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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Electrical Engineering, Technion, Haifa, 320003, Israel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Elad Hoffer, Itay Hubara, Daniel Soudry
|
https://iclr.cc/virtual/2018/poster/215
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Fix your classifier: the marginal value of training the last weight layer
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Generative Adversarial Networks;Deep Learning;Inverse Problems
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Anomaly Detection with Generative Adversarial Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Computer Science, Stanford University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Drew A. Hudson, Christopher Manning
|
https://iclr.cc/virtual/2018/poster/59
|
Deep Learning;Reasoning;Memory;Attention;VQA;CLEVR;Recurrent Neural Networks;Module Networks;Compositionality
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Compositional Attention Networks for Machine Reasoning
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic Optimization;Deep Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
| 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;Adversarial Examples;Difference Target Propagation;Generative Modelling;Classifiers;Explaining;Sympathetic Examples
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative adversarial networks;classification;benchmark;mode collapse;diversity
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
A Classification-Based Perspective on GAN Distributions
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model based reinforcement learning;Imitation learning;dynamics model
| null | 0 | null | null |
iclr
| -0.960769 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Model-based imitation learning from state trajectories
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conversational modeling;dialogue;chitchat;open-domain dialogue;topic model;neural variational inference;human evaluation;latent variable model;gaussian reparameterisation trick
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Latent Topic Conversational Models
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yan Wu, Greg Wayne, Alex Graves, Timothy Lillicrap
|
https://iclr.cc/virtual/2018/poster/105
|
memory;generative model;inference;neural network;hierarchical model
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
The Kanerva Machine: A Generative Distributed Memory
| null | null | 0 | 3 |
Poster
|
4;3;2
| null |
null |
Salesforce Research, Palo Alto, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alexander Trott, Caiming Xiong, richard socher
|
https://iclr.cc/virtual/2018/poster/195
|
Counting;VQA;Object detection
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Interpretable Counting for Visual Question Answering
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Institute for Advanced Study; The Hebrew University of Jerusalem
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nadav Cohen, Ronen Tamari, Amnon Shashua
|
https://iclr.cc/virtual/2018/poster/320
|
Deep Learning;Expressive Efficiency;Dilated Convolutions;Tensor Decompositions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
| null | null | 0 | 3.666667 |
Oral
|
4;3;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Relationships Extraction;Deep Learning;TreeLSTM;NLP
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
ShakeDrop regularization
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional neural networks;Audio processing;Speech processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
How do deep convolutional neural networks learn from raw audio waveforms?
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-task learning;MNIST;image recognition
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Multi-task Learning on MNIST Image Datasets
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
word embeddings;natural language semantics;entailment;unsupervised learning;distributional semantics
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;3;7
| null | null |
Unsupervised Learning of Entailment-Vector Word Embeddings
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Toyota Technological Institute, Chicago, IL - 60637; Department of Computer Science, The University of Chicago, Chicago, IL - 60637
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural networks;message passing;label propagation;high order representation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Covariant Compositional Networks For Learning Graphs
| null | null | 0 | 2.666667 |
Workshop
|
2;3;3
| null |
null |
Skolkovo Institute of Science and Technology, Institute of Numerical Mathematics RAS; Skolkovo Institute of Science and Technology; National Research University Higher School of Economics, Institute of Numerical Mathematics RAS
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Valentin Khrulkov, Alexander Novikov, Ivan Oseledets
|
https://iclr.cc/virtual/2018/poster/112
|
Recurrent Neural Networks;Tensor Train;tensor decompositions;expressive power
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Expressive power of recurrent neural networks
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Cross Language Text Classification;Neural Networks;Machine Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Zero-shot Cross Language Text Classification
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
ETH Zürich; Google DeepMind; IST Austria
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Antonio Polino, Razvan Pascanu, Dan Alistarh
|
https://iclr.cc/virtual/2018/poster/8
|
quantization;distillation;model compression
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Model compression via distillation and quantization
| null | null | 0 | 3.666667 |
Poster
|
4;2;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
parallel hyperparameter tuning;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Massively Parallel Hyperparameter Tuning
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
National Laboratory of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China.; McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA.
