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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Computer Science, University of Copenhagen, Denmark, Copenhagen 2100
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Simonsen, Christina Lioma
|
https://iclr.cc/virtual/2019/poster/1044
|
natural language processing;speed reading;recurrent neural network;classification
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Neural Speed Reading with Structural-Jump-LSTM
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
DeepMind, London, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli
|
https://iclr.cc/virtual/2019/poster/1136
|
agent evaluation;adversarial examples;robustness;safety;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
| null | null | 0 | 3 |
Poster
|
3;3;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
| 4 |
3;4;5
| null | null |
Modular Deep Probabilistic Programming
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Heriot-Watt University; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrew Brock, Jeff Donahue, Karen Simonyan
|
https://iclr.cc/virtual/2019/poster/937
|
GANs;Generative Models;Large Scale Training;Deep Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Large Scale GAN Training for High Fidelity Natural Image Synthesis
| null | null | 0 | 3.666667 |
Oral
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multisensory binding;expectation learning;unsupervised learning;Deep autoencoder;Growing-When-Required Network;animal recognition
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Unsupervised Expectation Learning for Multisensory Binding
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image Style Transfer;Deep Learning;Neural Network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Total Style Transfer with a Single Feed-Forward Network
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
novelty detection;learnt texture representation;one-class neural network;human-vision-inspired anomaly detection
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 2.5 |
1;3;3;3
| null | null |
Psychophysical vs. learnt texture representations in novelty detection
| null | null | 0 | 3.25 |
Reject
|
3;4;3;3
| null |
null |
DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau
|
https://iclr.cc/virtual/2019/poster/1076
|
reinforcement learning;generative models;model-based reinforcement learning;causal inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
| null | null | 0 | 2.666667 |
Poster
|
3;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Net Quantization;Neural Architecture Search
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
| null | null | 0 | 3.5 |
Reject
|
5;3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative model;grounded language;scene understanding;natural language
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning to encode spatial relations from natural language
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Unsupervised Learning;Generative Adversarial Networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
PAIRWISE AUGMENTED GANS WITH ADVERSARIAL RECONSTRUCTION LOSS
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Non-negative matrix factorisation;Variational autoencoder;Probabilistic
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;training memory;computation-memory trade off;optimal solution
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
Cutting Down Training Memory by Re-fowarding
| null | null | 0 | 2.75 |
Reject
|
2;3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stochastic gradient method;local smoothness;linear system;AMSGrad
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 2.75 |
2;2;3;4
| null | null |
Predictive Local Smoothness for Stochastic Gradient Methods
| null | null | 0 | 4.25 |
Reject
|
5;4;3;5
| null |
null |
University of Minnesota, Twin Cities; MIT-IBM Watson AI Lab, IBM Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong
|
https://iclr.cc/virtual/2019/poster/871
|
nonconvex optimization;zeroth-order algorithm;black-box adversarial attack
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 7 |
6;7;8
| null | null |
signSGD via Zeroth-Order Oracle
| null | null | 0 | 3.333333 |
Poster
|
2;5;3
| null |
null |
Not provided
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Program synthesis;tree2tree autoencoders;soft attention;doubly-recurrent neural networks;LSTM;nlp2tree
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Ain't Nobody Got Time for Coding: Structure-Aware Program Synthesis from Natural Language
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Deepmind; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ali Razavi, Aaron van den Oord, Ben Poole, Oriol Vinyals
|
https://iclr.cc/virtual/2019/poster/1047
|
Posterior Collapse;VAE;Autoregressive Models
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Preventing Posterior Collapse with delta-VAEs
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Stanford University; Princeton University; University of California, Berkeley; Facebook AI Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma
|
https://iclr.cc/virtual/2019/poster/887
|
model-based reinforcement learning;sample efficiency;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
|
https://github.com/roosephu/slbo
| null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distance learning;metric learning;hyperbolic geometry;hierarchy tree
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Lorentzian Distance Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multimodal;sentence;representation;embedding;grounding
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Learning Grounded Sentence Representations by Jointly Using Video and Text Information
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null |
University of Illinois at Urbana-Champaign
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Iou-Jen Liu, Jian Peng, Alex Schwing
|
https://iclr.cc/virtual/2019/poster/929
|
Transfer Learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Knowledge Flow: Improve Upon Your Teachers
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
regression;uncertainty;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Neural Network Regression with Beta, Dirichlet, and Dirichlet-Multinomial Outputs
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
memory networks;deep learning;medical image segmentation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
SHAMANN: Shared Memory Augmented Neural Networks
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M Kitani
|
https://iclr.cc/virtual/2019/poster/1072
|
