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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNN;sparse convolution;sparse kernel;sparsity;model utilization;image classification
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Sparse-Complementary Convolution for Efficient Model Utilization on CNNs
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Affiliation (Please provide the actual affiliation(s) if available); Another Affiliation (Please provide the actual affiliation(s) if available)
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;symbolic knowledge;semi-supervised learning;constraints
| null | 0 | null | null |
iclr
| -0.755929 | 0 |
(Please provide the project link if available)
|
main
| 5.333333 |
4;5;7
| null | null |
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
|
(Please provide the GitHub link if available)
| null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Department of Computer Science, Rice University, Houston, TX 77005, USA.
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
|
https://iclr.cc/virtual/2018/poster/89
|
Program generation;Source code;Program synthesis;Deep generative models
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Neural Sketch Learning for Conditional Program Generation
| null | null | 0 | 3 |
Oral
|
3;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;theory;optimization;local minima;loss landscape
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Exponentially vanishing sub-optimal local minima in multilayer neural networks
| null | null | 0 | 2.666667 |
Workshop
|
3;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky
|
https://iclr.cc/virtual/2018/poster/326
|
distributed training;federated learning;language modeling;differential privacy
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Distributed Fine-tuning of Language Models on Private Data
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;neural networks;formal verification;ground truths
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Ground-Truth Adversarial Examples
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;quantization;compression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Automatic Parameter Tying in Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
survival analysis;competing risks;siamese neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Siamese Survival Analysis with Competing Risks
| null | null | 0 | 4.333333 |
Reject
|
5;4;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
| 4.333333 |
3;4;6
| null | null |
UPS: optimizing Undirected Positive Sparse graph for neural graph filtering
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic segmentation;conditional denoising autoencoders;iterative inference
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Image Segmentation by Iterative Inference from Conditional Score Estimation
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visualization;loss surface;flatness;sharpness
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Visualizing the Loss Landscape of Neural Nets
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null |
TU Berlin; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pieter-Jan Kindermans, Kristof T Schütt, Maximilian Alber, Klaus R Muller, Dumitru Erhan, Been Kim, Sven Dähne
|
https://iclr.cc/virtual/2018/poster/322
|
machine learning;interpretability;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
6;8;8
| null | null |
Learning how to explain neural networks: PatternNet and PatternAttribution
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;generative adversarial network;black-box attack
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Generating Adversarial Examples with Adversarial Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Google Brain, Mountain View, CA, 94043, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chung-Cheng Chiu, Colin Raffel
|
https://iclr.cc/virtual/2018/poster/211
|
attention;sequence-to-sequence;speech recognition;document summarization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Monotonic Chunkwise Attention
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian;Deep Learning;Recurrent Neural Networks;LSTM
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Revisiting Bayes by Backprop
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Lambda-Returns
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning to Mix n-Step Returns: Generalizing Lambda-Returns for Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Carnegie Mellon University; University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;MLP;ResNet;residual network;exploding gradient problem;vanishing gradient problem;effective depth;batch normalization;covariate shift
| null | 0 | null | null |
iclr
| -0.433555 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
Gradients explode - Deep Networks are shallow - ResNet explained
| null | null | 0 | 2.333333 |
Workshop
|
2;4;1
| 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 |
Embedding Deep Networks into Visual Explanations
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
California Institute of Technology, Pasadena, CA; University of California, Irvine, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Forough Arabshahi, Sameer Singh, anima anandkumar
|
https://iclr.cc/virtual/2018/poster/110
|
symbolic reasoning;mathematical equations;recursive neural networks;neural programing
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
| null | null | 0 | 3.666667 |
Poster
|
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 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
withdrawn
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
University of Illinois at Urbana-Champaign; Google; Microsoft Research; Citadel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng
|
https://iclr.cc/virtual/2018/poster/306
|
Neural Machine Translation;Sequence to Sequence;Sequence Modeling
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Towards Neural Phrase-based Machine Translation
|
https://github.com/posenhuang/NPMT
| null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Georgia State University; Georgia Institute of Technology
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, huan xu, Hongyuan Zha
|
https://iclr.cc/virtual/2018/poster/268
|
mean field games;reinforcement learning;Markov decision processes;inverse reinforcement learning;deep learning;inverse optimal control;computational social science;population modeling
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8.666667 |
8;8;10
| null | null |
Learning Deep Mean Field Games for Modeling Large Population Behavior
| null | null | 0 | 4 |
Oral
|
4;3;5
| null |
null |
GRASP Laboratory, University of Pennsylvania
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, Kostas Daniilidis
|
https://iclr.cc/virtual/2018/poster/263
|
