computer Science and Engineering, Texas A&M University; Department of Mathematics, University of California, Los Angeles
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin
|
https://iclr.cc/virtual/2019/poster/803
|
sparse recovery;neural networks
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 8.666667 |
7;9;10
| null | null |
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
membership inference;memorization;attack;privacy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Déjà Vu: An Empirical Evaluation of the Memorization Properties of Convnets
| null | null | 0 | 2.666667 |
Reject
|
2;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural nets;generative models;semi-supervised learning;cross-entropy
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
modularity;compostionality;graphs;dynamics;network
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://doubleblindICLR19.github.io/self-assembly/
|
main
| 5 |
4;4;7
| null | null |
Learning to control self-assembling morphologies: a study of generalization via modularity
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
University of Toronto, Vector Institute
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse
|
https://iclr.cc/virtual/2019/poster/1120
|
Generalization;Regularization;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Three Mechanisms of Weight Decay Regularization
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
inter-layer locking;local critic network;backpropagation;convolutional neural network;structural optimization;progress inference;ensemble inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Local Critic Training of Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
subwords;representations;word embeddings;transfer learning;machine translation;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
What Is in a Translation Unit? Comparing Character and Subword Representations Beyond Translation
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
automated design;affordance learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Automatic generation of object shapes with desired functionalities
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantization;reduced precision;training;inference;activation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
ACIQ: Analytical Clipping for Integer Quantization of neural networks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalization analysis;Statistical estimation;Understanding GANs;Disconnected support
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Understanding GANs via Generalization Analysis for Disconnected Support
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semantic;Graph;Sequence;Embeddings
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
SENSE: SEMANTICALLY ENHANCED NODE SEQUENCE EMBEDDING
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Fake Sentence Detection as a Training Task for Sentence Encoding
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Domain Adaptation;Feature Representation Learning;Semantic Segmentation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Domain Adaptation for Structured Output via Disentangled Patch Representations
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
evolutionary strategies;optimization;gradient estimators;biased gradients
| null | 0 | null | null |
iclr
| 0.866025 | 0 |
redacted URL
|
main
| 5 |
4;5;6
| null | null |
Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Context-aware Forecasting for Multivariate Stationary Time-series
|
https://github.com/XXXX
| null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
|
https://iclr.cc/virtual/2019/poster/719
|
graph-to-graph translation;graph generation;molecular optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
|
https://github.com/wengong-jin/iclr19-graph2graph
| null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Defense;Robustness of Deep Convolutional Networks
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Multi-way Encoding for Robustness to Adversarial Attacks
| null | null | 0 | 2.75 |
Reject
|
4;2;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
exploration;model based reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Unsupervised Exploration with Deep Model-Based Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Google AI; Rice University; Arizona State University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ali Mousavi, Gautam Dasarathy, Richard Baraniuk
|
https://iclr.cc/virtual/2019/poster/1051
|
Sparsity;Compressive Sensing;Convolutional Network
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
RIKEN, Tokyo 103-0027, Japan; The University of Tokyo, Tokyo 113-0033, Japan; Google, New York, NY 10018, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
|
https://iclr.cc/virtual/2019/poster/807
|
learning from only unlabeled data;empirical risk minimization;unbiased risk estimator
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.5 |
7;7;8;8
| null | null |
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
| null | null | 0 | 3.5 |
Poster
|
4;4;3;3
| null |
null |
ITCS, IIIS, Tsinghua University; ByteDance Inc.; Google Inc.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Dengyong Zhou
|
https://iclr.cc/virtual/2019/poster/816
|
Neuro-Symbolic Computation;Logic Induction
| null | 0 | null | null |
iclr
| -0.981981 | 0 |
https://sites.google.com/view/neural-logic-machines
|
main
| 6 |
5;6;7
| null | null |
Neural Logic Machines
| null | null | 0 | 3.333333 |
Poster
|
5;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
explainable AI;interpreting deep neural networks;bias;attribution method;piecewise linear activation function;backpropagation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
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