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou
|
https://iclr.cc/virtual/2018/poster/42
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Stochastic Processes;Time Series Analysis
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Neural Networks for irregularly observed continuous-time Stochastic Processes
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational inference;bayesian inference;deep networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Bayesian Hypernetworks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Lifelong;Generative Modeling;Variational Autoencoder;VAE;Catastrophic Interference
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
4;4;9
| null | null |
Lifelong Generative Modeling
| null | null | 0 | 4 |
Reject
|
2;5;5
| null |
null |
University of Toronto; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Lukasz Kaiser, Aidan Gomez, Francois Chollet
|
https://iclr.cc/virtual/2018/poster/247
|
convolutions;neural machine translation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Depthwise Separable Convolutions for Neural Machine Translation
|
https://github.com/tensorflow/tensor2tensor
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
pruning;model sparsity;model compression;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Probability metrics;Wasserstein metric;stochastic gradient descent;GANs
| null | 0 | null | null |
iclr
| -0.928571 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
The Cramer Distance as a Solution to Biased Wasserstein Gradients
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null |
Department of Computer Science, University of California, San Diego; Department of Music, University of California, San Diego; Department of Genetics, Stanford University; Carnegie Mellon University, Amazon AI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chris Donahue, Zachary Lipton, Akshay Balsubramani, Julian McAuley
|
https://iclr.cc/virtual/2018/poster/281
|
disentangled representations;generative adversarial networks;generative modeling;image synthesis
| null | 0 | null | null |
iclr
| 0 | 0 |
https://chrisdonahue.github.io/sdgan
|
main
| 6.333333 |
6;6;7
| null | null |
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
|
https://github.com/chrisdonahue/sdgan
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;contextualized word embeddings
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Deep contextualized word representations
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Neural Network;Compression;Sparsity
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Abstract Machines;Neural Turing Machines;Neural Random Access Machines;Program Synthesis;Program Induction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Training Neural Machines with Partial Traces
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge base embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Revisiting Knowledge Base Embedding as Tensor Decomposition
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, University of Toronto; Department of Computer Science, University of Toronto, Vector Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
|
https://iclr.cc/virtual/2018/poster/66
|
reinforcement learning;transfer learning;graph neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
NerveNet: Learning Structured Policy with Graph Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;domain adaptation;unsupervised learning;autoencoders;multi-task learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Joint autoencoders: a flexible meta-learning framework
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Salesforce Research, Palo Alto, CA; Microsoft AI & Research, Sunnyvale, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ke Zhai, Huan Wang
|
https://iclr.cc/virtual/2018/poster/287
|
model complexity;regularization;deep learning;model generalization;adaptive dropout
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Adaptive Dropout with Rademacher Complexity Regularization
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
University Paris-Est, LIGM; Ecole des Ponts ParisTech
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Spyros Gidaris, Praveer Singh, Nikos Komodakis
|
https://iclr.cc/virtual/2018/poster/227
|
Unsupervised representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Unsupervised Representation Learning by Predicting Image Rotations
|
https://github.com/gidariss/FeatureLearningRotNet
| null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Been Kim, Justin Gilmer, Fernanda Viegas, Ulfar Erlingsson, Martin Wattenberg
| null | null | null | 0 | null | null |
iclr
| -0.948683 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
TCAV: Relative concept importance testing with Linear Concept Activation Vectors
| null | null | 0 | 3.5 |
Reject
|
5;3;4;2
| null |
null |
University of Cambridge; Google Brain; UC Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/118
|
manual reset;continual learning;reinforcement learning;safety
| null | 0 | null | null |
iclr
| -0.174078 | 0 |
https://sites.google.com/site/mlleavenotrace/
|
main
| 6.25 |
5;6;7;7
| null | null |
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
| null | null | 0 | 4.25 |
Poster
|
4;5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
embeddings;hyperbolic space;neural networks;geometry
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Hybed: Hyperbolic Neural Graph Embedding
| null | null | 0 | 2.75 |
Reject
|
3;3;3;2
| null |
null |
Facebook AI Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander Miller, Arthur Szlam, Douwe Kiela, Jason Weston
|
https://iclr.cc/virtual/2018/poster/176
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;second-order optimization;hessian free
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
BLOCK-DIAGONAL HESSIAN-FREE OPTIMIZATION FOR TRAINING NEURAL NETWORKS
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ronald Kemker, Christopher Kanan
|
https://iclr.cc/virtual/2018/poster/81
|
Incremental Learning;Lifelong Learning;Supervised Learning;Catastrophic Forgetting;Brain-Inspired;Neural Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
FearNet: Brain-Inspired Model for Incremental Learning
| null | null | 0 | 2.666667 |
Poster
|
2;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural networks;Curriculum learning;Self paced learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Training with Growing Sets: A Simple Alternative to Curriculum Learning and Self Paced Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Computer Science, University of Virginia; Department of Computer Science, University of California, Los Angeles
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wasi Ahmad, Kai-Wei Chang, Hongning Wang
|
https://iclr.cc/virtual/2018/poster/270
|
Multitask Learning;Document Ranking;Query Suggestion
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Multi-Task Learning for Document Ranking and Query Suggestion
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LSH;softmax;deep;learning;sub;linear;efficient;GPU
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures
| 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;neural network;attention;attention mechanism;interpretability;visualization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Modeling Latent Attention Within 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 |
unsupervised learning;representation learning;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Learning To Generate Reviews and Discovering Sentiment
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Auto Encoder;Signal Recovery;Sparse Coding
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
On Optimality Conditions for Auto-Encoder Signal Recovery
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Max Planck Institute for Intelligent Systems; Carnegie Mellon University; IBM Research AI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng
|
https://iclr.cc/virtual/2018/poster/98
|
GAN theory;Integral Probability Metrics;elliptic PDE and diffusion;GAN for discrete sequences;semi-supervised learning.
