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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Tencent AI Lab, Bellevue, WA, USA 98004; Department of Computer Science, University of Central Florida, Orlando, FL, USA 32816; School of Software Engineering, Beijing Jiaotong University, Beijing, China 100044
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang
|
https://iclr.cc/virtual/2018/poster/4
|
GAN;WGAN
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
| 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;Deep learning;Episodic memory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Now I Remember! Episodic Memory For Reinforcement Learning
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Université Côte d’Azur, OCA, Lagrange, UMR 7293, CNRS; Université de Bretagne Sud, IRISA, UMR 6074, CNRS; Université Côte d’Azur, I3S, UMR 7271, CNRS
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nicolas Courty, Rémi Flamary, Mélanie Ducoffe
|
https://iclr.cc/virtual/2018/poster/305
|
Wasserstein distance;metric embedding;Siamese architecture
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Learning Wasserstein Embeddings
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
MILA, Université de Montréal; Jagiellonian University; MILA, Université de Montréal, CIFAR Senior Fellow
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio
|
https://iclr.cc/virtual/2018/poster/5
|
fraternal dropout;activity regularization;recurrent neural networks;RNN;LSTM;faster convergence
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Fraternal Dropout
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph;Node Embeddings;Distributed Representations;Learning Representations
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Fast Node Embeddings: Learning Ego-Centric Representations
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
clustering;dimensionality reduction
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Deep Continuous Clustering
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Technical University of Denmark, Section for Cognitive Systems
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
|
https://iclr.cc/virtual/2018/poster/130
|
Generative models;Riemannian Geometry;Latent Space
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Latent Space Oddity: on the Curvature of Deep Generative Models
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transform learning;sparse representation;discrimininative prior;information preservation;discrimination power
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning non-linear transform with discriminative and minimum information loss priors
| null | null | 0 | 1.666667 |
Reject
|
2;1;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conversation systems;retrieval method;generation method
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sparse representation;Compression Deep Learning Models;L1 regularisation;Optimisation.
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Sparse Regularized Deep Neural Networks For Efficient Embedded Learning
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
privacy preserving deep learning;collaborative learning;adversarial attack
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Key Protected Classification for GAN Attack Resilient Collaborative Learning
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null |
UC Berkeley; CMU; OpenAI; UMass Amherst
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
|
https://iclr.cc/virtual/2018/poster/171
|
reinforcement learning;nonstationarity;meta-learning;transfer learning;multi-agent
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
| null | null | 0 | 3.333333 |
Oral
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNN;unitary approach;associative memory;language modeling
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Rotational Unit of Memory
| null | null | 0 | 3.666667 |
Workshop
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
confidence scoring;meta-model;linear classifier probes
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi
|
https://iclr.cc/virtual/2018/poster/304
|
binary deep neural networks;optimized implementation;bitwise computations
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
| null | null | 0 | 2.666667 |
Poster
|
3;4;1
| null |
null |
KAIST, Daejeon, South Korea; KAIST, Daejeon, South Korea; AITrics, Seoul, South Korea; UNIST, Ulsan, South Korea
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang
|
https://iclr.cc/virtual/2018/poster/37
|
Transfer learning;Lifelong learning;Selective retraining;Dynamic network expansion
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Lifelong Learning with Dynamically Expandable Networks
| null | null | 0 | 2.666667 |
Poster
|
3;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Latent representations;Predictive coding;Recurrent networks;Sequential data
| null | 0 | null | null |
iclr
| -0.229416 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Representing dynamically: An active process for describing sequential data
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null |
Baidu Research USA, Sunnyvale, CA 94089 USA; Snap Inc., Venice, CA 90291 USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille
|
https://iclr.cc/virtual/2018/poster/238
|
adversarial examples
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Mitigating Adversarial Effects Through Randomization
|
https://github.com/cihangxie/NIPS2017_adv_challenge_defense
| null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Facebook AI Research; Carnegie Mellon University; University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Simon Du, Jason D Lee, Yuandong Tian
|
https://iclr.cc/virtual/2018/poster/317
|
deep learning;convolutional neural network;non-convex optimization;convergence analysis
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
When is a Convolutional Filter Easy to Learn?
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Embedding spaces;feature extraction;transfer learning.
