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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
DEEPCAST : UNIVERSAL TIME-SERIES FORECASTER
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conversation model;multimodal embedding;attention mechanism;natural language processing;encoder-decoder model
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Associative Conversation Model: Generating Visual Information from Textual Information
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
The Hebrew University of Jerusalem
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural networks;deep networks;correlations;long term memory;tensor networks;tensor analysis
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Benefits of Depth for Long-Term Memory of Recurrent Networks
| null | null | 0 | 2.666667 |
Workshop
|
2;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Learning;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;tensor product representation;LSTM;image captioning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Neural-Symbolic Approach to Natural Language Tasks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;one-shot learning;metalearning;pixelcnn;hierarchical bayesian;omniglot
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
fast weights;RNN;associative retrieval;time-varying variables
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
College of Information Sciences and Technology, The Pennsylvania State University; Adobe Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jianbo Ye, Xin Lu, Zhe Lin, James Z Wang
|
https://iclr.cc/virtual/2018/poster/315
|
model pruning;batch normalization;convolutional neural network;ISTA
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
| null | null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalization;Neural Networks;Fourier Analysis
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
A Spectral Approach to Generalization and Optimization in Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
|
https://iclr.cc/virtual/2018/poster/65
|
generalization;complexity;experimental study;linear regions;Jacobian
| null | 0 | null | null |
iclr
| -0.240192 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Sensitivity and Generalization in Neural Networks: an Empirical Study
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
Cornell University, Ithaca, NY 14850, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ben Athiwaratkun, Andrew Wilson
|
https://iclr.cc/virtual/2018/poster/7
|
embeddings;word embeddings;probabilistic embeddings;hierarchical representation;probabilistic representation;order embeddings;wordnet;hyperlex
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Hierarchical Density Order Embeddings
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
objects;unsupervised;reinforcement learning;atari
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Learning objects from pixels
| 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;Computer Vision;Approximation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Heterogeneous Bitwidth Binarization in 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 |
Adversarial Examples;Detection;Saliency;Model Interpretation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
DETECTING ADVERSARIAL PERTURBATIONS WITH SALIENCY
| null | null | 0 | 4.25 |
Withdraw
|
5;4;4;4
| null |
null |
Department of Precision Instrument, Center for Brain Inspired Computing Research, Beijing Innovation Center for Future Chip, Tsinghua University; Department of Automation, Tsinghua University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Shuang Wu, Guoqi Li, Feng Chen, Luping Shi
|
https://iclr.cc/virtual/2018/poster/330
|
quantization;training;bitwidth;ternary weights
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Training and Inference with Integers in Deep Neural Networks
| null | null | 0 | 3.666667 |
Oral
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meshes;convolutions;faces;autoencoder
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
2;4;6
| null | null |
Convolutional Mesh Autoencoders for 3D Face Representation
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
DeepMind, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Angeliki Lazaridou, Karl M Hermann, Karl Tuyls, Stephen Clark
|
https://iclr.cc/virtual/2018/poster/138
|
disentanglement;communication;emergent language;compositionality;multi-agent
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
| null | null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null |
Under Review at ICLR 2018
|
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 |
Prediction Under Uncertainty with Error Encoding Networks
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null |
Boston University; The University of Tokyo, RIKEN; The University of Tokyo
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko
|
https://iclr.cc/virtual/2018/poster/137
|
domain adaptation;computer vision;generative models
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Adversarial Dropout Regularization
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNNs;time series forecasting;nonlinear dynamics;tensor-train
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Long-term Forecasting using Tensor-Train RNNs
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Carnegie Mellon University; Google Brain; Georgia Tech
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Krzysztof Choromanski, Carlton Downey, Byron Boots
|
https://iclr.cc/virtual/2018/poster/109
|
recurrent neural networks;orthogonal random features;predictive state representations
| null | 0 | null | null |
iclr
| -0.576557 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
5;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Parsimonious Deep Feed-forward Networks;structure learning;classification;overfitting;fewer parameters;high interpretability
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Parsimonious Deep Feed-forward Networks
| null | null | 0 | 3 |
Reject
|
2;2;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image captioning;representation learning;interpretability;rnn;multimodal;vision to language
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
What is image captioning made of?