Imitation Learning;Reinforcement Learning;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computational Neuroscience;Brain-Inspired;Neural Networks;Simplified Models;Recurrent Neural Networks;Computer Vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian nonparametric;robust;deep neural network;classifier;unsupervised learning;geometric
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
GEOMETRIC AUGMENTATION FOR ROBUST NEURAL NETWORK CLASSIFIERS
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation learning;generative model;adversarial learning;implicit 3D generation;scene generation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Pix2Scene: Learning Implicit 3D Representations from Images
| null | null | 0 | 3 |
Reject
|
4;1;4
| null |
null |
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Richard Baraniuk, Jack Wang, Randall Balestriero
|
https://iclr.cc/virtual/2019/poster/830
|
RNN;max-affine spline operators
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
A Max-Affine Spline Perspective of Recurrent Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Google AI
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur
|
https://iclr.cc/virtual/2019/poster/862
|
navigating web pages;reinforcement learning;q learning;curriculum learning;meta training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Learning to Navigate the Web
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;rotation equivariance;bioimaging analysis
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Rotation Equivariant Networks via Conic Convolution and the DFT
| null | null | 0 | 3 |
Withdraw
|
4;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
end-user privacy;utility;feature learning;adversarial training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Better Accuracy with Quantified Privacy: Representations Learned via Reconstructive Adversarial Network
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Visualization;Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Visualizing and Discovering Behavioural Weaknesses in Deep Reinforcement Learning
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null |
Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kai Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, Aleksander Madry
|
https://iclr.cc/virtual/2019/poster/1028
|
verification;adversarial robustness;adversarial examples;stability;deep learning;regularization
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
| null | null | 0 | 2.666667 |
Poster
|
3;3;2
| null |
null |
Imagia Inc.; Dalhousie University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiang Jiang, Seyed Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin
|
https://iclr.cc/virtual/2019/poster/1038
|
meta-learning;learning to learn;few-shot learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Learning to Learn with Conditional Class Dependencies
| null | null | 0 | 3.666667 |
Poster
|
5;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
disentangled representations;factored representations;generative adversarial networks;variational auto encoders;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Adversarial Information Factorization
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, SIQI LIU, Dhruva Tirumala, Nicolas Heess, Greg Wayne
|
https://iclr.cc/virtual/2019/poster/685
|
hierarchical reinforcement learning;motor control;motion capture
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Hierarchical Visuomotor Control of Humanoids
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bias;Simulation;Optimization;Face Detection
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Identifying Bias in AI using Simulation
| null | null | 0 | 3.666667 |
Reject
|
4;2;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Kolmogorov model;interpretable models;causal relations mining;non-convex optimization;relaxations
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Learning Kolmogorov Models for Binary Random Variables
| null | null | 0 | 3.333333 |
Reject
|
2;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
continual learning;deep learning;lifelong learning;new task learning;representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robustness;spatial transformations;invariance;rotations;data augmentation;robust optimization
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null |
Sorbonne Universités, UMR 7606, LIP6, F-75005 Paris, France; Criteo AI Lab, Paris, France
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Arthur Pajot, Emmanuel de Bézenac, patrick Gallinari
|
https://iclr.cc/virtual/2019/poster/906
|
Deep Learning;Adversarial;MAP;GAN;neural networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Unsupervised Adversarial Image Reconstruction
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
The Center on Frontiers of Computing Studies, Peking University; School of Electronics Engineering and Computer Science, Peking University; Nat’l Eng. Lab. for Video Technology, Computer Science Dept., Peking University, Cooperative Medianet Innovation Center, PengCheng Lab, Deepwise AI Lab
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang
|
https://iclr.cc/virtual/2019/poster/665
|
crowdsourcing;information theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Text Generation;Machine Translation;Deep Learning;GAN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Bilingual-GAN: Neural Text Generation and Neural Machine Translation as Two Sides of the Same Coin
| null | null | 0 | 4.333333 |
Withdraw
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Klaus Greff
| null |
Objects;Compositionality;Generative Models;GAN;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
A Case for Object Compositionality in Deep Generative Models of Images
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic segmentation;stacked u-nets;classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sentence;embeddings;zero-shot;multilingual;multi-task;cross-lingual
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing
|
https://iclr.cc/virtual/2019/poster/1063
|
Meta Learning;AutoML;Optimization Schedule
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
AutoLoss: Learning Discrete Schedule for Alternate Optimization
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Department of Cognitive Linguistic & Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, RI 02912
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre
|
https://iclr.cc/virtual/2019/poster/1007
|
Attention models;human feature importance;object recognition;cognitive science
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Learning what and where to attend
| null | null | 0 | 3.333333 |
Poster
|
4;3;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
| 5 |