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph embedding;CNN
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Ego-CNN: An Ego Network-based Representation of Graphs Detecting Critical Structures
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta learning;activation functions
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Searching for Activation Functions
| null | null | 0 | 4.666667 |
Workshop
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ensemble;confidence level
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational autoencoder;information theory;noise modelling;representation learning;generative model;disentanglement
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Preliminary theoretical troubleshooting in Variational Autoencoder
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial machine learning;black-box attacks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Exploring the Space of Black-box Attacks on Deep Neural Networks
|
https://github.com/anonymous1
| null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani
|
https://iclr.cc/virtual/2018/poster/220
|
Voice Synthesis;Multi-Speaker;Differentiable Memory;Text-to-Speech
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
| 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 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Oxford; DeepMind; DeepMind and University of Oxford
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dani Yogatama, yishu miao, Gábor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom
|
https://iclr.cc/virtual/2018/poster/205
| null | null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Memory Architectures in Recurrent Neural Network Language Models
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Network Iterative Learning;Morphism
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Network Iterative Learning for Dynamic Deep Neural Networks via Morphism
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null |
School of Electrical Engineering, KAIST, Daejeon, South Korea
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kangwook Lee, Hoon Kim, Changho Suh
|
https://iclr.cc/virtual/2018/poster/38
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
3;6;6
| null | null |
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;deep learning;regularization;data augmentation;network architecture;loss function;dropout;residual learning;optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Regularization for Deep Learning: A Taxonomy
| null | null | 0 | 4.666667 |
Reject
|
5;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
| 5.333333 |
5;5;6
| null | null |
Regularization Neural Networks via Constrained Virtual Movement Field
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
relational reasoning;graph neural networks
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Recurrent Relational Networks for complex relational reasoning
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural language models;word embeddings;neural networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Word2net: Deep Representations of Language
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoder;recommendations;collaborative filtering;selu
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Training Deep AutoEncoders for Recommender Systems
|
https://github.com/Anonymous
| null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology; Salesforce Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;architecture search;ranking function;recurrent neural networks;recursive neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
A Flexible Approach to Automated RNN Architecture Generation
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.720577 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Cortexica Vision Systems Ltd., London, UK; AI Theory Lab (Noah’s Ark), Huawei Technologies R&D, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null |
Dept. of Computer Science, New York University; Facebook AI Research, New York
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus
|
https://iclr.cc/virtual/2018/poster/146
|
self-play;automatic curriculum;intrinsic motivation;unsupervised learning;reinforcement learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
5;8;8
| null | null |
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Zalando Research, Mühlenstraße 25, 10243 Berlin, Germany; LIT AI Lab & Institute of Bioinformatics, Johannes Kepler University Linz, Austria
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Thomas Unterthiner
|
https://iclr.cc/virtual/2018/poster/251
|
Deep Learning;Generative Adversarial Network;GAN;Generative Model;Potential Field
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transformer;branching;attention;machine translation
| null | 0 | null | null |
iclr
| -0.114708 | 0 | null |
main
| 6.333333 |
4;6;9
| null | null |
Weighted Transformer Network for Machine Translation
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Character Level Convolutional Networks;Text Classification;Word Compressing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Compact Encoding of Words for Efficient Character-level Convolutional Neural Networks Text Classification
| null | null | 0 | 5 |
Reject
|
5;5;5
| 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
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep generative models;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Learning Deep Generative Models With Discrete Latent Variables
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
|
https://iclr.cc/virtual/2018/poster/175
|
structure;neural networks;logic;dataset
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Can Neural Networks Understand Logical Entailment?
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Convolution;Deep Learning;Network of Networks
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Network of Graph Convolutional Networks Trained on Random Walks
| null | null | 0 | 3.666667 |
Reject
|
2;4;5
| null |
null |
University College London, Alan Turing Institute; University College London
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hippolyt Ritter, Aleksandar Botev, David Barber
|
https://iclr.cc/virtual/2018/poster/224
|
deep learning;neural networks;laplace approximation;bayesian deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;6;9
| null | null |
A Scalable Laplace Approximation for Neural Networks
| 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 | 0 | null |
main
| 0 | null | null | null |
Distributed Restarting NewtonCG Method for Large-Scale Empirical Risk Minimization
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model-parallel;parallelization;software platform
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Statestream: A toolbox to explore layerwise-parallel deep neural 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 |
convolution neural networks;architecture search;meta-learning;representational capacity
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Building effective deep neural networks one feature at a time
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Stanford University; Stanford University, Google Brain; Tsinghua University, Beijing National Research Center for Information Science and Technology; Stanford University, NVIDIA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yujun Lin, song han, , Yu Wang, Bill Dally
|
https://iclr.cc/virtual/2018/poster/68
|
distributed training
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;physics;field theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Spontaneous Symmetry Breaking in Deep Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
University of Maryland, College Park, MD 20740, USA.