equivariance;invariance;canonical coordinates
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Polar Transformer Networks
|
http://github.com/daniilidis-group//polar-transformer-networks
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;inverse reinforcement learning;imitation learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Reward Estimation via State Prediction
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Illinois at Urbana Champaign
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jiaqi Mu, Pramod Viswanath
|
https://iclr.cc/virtual/2018/poster/298
| null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Baylor College of Medicine & Rice University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Emin Orhan, Xaq Pitkow
|
https://iclr.cc/virtual/2018/poster/73
|
deep learning;optimization;skip connections
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
6;8;8
| null | null |
Skip Connections Eliminate Singularities
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Universitat Polit `ecnica de Catalunya; Barcelona Supercomputing Center; Columbia University; Google Inc
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Víctor Campos, Brendan Jou, Xavier Giro-i-Nieto, Jordi Torres, Shih-Fu Chang
|
https://iclr.cc/virtual/2018/poster/312
|
recurrent neural networks;dynamic learning;conditional computation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
|
https://imatge-upc.github.io/skiprnn-2017-telecombcn/
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
School of Computer Science, Carnegie Mellon University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W Cohen
|
https://iclr.cc/virtual/2018/poster/70
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
|
https://github.com/zihangdai/mos
| null | 0 | 4.333333 |
Oral
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Modeling;Generative Adversarial Networks;Density Estimation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
WHAT ARE GANS USEFUL FOR?
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
NVIDIA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie
|
https://iclr.cc/virtual/2018/poster/200
|
Sparsity;Pruning;Compression;RNN;LSTM;Persistent;RF-Resident;GPU
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
| null | null | 0 | 2.666667 |
Poster
|
2;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Incremental learning;energy-efficient learning;supervised learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Model of Graphs
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Deep Generative Models of Graphs
| null | null | 0 | 3.333333 |
Workshop
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine reading;adversarial training
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Adversarial reading networks for machine comprehension
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
David Ha, Douglas Eck
|
https://iclr.cc/virtual/2018/poster/293
|
applications;image modelling;computer-assisted;drawing;art;creativity;dataset
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
5;8;8
| null | null |
A Neural Representation of Sketch Drawings
|
https://magenta.tensorflow.org/sketch_rnn
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
novelty detection;GAN;feature matching;semi-supervised
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Novelty Detection with GAN
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, University of Texas at Austin; Department of Electrical and Computer Engineering, University of Texas at Austin
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ashish Bora, Eric Price, Alexandros Dimakis
|
https://iclr.cc/virtual/2018/poster/231
|
Generative models;Adversarial networks;Lossy measurements
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
AmbientGAN: Generative models from lossy measurements
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Predictive coding;deep neural network;generative model;unsupervised learning;learning latent representations
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
A Deep Predictive Coding Network for Learning Latent Representations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
word representation;unsupervised learning;computational linguistics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Tensor-Based Preposition Representation
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameter optimization;random search;determinantal point processes;low discrepancy sequences
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Open Loop Hyperparameter Optimization and Determinantal Point Processes
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;Markov decision processes;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Time Limits in Reinforcement Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;GANs;deep learning;unsupervised learning;generative models;adversarial learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Towards Effective GANs for Data Distributions with Diverse Modes
| null | null | 0 | 3.666667 |
Workshop
|
3;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural networks;vanishing gradients;exploding gradients;orthogonal;unitary;long term dependencies;uRNN
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalization;Reservoir Computing;dynamical system;Siamese Neural Network;image classification;similarity;dimensionality reduction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Generalization of Learning using Reservoir Computing
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; University of British Columbia, Vancouver, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wu Lin, Nicolas Daniel Hubacher, Mohammad Emtiyaz Khan
|
https://iclr.cc/virtual/2018/poster/136
|
Variational Inference;Variational Message Passing;Variational Auto-Encoder;Graphical Models;Structured Models;Natural Gradients
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Variational Message Passing with Structured Inference Networks
| null | null | 0 | 3 |
Poster
|
4;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language models;vector spaces;word embedding;similarity
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Comparison of Paragram and GloVe Results for Similarity Benchmarks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Neural Networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Neural Networks with Block Diagonal Inner Product Layers
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Unknown Affiliation
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Attacks;Unsupervised Defense;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
BinaryFlex: On-the-Fly Kernel Generation in Binary Convolutional Networks
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-task learning;transfer learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Large Scale Multi-Domain Multi-Task Learning with MultiModel