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
University of Maryland; Microsoft Research NYC; University of Maryland & Microsoft Research NYC
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Structured Prediction;Contextual Bandits;Learning Reduction
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
| null | null | 0 | 3.666667 |
Poster
|
4;2;5
| 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
| 6 |
5;6;7
| null | null |
Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum
| 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;Computer Vision;Multi-modal fusion;Language Grounding
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Learning to navigate by distilling visual information and natural language instructions
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reading comprehension;question answering;CNN;ConvNet;Inference
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
FAST READING COMPREHENSION WITH CONVNETS
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimization;generalization;Adam;SGD
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Normalized Direction-preserving Adam
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;regularization;deep learning;graphical models;model diagnostics;survival analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Contextual Explanation Networks
| null | null | 0 | 3.333333 |
Reject
|
2;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;imitation learning;robotics;visuomotor skills
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Google Brain, Toronto, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Geoffrey E Hinton, Sara Sabour, Nicholas Frosst
|
https://iclr.cc/virtual/2018/poster/87
|
Computer Vision;Deep Learning;Dynamic routing
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Matrix capsules with EM routing
| null | null | 0 | 2.666667 |
Poster
|
2;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Compression;Deep Learning;Parent-Teacher Networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Deep Net Triage: Assessing The Criticality of Network Layers by Structural Compression
| null | null | 0 | 3.666667 |
Withdraw
|
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 |
title
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
localist;pdp;neural network;representation;psychology;cognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
When and where do feed-forward neural networks learn localist representations?
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conversation model;seq2seq;self-play;reinforcement learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
A Goal-oriented Neural Conversation Model by Self-Play
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GANs;first order dynamics;convergence;mode collapse
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
On the limitations of first order approximation in GAN dynamics
| null | null | 0 | 3.333333 |
Workshop
|
4;3;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
| 4.666667 |
3;5;6
| null | null |
A comparison of second-order methods for deep convolutional neural networks
| null | null | 0 | 4 |
Reject
|
4;5;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
| 5.666667 |
5;5;7
| null | null |
Byte-Level Recursive Convolutional Auto-Encoder for Text
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Spectral Method;Multi-label Learning;Tensor Factorisation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Weizmann Institute of Science, Rehovot, Israel; Yale University, New Haven, CT, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Uri Shaham, Kelly Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger
|
https://iclr.cc/virtual/2018/poster/290
|
unsupervised learning;spectral clustering;siamese networks
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
SpectralNet: Spectral Clustering using Deep Neural Networks
|
https://github.com/kstant0725/SpectralNet
| null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pruning;block sparsity;structured sparsity;Recurrent Neural Networks;Speech Recognition
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Block-Sparse Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Princeton University; MIT; University of Toronto; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, Richard Zemel
|
https://iclr.cc/virtual/2018/poster/88
|
Few-shot learning;semi-supervised learning;meta-learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Meta-Learning for Semi-Supervised Few-Shot Classification
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Privacy-preserving deep learning;Neural network training
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CNN;ensemble;image recognition
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yang Fan, Fei Tian, Tao Qin, Tie-Yan Liu
|
https://iclr.cc/virtual/2018/poster/193
| null | null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 7.333333 |
5;8;9
| null | null |
Learning to Teach
| 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
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Bit-Regularized Optimization of Neural Nets
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Stanford University; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nishal Shah, Sasidhar Madugula, E.J. Chichilnisky, Yoram Singer, Jonathon Shlens
|
https://iclr.cc/virtual/2018/poster/206
|
Metric learning;Computational Neuroscience;Retina;Neural Prosthesis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning a neural response metric for retinal prosthesis
