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Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
Earlier work this paper cites.
Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
R.P.N. Rao and D.H. Ballard · 1999
Earlier work this paper cites.
Predictive coding as a model of biased competition in visual attention
M.W. Spratling · 2008
Earlier work this paper cites.
Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
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Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
Earlier work this paper cites.
Deep predictive coding networks
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Rakesh Chalasani and Jose C. Principe · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Learning to linearize under uncertainty
Original
Ross Goroshin, Michaël Mathieu, and Yann LeCun · 2015
Earlier work this paper cites.

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2017

Henaff, Mikael, Zhao, Junbo, LeCun, Yann

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.

It is based on a simple idea of disentangling components of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into a forward model.
Our method uses a supervised training objective which is fast and easy to train.
We evaluate it in the context of video prediction on multiple datasets and show that it is able to consistently generate diverse predictions without the need for alternating minimization over a latent space or adversarial training.

Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
Earlier work this paper cites.
Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
R.P.N. Rao and D.H. Ballard · 1999
Earlier work this paper cites.
Predictive coding as a model of biased competition in visual attention
M.W. Spratling · 2008
Earlier work this paper cites.
Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Original
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
Earlier work this paper cites.
Deep predictive coding networks
Original
Rakesh Chalasani and Jose C. Principe · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
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Original
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Earlier work this paper cites.
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Original
Ross Goroshin, Michaël Mathieu, and Yann LeCun · 2015
Earlier work this paper cites.

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Original
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Cited alongside, same era.
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Cited alongside, same era.
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Original
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
Asynchronous methods for deep reinforcement learning
Original
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Later among the works it cites.
Improved techniques for training gans
Original
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Later among the works it cites.
Query-efficient imitation learning for end-to-end autonomous driving