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Department of Computer Science, University of Toronto, Toronto, Canada; DeepMind; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
James Martens, Jimmy Ba, Matthew Johnson
|
https://iclr.cc/virtual/2018/poster/12
|
optimization;K-FAC;natural gradient;recurrent neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Kronecker-factored Curvature Approximations for Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Language Model;discriminative model
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Large Margin Neural Language Models
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
supervised representation learning;causality;interpretability;transfer learning
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Grouping-By-ID: Guarding Against Adversarial Domain Shifts
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay
|
https://iclr.cc/virtual/2018/poster/324
|
adversarial machine learning;embedding;regularization;adversarial attack
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Google Research, New York, NY 10011, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Swabha Swayamdipta, Ankur Parikh, Tom Kwiatkowski
|
https://iclr.cc/virtual/2018/poster/95
|
reading comprehension;multi-loss;question answering;scalable;TriviaQA;feed-forward;latent variable;attention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Multi-Mention Learning for Reading Comprehension with Neural Cascades
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NLP;morphology;seq2seq
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 4.333333 |
2;5;6
| null | null |
Achieving morphological agreement with Concorde
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Department of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA∗; [email protected]
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Eric Martin, Christopher Cundy
|
https://iclr.cc/virtual/2018/poster/249
|
rnn;sequence;parallel;qrnn;sru;gilr;gilr-lstm
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Parallelizing Linear Recurrent Neural Nets Over Sequence Length
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
developmental robotics;intrinsic motivation;strategic learning;complex motor policies
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning a set of interrelated tasks by using a succession of motor policies for a socially guided intrinsically motivated learner
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Computer Science, University of California, Los Angeles
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pratik A Chaudhari, Stefano Soatto
|
https://iclr.cc/virtual/2018/poster/152
|
sgd;variational inference;gradient noise;out-of-equilibrium
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;ConvNets;RNNs;pattern matching;semi-supervised clustering
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Residual Gated Graph ConvNets
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative;hierarchical;unsupervised;semisupervised;latent;ALI;GAN
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Hierarchical Adversarially Learned Inference
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
soft Q-learning;policy gradients;entropy;Legendre transformation;duality;convex analysis;Donsker-Varadhan
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Machine Intelligence and Perception, Microsoft Research, Cambridge, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sebastian Nowozin
|
https://iclr.cc/virtual/2018/poster/114
|
variational inference;approximate inference;generative models
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
|
https://github.com/Microsoft/jackknife-variational-inference
| null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;representation learning;variational auto-encoders;variational inference;generative models
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Feature Map Variational Auto-Encoders
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
one-shot learning;few-shot learning;Omniglot
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
GRASP Laboratory, University of Pennsylvania
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D Lee
|
https://iclr.cc/virtual/2018/poster/321
|
planning;memory networks;deep learning;robotics
| null | 0 | null | null |
iclr
| -0.216777 | 0 | null |
main
| 6.333333 |
4;6;9
| null | null |
Memory Augmented Control Networks
| null | null | 0 | 3.666667 |
Poster
|
5;2;4
| null |
null |
Google Brain, Mountain View, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
|
https://iclr.cc/virtual/2018/poster/121
|
abstractive summarization;Transformer;long sequences;natural language processing;sequence transduction;Wikipedia;extractive summarization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Generating Wikipedia by Summarizing Long Sequences
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
University of Freiburg; Intel Labs
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox
|
https://iclr.cc/virtual/2018/poster/190
|
deep learning;reinforcement learning;temporal difference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic gradient descent;autoencoders;nonconvex optimization;representation learning;theory
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
automatic speech recognition;letter-based acoustic model;gated convnets
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Gated ConvNets for Letter-Based ASR
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chelsea Finn, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/185
|
meta-learning;learning to learn;universal function approximation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
| null | null | 0 | 1.666667 |
Poster
|
1;3;1
| null |
null |
Toyota Central R&D Labs, Inc., Nagakute, Aichi 480-1192, Japan
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
vanishing gradient problem;multilayer perceptron;angle bias
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Linearly Constrained Weights: Resolving the Vanishing Gradient Problem by Reducing Angle Bias
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;transfer learning;adjacency matrices;image feature representation;Caffe;graph classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Neural Networks;Information Theory;Generative models;GAN;Adversarial
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
MINE: Mutual Information Neural Estimation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Visual Relations;Visual Reasoning;SVRT;Attention;Working Memory;Convolutional Neural Network;Deep Learning;Relational Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;autoencoder;generative;feed-back
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Style Memory: Making a Classifier Network Generative
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
