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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;disentange;siamese networks;semantic
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Unlabeled Disentangling of GANs with Guided Siamese Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
TarMAC: Targeted Multi-Agent Communication
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Structured Prediction;Reinforcement Learning;NLP
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
EFFICIENT SEQUENCE LABELING WITH ACTOR-CRITIC TRAINING
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GSP;robustness;noise;deep learning;neural networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
5;5;9
| null | null |
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks Robustness
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 |
https://mugandemo.github.io/mugandemo/
|
main
| 5 |
4;5;6
| null | null |
Adversarial Audio Super-Resolution with Unsupervised Feature Losses
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ride-sharing;generative modeling;parallelization;application
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta-Learning;Reinforcement Learning;Exploration;Unsupervised
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Unsupervised Meta-Learning for Reinforcement Learning
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Classification;Partial Differential Equations
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Low-Cost Parameterizations of Deep Convolutional Neural Networks
| null | null | 0 | 4 |
Withdraw
|
4;3;5
| null |
null |
Computer Science Department, Stanford University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Aditya Grover, Eric J. Wang, Aaron Zweig, Stefano Ermon
|
https://iclr.cc/virtual/2019/poster/920
|
continuous relaxations;sorting;permutation;stochastic computation graphs;Plackett-Luce
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Stochastic Optimization of Sorting Networks via Continuous Relaxations
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational autoencoders;generative model;deep neural network;text modeling;unsupervised learning;multimodal
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
1;4;4
| null | null |
Variational Autoencoders for Text Modeling without Weakening the Decoder
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;cnn;structural modification;optimization;saddle point
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Escaping Flat Areas via Function-Preserving Structural Network Modifications
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
The University of Tokyo, Tokyo, Japan; Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan; Japan Digital Design, Tokyo, Japan
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Taiji Suzuki
|
https://iclr.cc/virtual/2019/poster/895
|
deep learning theory;approximation analysis;generalization error analysis;Besov space;minimax optimality
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
| null | null | 0 | 2 |
Poster
|
2;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
WGAN;gradient penalty;stability;measure valued differentiation
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Knowledge Technology, Department of Informatics, Universität Hamburg
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tobias Hinz, Stefan Heinrich, Stefan Wermter
|
https://iclr.cc/virtual/2019/poster/736
|
controllable image generation;text-to-image synthesis;generative model;generative adversarial network;gan
| null | 0 | null | null |
iclr
| 0 | 0 |
https://www.inf.uni-hamburg.de/en/inst/ab/wtm/
|
main
| 7 |
6;7;8
| null | null |
Generating Multiple Objects at Spatially Distinct Locations
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gated Recurrent Units;Recurrent Neural Network;Time Series Predictions;interpretable;Nonlinear Dynamics;Dynamical Systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
The Expressive Power of Gated Recurrent Units as a Continuous Dynamical System
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain, Also at UC Berkeley; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
|
https://iclr.cc/virtual/2019/poster/713
|
representation hierarchy reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/representation-hrl
|
main
| 8 |
7;8;9
| null | null |
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
|
https://github.com/tensorflow/models/tree/master/research/efficient-hrl
| null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null |
Department of Computer Science, Duke University, Durham, NC 27708, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Abraham Frandsen, Rong Ge
|
https://iclr.cc/virtual/2019/poster/746
|
word embeddings;semantic composition;tensor decomposition
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Understanding Composition of Word Embeddings via Tensor Decomposition
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Response Generation;Universal Replies;Optimization Goal Analysis;Max-Marginal Ranking Regularization
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 3.666667 |
1;3;7
| null | null |
Why Do Neural Response Generation Models Prefer Universal Replies?
