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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Computer Science, Rice University, Houston, TX 77005, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic Gradient Descent;Optimization;Sampling;Estimation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
LSH-SAMPLING BREAKS THE COMPUTATIONAL CHICKEN-AND-EGG LOOP IN ADAPTIVE STOCHASTIC GRADIENT ESTIMATION
| null | null | 0 | 4.666667 |
Workshop
|
5;5;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
| 4.666667 |
3;5;6
| null | null |
Deep Asymmetric Multi-task Feature Learning
| null | null | 0 | 4 |
Reject
|
4;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 |
https://goo.gl/WG7nkD
|
main
| 4.666667 |
4;5;5
| null | null |
Kernel Graph Convolutional Neural Nets
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Not provided
|
2018
| 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 |
Withdrawn
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science; Henry M. Gunn High School; Work done as an intern at OpenAI; OpenAI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kevin Frans, Jonathan Ho, , Pieter Abbeel, John Schulman
|
https://iclr.cc/virtual/2018/poster/149
|
hierarchal reinforcement learning;meta-learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 |
https://sites.google.com/site/mlshsupplementals
|
main
| 5.666667 |
4;6;7
| null | null |
META LEARNING SHARED HIERARCHIES
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
softmax;center loss;triplet loss;convolution neural network;supervised learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Softmax Supervision with Isotropic Normalization
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;image segmentation;generative models;adversarial networks;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Counterfactual Image Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Carlos Riquelme, George Tucker, Jasper Snoek
|
https://iclr.cc/virtual/2018/poster/178
|
exploration;Thompson Sampling;Bayesian neural networks;bandits;reinforcement learning;variational inference;Monte Carlo
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tübingen, Germany
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wieland Brendel, Jonas Rauber, Matthias Bethge
|
https://iclr.cc/virtual/2018/poster/19
|
adversarial attacks;adversarial examples;adversarials;robustness;security
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
|
https://github.com/bethgelab/foolbox
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
DeepMind, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Gabriel Barth-maron, Matthew Hoffman, David Budden, Will Dabney, Daniel Horgan, Dhruva Tirumala, Alistair Muldal, Nicolas Heess, Timothy Lillicrap
|
https://iclr.cc/virtual/2018/poster/25
|
policy gradient;continuous control;actor critic;reinforcement learning
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 6.666667 |
5;6;9
| null | null |
Distributed Distributional Deterministic Policy Gradients
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep reinforcement learning;Hybrid action space;DQN;DDPG
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
| 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 learning;optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 |
Not provided
|
main
| 4.666667 |
4;5;5
| null | null |
On Batch Adaptive Training for Deep Learning: Lower Loss and Larger Step Size
|
Not provided
| null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Anonymous
| null |
robot locomotion;reinforcement learning;policy gradients;physical design;deep learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6 |
4;5;9
| null | null |
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
| null | null | 0 | 4 |
Workshop
|
4;3;5
| null |
null |
Amazon Web Services; University of Texas at Austin; Carnegie Mellon University; University of Massachusetts, Amherst
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum
|
https://iclr.cc/virtual/2018/poster/50
|
Knowledge Graphs;Reinforcement Learning;Query Answering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
ETH Zürich; Intel Labs
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun
|
https://iclr.cc/virtual/2018/poster/61
|
deep learning;navigation;memory
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Semi-parametric topological memory for navigation
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Multi-task Learning;Curriculum Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/goalgeneration4rl
|
main
| 6 |
4;6;8
| null | null |
Automatic Goal Generation for Reinforcement Learning Agents
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Convolutional Neural Network;Disguised Face Identification;Fraudulent Transaction;ATM;Impersonation;
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2 |
1;2;3
| null | null |
APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREVENT ATM FRAUD BY FACIAL DISGUISE IDENTIFICATION
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Carnegie Mellon University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aayush Bansal, Yaser Sheikh, Deva Ramanan
|
https://iclr.cc/virtual/2018/poster/209
|
conditional image synthesis;nearest neighbors
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
PixelNN: Example-based Image Synthesis
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;deep learning;structured prediction;sequential prediction
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Loss Functions for Multiset Prediction
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised;learning;evolutionary;sparse;coding;noisyOR;BSC;EM;expectation-maximization;variational EM;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Evolutionary Expectation Maximization for Generative Models with Binary Latents
