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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
question answering;knowledge graph;compositional model;semantics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Neural Compositional Denotational Semantics for Question Answering
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google, Mountain View, CA 94043, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston
|
https://iclr.cc/virtual/2018/poster/48
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Variational image compression with a scale hyperprior
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
game-theory;reinforcement-learning;guided-policy-search;dynamic-programming
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
A dynamic game approach to training robust deep policies
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continuous-time flows;efficient inference;density estimation;deep generative models
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Continuous-Time Flows for Efficient Inference and Density Estimation
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequence modelling;language;recurrent neural networks;adaptation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Dynamic Evaluation of Neural Sequence Models
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
HyperNetworks with statistical filtering for defending adversarial examples
| null | null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Network;Integral Probability Metric;Meta-Adversarial Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Variance Regularizing Adversarial Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
DeepMind, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pablo Sprechmann, Siddhant Jayakumar, Jack Rae, Alexander Pritzel, Adria Puigdomenech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell
|
https://iclr.cc/virtual/2018/poster/60
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Memory-based Parameter Adaptation
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Subjective search;Reinforcement Learning;Conversational Agent;Virtual user model;A3C;Context aggregation
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Improving Search Through A3C Reinforcement Learning Based Conversational Agent
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Department of Computer Science and Operations Research, Universit de Montral, Montral, QC H3C3J7, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yikang Shen, Zhouhan Lin, Chin-Wei Huang, Aaron Courville
|
https://iclr.cc/virtual/2018/poster/124
|
Language model;unsupervised parsing
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Neural Language Modeling by Jointly Learning Syntax and Lexicon
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;planning;prediction;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Learning to Imagine Manipulation Goals for Robot Task Planning
| null | null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Distributed non-parametric deep and wide networks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Department of Electrical Engineering, North Carolina A&T State University, Greensboro, NC 27410, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Travis Williams, Robert Li
|
https://iclr.cc/virtual/2018/poster/262
|
Pooling;Wavelet;CNN;Neural Network;Deep Learning;Classification;Machine Learning;Object Recognition
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 6.666667 |
4;7;9
| null | null |
Wavelet Pooling for Convolutional Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Network architecture;Learned representation space;absolute valued function;bidirectional neuron
| null | 0 | null | null |
iclr
| 0.240192 | 0 | null |
main
| 3.666667 |
2;3;6
| null | null |
AANN: Absolute Artificial Neural Network
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
resnet;residual;shortcut;convolutional;linear;skip;highway
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Tandem Blocks in Deep Convolutional Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
video prediction;visual analogy network;unsupervised;hierarchical
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Unsupervised Hierarchical Video Prediction
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Massachusetts-Amherst, Amherst, MA, USA; PRaDA Centre, Deakin University, Geelong, Australia
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Quan Hoang, Tu D Nguyen, Trung Le, Dinh Phung
|
https://iclr.cc/virtual/2018/poster/318
|
GANs;Mode Collapse;Mixture;Jensen-Shannon Divergence;Inception Score;Generator;Discriminator;CIFAR-10;STL-10;ImageNet
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
MGAN: Training Generative Adversarial Nets with Multiple Generators
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Text;Empirical Investigation;Model Capacity;Generalization Ability;Neural Networks;Deep Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Empirical Investigation on Model Capacity and Generalization of Neural Networks for Text
| null | null | 0 | 4.666667 |
Withdraw
|
4;5;5
| null |
null |
DeepMind, London, UK; Georgia Institute of Technology, Atlanta, GA, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Edward Choi, Angeliki Lazaridou, Nando de Freitas
|
https://iclr.cc/virtual/2018/poster/265
|
compositional language;obverter;multi-agent communication;raw pixel input
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
3;6;9
| null | null |
Compositional Obverter Communication Learning from Raw Visual Input
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
The Multilinear Structure of ReLU Networks
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
School of Computing Sciences, University of East Anglia, Norwich, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Geoff W French, Michal Mackiewicz, Mark Fisher
|
https://iclr.cc/virtual/2018/poster/207
|
deep learning;neural networks;domain adaptation;images;visual;computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Self-ensembling for visual domain adaptation
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Scattering Network;Continuous Wavelet Thresholding;Sparse Activations;Time-frequency represenation;Multi-Family;Wavelets;Convolutional Network;Bird Detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6
| null | null |
Sparse Deep Scattering Croisé Network
| null | null | 0 | 4 |
Withdraw
|
4
| null |
null |
Google; Google DeepMind; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, George Dahl, Geoffrey E Hinton
|
https://iclr.cc/virtual/2018/poster/255
|
distillation;distributed training;neural networks;deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Large scale distributed neural network training through online distillation
