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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Center for Brains, Minds, and Machines (CBMM), Department of Brain and Cognitive Sciences, Children’s Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Center for Brains, Minds, and Machines (CBMM), Massachusetts Institute of Technology, USA; Center for Brains, Minds, and Machines (CBMM), Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix
|
https://iclr.cc/virtual/2019/poster/679
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Engineering Science, University of Oxford, Alan Turing Institute; Department of Engineering Science, University of Oxford; Department of Statistics, University of Oxford
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
|
https://iclr.cc/virtual/2019/poster/767
|
neural network verification;multi-level splitting;formal verification
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
A Statistical Approach to Assessing Neural Network Robustness
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Similarity learning;structured objects;graph matching networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
calibration;deep models;bayesian neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Offline Deep models calibration with bayesian neural networks
|
https://github.com/2019submission/bnn.2019
| null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial examples;generalization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Adversarial Examples Are a Natural Consequence of Test Error in Noise
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Department of Applied Physics, Stanford; Google Brain, Mountain View, CA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jack Lindsey, Samuel Ocko, Surya Ganguli, Stephane Deny
|
https://iclr.cc/virtual/2019/poster/732
|
visual system;convolutional neural networks;efficient coding;retina
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
|
https://github.com/ganguli-lab/RetinalResources
| null | 0 | 4.333333 |
Oral
|
3;5;5
| null |
null |
Duke University; Microsoft Dynamics 365 AI Research; Baidu Research; SUNY at Buffalo; Microsoft Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin
|
https://iclr.cc/virtual/2019/poster/831
|
NLP;optimal transport;sequence to sequence;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Improving Sequence-to-Sequence Learning via Optimal Transport
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-view;learning;sentence;representation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Improving Sentence Representations with Multi-view Frameworks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bias-variance trade-off;James-stein estimator;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning
| null | null | 0 | 3 |
Reject
|
4;2;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 |
James Jordon, Jinsung Yoon, Mihaela Schaar
|
https://iclr.cc/virtual/2019/poster/880
|
Synthetic data generation;Differential privacy;Generative adversarial networks;Private Aggregation of Teacher ensembles
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
PATE-GAN: Generating Synthetic Data with Differential Privacy 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 |
reinforcement learning;off-policy;imitation;batch reinforcement learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Where Off-Policy Deep Reinforcement Learning Fails
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Google, Mountain View, CA 94043, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Johannes Ballé, Nick Johnston, David Minnen
|
https://iclr.cc/virtual/2019/poster/1017
|
data compression;variational models;network quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Integer Networks for Data Compression with Latent-Variable Models
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
maximum entropy RL;policy composition;deep rl
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Composing Entropic Policies using Divergence Correction
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;planning;deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Dynamic Planning Networks
| null | null | 0 | 4 |
Reject
|
5;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine translation;syntax;diversity;code learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
Discrete Structural Planning for Generating Diverse Translations
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip Torr, Nicolas Usunier
|
https://iclr.cc/virtual/2019/poster/1077
|
Reinforcement Learning;Value Iteration;Navigation;Convolutional Neural Networks;Learning to plan
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Value Propagation Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Gilwoo Lee, Brian Hou, Aditya Mandalika, Jeongseok Lee, Sanjiban Choudhury, Siddhartha Srinivasa
|
https://iclr.cc/virtual/2019/poster/823
|
Bayes-Adaptive Markov Decision Process;Model Uncertainty;Bayes Policy Optimization
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Bayesian Policy Optimization for Model Uncertainty
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial Neural Networks;Neural Networks;ReLU;GaLU;Deep Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Decoupling Gating from Linearity
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised;Semi-supervised;Generative;Adversarial;Clustering
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Adversarially Learned Mixture Model
| null | null | 0 | 2.333333 |
Reject
|
4;2;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
lifelong learning;continual learning;sequential learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Uncertainty-guided Lifelong Learning in Bayesian Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