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;adversarial training;universal perturbations;safety;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Working Memory;Learning Rules;Stimulus Representations
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
A Self-Organizing Memory Network
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep convolution network;partial differential equation;physical laws
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
PDE-Net: Learning PDEs from Data
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Regression;SSL;Autonomous Driving
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Semi-supervised Regression with Generative Adversarial Networks for End to End Learning in Autonomous Driving
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Microsoft Research, Montréal, Canada; Orange Labs, Lannion, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Romain Laroche, Raphaël Féraud
|
https://iclr.cc/virtual/2018/poster/295
|
Reinforcement Learning;Multi-Armed Bandit;Algorithm Selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Reinforcement Learning Algorithm Selection
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
superresolution;convolutional neural network;sparse representation;inverse problem
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
CNNs as Inverse Problem Solvers and Double Network Superresolution
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null |
College of Computing, Georgia Institute of Technology; Department of Computer Science, Stony Brook University; College of Computing, Georgia Institute of Technology and Ant Financial
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song
|
https://iclr.cc/virtual/2018/poster/32
|
generative model for structured data;syntax-directed generation;molecule and program optimization;variational autoencoder
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Syntax-Directed Variational Autoencoder for Structured Data
| null | null | 0 | 2 |
Poster
|
2;1;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representations;multi-attribute images;generative adversarial networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
| null | null | 0 | 4.333333 |
Workshop
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adaptive optimizer;momentum;hyperparameter tuning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
YellowFin and the Art of Momentum Tuning
| null | null | 0 | 3 |
Reject
|
3;5;1
| null |
null |
Department of Electrical and Computer Engineering, The Ohio State University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yi Zhou, Yingbin Liang
|
https://iclr.cc/virtual/2018/poster/169
|
neural networks;critical points;analytical form;landscape
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties
| null | null | 0 | 4 |
Poster
|
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
| 3.666667 |
2;4;5
| null | null |
Tree2Tree Learning with Memory Unit
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative networks;two sample tests;bias correction;maximum mean discrepancy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Directing Generative Networks with Weighted Maximum Mean Discrepancy
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dataset;computer vision;deep learning;visual reasoning;relational reasoning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
FigureQA: An Annotated Figure Dataset for Visual Reasoning
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Learning;AutoML
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised word embedding;unsupervised hypernym detection;distributional inclusion hypothesis;non-negative matrix factorization;word sense disambiguation;hypernym scoring functions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null |
The Hebrew University of Jerusalem
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yoav Levine, David Yakira, Nadav Cohen, Amnon Shashua
|
https://iclr.cc/virtual/2018/poster/310
|
deep learning;quantum entanglement;quantum physics;many body physics;data correlations;inductive bias;tensor networks
| null | 0 | null | null |
iclr
| 0.6742 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
| null | null | 0 | 3.5 |
Poster
|
2;4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;reinforcement learning;dataset;natural language processing;natural language interface;sql
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Seq2SQL: Generating Structured Queries From Natural Language Using 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 |
Healthcare;Gaussian Process;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Salesforce Research, Palo Alto, CA 94301, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Stephen Merity, Nitish Shirish Keskar, richard socher
|
https://iclr.cc/virtual/2018/poster/316
|
language model;LSTM;regularization;optimization;ASGD;dropconnect
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Regularizing and Optimizing LSTM Language Models
|
https://github.com/salesforce/awd-lstm-lm
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
University of Toronto and Vector Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
|
https://iclr.cc/virtual/2018/poster/192
|
optimization;machine learning;variational inference;reinforcement learning;gradient estimation;deep learning;discrete optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical labels;weak labels;pairwise constraints;clustering;classification
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4 |
3;3;6
| null | null |
Cluster-based Warm-Start Nets
| null | null | 0 | 4.333333 |
Withdraw
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoders;CNN;image synthesis;latent space
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Taking Apart Autoencoders: How do They Encode Geometric Shapes ?