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Alan Turing Institute, University of Cambridge; University of Cambridge; Alan Turing Institute, University of Warwick
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J Kusner, José Miguel Hernández Lobato
|
https://iclr.cc/virtual/2018/poster/29
|
Active learning;Reinforcement learning;Molecules
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning a Generative Model for Validity in Complex Discrete Structures
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNN
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Unbiasing Truncated Backpropagation Through Time
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Withdraw
|
https://github.com/
| null | 0 | 4.333333 |
Withdraw
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Multi-Advisor Reinforcement Learning
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Lipschitz neural network
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Deep Lipschitz networks and Dudley GANs
| null | null | 0 | 2.666667 |
Reject
|
3;1;4
| null |
null |
University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Tree-to-tree Neural Networks for Program Translation
| null | null | 0 | 3.666667 |
Workshop
|
3;4;4
| null |
null |
Bosch Center for Artificial Intelligence, Robert Bosch GmbH, Renningen, Germany; RWTH Aachen University, Institute for Theoretical Information Technology, Aachen, Germany
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jan Achterhold, Jan Koehler, Anke Schmeink, Tim Genewein
|
https://iclr.cc/virtual/2018/poster/131
|
Network compression;variational inferene;ternary network;Bayesian neural network;weight quantization;weight sharing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Variational Network Quantization
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Amazon Web Services, Seattle, WA 98101; UT Austin, Austin, TX 78712
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, anima anandkumar
|
https://iclr.cc/virtual/2018/poster/125
|
active learning;deep learning;named entity recognition
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Deep Active Learning for Named Entity Recognition
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
IVE-GAN: Invariant Encoding Generative Adversarial Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
The Edmond and Lily Safra Center for Brain Sciences, Departments of Neurobiology and Cognitive Sciences and the Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; School of Computer Science and Engineering and Department of Cognitive Sciences, The Hebrew University of Jerusalem
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Lior Fox, Leshem Choshen, Yonatan Loewenstein
|
https://iclr.cc/virtual/2018/poster/261
|
Reinforcement Learning;Exploration;Model-Free
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
|
https://github.com/borgr/DORA/
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
self-training;generative adversarial networks;semi-supervised
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
A Self-Training Method for Semi-Supervised GANs
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial training;semi-supervised training;collaborative training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Improve Training Stability of Semi-supervised Generative Adversarial Networks with Collaborative Training
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Causal structure discovery;Generative neural networks;Cause-effect pair problem;Functional causal model;Maximum Mean Discrepancy;Structural Equation Models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Causal Generative Neural Networks
| null | null | 0 | 0 |
Reject
| null | null |
null |
Machine Learning Department, School of Computer Science, Carnegie Mellon University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov
|
https://iclr.cc/virtual/2018/poster/319
| null | null | 0 | null | null |
iclr
| 0.866025 | 0 |
https://devendrachaplot.github.io/projects/Neural-Localization
|
main
| 7 |
6;7;8
| null | null |
Active Neural Localization
|
https://github.com/devendrachaplot/Neural-Localization
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Council for Scientific and Industrial Research, Pretoria, South Africa, and Department of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa; Center for Brain Science, Harvard University, MA, USA; Department of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Adam Earle, Andrew Saxe, Benjamin Rosman
|
https://iclr.cc/virtual/2018/poster/56
|
Reinforcement Learning;Hierarchy;Subtask Discovery;Linear Markov Decision Process
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization
| null | null | 0 | 2.333333 |
Poster
|
2;2;3
| null |
null |
Under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
2;4;9
| null | null |
BLOCK-NORMALIZED GRADIENT METHOD: AN EMPIRICAL STUDY FOR TRAINING DEEP NEURAL NETWORK
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
College of Information and Computer Sciences, University of Massachusetts Amherst; IBM Research AI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Clemens Rosenbaum, Tim Klinger, Matt Riemer
|
https://iclr.cc/virtual/2018/poster/58
|
multi-task;transfer;routing;marl;multi-agent;reinforcement;self-organizing
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generalization error;neural networks;statistical learning theory;PAC-Bayes theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
word2vec;glove;word analogy;word relationships;word vectors
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
A closer look at the word analogy problem
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
feature norm;regularization;softmax loss;feature incay
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Feature Incay for Representation Regularization
| null | null | 0 | 3 |
Workshop
|
3;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
William A Falcon, Henning Schulzrinne
|
https://iclr.cc/virtual/2018/poster/123
|
Recurrent Neural Networks;RNN;LSTM;Mobile Device;Sensors
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
gru4rec;session-based recommendations;recommender systems;recurrent neural network
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial learning;domain adaptation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Multiple Source Domain Adaptation with Adversarial Learning
| null | null | 0 | 4.25 |
Workshop
|
4;5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
music;lstm;gan;generation;rnn;hmm
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Melody Generation for Pop Music via Word Representation of Musical Properties