variational inference;time series;nonlinear dynamics;neuroscience
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sentence embedding;generative models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;guided exploration;RL training speed up
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Guided Exploration in Deep Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
complementary labels;weak supervision
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Complementary-label learning for arbitrary losses and models
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Normalizing Flows
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Boosting Trust Region Policy Optimization by Normalizing flows Policy
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Information Diffusion;Recurrent Neural Network;Black Box Inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
A RECURRENT NEURAL CASCADE-BASED MODEL FOR CONTINUOUS-TIME DIFFUSION PROCESS
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Cheng Zhang, Frederick A Matsen
|
https://iclr.cc/virtual/2019/poster/1060
|
Bayesian phylogenetic inference;Variational inference;Subsplit Bayesian networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Variational Bayesian Phylogenetic Inference
| null | null | 0 | 2.333333 |
Poster
|
3;3;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine learning;privacy;adversarial training;information theory;data-driven privacy
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning data-derived privacy preserving representations from information metrics
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
EPFL; University of Queensland; Monash University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann
|
https://iclr.cc/virtual/2019/poster/1036
|
Open Set Domain Adaptation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional neural network;Early terminating;Dynamic model optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Dynamic Early Terminating of Multiply Accumulate Operations for Saving Computation Cost in Convolutional Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
IBM Thomas J. Watson Research Center; University of Notre Dame
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yukun Ding, Jinglan Liu, Jinjun Xiong, Yiyu Shi
|
https://iclr.cc/virtual/2019/poster/699
|
Quantized Neural Networks;Universial Approximability;Complexity Bounds;Optimal Bit-width
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
statistical mechanics;self-regularization;random matrix;glassy behavior;heavy-tailed
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Traditional and Heavy Tailed Self Regularization in Neural Network Models
| null | null | 0 | 3.333333 |
Reject
|
1;4;5
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Henok Ghebrechristos
| null |
CNN;Deep Learning;Feature Extraction;Patch Ordering;Convergence;Image Classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
EXPLORING DEEP LEARNING USING INFORMATION THEORY TOOLS AND PATCH ORDERING
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;bio-plausibility;random projections;spiking networks;unsupervised learning;MNIST;spike timing dependent plasticity
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Localized random projections challenge benchmarks for bio-plausible deep learning
| null | null | 0 | 4 |
Reject
|
5;4;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
| 6 |
4;6;8
| null | null |
DADAM: A consensus-based distributed adaptive gradient method for online optimization
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Politecnico di Torino, Torino, Italy
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Diego Valsesia, Giulia Fracastoro, Enrico Magli
|
https://iclr.cc/virtual/2019/poster/721
|
GAN;graph convolution;point clouds
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
| 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
| 0 | null | null | null |
PASS: Phased Attentive State Space Modeling of Disease Progression Trajectories
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Attacks;Deep Neural Networks
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 6.666667 |
5;6;9
| null | null |
Detecting Adversarial Examples Via Neural Fingerprinting
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Classification;Prediction;Cautious Methods
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Cautious Deep Learning
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
program translation;tree structures;transformer
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Novel positional encodings to enable tree-structured transformers
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;metric learning;bayesian nonparametrics;few-shot learning;deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Variadic Learning by Bayesian Nonparametric Deep Embedding
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
theory and analysis of RNNs architectures;reversibe evolution;stability of deep neural network;learning representations of outputs or states;quantum inspired embedding
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Unification of Recurrent Neural Network Architectures and Quantum Inspired Stable Design
| null | null | 0 | 2.25 |
Reject
|
3;2;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary Network;Binary Training;Model Compression;Quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;6;8
| null | null |
BNN+: Improved Binary Network Training
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Columbia Business School, Columbia University, New York City, NY 10027, USA; Department of Industrial Engineering & Management Science, Northwestern University, Evanston, IL 60201, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang