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
University of Southern California, USC Institute for Creative Technologies, Pinscreen; Shanghai Jiao Tong University; University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li
|
https://iclr.cc/virtual/2018/poster/266
|
motion synthesis;motion prediction;human pose;human motion;recurrent networks;lstm
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
| null | null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Planning;Compositionality;Attributes;Reinforcement learning
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Composable Planning with Attributes
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reward Design;Cooperative Multi-agent Reinforcement Learning;Packet Routing
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image generation;super-resolution;self-attention;transformer
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Image Transformer
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ensemble learning;neural network;small-sample;overfitting;variance
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Interactive Boosting of Neural Networks for Small-sample Image Classification
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-task learning;soft parameter sharing;facial landmark detection
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
task-oriented dialog;goal-oriented dialog;nlg evaluation;natural language generation;automated metrics for nlg
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNN;memory;LSTM;GRU;BRNN;encoder-decoder;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Dependent Bidirectional RNN with Extended-long Short-term Memory
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
code synthesis;program synthesis;genetic algorithm;reinforcement learning;policy gradient;reinforce;priority queue;topk buffer;bf;code golf;rnn
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Code Synthesis with Priority Queue Training
| 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
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
The Principle of Logit Separation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Wasserstein;Generalization;PCA
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Understanding GANs: the LQG Setting
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| 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
| 3.666667 |
3;4;4
| null | null |
Dense Transformer Networks
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
discriminative k-shot learning;probabilistic inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Discriminative k-shot learning using probabilistic models
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
FAIR; MIT
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, David Lopez-Paz
|
https://iclr.cc/virtual/2018/poster/177
|
empirical risk minimization;vicinal risk minimization;generalization;data augmentation;image classification;generative adversarial networks;adversarial examples;random labels
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
mixup: Beyond Empirical Risk Minimization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Computer Science, Indian Institute of Technology Bombay and Adobe Research; Department of Computer Science, Indian Institute of Technology Bombay
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi
|
https://iclr.cc/virtual/2018/poster/83
|
domain generalization;domain adaptation;adversarial learning;adversarial examples
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 7.25 |
7;7;7;8
| null | null |
Generalizing Across Domains via Cross-Gradient Training
| null | null | 0 | 4.5 |
Poster
|
4;5;5;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
| 3.333333 |
2;2;6
| null | null |
Estimation of cross-lingual news similarities using text-mining methods
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Carnegie Mellon University; DeepMind, CIFAR; DeepMind; University of Montreal
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil
|
https://iclr.cc/virtual/2018/poster/16
|
Awareness;Prediction;Seq2seq;Robots
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://goo.gl/mZuqAV
|
main
| 5 |
4;4;7
| null | null |
Learning Awareness Models
| null | null | 0 | 4.333333 |
Poster
|
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
| 5 |
5;5;5
| null | null |
Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning
| null | null | 0 | 2.666667 |
Reject
|
2;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Batch normalization;gradient backpropagation;Residual network;wide residual network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3 |
1;4;4
| null | null |
ANALYSIS ON GRADIENT PROPAGATION IN BATCH NORMALIZED RESIDUAL NETWORKS
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
École normale supérieure, Collège de France, PSL Research University, 75005 Paris, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tomás Angles, Stéphane Mallat
|
https://iclr.cc/virtual/2018/poster/36
|
Unsupervised Learning;Inverse Problems;Convolutional Networks;Generative Models;Scattering Transform
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Generative networks as inverse problems with Scattering transforms
| null | null | 0 | 4 |
Poster
|
4;4;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
| 4.666667 |
4;5;5
| null | null |
Deep Hyperspherical Defense against Adversarial Perturbations
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning interpretability;understanding
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
The (Un)reliability of saliency methods
| 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 learning;CNN;fuzzy logic;generalized hamming distance
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Deep Epitome for Unravelling Generalized Hamming Network: A Fuzzy Logic Interpretation of Deep Learning
| null | null | 0 | 3 |
Withdraw
|
3;4;2
| 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
| 4.666667 |
4;4;6
| null | null |
Adversarial Examples for Natural Language Classification Problems
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
density estimation;autoregressive models;RNNs
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Transformation Autoregressive Networks
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Extreme Classification;Large-scale learning;hashing;GPU;High Performance Computing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
MACH: Embarrassingly parallel $K$-class classification in $O(d\log{K})$ memory and $O(K\log{K} + d\log{K})$ time, instead of $O(Kd)$
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Mathematics, Department of Economics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Engineering Science, University of Oxford, Oxford, UK; Alan Turing Institute, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jinsung Yoon, William R Zame, Mihaela v Schaar
|
https://iclr.cc/virtual/2018/poster/239
|
Active Sensing;Timely Prediction;Irregular Sampling;Missing Data
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;continuous control;deep learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Discrete Sequential Prediction of Continuous Actions for Deep RL
| null | null | 0 | 3.666667 |
Reject
|
5;1;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Monte-Carlo Tree Search;search;planning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Learning to search with MCTSnets
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Edinburgh, Edinburgh EH8 9AB, UK; University of Pennsylvania, Philadelphia, PA 19104; Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, Stefano Panzeri