| 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
| 0.755929 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Better Generalization by Efficient Trust Region Method
| null | null | 0 | 3.333333 |
Reject
|
2;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;maximum entropy learning;stochastic actor-critic
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
asynchronous;neural network;deep learning;graph;tree;rnn
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Word2Vec;Word Mover's Distance;Document Embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Word Mover's Embedding: From Word2Vec to Document Embedding
| null | null | 0 | 0 |
Active
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Classification;Feature Combination;Feature Mapping;Feed-Forward Neural Network;Genetic Algorithm;Linear Transfer Function;Non-Linear Transfer Function
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2 |
1;2;3
| null | null |
ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
human in the loop;GANs;generative adversarial networks;image generative models;computer vision
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Improving image generative models with human interactions
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial;video prediction;flow
| null | 0 | null | null |
iclr
| 0.333333 | 0 |
https://sites.google.com/site/omvideoprediction
|
main
| 4 |
3;3;3;7
| null | null |
Self-Supervised Learning of Object Motion Through Adversarial Video Prediction
| null | null | 0 | 4.75 |
Reject
|
5;4;5;5
| null |
null |
University of Amsterdam; Now at Bethgelab, University of T ¨ubingen; University of Amsterdam; CVN, CentraleSup ´elec, Universit ´e Paris-Saclay; Galen team, INRIA Saclay; SequeL team, INRIA Lille; DI, ENS, Universit ´e PSL
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Joern-Henrik Jacobsen, Arnold W Smeulders, Edouard Oyallon
|
https://iclr.cc/virtual/2018/poster/103
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
i-RevNet: Deep Invertible Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
factorization;general-purpose methods
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Structured Deep Factorization Machine: Towards General-Purpose Architectures
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Baidu Research; University of California, Berkeley; OpenAI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Arik, Ajay Kannan, SHARAN NARANG, Jonathan Raiman, John Miller
|
https://iclr.cc/virtual/2018/poster/323
|
2000-Speaker Neural TTS;Monotonic Attention;Speech Synthesis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameters;optimization;SGD;Adam;Bayesian
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Dynamically Learning the Learning Rates: Online Hyperparameter Optimization
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Department of Biology, University of Utrecht, The Netherlands; Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte
|
https://iclr.cc/virtual/2018/poster/229
|
DNA sequences;Hilbert curves;Convolutional neural networks;chromatin structure
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
An image representation based convolutional network for DNA classification
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;probabilistic modelling;reinforcement learning;state-space models;planning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xingyu Liu, Jeff Pool, song han, Bill Dally
|
https://iclr.cc/virtual/2018/poster/297
|
deep learning;convolutional neural network;pruning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Efficient Sparse-Winograd Convolutional 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 |
the manifold assumption;adversarial perturbation;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
The Manifold Assumption and Defenses Against Adversarial Perturbations
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
University of Basel, Switzerland
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth
|
https://iclr.cc/virtual/2018/poster/156
|
Information Bottleneck;Deep Information Bottleneck;Deep Variational Information Bottleneck;Variational Autoencoder;Sparsity;Disentanglement;Interpretability;Copula;Mutual Information
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
| null | null | 0 | 2.75 |
Poster
|
4;1;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble learning;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Coupled Ensembles of Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research; Cornell University; Tsinghua University; Fudan University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Weinberger
|
https://iclr.cc/virtual/2018/poster/278
|
efficient learning;budgeted learning;deep learning;image classification;convolutional networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8.333333 |
7;8;10
| null | null |
Multi-Scale Dense Networks for Resource Efficient Image Classification
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null |
Osaro Inc.; EECS Department, UC Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore
|
https://iclr.cc/virtual/2018/poster/53
|
multi-modal imitation learning;deep learning;generative models;stochastic neural networks
| null | 0 | null | null |
iclr
| 1 | 0 |
https://vimeo.com/240212286/fd401241b9
|
main
| 5.333333 |
4;6;6
| null | null |
Imitation Learning from Visual Data with Multiple Intentions