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Microsoft Research, Cambridge, United Kingdom
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
|
https://iclr.cc/virtual/2019/poster/998
|
Summarization;Graphs;Source Code
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Structured Neural Summarization
|
https://github.com/CoderPat/structured-neural-summarization
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng
|
https://iclr.cc/virtual/2019/poster/710
|
graph convolution;graph wavelet transform;graph Fourier transform;semi-supervised learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Graph Wavelet Neural Network
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;entity synonym
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
omnidirectional images;classification;deep learning;graph signal processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Geometry aware convolutional filters for omnidirectional images representation
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexander Ecker, Fabian H Sinz, Emmanouil Froudarakis, Paul Fahey, Santiago Cadena, Edgar Walker, Erick M Cobos, Jacob Reimer, Andreas Tolias, Matthias Bethge
|
https://iclr.cc/virtual/2019/poster/922
|
rotation equivariance;equivariance;primary visual cortex;V1;neuroscience;system identification
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
A rotation-equivariant convolutional neural network model of primary visual cortex
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;training with constraints;querying networks;semantic training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
DL2: Training and Querying Neural Networks with Logic
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Filter Pruning;Model Compression;Efficient Convolutional Neural Networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Globally Soft Filter Pruning For Efficient Convolutional Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
monocular depth estimation;unsupervised learning;image warping
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
UNSUPERVISED MONOCULAR DEPTH ESTIMATION WITH CLEAR BOUNDARIES
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Semi-supervised learning;Adversarial training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Fast adversarial training for semi-supervised learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Courant Institute of Mathematical Sciences, New York University, New York, NY; Amplify Partners, San Francisco, CA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Zhengdao Chen, Xiang Li, Joan Bruna
|
https://iclr.cc/virtual/2019/poster/1059
|
community detection;graph neural networks;belief propagation;energy landscape;non-backtracking matrix
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
Supervised Community Detection with Line Graph Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples
| null | 0 | null | null |
iclr
| 0 | 0 |
Not provided
|
main
| 3 |
2;3;4
| null | null |
Evaluation Methodology for Attacks Against Confidence Thresholding Models
|
Not provided
| null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Facebook AI Research; Facebook AI Research, Sorbonne Universit ´es, UPMC Univ Paris 06; MILA, Universit ´e de Montr ´eal
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau
|
https://iclr.cc/virtual/2019/poster/1011
|
controllable text generation;generative models;conditional generative models;style transfer
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Multiple-Attribute Text Rewriting
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
IBM Research & MIT-IBM Watson AI Lab
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero Nogueira dos Santos, Tom Sercu
|
https://iclr.cc/virtual/2019/poster/1048
|
Wasserstein barycenter model ensembling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Wasserstein Barycenter Model Ensembling
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wenhao Yu, C. Liu, Greg Turk
|
https://iclr.cc/virtual/2019/poster/918
|
transfer learning;reinforcement learning;modeling error;strategy optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Policy Transfer with Strategy Optimization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
A Study of Robustness of Neural Nets Using Approximate Feature Collisions
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
word embeddings;hyperbolic;skip-gram
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Skip-gram word embeddings in hyperbolic space
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Facebook AI Research; Technion
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Uri Alon, Shaked Brody, Omer Levy, Eran Yahav
|
https://iclr.cc/virtual/2019/poster/646
|
source code;programs;code2seq
| null | 0 | null | null |
iclr
| 0 | 0 |
http://code2seq.org
|
main
| 6 |
5;6;7
| null | null |
code2seq: Generating Sequences from Structured Representations of Code
|
http://github.com/tech-srl/code2seq
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Technical University of Munich, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann
|
https://iclr.cc/virtual/2019/poster/1117
|
Graph;GCN;GNN;Neural network;Graph neural network;Message passing neural network;Semi-supervised classification;Semi-supervised learning;PageRank;Personalized PageRank
| null | 0 | null | null |
iclr
| 0 | 0 |
https://www.kdd.in.tum.de/ppnp
|
main
| 5.666667 |
5;5;7
| null | null |
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Illinois at Urbana-Champaign; ByteDance Inc.; Snap Inc.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang
|
https://iclr.cc/virtual/2019/poster/796
|
Slimmable neural networks;mobile deep learning;accuracy-efficiency trade-offs
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Slimmable Neural Networks
|
https://github.com/JiahuiYu/slimmable_networks
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
point processes;wavelets;temporal neural networks;Hawkes processes
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Clinical Risk: wavelet reconstruction networks for marked point processes
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
|
https://iclr.cc/virtual/2019/poster/933