|
https://iclr.cc/virtual/2019/poster/894
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Network interpretability;deep learning;knowledge distillation;convolutional neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Explaining Neural Networks Semantically and Quantitatively
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null |
School Of Mathematical Science, Peking University; Institute of Computer Science and Technology, Peking University; Beijing International Center for Mathematical Research, Peking University; Center for Data Science, Peking University; Beijing Institute of Big Data Research, Beijing, China
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong
|
https://iclr.cc/virtual/2019/poster/674
|
image restoration;differential equation
| null | 0 | null | null |
iclr
| 0 | 0 |
http://www.jet.pku.edu.cn/durr/
|
main
| 6.333333 |
6;6;7
| null | null |
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameter optimization;black box optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Open Loop Hyperparameter Optimization and Determinantal Point Processes
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Computer Science Department, Technion – Israel Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yonatan Geifman, Guy Uziel, Ran El-Yaniv
|
https://iclr.cc/virtual/2019/poster/963
|
Uncertainty estimation;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
| null | null | 0 | 3 |
Poster
|
2;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 |
Realistic Adversarial Examples in 3D Meshes
| null | null | 0 | 3 |
Withdraw
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
teaching to teach;dark knowledge;curriculum learning;teaching
| null | 0 | null | null |
iclr
| 0.419314 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Teaching to Teach by Structured Dark Knowledge
| null | null | 0 | 3.333333 |
Reject
|
4;1;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
malware;execution;control;deep reinforcement learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING
| null | null | 0 | 2.333333 |
Reject
|
3;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Batch normalization;Convergence analysis;Gradient descent;Ordinary least squares;Deep neural network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
On the Convergence and Robustness of Batch Normalization
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Tencent A.I. Lab; Department of Computer Science, University of Central Florida; Computational and Information Sciences Directorate, U.S. Army Research Laboratory
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yang Zhang, Hassan Foroosh, Phiip David, Boqing Gong
|
https://iclr.cc/virtual/2019/poster/645
|
Adversarial Attack;Object Detection;Synthetic Simulation
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Department of Mathematics & Information Technology, The Education University of Hong Kong; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiaopeng Li, Zhourong Chen, Leonard Poon, Nevin Zhang
|
https://iclr.cc/virtual/2019/poster/838
|
latent tree model;variational autoencoder;deep learning;latent variable model;bayesian network;structure learning;stepwise em;message passing;graphical model;multidimensional clustering;unsupervised learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Policy Robustness;Policy generalization;Automated Curriculum
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
The University of Edinburgh, FiveAI
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Svetlin Penkov, Subramanian Ramamoorthy
|
https://iclr.cc/virtual/2019/poster/1107
|
representation learning;structured representations;symbols;programs
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Programmatically Structured Representations with Perceptor Gradients
| null | null | 0 | 3 |
Poster
|
3;1;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
| 2.666667 |
2;2;4
| null | null |
Multiple Encoder-Decoders Net for Lane Detection
| 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;generalization;semantic structure;model-based
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Learning and Planning with a Semantic Model
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
School of Computing Science, Simon Fraser University; ; California Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jiawei He, Yu Gong, Joe Marino, Greg Mori, Andreas Lehrmann
|
https://iclr.cc/virtual/2019/poster/953
|
deep generative models;structure learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null |
Institute for Infocomm Research, A*STAR; Nanyang Technological University (NTU); National University of Singapore (NUS); Singapore University of Technology and Design
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yasin YAZICI, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar
|
https://iclr.cc/virtual/2019/poster/1095
|
Generative Adversarial Networks (GANs);Moving Average;Exponential Moving Average;Convergence;Limit Cycles
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
The Unusual Effectiveness of Averaging in GAN Training
|
https://github.com/yasinyazici/EMA_GAN
| null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;model compression;pruning;PCA
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A SINGLE SHOT PCA-DRIVEN ANALYSIS OF NETWORK STRUCTURE TO REMOVE REDUNDANCY
| null | null | 0 | 4.666667 |
Withdraw
|
4;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Conditional GAN;Video Generation;Text-to-Video Synthesis;Conditional Generative Models;Deep Generative Models