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantum machine learning;tensor network;quantum information
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
| null | null | 0 | 2.666667 |
Reject
|
2;3;3
| null |
null |
Departments of Computer Science & Linguistics, University of Illinois at Urbana-Champaign, Urbana, IL 61801; Department of Linguistics, University of Illinois at Urbana-Champaign, Urbana, IL 61801; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Richard Wei, Lane Schwartz, and Vikram Adve
| null |
deep learning;automatic differentiation;algorithmic differentiation;domain specific languages;neural networks;programming languages;DSLs
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
DLVM: A modern compiler infrastructure for deep learning systems
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Klaus Greff
|
https://iclr.cc/virtual/2018/poster/15
|
Common-sense Physical Reasoning;Intuitive Physics;Representation Learning;Model building
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised embedding;convolutional neural network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
2;4;6
| null | null |
Learning Document Embeddings With CNNs
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial Networks;Deep Generative models;Kernel Methods
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
DOUBLY STOCHASTIC ADVERSARIAL AUTOENCODER
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Network acceleration;Low Precision neural networks.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Binarized Back-Propagation: Training Binarized Neural Networks with Binarized Gradients
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational auto-encoder;unsupervised learning;image generation;spatial information;matrix-variate normal distribution
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
| null | null | 0 | 3.666667 |
Withdraw
|
5;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Model Specialization for Inference Via End-to-End Distillation, Pruning, and Cascades
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Learning;Deep Learning;Coreset;Deep Representation;Compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Deep Active Learning over the Long Tail
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sashank Reddi, Satyen Kale, Sanjiv Kumar
|
https://iclr.cc/virtual/2018/poster/78
|
optimization;deep learning;adam;rmsprop
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
On the Convergence of Adam and Beyond
| null | null | 0 | 4 |
Oral
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational Autoencoder;Sparse Topical Coding;Neural Variational Inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Neural Variational Sparse Topic Model
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France; Ecole Centrale Marseille; Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6, F-75005, Paris, France; Criteo Research; Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6, F-75005, Paris, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mickael Chen, Ludovic Denoyer, thierry artieres
|
https://iclr.cc/virtual/2018/poster/104
|
multi-view;adversarial learning;generative model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Multi-View Data Generation Without View Supervision
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Evan Liu, Kelvin Guu, Panupong Pasupat, Tim Shi, Percy Liang
|
https://iclr.cc/virtual/2018/poster/28
|
reinforcement learning;sparse rewards;web;exploration
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wen Sun, J. A Bagnell, Byron Boots
|
https://iclr.cc/virtual/2018/poster/33
|
Imitation Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.960769 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;recommendation system;uncertainty;context-based and collaborative filtering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
DEEP DENSITY NETWORKS AND UNCERTAINTY IN RECOMMENDER SYSTEMS
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep convolutional network;residual network;dynamic system;stochastic dynamic system;modified equation
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
| null | null | 0 | 2.25 |
Workshop
|
3;1;1;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Link Weight Prediction with Node Embeddings
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Topic Classification;Sentiment Analysis;Natural Language Processing
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Variational Autoencoder;Generative Adversarial Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;General Value Functions;Predictive Representations
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Discovery of Predictive Representations With a Network of General Value Functions
| null | null | 0 | 3 |
Reject
|
1;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D Tracey, David D Cox
|
https://iclr.cc/virtual/2018/poster/63
|
information bottleneck;deep learning;deep linear networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
On the Information Bottleneck Theory of Deep Learning
| null | null | 0 | 2.666667 |
Poster
|
2;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Time Series;Time Series Classification
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Jiffy: A Convolutional Approach to Learning Time Series Similarity
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hyeji Kim, Yihan Jiang, Ranvir B Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
|
https://iclr.cc/virtual/2018/poster/20
|
coding theory;recurrent neural network;communication
| null | 0 | null | null |
iclr
| 0.821995 | 0 | null |
main
| 5.666667 |
2;6;9
| null | null |
Communication Algorithms via Deep Learning
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational inference;vae;variational autoencoders;generative modeling;representation learning;classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Towards Unsupervised Classification with Deep Generative Models
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sequence-to-Sequence Models;Speech Recognition;Language Models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
COLD FUSION: TRAINING SEQ2SEQ MODELS TOGETHER WITH LANGUAGE MODELS
| null | null | 0 | 5 |
Workshop
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer Learning;Applications;Neural decoding
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Sequence Transfer Learning for Neural Decoding
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Brock, Theo Lim, James Ritchie, Nick Weston
|
https://iclr.cc/virtual/2018/poster/338
|
meta-learning;architecture search;deep learning;computer vision
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
SMASH: One-Shot Model Architecture Search through HyperNetworks