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Department of Engineering Science, University of Oxford; Department of Engineering Science, University of Oxford and Alan Turing Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
|
https://iclr.cc/virtual/2018/poster/170
| null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Smooth Loss Functions for Deep Top-k Classification
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
DeepMind, London, UK; Department of Computer Science and Technology, University of Cambridge, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark
|
https://iclr.cc/virtual/2018/poster/210
|
multi-agent learning;reinforcement learning;game theory;emergent communication
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Emergent Communication through Negotiation
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Departments of Electrical Engineering and Statistics, Stanford University; Departments of Management Science & Engineering, Stanford University; Departments of Electrical Engineering, Stanford University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aman Sinha, Hong Namkoong, John Duchi
|
https://iclr.cc/virtual/2018/poster/147
|
adversarial training;distributionally robust optimization;deep learning;optimization;learning theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 9 |
9;9;9
| null | null |
Certifying Some Distributional Robustness with Principled Adversarial Training
| null | null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null |
Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Brian Bullins, Cyril Zhang, Yi Zhang
|
https://iclr.cc/virtual/2018/poster/283
|
kernel learning;random features;online learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Not-So-Random Features
| null | null | 0 | 4.333333 |
Poster
|
5;5;3
| null |
null |
University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Prior knowledge;Reinforcement learning;Cognitive Science
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Investigating Human Priors for Playing Video Games
| null | null | 0 | 3.666667 |
Workshop
|
3;4;4
| null |
null |
NVIDIA and Aalto University; NVIDIA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen
|
https://iclr.cc/virtual/2018/poster/204
|
generative adversarial networks;unsupervised learning;hierarchical methods
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
1;8;8
| null | null |
Progressive Growing of GANs for Improved Quality, Stability, and Variation
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null |
Microsoft Research, Redmond; Google; Computer Science, Lehigh University; Computer Science, Dartmouth College
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He
|
https://iclr.cc/virtual/2018/poster/248
|
generative adversarial network;discrimination;generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
On the Discrimination-Generalization Tradeoff in GANs
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Salesforce Research, Palo Alto, CA 94301, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Romain Paulus, Caiming Xiong, richard socher
|
https://iclr.cc/virtual/2018/poster/279
|
deep learning;natural language processing;reinforcement learning;text summarization;sequence generation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
A Deep Reinforced Model for Abstractive Summarization
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;deep reinforcement learning;combinatorial games;optimality
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games?
| null | null | 0 | 3 |
Workshop
|
3;3;3
| null |
null |
Google Inc.; Georgia Tech
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kevin Murphy
|
https://iclr.cc/virtual/2018/poster/218
|
variational autoencoders;generative models;language;vision;abstraction;compositionality;hierarchy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Generative Models of Visually Grounded Imagination
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
complex numbers;complex-valued;neural;network;multi-layer;perceptron;architecture
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Complex- and Real-Valued Neural Network Architectures
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null |
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; SKOLTECH, Moscow, Russia & RIKEN BSI, Japan; RIKEN Center for Advanced Intelligence Project & Saitama Institute of Technology, Japan; RIKEN Center for Advanced Intelligence Project & The University of Tokyo, Tokyo, Japan
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Tensor Decomposition;Tensor Networks;Stochastic Gradient Descent
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning Efficient Tensor Representations with Ring Structure Networks
| null | null | 0 | 3.666667 |
Workshop
|
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
| 5.666667 |
5;5;7
| null | null |
Auxiliary Guided Autoregressive Variational Autoencoders
| 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;Chemistry;Interpretable Models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Using Deep Reinforcement Learning to Generate Rationales for Molecules
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
MPI for Intelligent Systems, Tübingen, Germany; Google Brain, Zürich, Switzerland
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf
|
https://iclr.cc/virtual/2018/poster/182
|
auto-encoder;generative models;GAN;VAE;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Wasserstein Auto-Encoders