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
TFGAN: Improving Conditioning for Text-to-Video Synthesis
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null |
McGill University; Carnegie Mellon University; University of Toronto
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson
|
https://iclr.cc/virtual/2019/poster/1132
|
adversarial examples;norm-balls;differentiable renderer
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Compound Question Decomposition;Reinforcement Learning;Knowledge-Based Question Answering;Learning-to-decompose
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning to Decompose Compound Questions with Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning from demonstration;imitation learning;behavioral cloning;reinforcement learning;off-policy;continuous control;autonomous vehicles;deep learning;machine learning;policy gradient
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
ReNeg and Backseat Driver: Learning from demonstration with continuous human feedback
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
global interpretability;additive explanations;model distillation;neural nets;tabular data
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://youtu.be/ATNcgurNHhc
|
main
| 5.333333 |
4;6;6
| null | null |
Learning Global Additive Explanations for Neural Nets Using Model Distillation
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
personalized learning;e-learning;text embedding;Skip-gram;imbalanced data set;data level classification methods
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Agent Modeling;Theory of Mind;Deep Reinforcement Learning;Multi-agent Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://www.dropbox.com/s/8mz6rd3349tso67/Probing_Demo.mov?dl=0
|
main
| 6 |
6;6;6;6
| null | null |
Interactive Agent Modeling by Learning to Probe
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Machine Learning;Knowledge Distill;Model Compression
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3 |
1;3;5
| null | null |
KNOWLEDGE DISTILL VIA LEARNING NEURON MANIFOLD
| null | null | 0 | 4 |
Withdraw
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation learning. Generative Adversarial Network (GAN). Positive Unlabeled learning. Image classification
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
| null | null | 0 | 3.333333 |
Reject
|
5;4;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Translational invariance;CNN;Capsule Network
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
A quantifiable testing of global translational invariance in Convolutional and Capsule Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
new learning criterion;penalized maximum likelihood;posterior inference in deep generative models;input forgetting issue;latent variable collapse issue
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Learning with Reflective Likelihoods
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distribution regression;Distribution sequence;Forward prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
An Efficient Network for Predicting Time-Varying Distributions
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| 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
| 4.666667 |
4;5;5
| null | null |
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional neural networks;channel-selectivity;channel re-wiring;bottleneck architectures;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Selective Convolutional Units: Improving CNNs via Channel Selectivity
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical model;RNN;generative model;automatic composing
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
HAPPIER: Hierarchical Polyphonic Music Generative RNN
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
UC Berkeley, Google Brain; Carnegie Mellon University, Google Brain; Google Brain; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine
|
https://iclr.cc/virtual/2019/poster/720
|
reinforcement learning;unsupervised learning;skill discovery
| null | 0 | null | null |
iclr
| 0.5 | 0 |
https://sites.google.com/view/diayn/
|
main
| 7.333333 |
7;7;8
| null | null |
Diversity is All You Need: Learning Skills without a Reward Function
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;quantization;optimization;low-precision;convolutional network;recurrent network
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Precision Highway for Ultra Low-precision Quantization
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial nets;minimax duality gap;equilibrium
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
| 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
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Functional Bayesian Neural Networks for Model Uncertainty Quantification
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;machine learning;multimodal;generative adversarial networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Generating Images from Sounds Using Multimodal Features and GANs
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
New York University; University of California, San Diego; New York University/New York University Shanghai
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Quan Vuong, Yiming Zhang, Keith Ross
|
https://iclr.cc/virtual/2019/poster/744
|
Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Supervised Policy Update for Deep Reinforcement Learning
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;bilingual dictionary induction;unsupervised learning;generative adversarial networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Empirical observations on the instability of aligning word vector spaces with GANs
