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It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation.
Asymptotic evaluation of certain markov process expectations for large time, i
Monroe D Donsker and SR Srinivasa Varadhan · 1975
Earlier work this paper cites.
Causality, feedback and directed information
James Massey · 1990
Earlier work this paper cites.
Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
Earlier work this paper cites.
The MNIST database of handwritten digits
Yann LeCun · 1998
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Temporal abstraction in reinforcement learning
Doina Precup · 2000
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
Earlier work this paper cites.
Learning deep architectures for AI
Yoshua Bengio · 2009
Earlier work this paper cites.
What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
Earlier work this paper cites.
Tighter variational representations of f-divergences via restriction to probability measures
Avraham Ruderman, Mark Reid, Darío García-García, and James Petterson · 2012
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Christoph Salge, Cornelius Glackin, and Daniel Polani · 2013
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NICE: Non-linear Independent Components Estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Durk P. Kingma and Max Welling · 2014
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Brian D Ziebart · 2015
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen and Hiroshi Morioka · 2016
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
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Stochastic neural networks for hierarchical reinforcement learning
Carlos Florensa, Yan Duan, and Pieter Abbeel · 2017
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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MazeBase: A sandbox for learning from games
Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, and Rob Fergus · 2015
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Intrinsically motivated learning of hierarchical collections of skills
Andrew G Barto, Satinder Singh, and Nuttapong Chentanez
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Variational Intrinsic Control
K. Gregor, D. Jimenez Rezende, and D. Wierstra · 2017
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Independently controllable factors
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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