|
mathematics;dataset;algebraic;reasoning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Analysing Mathematical Reasoning Abilities of Neural Models
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
memory analysis;recurrent neural network;LSTM;neural Turing machine;neural stack;differentiable neural computers
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Analysis of Memory Organization for Dynamic Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Department of Mathematics, Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiuyuan Cheng, Qiang Qiu, Robert Calderbank, Guillermo Sapiro
|
https://iclr.cc/virtual/2019/poster/1053
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Transfer Learning;Control;Value function
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null |
Citadel Securities; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xinyun Chen, Chang Liu, Dawn Song
|
https://iclr.cc/virtual/2019/poster/760
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Execution-Guided Neural Program Synthesis
| null | null | 0 | 3.666667 |
Poster
|
5;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Uncertainty;Prior Networks;Adversarial Attacks;Detection
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Prior Networks for Detection of Adversarial Attacks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;deep learning;summarization;embeddings;word embeddings;source code;programming languages;programming language processing
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
The Effectiveness of Pre-Trained Code Embeddings
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Computer Science, University of California, Santa Barbara; Center for Brain Inspired Computing Research, Department of Precision Instrument, Tsinghua University; Department of Electrical and Computer Engineering, University of California, Santa Barbara
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie
|
https://iclr.cc/virtual/2019/poster/650
|
Sparsity;compression;training;acceleration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Dynamic Sparse Graph for Efficient Deep Learning
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
Stanford University; MIT; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hongyi Zhang, Yann Dauphin, Tengyu Ma
|
https://iclr.cc/virtual/2019/poster/643
|
deep learning;residual networks;initialization;batch normalization;layer normalization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Fixup Initialization: Residual Learning Without Normalization
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;ensembles;deep learning;neural network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentanglement;vae;clustering;prior imposition;deep generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
|
https://iclr.cc/virtual/2019/poster/1088
|
Generative Adversarial Networks;Bayesian Deep Learning;Mode Collapse;Inception Score;Generator;Discriminator;CIFAR-10;STL-10;ImageNet
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 6.666667 |
5;6;9
| null | null |
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial training;Adversarial examples;Riemannian Geometry;Machine Learning;Deep Learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
| null | null | 0 | 4 |
Reject
|
5;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spatial perception;grounding;sensorimotor prediction;unsupervised learning;representation learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regression-via-classification;discretization;regression tree;neural model;optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Neural Regression Tree
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Batch size;Optimization;Mini-batch gradient descent;Multi-armed bandit
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual Learning;Catastrophic Forgetting;Dynamic Network Expansion
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;4;8
| null | null |
Learning to remember: Dynamic Generative Memory for Continual Learning
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
|
https://iclr.cc/virtual/2019/poster/1093
|
reinforcement learning;exploration;curiosity
| null | 0 | null | null |
iclr
| 0.377964 | 0 | null |
main
| 7.5 |
4;7;9;10
| null | null |
Exploration by random network distillation
| null | null | 0 | 4.25 |
Poster
|
4;4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Neural Networks;Initialization;Gaussian Processes
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
On the Selection of Initialization and Activation Function for Deep Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
committor function;rare event;deep learning;importance sampling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Computing committor functions for the study of rare events using deep learning with importance sampling
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
feature attribution;feature selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Feature Attribution As Feature Selection
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
audio;synthesizers;music;convolutional neural networks;generative models;autoregressive models
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Synthnet: Learning synthesizers end-to-end
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
positive-unlabeled learning;dataset shift;empirical risk minimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Classification from Positive, Unlabeled and Biased Negative Data
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial training;large batch size;neural network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SGD;distributed asynchronous training;deep learning;optimisation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Asynchronous SGD without gradient delay for efficient distributed training
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
The School of Computer Science, Tel Aviv University; Facebook AI Research & The School of Computer Science, Tel Aviv University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Lior Wolf, Sagie Benaim, Tomer Galanti
|
https://iclr.cc/virtual/2019/poster/745
|
