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We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data.
Modeling by shortest data description
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, Geoffrey E and Van Camp, Drew · 1993
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Autoencoders, minimum description length, and helmholtz free energy
Hinton, Geoffrey E and Zemel, Richard S · 1994
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A review of simulation optimization techniques
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Graphical models for machine learning and digital communication
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LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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MacKay, David JC · 2003
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Ranzato, Marc’Aurelio, Boureau, Y-lan, and Cun, Yann L · 2007
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Hinton, Geoffrey · 2012
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Variational bayesian inference with stochastic search
Paisley, John, Blei, David, and Jordan, Michael · 2012
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Rifai, Salah, Bengio, Yoshua, Dauphin, Yann N, and Vincent, Pascal · 2012
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UCI machine learning repository, 2013
Bache, K. and Lichman, M · 2013
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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