5;5;5
| null | null |
Learning to Progressively Plan
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Department of Statistics and Data Science, Yale University, New Haven, CT 06511 USA; Department of Statistics, University of Chicago, Chicago, IL 60637 USA; Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chao Gao, Jiyi Liu, Yuan Yao, Weizhi ZHU
|
https://iclr.cc/virtual/2019/poster/1133
|
robust statistics;neural networks;minimax rate;data depth;contamination model;Tukey median;GAN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative modeling;Generative Adversarial Networks (GANs);Wasserstein GAN;Optimal transport
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Generative model based on minimizing exact empirical Wasserstein distance
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Continuous Control;Evolutionary Computation;Genetic Algorithms;Evolving Morphology;Baldwin Effect;Population Based Training
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
The Body is not a Given: Joint Agent Policy Learning and Morphology Evolution
| null | null | 0 | 3.75 |
Withdraw
|
4;3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Evaluation Metric;Local Intrinsic Dimensionality
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep clustering;mixture of experts;mixture of autoencoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Deep clustering based on a mixture of autoencoders
| null | null | 0 | 3.666667 |
Withdraw
|
3;5;3
| null |
null |
University of Cambridge, UK; Department of Electrical and Computer Engineering, UCLA, California, USA; Alan Turing Institute, London, UK; Department of Electrical and Computer Engineering, UCLA, California, USA; Engineering Science Department, University of Oxford, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jinsung Yoon, James Jordon, Mihaela Schaar
|
https://iclr.cc/virtual/2019/poster/1022
|
Instance-wise feature selection;interpretability;actor-critic methodology
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
INVASE: Instance-wise Variable Selection using Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
joint source-channel coding;deep generative models;unsupervised learning
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
NECST: Neural Joint Source-Channel Coding
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
DeepMind, London, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell
|
https://iclr.cc/virtual/2019/poster/839
|
meta-learning;few-shot;miniImageNet;tieredImageNet;hypernetworks;generative;latent embedding;optimization
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Meta-Learning with Latent Embedding Optimization
| null | null | 0 | 4.333333 |
Poster
|
3;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine translation;latent variable models;diverse decoding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
3;5;6;7
| null | null |
Diverse Machine Translation with a Single Multinomial Latent Variable
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;adversarial attacks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Neural Networks with Structural Resistance to Adversarial Attacks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Princeton University, Princeton, NJ; Columbia University, New York, NY
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, Peter Orbanz
|
https://iclr.cc/virtual/2019/poster/806
|
generalization;deep-learning;pac-bayes
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
model compression;RNN;perforamnce optimization;langugage model;machine reading comprehension;knowledge distillation;teacher-student
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
AntMan: Sparse Low-Rank Compression To Accelerate RNN Inference
| null | null | 0 | 3.666667 |
Reject
|
4;2;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
supervised classification;information theory;deep learning;regularization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Information Regularized Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semantic segmentation;video;efficient inference;video segmentation;video compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Generative Adversarial Nets
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Surprising Negative Results for Generative Adversarial Tree Search
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation learning;Generative adversarial imitation learning
| null | 0 | null | null |
iclr
| -0.760886 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null |
Language Technology Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Microsoft Research, Cambridge, CB1 2FB, United Kingdom
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander Gaunt
|
https://iclr.cc/virtual/2019/poster/1070
|
Representation Learning;Source Code;Natural Language;edit
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning to Represent Edits
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
DeepMind, London, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
|
https://iclr.cc/virtual/2019/poster/684
|
Motor Primitives;Distillation;Reinforcement Learning;Continuous Control;Humanoid Control;Motion Capture;One-Shot Imitation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Neural Probabilistic Motor Primitives for Humanoid Control
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;language modelling;natural language processing;uncertainty;random projections
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Neural Random Projections for Language Modelling
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Catastrophic Forgetting;Life-Long Learning;adversarial examples
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Overcoming catastrophic forgetting through weight consolidation and long-term memory
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
kernels;Nyström approximation;deep convnets
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Deepström Networks
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep neural networks;stochastic gradient descent;sequenced-replacement sampling
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Sequenced-Replacement Sampling for Deep Learning
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Activation functions;Kernel methods;Recurrent networks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
cortical models;spatiotemporal memory;video prediction;predictive coding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
A Model Cortical Network for Spatiotemporal Sequence Learning and Prediction
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional networks;geometry
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Geometry of Deep Convolutional Networks