Unsupervised Learning;One-class Classification;Multi-player Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Unsupervised Learning of the Set of Local Maxima
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;causal reasoning;deep reinforcement learning;artificial intelligence
| null | 0 | null | null |
iclr
| 0.471405 | 0 | null |
main
| 5 |
4;4;5;7
| null | null |
Causal Reasoning from Meta-reinforcement learning
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;high-dimensional geometry
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
On the Geometry of Adversarial Examples
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
ECE, University of Minnesota - Twin Cities; MIT-IBM Watson AI Lab, IBM Research; ISE, University of Illinois at Urbana-Champaign
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong
|
https://iclr.cc/virtual/2019/poster/774
|
nonconvex optimization;Adam;convergence analysis
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
| null | null | 0 | 2.666667 |
Poster
|
3;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
out-of-distribution detection;semantic segmentation
| null | 0 | null | null |
iclr
| 0.960769 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Discriminative out-of-distribution detection for semantic segmentation
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;meta-learning;higher order derivatives;gradient estimation;stochastic computation graphs
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Privacy;Fairness;Adversarial Learning;Generative Adversarial Networks;Minimax Games;Information Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Generative Adversarial Models for Learning Private and Fair Representations
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Department of Computer Science and Engineering, Shanghai Jiao Tong University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Huan Zhang, hai zhao
|
https://iclr.cc/virtual/2019/poster/819
|
sequence to sequence;training criteria
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;reinforcement learning;multi-party computation
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 6 |
4;5;9
| null | null |
Scaling shared model governance via model splitting
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
conditional random fields;semantic segmentation;computer vision;structured learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Convolutional CRFs for Semantic Segmentation
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer Learning;Domain Adaptation;Multi-source Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
GradMix: Multi-source Transfer across Domains and Tasks
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Aggregation;Budget Learning;Speed Up;Faster Inference;Robust Classifier
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Robust Text Classifier on Test-Time Budgets
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dropout;Saliency;Deep Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Google AI, Mountain View, CA 94043, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jesse Engel, Kumar Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts
|
https://iclr.cc/virtual/2019/poster/1004
|
GAN;Audio;WaveNet;NSynth;Music
| null | 0 | null | null |
iclr
| 0 | 0 |
http://goo.gl/magenta/gansynth-demo
|
main
| 7 |
6;7;8
| null | null |
GANSynth: Adversarial Neural Audio Synthesis
|
http://goo.gl/magenta/gansynth-code
| null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
College of Information and Computer Sciences, University of Massachusetts Amherst
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum
|
https://iclr.cc/virtual/2019/poster/1010
|
embeddings;order embeddings;knowledge graph embedding;relational learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Smoothing the Geometry of Probabilistic Box Embeddings
| null | null | 0 | 3.333333 |
Oral
|
3;4;3
| null |
null |
Department of Electrical Engineering, University of Virginia, Charlottesville, VA, USA; Information and Systems Sciences Laboratory, HRL Laboratories, LLC., Malibu, CA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Soheil Kolouri, Phillip Pope, Charles Martin, Gustavo Rohde
|
https://iclr.cc/virtual/2019/poster/1081
|
optimal transport;Wasserstein distances;auto-encoders;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Sliced Wasserstein Auto-Encoders
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Computer Science Department, Duke University; Institute for Interdisciplinary Information Sciences, Tsinghua University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
|
https://iclr.cc/virtual/2019/poster/872
|
Neural Network;Optimization;Symmetric Inputs;Moment-of-moments
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Two-layer Neural Networks with Symmetric Inputs
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
NYU; FAIR; NYU, FAIR
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Exploration;Games;Pommerman;Bomberman;AI;Reinforcement Learning;Machine Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Backplay: 'Man muss immer umkehren'
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning with noisy labels;deep learning;convolutional neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Investigating CNNs' Learning Representation under label noise
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
capsule;capsule network;semantic segmentation;FCN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Trace-back along capsules and its application on semantic segmentation
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Mila, Université de Montréal; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette
|
https://iclr.cc/virtual/2019/poster/734
|
instruction following;reward modelling;language understanding
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning to Understand Goal Specifications by Modelling Reward
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan
|
https://iclr.cc/virtual/2019/poster/1015
|
deep generative models;out-of-distribution inputs;flow-based models;uncertainty;density
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Do Deep Generative Models Know What They Don't Know?