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;GANs;conditional GANs;Discriminator;Fusion
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Structured Prediction using cGANs with Fusion Discriminator
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Paper under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial sample;Text;Black-box;MCTS;Homoglyph
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.5 |
3;4
| null | null |
MCTSBug: Generating Adversarial Text Sequences via Monte Carlo Tree Search and Homoglyph Attack
| null | null | 0 | 3.5 |
Withdraw
|
4;3
| null |
null |
Massachusetts Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Victoria Xia, Zi Wang, Kelsey Allen, Tom Silver, Leslie Kaelbling
|
https://iclr.cc/virtual/2019/poster/934
|
Deictic reference;relational model;rule-based transition model
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning sparse relational transition models
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
School of Electrical Engineering, KAIST
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Daewoo Kim, Sangwoo Moon, David Earl Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi
|
https://iclr.cc/virtual/2019/poster/931
|
Multi agent reinforcement learning;deep reinforcement learning;Communication
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Learning to Schedule Communication in Multi-agent Reinforcement Learning
| null | null | 0 | 3.333333 |
Poster
|
2;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dialogue models;adversarial networks;dialogue generation
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Multi-turn Dialogue Response Generation in an Adversarial Learning Framework
| null | null | 0 | 4.25 |
Reject
|
4;4;5;4
| null |
null |
Carnegie Mellon University, Facebook AI Research; New York University, Facebook AI Research; Facebook AI Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kenneth Marino, Abhinav Gupta, Rob Fergus, Arthur Szlam
|
https://iclr.cc/virtual/2019/poster/1042
| null | null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/hrl-ep3
|
main
| 6.666667 |
6;7;7
| null | null |
Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
DEEP GRAPH TRANSLATION
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null |
Georgia Institute of Technology; Georgia Tech Research Institute; Georgia Institute of Technology and Georgia Tech Research Institute
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
|
https://iclr.cc/virtual/2019/poster/737
|
classification;unsupervised learning;semi-supervised learning;problem reduction;weak supervision;cross-task;learning;deep learning;neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Multi-class classification without multi-class labels
|
https://github.com/GT-RIPL/L2C
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
New York University; Brown University; Swarthmore College; Google AI Language; Johns Hopkins University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, Tom McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, Ellie Pavlick
|
https://iclr.cc/virtual/2019/poster/1009
|
natural language processing;word embeddings;transfer learning;interpretability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
What do you learn from context? Probing for sentence structure in contextualized word 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 network;pruning;Winograd convolution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
DeepMind; Google Brain
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
David Pfau, Stig Petersen, Ashish Agarwal, David Barrett, Kimberly Stachenfeld
|
https://iclr.cc/virtual/2019/poster/962
|
spectral learning;unsupervised learning;manifold learning;dimensionality reduction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Spectral Inference Networks: Unifying Deep and Spectral Learning
|
https://github.com/deepmind/spectral_inference_networks
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Continual Learning;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Generative Adversarial Network Training is a Continual Learning Problem
| 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 learning;low rank representations;adversarial robustness
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Intriguing Properties of Learned Representations
| null | null | 0 | 2.666667 |
Reject
|
4;2;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning representation;decomposition;adversarial training;style transfer
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Adversarial Decomposition of Text Representation
| null | null | 0 | 3.333333 |
Withdraw
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;generalization error bound;convolutional neural networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
USI, Switzerland; Fabula AI, UK; Intel Perceptual Computing, Israel; NNAISENSE, Switzerland; Stanford University, USA; Imperial College London, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas
|
https://iclr.cc/virtual/2019/poster/853
|
peernet;peernets;graph;geometric deep learning;adversarial;perturbation;defense;peer regularization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.5 |
6;7
| null | null |
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
| null | null | 0 | 4.5 |
Poster
|
5;4
| null |
null |
DeepMind, University of Oxford; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
|
https://iclr.cc/virtual/2019/poster/1006
|
Neural Processes;Conditional Neural Processes;Stochastic Processes;Regression;Attention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Attentive Neural Processes
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
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