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Sobolev GAN
|
https://github.com/tomsercu/SobolevGAN-SSL
| null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Parallelism of Convolutional Neural Networks;Accelerating Convolutional Neural Networks
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Exploring the Hidden Dimension in Accelerating Convolutional 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 |
recurrent neural networks;long-term dependencies;back-propagation through time;truncated back-propagation;biological inspiration;self-attention
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent 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 |
Weight-sharing;Weight sharing;Weight tying;neural networks;entropy;hash function;hash table;balance;sparse;sparsity;hashednets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Balanced and Deterministic Weight-sharing Helps Network Performance
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural sequence prediction;Embedding;LSTM;Regularization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Time-Dependent Representation for Neural Event Sequence Prediction
| null | null | 0 | 4 |
Workshop
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;local minima;global minima;saddle point;optimization;loss surface;rectified linear unit;loss surface decomposition;gradient descent
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Understanding Local Minima in Neural Networks by Loss Surface Decomposition
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised deep learning;Temporal clustering;Event Visualization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Deep Temporal Clustering: Fully unsupervised learning of time-domain features
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hypernetworks;hyperparameter optimization;metalearning;neural networks;Bayesian optimization;game theory;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Stochastic Hyperparameter Optimization through Hypernetworks
| null | null | 0 | 2.666667 |
Reject
|
4;1;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
depth separation;neural networks;weights-width trade-off
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Depth separation and weight-width trade-offs for sigmoidal neural networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
University of California, Los Angeles; Salesforce Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tianmin Shu, Caiming Xiong, richard socher
|
https://iclr.cc/virtual/2018/poster/128
|
Hierarchical Policy;Interpretable Policy;Deep Reinforcement Learning;Multi-task Reinforcement Learning;Skill Acquisition;Language Grounding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Berkeley AI Research, University of California, Berkeley, Berkeley, CA 94709
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel
|
https://iclr.cc/virtual/2018/poster/111
|
model-based reinforcement learning;model ensemble;reinforcement learning;model bias
| null | 0 | null | null |
iclr
| 0.866025 | 0 |
https://sites.google.com/view/me-trpo
|
main
| 6.666667 |
6;7;7
| null | null |
Model-Ensemble Trust-Region Policy Optimization
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
MIT, EECS; Microsoft Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Constantinos C Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
|
https://iclr.cc/virtual/2018/poster/127
|
GANs;Optimistic Mirror Decent;Cycling;Last Iterate Convergence;Optimistic Adam
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Training GANs with Optimism
|
https://github.com/vsyrgkanis/optimistic_GAN_training
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Pennsylvania
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Jaegle, Stephen Phillips, Daphne Ippolito, Kostas Daniilidis
|
https://iclr.cc/virtual/2018/poster/84
|
vision;motion;recurrent neural networks;self-supervised learning;unsupervised learning;group theory
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Understanding image motion with group representations
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
DenseNet;sparse shortcut connections;network architecture;scene parsing;image classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Log-DenseNet: How to Sparsify a DenseNet
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Security;Privacy;Model Publication;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier
| 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;segmentation;automatic measurement;semiconductor;Scanning Electron Microscope
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Automatic Measurement on Etched Structure in Semiconductor Using Deep Learning Approach
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep neural networks;convolutional networks;influence measures;explanations
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Influence-Directed Explanations for Deep Convolutional Networks
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Janelia Research Campus, HHMI, AIFounded Inc.; Janelia Research Campus, HHMI; University of Guelph, Vector Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Daniel Im, He Ma, Graham W Taylor, Kristin Branson
|
https://iclr.cc/virtual/2018/poster/41
|
Generative adversarial networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Quantitatively Evaluating GANs With Divergences Proposed for Training
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic segmentation;adversarial learning;semi-supervised learning;self-taught learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Adversarial Learning for Semi-Supervised Semantic Segmentation
| 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
| 5 |
4;4;5;7
| null | null |
SHADE: SHAnnon DEcay Information-Based Regularization for Deep Learning
| null | null | 0 | 3.25 |
Reject
|
3;3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Backpropagation;Fixed Point Recurrent Neural Network;Biologically Plausible Learning;Feedback Alignment;Dynamical System;Gradient-Free Optimization
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Extending the Framework of Equilibrium Propagation to General Dynamics
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fairness;IDK;Calibration;Automated decision-making;Transparency;Accountability
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Predict Responsibly: Increasing Fairness by Learning to Defer
| null | null | 0 | 3.666667 |
Workshop
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Selection Problem;Job Dispatching;Convolution Neural Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Learning to Select: Problem, Solution, and Applications
