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Joint Perception and Control as Inference with an Object-based
  Implementation
v1v2v3 (latest)

Joint Perception and Control as Inference with an Object-based Implementation

4 March 2019
Minne Li
Zheng Tian
Pranav Nashikkar
Ian Davies
Ying Wen
Jun Wang
ArXiv (abs)PDFHTML

Papers citing "Joint Perception and Control as Inference with an Object-based Implementation"

12 / 12 papers shown
Title
Deep Variational Reinforcement Learning for POMDPs
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl
L. Zintgraf
T. Le
Frank Wood
Shimon Whiteson
BDLOffRL
66
262
0
06 Jun 2018
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Vikrant Goel
James Weng
Pascal Poupart
OCL
69
66
0
20 May 2018
Relational Neural Expectation Maximization: Unsupervised Discovery of
  Objects and their Interactions
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Sjoerd van Steenkiste
Michael Chang
Klaus Greff
Jürgen Schmidhuber
BDLOCLDRL
206
291
0
28 Feb 2018
A simple neural network module for relational reasoning
A simple neural network module for relational reasoning
Adam Santoro
David Raposo
David Barrett
Mateusz Malinowski
Razvan Pascanu
Peter W. Battaglia
Timothy Lillicrap
GNNNAI
181
1,614
0
05 Jun 2017
A Compositional Object-Based Approach to Learning Physical Dynamics
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang
T. Ullman
Antonio Torralba
J. Tenenbaum
AI4CEOCL
382
441
0
01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CEOCLPINNGNN
541
1,410
0
01 Dec 2016
Deep Visual Foresight for Planning Robot Motion
Deep Visual Foresight for Planning Robot Motion
Chelsea Finn
Sergey Levine
119
785
0
03 Oct 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing
  Generative Adversarial Nets
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen
Yan Duan
Rein Houthooft
John Schulman
Ilya Sutskever
Pieter Abbeel
GAN
159
4,235
0
12 Jun 2016
Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih
Adria Puigdomenech Badia
M. Berk Mirza
Alex Graves
Timothy Lillicrap
Tim Harley
David Silver
Koray Kavukcuoglu
199
8,859
0
04 Feb 2016
Embed to Control: A Locally Linear Latent Dynamics Model for Control
  from Raw Images
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Manuel Watter
Jost Tobias Springenberg
Joschka Boedecker
Martin Riedmiller
BDL
82
846
0
24 Jun 2015
Playing Atari with Deep Reinforcement Learning
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih
Koray Kavukcuoglu
David Silver
Alex Graves
Ioannis Antonoglou
Daan Wierstra
Martin Riedmiller
127
12,231
0
19 Dec 2013
Deep Learning of Representations: Looking Forward
Deep Learning of Representations: Looking Forward
Yoshua Bengio
218
682
0
02 May 2013
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