|
https://iclr.cc/virtual/2018/poster/143
|
GANs;Wasserstein-GANs;convolutional networks;neuroscience;spike train patterns;spike train analysis
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Department of ECE, National University of Singapore; Department of EE, Technion; Department of CS, Technion; Department of ISE, National University of Singapore
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robustness;Generalization;Deep Learning;Adversarial Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
| null | null | 0 | 4 |
Workshop
|
5;3;4
| 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
| 4.666667 |
4;5;5
| null | null |
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null |
Technical University of Munich, Germany
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aleksandar Bojchevski, Stephan Günnemann
|
https://iclr.cc/virtual/2018/poster/188
|
node embeddings;graphs;unsupervised learning;inductive learning;uncertainty;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural architecture search;language tasks;neural machine translation;reading comprehension;SQuAD
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Security in Machine Learning;Fairness and Security;Model Compression
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent autoencoder;seq2seq;rnn;multidimensional time series;clustering;sensor;signal analysis;industrial application
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;2;4
| null | null |
Recurrent Auto-Encoder Model for Multidimensional Time Series Representation
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
New York University, New York, USA; Horizon Robotics, Inc., Beijing, China
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yichen Gong, Heng Luo, Jian Zhang
|
https://iclr.cc/virtual/2018/poster/334
|
natural language inference;attention;SoTA;natural language understanding
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Natural Language Inference over Interaction Space
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural networks;state space models;sequential Monte Carlo
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
State Space LSTM Models with Particle MCMC Inference
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Baidu Research; NVIDIA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Paulius Micikevicius, SHARAN NARANG, Jonah Alben, Gregory Diamos, Erich K Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu
|
https://iclr.cc/virtual/2018/poster/288
|
Half precision;float16;Convolutional neural networks;Recurrent neural networks
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Mixed Precision Training
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
low-precision;neural networks;resource efficient;variational inference;Bayesian
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Discrete-Valued Neural Networks Using Variational Inference
| null | null | 0 | 3 |
Reject
|
4;4;1
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic program repair;neural program embeddings;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Semantic Code Repair using Neuro-Symbolic Transformation Networks
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null |
DeepMind, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ari Morcos, David Barrett, Neil C Rabinowitz, Matthew Botvinick
|
https://iclr.cc/virtual/2018/poster/232
|
generalization;analysis;deep learning;selectivity
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
5;7;9
| null | null |
On the importance of single directions for generalization
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;text summarization;adversarial training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequence generation;reinforcement learning;unsupervised learning;RNN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Toward learning better metrics for sequence generation training with policy gradient
| null | null | 0 | 2.333333 |
Reject
|
3;1;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generalization;maximum entropy;deep learning
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 3.666667 |
2;3;6
| null | null |
Understanding Deep Learning Generalization by Maximum Entropy
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Less-overlapness;regularization;near-orthogonality;sparsity
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Learning Less-Overlapping Representations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Computer Science & Technology, THU Lab for Brain and AI, Tsinghua University; Department of Computer Science, University of Toronto
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jiaxin Shi, Shengyang Sun, Jun Zhu
|
https://iclr.cc/virtual/2018/poster/11
|
Variational inference;Bayesian neural networks;Implicit distribution
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Kernel Implicit Variational Inference
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mikolaj Binkowski, [deadname] Sutherland, Michael Arbel, Arthur Gretton
|
https://iclr.cc/virtual/2018/poster/54
|
gans;mmd;ipms;wgan;gradient penalty;unbiased gradients
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Demystifying MMD GANs
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning to Write by Learning the Objective
| null | null | 0 | 4.666667 |
Workshop
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNNs;GANs;Variational RNNs;Sketch RNNs
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
action segmentation;video labeling;temporal networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Video Action Segmentation with Hybrid Temporal Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural networks;long-term dependencies;long short-term memory;LSTM
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
| null | null | 0 | 4.333333 |
Workshop
|
4;4;5
| null |
null |
Toyota Technological Institute at Chicago, Chicago, Illinois 60637, USA; Department of Electrical Engineering, Technion, Haifa, 320003, Israel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro
|
https://iclr.cc/virtual/2018/poster/236
|
gradient descent;implicit regularization;generalization;margin;logistic regression;loss functions;optimization;exponential tail;cross-entropy
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
The Implicit Bias of Gradient Descent on Separable Data
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
coresets;data compression
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Small Coresets to Represent Large Training Data for Support Vector Machines
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
set;unsupervised learning;representation learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
The Set Autoencoder: Unsupervised Representation Learning for Sets
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ensemble learning;SG-MCMC;group sparse prior;network pruning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;representation learning;autoencoder
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Neighbor-encoder
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
University of Amsterdam & CIFAR; University of Amsterdam
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Emiel Hoogeboom, Jorn Peters, Taco Cohen, Max Welling
|
https://iclr.cc/virtual/2018/poster/77
|
hexagonal;group;symmetry;representation learning;rotation;equivariance;invariance
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
HexaConv
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
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