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Networks;Dynamic Networks;Faster Inference;Reasoning;QA
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Adaptive Memory Networks
| null | null | 0 | 4 |
Workshop
|
3;4;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
| 5 |
4;5;6
| null | null |
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science, Carnegie Mellon University; Microsoft Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
anytime;neural network;adaptive prediction;budgeted prediction
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://arxiv.org/abs/1708.06832
|
main
| 5.666667 |
5;5;7
| null | null |
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null |
Facebook, Montreal, Canada; MILA, Université de Montréal, Canada; Aalto University, Finland; MILA, Université de Montréal, Canada & CIFAR Senior Fellow; Jagiellonian University, Cracow, Poland
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio
|
https://iclr.cc/virtual/2018/poster/267
|
residual network;iterative inference;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Residual Connections Encourage Iterative Inference
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
University of Amsterdam; Cornell University; Microsoft Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
|
https://iclr.cc/virtual/2018/poster/282
|
Batch Learning from Bandit Feedback;Counterfactual Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Deep Learning with Logged Bandit Feedback
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;stable training;low-dimensional projections;deep learning
| null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
Stabilizing GAN Training with Multiple Random Projections
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Laboratoire de Recherche en Informatique, Université Paris Sud, Gif-sur-Yvette, 91190, France; Facebook Articial Intelligence Research, Paris, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Corentin Tallec, Yann Ollivier
|
https://iclr.cc/virtual/2018/poster/181
|
RNN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Can recurrent neural networks warp time?
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;knowledge graphs;relational inference;link prediction;multi-label classification;knowledge base completion
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Generalized Graph Embedding Models
| 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;robotics;dexterous manipulation;off-policy learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;DDPG;Multiple Action Prediction
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Predicting Multiple Actions for Stochastic Continuous Control
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Cambridge, Cambridge, CB2 1PZ, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yingzhen Li, Richard E Turner
|
https://iclr.cc/virtual/2018/poster/252
|
Implicit Models;Approximate Inference;Deep Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Gradient Estimators for Implicit Models
| null | null | 0 | 2.666667 |
Poster
|
2;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
contextual bandit;memory network;proactive dialog engagement
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Contextual memory bandit for pro-active dialog engagement
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
California Institute of Technology; University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu
|
https://iclr.cc/virtual/2018/poster/80
|
Traffic prediction;spatiotemporal forecasting;diffusion;graph convolution;random walk;long-term forecasting
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 6 |
4;5;9
| null | null |
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Anomaly Detection;Fault diagnosis;Generative Adversarial Networks;Network Operation;TCP/IP
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Detecting Anomalies in Communication Packet Streams based on Generative Adversarial Networks
| null | null | 0 | 4 |
Withdraw
|
5;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
| 5.333333 |
5;5;6
| null | null |
Learning Independent Causal Mechanisms
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph;generative model;autoencoder
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
feature map;representation;compression;quantization;finite-field
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
DNN Feature Map Compression using Learned Representation over GF(2)
|
https://github.com/anonymous-author-if-available
| null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Survival Analysis;Kuiper statistics;model-free
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
A Deep Learning Approach for Survival Clustering without End-of-life Signals
| null | null | 0 | 3.333333 |
Reject
|
4;1;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data augmentation;Image classification
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Data Augmentation by Pairing Samples for Images Classification
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multilingual;embedding;representation learning;multi-task learning;low resource
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Multitask learning of Multilingual Sentence Representations
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
pruning;generalisation error;DC optimisation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Cheap DNN Pruning with Performance Guarantees
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary Neural Networks;Residual Binarization;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
ResBinNet: Residual Binary Neural Network
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Metric Learning;K-means;CPD;Clustering
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
UNSUPERVISED METRIC LEARNING VIA NONLINEAR FEATURE SPACE TRANSFORMATIONS
| 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
| 0 | 0 | null |
main
| 0 | null | null | null |
withdraw
|
https://github.com/
| null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversary;Robust;Reinforcement Learning;A3C
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Adversary A3C for Robust Reinforcement 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 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Graph2Seq: Scalable Learning Dynamics for Graphs
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Low-Shot Learning;class incremental learning;Network expansion;Generative model;Distillation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
GENERATIVE LOW-SHOT NETWORK EXPANSION
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
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