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein
|
https://iclr.cc/virtual/2018/poster/213
|
adversarial networks;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
4;7;9
| null | null |
Stabilizing Adversarial Nets with Prediction Methods
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;rl;exploration;meta learning;meta reinforcement learning;curiosity
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spatial memory;egocentric vision;deep neural network;navigation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Egocentric Spatial Memory Network
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangling;factors;attribute;transfer;autoencoder;GAN
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Challenges in Disentangling Independent Factors of Variation
| null | null | 0 | 3.333333 |
Workshop
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continuous fidelity;Bayesian optimization;fast;knowledge gradient;hyperparameter optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Continuous-fidelity Bayesian Optimization with Knowledge Gradient
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xinyun Chen, Chang Liu, Dawn Song
|
https://iclr.cc/virtual/2018/poster/269
| null | null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Towards Synthesizing Complex Programs From Input-Output Examples
| null | null | 0 | 3 |
Poster
|
2;4;3
| 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 |
4;6;7
| null | null |
Variational Bi-LSTMs
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Learning Representations for Faster Similarity Search
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
residual network;boosting theory;training error guarantee
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Deep ResNet Blocks Sequentially using Boosting Theory
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;unsupervised learning;classification;semantic segmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;5;9
| null | null |
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
value iteration networks;robotics;space robotics;imitation learning;convolutional neural networks;path planning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Soft Value Iteration Networks for Planetary Rover Path Planning
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image retrieval;visual similarity;non-metric learning
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Learning Non-Metric Visual Similarity for Image Retrieval
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Cluttering;deep learning;human learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
3;4
| null | null |
Human-like Clustering with Deep Convolutional Neural Networks
| null | null | 0 | 5 |
Withdraw
|
5;5
| null |
null |
University of Cambridge, Max Planck Institute, Google Brain; University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/108
|
model-based reinforcement learning;model-free reinforcement learning;temporal difference learning;predictive learning;predictive models;optimal control;off-policy reinforcement learning;deep learning;deep reinforcement learning;q learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SVD;Latent Dimensions;Dimension Reductions;Machine Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2 |
1;2;3
| null | null |
A novel method to determine the number of latent dimensions with SVD
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
IBM Watson, Prague AI Research & Development Lab; IBM Watson, Prague AI Research & Development Lab; Deepmind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
| null |
evaluation;methodology
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
A Boo(n) for Evaluating Architecture Performance
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
topic model;Bayesian nonparametric;variational auto-encoder;document modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 |
http://airnet.caiyunapp.com
|
main
| 4.333333 |
4;4;5
| null | null |
AirNet: a machine learning dataset for air quality forecasting
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Toyota Technological Institute at Chicago
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
|
https://iclr.cc/virtual/2018/poster/93
|
Neural Networks;Generalization;PAC-Bayes;Sharpness
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
University of Washington; Seoul National University; Clova AI Research, University of Washington; University of Washington, Allen Institute for Artificial Intelligence, XNOR.AI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi
|
https://iclr.cc/virtual/2018/poster/79
|
Natural Language Processing;RNN;Inference Speed
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Neural Speed Reading via Skim-RNN
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
rectifier networks;maxout networks;piecewise linear functions;linear regions;mixed-integer programming
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Bounding and Counting Linear Regions of Deep Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
OpenAI; UMass Amherst
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch
|
https://iclr.cc/virtual/2018/poster/260
|
multi-agent systems;multi-agent competition;self-play;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0.981981 | 0 |
https://goo.gl/eR7fbX
|
main
| 6.333333 |
3;7;9
| null | null |
Emergent Complexity via Multi-Agent Competition
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;evaluation metric
| null | 0 | null | null |
iclr
| -0.406181 | 0 | null |
main
| 6.25 |
5;5;7;8
| null | null |
An empirical study on evaluation metrics of generative adversarial networks
| null | null | 0 | 3.75 |
Reject
|
3;5;4;3
| null |
null |
Institute of Neuroinformatics, University Zürich and ETH Zürich; Department of Computer Science, ETH Zurich, Switzerland
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross
|
https://iclr.cc/virtual/2018/poster/302
|
Deep Neural Networks;Attribution methods;Theory of deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
IXA NLP Group, University of the Basque Country (UPV/EHU); New York University, CIFAR Azrieli Global Scholar
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho
|
