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Intelligent agents can learn to represent the action spaces of other agents simply by observing them act.
Premotor cortex and the recognition of motor actions
Giacomo Rizzolatti, Luciano Fadiga, Vittorio Gallese, and Leonardo Fogassi · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek · 1999
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Neuronal correlates of a perceptual decision in ventral premotor cortex
Ranulfo Romo, Adrián Hernández, and Antonio Zainos · 2004
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The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning
Reuven Y. Rubinstein and Dirk P. Kroese · 2004
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Learning movement primitives
Stefan Schaal, Jan Peters, Jun Nakanishi, and Auke Ijspeert · 2005
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Dynamic Movement Primitives - A Framework for Motor Control in Humans and Humanoid Robotics
Stefan Schaal · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Locomotor primitives in newborn babies and their development
Nadia Dominici, Yuri Ivanenko, Germana Cappellini, Andrea d’Avella, Vito Mondì, Marika Cicchese, Adele Fabiano, Tiziana Silei, Ambrogio Di Paolo, Carlo Giannini, Richard Poppele, and Francesco Lacquaniti · 2011
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How do you learn to walk? thousands of steps and dozens of falls per day
Karen Adolph, Whitney Cole, Meghana Komati, Jessie Garciaguirre, Daryaneh Badaly, Jesse Lingeman, Gladys Chan, and Rachel Sotsky · 2012
Earlier work this paper cites.
Principles of Neural Science
Eric R. Kandel, James H. Schwartz, Thomas M. Jessell, Steven A. Siegelbaum, and A. J. Hudspeth (eds.) · 2012
Earlier work this paper cites.
From simple innate biases to complex visual concepts
Shimon Ullman, Daniel Harari, and Nimrod Dorfman · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Learning to linearize under uncertainty
Ross Goroshin, Michael F Mathieu, and Yann LeCun · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Learning grounded finite-state representations from unstructured demonstrations
Scott Niekum, Sarah Osentoski, George Konidaris, Sachin Chitta, Bhaskara Marthi, and Andrew G Barto · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, and Satinder Singh · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Unsupervised learning of video representations using LSTMs
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Prediction under uncertainty with error-encoding networks
Mikael Henaff, Junbo Zhao, and Yann LeCun · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Independently controllable features
Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, and Yoshua Bengio · 2017
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Decomposing motion and content for natural video sequence prediction
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Cited alongside, same era.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Toward an integration of deep learning and neuroscience
Adam H Marblestone, Greg Wayne, and Konrad P Kording · 2016
Cited alongside, same era.
Anticipating visual representations from unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
Cited alongside, same era.
The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
Cited alongside, same era.
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Imagination-augmented agents for deep reinforcement learning
Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, David Silver, and Daan Wierstra · 2017
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Fixing a broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A. Saurous, and Kevin Murphy · 2018
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2018
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Understanding disentangling in β \beta -VAE
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Stochastic Video Generation with a Learned Prior
Emily Denton and Rob Fergus · 2018
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David Ha and Jurgen Schmidhuber · 2018
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Understanding image motion with group representations
Andrew Jaegle, Stephen Phillips, Daphne Ippolito, and Kostas Daniilidis · 2018
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Roboschool: Open-source software for robot simulation, 2018
Oleg Klimov and John Schulman · 2018
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Stochastic adversarial video prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Driving policy transfer via modularity and abstraction
Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem, and Vladen Koltun · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Unsupervised predictive memory in a goal-directed agent
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew Botvinick, Demis Hassabis, and Timothy P. Lillicrap · 2018
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Shi Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2018
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