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Qatar Computing Research Institute, HBKU Research Complex, Doha 5825, Qatar; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
David A Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James R Glass
|
https://iclr.cc/virtual/2019/poster/654
|
neural machine translation;individual neurons;unsupervised;analysis;correlation;translation control;distributivity;localization
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 7.666667 |
6;7;10
| null | null |
Identifying and Controlling Important Neurons in Neural Machine Translation
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
DNN robustness;Adversarial attack;Data augmentation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.5 |
3;4
| null | null |
Bamboo: Ball-Shape Data Augmentation Against Adversarial Attacks from All Directions
| null | null | 0 | 4 |
Withdraw
|
5;3
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
DON’T JUDGE A BOOK BY ITS COVER - ON THE DYNAMICS OF RECURRENT NEURAL NETWORKS
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Baylor College of Medicine, Rice University; Rice University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Joshua Michalenko, Ameesh Shah, Abhinav Verma, Richard Baraniuk, Swarat Chaudhuri, Ankit B Patel
|
https://iclr.cc/virtual/2019/poster/1023
|
Language recognition;Recurrent Neural Networks;Representation Learning;deterministic finite automaton;automaton
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
KU Leuven, ESAT-PSI
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
José Antonio Oramas Mogrovejo, Kaili Wang, Tinne Tuytelaars
|
https://iclr.cc/virtual/2019/poster/879
|
model explanation;model interpretation;explainable ai;evaluation
| null | 0 | null | null |
iclr
| -0.240192 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Gradient-based learning for F-measure and other performance metrics
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Department of Computer Science, University of Texas
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Brady Zhou, Philipp Krähenbühl
|
https://iclr.cc/virtual/2019/poster/1086
|
GAN;generative models;computer vision
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Don't let your Discriminator be fooled
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;unsupervised;encoder discriminator
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Encoder Discriminator Networks for Unsupervised Representation Learning
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Negotiation;Team Formation;Cooperative Game Theory;Shapley Value
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Negotiating Team Formation Using Deep Reinforcement Learning
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;reinforcement learning;imitation learning
| null | 0 | null | null |
iclr
| -0.730297 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Learning to Reinforcement Learn by Imitation
| null | null | 0 | 3 |
Reject
|
5;2;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;wasserstein distance;wasserstein barycenter;entailment
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Skolkovo Institute of Science and Technology (Skoltech), Russia; Currently at Stony Brook University.; Skolkovo Institute of Science and Technology (Skoltech), Russia; Skolkovo Institute of Science and Technology (Skoltech), Russia; Currently also with Samsung AI Center, Moscow.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky
|
https://iclr.cc/virtual/2019/poster/773
|
latent models;convolutional networks;unsupervised learning;deep learning;modeling natural images;image restoration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Latent Convolutional Models
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null |
Facebook AI Research; Facebook AI Research & Tel Aviv Uni.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman
|
https://iclr.cc/virtual/2019/poster/993
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
A Universal Music Translation Network
|
https://github.com/musictranslation
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generalization theory;implicit regularization;generalization;over-parametrization;theory;deep learning theory;margin
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
On the Margin Theory of Feedforward Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
OpenAI, University of Edinburgh; University of Edinburgh
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Antreas Antoniou, Harrison Edwards, Amos Storkey
|
https://iclr.cc/virtual/2019/poster/1106
|
meta-learning;deep-learning;few-shot learning;supervised learning;neural-networks;stochastic optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
How to train your MAML
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
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