https://iclr.cc/virtual/2018/poster/168
|
neural machine translation;unsupervised learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Unsupervised Neural Machine Translation
|
https://github.com/artetxem/undreamt
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language inference;external knowledge;state of the art
| null | 0 | null | null |
iclr
| -0.507093 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Natural Language Inference with External Knowledge
| null | null | 0 | 4.5 |
Workshop
|
5;4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
connectivity learning;multi-branch networks;image categorization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Connectivity Learning in Multi-Branch Networks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
activation manifold;dimension;deep neural network;singular value decomposition
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
THE LOCAL DIMENSION OF DEEP MANIFOLD
| null | null | 0 | 3.666667 |
Withdraw
|
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
| 5.333333 |
4;6;6
| null | null |
DropMax: Adaptive Stochastic Softmax
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
On the Use of Word Embeddings Alone to Represent Natural Language Sequences
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Google Brain, San Francisco, CA, USA; Google Inc., San Francisco, CA, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jesse Engel, Matthew D Hoffman, Adam Roberts
|
https://iclr.cc/virtual/2018/poster/62
|
VAE;GAN;generative networks;conditional generation;latent-variable models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning to Learn;Meta learning;Reinforcement learning;Transfer learning
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Transfer Learning to Learn with Multitask Neural Model Search
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;GANs;latent space operations;optimal transport
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dustin Tran, David Blei
|
https://iclr.cc/virtual/2018/poster/274
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Implicit Causal Models for Genome-wide Association Studies
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial Intelligence;Signal processing;Philosophy;Analogy;ALE;Slot Car
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Learning to play slot cars and Atari 2600 games in just minutes
| null | null | 0 | 2.666667 |
Reject
|
5;2;1
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Feature Learning;Convolutional Neural Networks;Visual Recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Simple Fast Convolutional Feature Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research; Cornell University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten
|
https://iclr.cc/virtual/2018/poster/139
|
adversarial example;machine learning security;computer vision;image classification
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Countering Adversarial Images using Input Transformations
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Xtract Technologies Inc., Vancouver, Canada; University of British Columbia & Xtract Technologies Inc., Vancouver, Canada; Simon Fraser University, Burnaby, Canada; University of British Columbia, Vancouver, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert
|
https://iclr.cc/virtual/2018/poster/253
|
residual networks;dynamical systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Multi-level Residual Networks from Dynamical Systems View
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial learning;generative model;domain adaptation;two-sample test
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Distributional Adversarial Networks
| null | null | 0 | 3.333333 |
Workshop
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recurrent Neural Network;Vanishing Gradient;Exploding Gradient;Linear Algebra;Householder Reflections
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of Electrical Engineering and Computer Science, York University, Toronto, M3J 1P3, Canada; Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang
|
https://iclr.cc/virtual/2018/poster/332
|
Warped residual networks;residual networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Decoupling the Layers in Residual Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Department of Electrical and Computer Engineering, University of Wisconsin - Madison
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Soroosh Khoram, Jing Li
|
https://iclr.cc/virtual/2018/poster/271
|
Deep Neural Networks;Model Quantization;Model Compression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Adaptive Quantization of 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 |
Counterfactual Inference;Off-Policy Learning;Variance Regularization
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Massachusetts Institute of Technology
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
David Rolnick, Max Tegmark
|
https://iclr.cc/virtual/2018/poster/39
|
expressivity of neural networks;depth of neural networks;universal approximators;function approximation;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
The power of deeper networks for expressing natural functions
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Hyperparameter Optimization;Architecture Search;Convolutional Neural Networks;Network Morphism;Network Transformation;SGDR;Cosine annealing;hill climbing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Simple and efficient architecture search for Convolutional Neural Networks
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;VAE;causality
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
2;7;7
| null | null |
On the difference between building and extracting patterns: a causal analysis of deep generative models.
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Natural Language Processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
From Information Bottleneck To Activation Norm Penalty
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Examples;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Adversarial Spheres
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
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