vanishing gradient descent;recurrent neural networks;identity mapping
| null | 0 | null | null |
iclr
| -0.114708 | 0 | null |
main
| 4.333333 |
2;4;7
| null | null |
Overcoming the vanishing gradient problem in plain recurrent 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 |
CLEVR;VQA;Visual Question Answering;Neural Programmer
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer
| null | null | 0 | 2 |
Reject
|
2;2;2
| null |
null |
Google Brain, Also at the Department of Computing Science, University of Alberta; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans
|
https://iclr.cc/virtual/2018/poster/167
|
Reinforcement learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
|
https://github.com/tensorflow/models/tree/master/research/pcl_rl
| null | 0 | 3 |
Poster
|
4;1;4
| null |
null |
Under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
subspace;censor;multi-task;deep network
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Structured Variational Inference;Multi-agent Coordination;Multi-agent Learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Structured Exploration via Hierarchical Variational Policy Networks
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conditional sequence generation;generative adversarial network;REINFORCE;dialogue generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Withdrawn
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sign;stochastic;gradient;non-convex;optimization;gradient;quantization;convergence;rate
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Convergence rate of sign stochastic gradient descent for non-convex functions
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantum technique;convolution networks;shape detection
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Towards Quantum Inspired Convolution Networks
| null | null | 0 | 3.666667 |
Withdraw
|
5;3;3
| null |
null |
Yitu Tech; Shanghai Jiao Tong University; University College London
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu
|
https://iclr.cc/virtual/2018/poster/221
|
Generative Adversarial Nets;GANs;Evaluation Metrics;Generative Model;Deep Learning;Adversarial Learning;Inception Score;AM Score
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Activation Maximization Generative Adversarial Nets
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of California, Berkeley, USA; Massachusetts Institute of Technology, MA, USA; University of Michigan, Ann Arbor, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song
|
https://iclr.cc/virtual/2018/poster/18
|
adversarial examples;spatial transformation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.666667 |
7;7;9
| null | null |
Spatially Transformed Adversarial Examples
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Engineering Science, University of Oxford
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip Torr
|
https://iclr.cc/virtual/2018/poster/34
|
deep learning;attention-aware representations;image classification;weakly supervised segmentation;domain shift;classifier generalisation;robustness to adversarial attack
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learn to Pay Attention
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sentence Vectors;Vector Semantics;Automatic Summarization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
Exploring Sentence Vectors Through Automatic Summarization
| null | null | 0 | 5 |
Reject
|
5;5;5
| 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 |
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Spectral Graph Convolutional Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;8
| null | null |
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
tensor contraction;tensor regression;network compression;deep neural networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Tensor Contraction & Regression Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning representation;clustering;loss
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Forced Apart: Discovering Disentangled Representations Without Exhaustive Labels
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Google Brain, Mountain View, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow
|
https://iclr.cc/virtual/2018/poster/120
|
Adversarial examples;robust neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null |
Affiliation not provided
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bioinformatics;multi-label classification;matching networks;prototypes;memory networks;attention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Search Algorithm Team, Alibaba Group, China; AI-LAB, Alibaba Group, China; Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, China
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chen Xu, Jianqiang Yao, Zhouchen Lin, Baigui Sun, Yuanbin Cao, Zhirong Wang, Hongbin Zha
|
https://iclr.cc/virtual/2018/poster/235
|
Alternating Minimization;Quantized Recurrent Neural Network;Binary Search Tree
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Alternating Multi-bit Quantization for Recurrent Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
abbas abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Yuval Tassa, Remi Munos
|
https://iclr.cc/virtual/2018/poster/9
|
Reinforcement Learning;Variational Inference;Control
| null | 0 | null | null |
iclr
| 0.240192 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Maximum a Posteriori Policy Optimisation
| null | null | 0 | 3.333333 |
Poster
|
4;1;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.59604 | 0 | null |
main
| 6.333333 |
4;6;9
| null | null |
Data Augmentation Generative Adversarial Networks
| null | null | 0 | 4 |
Workshop
|
4;3;5
| 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 |
Pseudo sequence based deep neural network compression
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Google
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang.
|
https://iclr.cc/virtual/2018/poster/107
|
machine translation;paraphrasing;question answering;reinforcement learning;agents
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
| null | null | 0 | 4 |
Oral
|
4;5;3
| null |
null |
IBM Thomas J. Watson Research Center, USA; POSTECH, Department of Creative IT Engineering, Korea
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce I Chuang, Jae-Joon Kim
|
https://iclr.cc/virtual/2018/poster/256
|
pruning;sparse matrix;memory footprint;model size;model compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Flowers Team, Inria and Ensta-ParisTech, France; Flowers Team, Inria, Ensta-ParisTech and UPMC, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer
|
https://iclr.cc/virtual/2018/poster/309
|
exploration; autonomous goal setting; diversity; unsupervised learning; deep neural network
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
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