| null | null | 0 | 3.666667 |
Withdraw
|
5;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Examples;Neural Network Security;Deep Neural Network;Checkerboard Artifact
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks
| null | null | 0 | 2 |
Reject
|
3;2;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Parallelization;Speech Recognition;Sequence Modeling;Recurrent Neural Network;Embedded Systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
EXPLORATION OF EFFICIENT ON-DEVICE ACOUSTIC MODELING WITH NEURAL NETWORKS
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
ILLC, University of Amsterdam; ILCC, School of Informatics, University of Edinburgh
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Caio Corro, Ivan Titov
|
https://iclr.cc/virtual/2019/poster/984
|
differentiable dynamic programming;variational auto-encoder;dependency parsing;semi-supervised learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional neural network;tensor decomposition;sample complexity;approximation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition
| null | null | 0 | 3 |
Withdraw
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Open-domain question answering
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
| null | null | 0 | 4 |
Withdraw
|
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
| 7 |
6;6;9
| null | null |
Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep generative models;graphical models;trajectory forecasting;GANs;density estimation;structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning Gibbs-regularized GANs with variational discriminator reparameterization
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial machine learning;machine learning security
| null | 0 | null | null |
iclr
| 0.114708 | 0 | null |
main
| 4.333333 |
2;4;7
| null | null |
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
| null | null | 0 | 4.333333 |
Withdraw
|
5;3;5
| null |
null |
Department of Computer Science, Purdue University; Department of Statistics, Purdue University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ryan L Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
|
https://iclr.cc/virtual/2019/poster/923
|
representation learning;permutation invariance;set functions;feature pooling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Microsoft Research Montreal; MILA, Université de Montréal CIFAR Senior Fellow; Carnegie Mellon University; Microsoft Research Montreal Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mariya Toneva, Alessandro Sordoni, Remi Combes, Adam Trischler, Yoshua Bengio, Geoffrey Gordon
|
https://iclr.cc/virtual/2019/poster/753
|
catastrophic forgetting;sample weighting;deep generalization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
An Empirical Study of Example Forgetting during Deep Neural Network Learning
|
https://github.com/mtoneva/example_forgetting
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
competition;supervision;deep learning;adversarial;debate
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
Advocacy Learning
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null |
Laboratoire de Linguistique Formelle, CNRS - Université Paris Diderot - Sorbonne Paris Cité; Microsoft Research AI, Redmond, WA USA; Department of Cognitive Science, Johns Hopkins University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tom McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky
|
https://iclr.cc/virtual/2019/poster/1139
|
tensor-product representations;compositionality;neural network interpretability;recurrent neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
RNNs implicitly implement tensor-product representations
| 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;Convolutional Neural Networks;Low-precision inference;Network quantization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimal Transportation;Deep Learning;Generative Adversarial Networks;Wasserstein Distance
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
W2GAN: RECOVERING AN OPTIMAL TRANSPORT MAP WITH A GAN
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generalization;PAC-Bayes;Hessian;perturbation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Identifying Generalization Properties in Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Network;Lifelong Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
pruning;saliency;neural networks;optimization;redundancy;model compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Mean Replacement Pruning
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge Distillation;Ensemble Effect;Knowledge Transfer
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
FEED: Feature-level Ensemble Effect for knowledge Distillation
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
saliency maps;explainable AI;convolutional neural networks;generative adversarial training;classification
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Classifier-agnostic saliency map extraction
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spiking neural networks;autoencoders;representation learning;backpropagation;multimodal
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Learning Spatio-Temporal Representations Using Spike-Based Backpropagation
| null | null | 0 | 4.666667 |
Withdraw
|
4;5;5
| null |
null |
Microsoft, Redmond, WA 98052
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
|
https://iclr.cc/virtual/2019/poster/750
|
Neuro-Symbolic Methods;Circuit Satisfiability;Neural SAT Solver;Graph Neural Networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
Shenzhen Institutes of Advanced Technology, Shenzhen, China; University of Macau, Macau, China; University of Cambridge, Cambridge, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-zhong Xu
|
https://iclr.cc/virtual/2019/poster/857
|
dynamic network;faster CNNs;channel pruning
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 6.5 |
6;6;7;7
| null | null |
Dynamic Channel Pruning: Feature Boosting and Suppression
| null | null | 0 | 4 |
Poster
|
4;3;4;5
| null |
null |
UC Irvine; Caltech; Nanjing University of Aeronautics and Astronautics
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar
|
https://iclr.cc/virtual/2019/poster/876
|
large-scale learning;distributed systems;communication efficiency;convergence rate analysis;robust optimisation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
signSGD with Majority Vote is Communication Efficient and Fault Tolerant
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;deep multi-task learning;tensor factorization;tensor ring nets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
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