| null | null | 0 | 3 |
Poster
|
2;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer Learning;convolutional networks;fine-tuning;regularization;induction bias
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Explicit Induction Bias for Transfer Learning with Convolutional Networks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Generative Adversarial Networks;Mode Collapse;Stability;Game Theory;Regret Minimization;Convergence;Gradient Penalty
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 4 |
3;4;5
| null | null |
On Convergence and Stability of GANs
| null | null | 0 | 3.333333 |
Reject
|
3;5;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin
|
https://iclr.cc/virtual/2018/poster/225
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer Learning;Learning without forgetting;Multitask Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Incremental Learning through Deep Adaptation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;latent variable models;image generation;generative adversarial networks;convolutional neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Optimizing the Latent Space of Generative Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;structure learning;deep belief networks;probabilistic graphical models;Bayesian networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Unsupervised Deep Structure Learning by Recursive Dependency Analysis
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Noise-Based Regularizers for Recurrent Neural Networks
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zhiting Hu, , Ruslan Salakhutdinov, Eric P Xing
|
https://iclr.cc/virtual/2018/poster/166
|
deep generative models;generative adversarial networks;variational autoencoders;variational inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
On Unifying Deep Generative Models
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph convolutional networks;stochastic gradient descent;variance reduction;control variate
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Stochastic Training of Graph Convolutional Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;A3C;deep learning;Atari games
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Anticipatory Asynchronous Advantage Actor-Critic (A4C): The power of Anticipation in Deep Reinforcement Learning
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Adversarial Networks;Object Instance Recognition;Cognitive AI
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Learning with Mental Imagery
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dejiao Zhang, Haozhu Wang, Mario Figueiredo, Laura Balzano
|
https://iclr.cc/virtual/2018/poster/27
|
Compressing neural network;simultaneously parameter tying and sparsification;group ordered l1 regularization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Weakly Supervised Learning;Medical Imaging;Histopathology;Deep Feature Extraction
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mark D. McDonnell
|
https://iclr.cc/virtual/2018/poster/311
|
wide residual networks;model compression;quantization;1-bit weights
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Training wide residual networks for deployment using a single bit for each weight
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jie Chen, Tengfei Ma, Cao Xiao
|
https://iclr.cc/virtual/2018/poster/145
|
Graph convolutional networks;importance sampling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;7;8
| null | null |
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
| null | null | 0 | 3.5 |
Poster
|
4;2;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chunyuan Li, Heerad Farkhoor, Ruoqian Liu, Jason Yosinski
|
https://iclr.cc/virtual/2018/poster/122
|
machine learning;neural networks;intrinsic dimension;random subspace;model understanding
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Measuring the Intrinsic Dimension of Objective Landscapes
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
black-box attack;adversarial example;deep learning;transferability
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Microsoft Research Redmond, Redmond, WA 98052, USA; Microsoft Research India, Bengaluru, Karnataka 560001, India; School of Interactive Computing, Georgia Tech, Atlanta, GA 30308, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ashwin Vijayakumar, Abhishek Mohta, Alex Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani
|
https://iclr.cc/virtual/2018/poster/141
|
Program synthesis;deductive search;deep learning;program induction;recurrent neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg
|
https://iclr.cc/virtual/2018/poster/308
|
Deep Reinforcement Learning;Exploration;Neural Networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Noisy Networks For Exploration
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial networks;ica;unsupervised;independence
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Learning Independent Features with Adversarial Nets for Non-linear ICA
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;gans;deep learning;image modeling;image generation;energy based models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Image Quality Assessment Techniques Improve Training and Evaluation of Energy-Based Generative Adversarial Networks
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural causal learning;learnable noise
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
Neural Causal Discovery with Learnable Input Noise
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Autonomous car;convolution network;image segmentation;depth estimation;generalization ability;explanation ability;multi-task learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimal transport;wasserstein autoencoder;variational autoencoder;latent variable modeling;generative modeling;discrete latent variables
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Improving Gaussian mixture latent variable model convergence with Optimal Transport
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
attention;hierarchical;machine reading comprehension;poem generation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Hierarchical Attention: What Really Counts in Various NLP Tasks
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Stanford University; Technical University of Munich; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chiyu Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Niessner
|
https://iclr.cc/virtual/2019/poster/709
|
Non-uniform Fourier Transform;3D Learning;CNN;surface reconstruction
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
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