| null | null | 0 | 3.333333 |
Oral
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantum Annealing;Reinforcement Learning;Boltzmann Machines;Markov Chain Monte Carlo
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Electrical Engineering Department, University of South Florida, Tampa, FL 33620
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ozsel Kilinc, Ismail Uysal
|
https://iclr.cc/virtual/2018/poster/250
|
representation learning;unsupervised clustering;pseudo supervision;graph-based activity regularization;auto-clustering output layer
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null |
The Hebrew University of Jerusalem
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Or Sharir, Amnon Shashua
|
https://iclr.cc/virtual/2018/poster/230
|
Deep Learning;Expressive Efficiency;Overlapping;Receptive Fields
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
On the Expressive Power of Overlapping Architectures of Deep Learning
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph classification;convolutional neural networks;2D CNN;representation
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Graph Classification with 2D Convolutional Neural 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 | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Combining Model-based and Model-free RL via Multi-step Control Variates
| 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 | 0 | null |
main
| 2.666667 |
1;3;4
| null | null |
Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs)
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null |
School of Engineering, University of Guelph, Canada; Canadian Institute for Advanced Research; Vector Institute for Artificial Intelligence, Canada; School of Engineering, University of Guelph, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Angus Galloway, Graham W Taylor, Medhat Moussa
|
https://iclr.cc/virtual/2018/poster/47
|
adversarial examples;adversarial attacks;binary;binarized neural networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Attacking Binarized Neural Networks
|
https://github.com/AngusG/cleverhans-attacking-bnns
| null | 0 | 4 |
Poster
|
5;4;3
| null |
null |
Université de Montréal & Montreal Institute for Learning Algorithms (MILA); Canadian Institute for Advanced Research (CIFAR); Département d’informatique de l’ENS, Paris, France; INRIA, École normale supérieure, CNRS, PSL Research University; National Research University Higher School of Economics, Moscow, Russia; Département d’informatique de l’ENS, Paris, France; INRIA, École normale supérieure, CNRS, PSL Research University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien
|
https://iclr.cc/virtual/2018/poster/191
|
Structured prediction;RNNs
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
SEARNN: Training RNNs with global-local losses
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
ETH Zurich, KU Leuven; ETH Zurich, Merantix; ETH Zurich
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Róbert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
|
https://iclr.cc/virtual/2018/poster/21
| null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Towards Image Understanding from Deep Compression Without Decoding
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
QUV A Lab, University of Amsterdam, Amsterdam, Netherlands
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Peter OConnor, Efstratios Gavves, Matthias Reisser, Max Welling
|
https://iclr.cc/virtual/2018/poster/135
|
online learning;spiking networks;deep learning;temporal
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Temporally Efficient Deep Learning with Spikes
| null | null | 0 | 4.333333 |
Poster
|
4;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
| 5 |
4;5;6
| null | null |
DENSELY CONNECTED RECURRENT NEURAL NETWORK FOR SEQUENCE-TO-SEQUENCE LEARNING
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applied deep learning;Image segmentation;Hyperspectral Imaging;Feature sampling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
| 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
| 3.666667 |
3;3;5
| null | null |
Convolutional Normalizing Flows
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
EPFL; University of Amsterdam & CIFAR; University of Amsterdam
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Taco Cohen, Mario Geiger, Jonas Koehler, Max Welling
|
https://iclr.cc/virtual/2018/poster/144
|
deep learning;equivariance;convolution;group convolution;3D;vision;omnidirectional;shape recognition;molecular energy regression
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Spherical CNNs
| null | null | 0 | 3.666667 |
Oral
|
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
| 4.333333 |
4;4;5
| null | null |
The Mutual Autoencoder: Controlling Information in Latent Code Representations
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Google
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V Le, Jeff Dean
|
https://iclr.cc/virtual/2018/poster/140
|
deep learning;device placement;policy gradient optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6 |
5;5;8
| null | null |
A Hierarchical Model for Device Placement
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;molecule design;de novo design;ppo;sample-efficient reinforcement learning
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design
| null | null | 0 | 3 |
Workshop
|
2;3;4
| null |
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