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Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well.
Connectionist learning of belief networks
Neal, Radford M · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, Ronald J · 1992
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Autoencoders, minimum description length, and Helmholtz free energy
Hinton, Geoffrey E and Zemel, Richard S · 1994
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The helmholtz machine
Dayan, Peter, Hinton, Geoffrey E, Neal, Radford M, and Zemel, Richard S · 1995
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The "wake-sleep" algorithm for unsupervised neural networks
Hinton, Geoffrey E, Dayan, Peter, Frey, Brendan J, and Neal, Radford M · 1995
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Varieties of helmholtz machine
Dayan, Peter and Hinton, Geoffrey E · 1996
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Mean field theory for sigmoid belief networks
Saul, Lawrence K., Jaakkola, Tommi, and Jordan, Michael I · 1996
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An introduction to variational methods for graphical models
Jordan, Michael I., Ghahramani, Zoubin, Jaakkola, Tommi S., and Saul, Lawrence K · 1999
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The optimal reward baseline for gradient-based reinforcement learning
Weaver, Lex and Tao, Nigel · 2001
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Variance reduction techniques for gradient estimates in reinforcement learning
Greensmith, Evan, Bartlett, Peter L., and Baxter, Jonathan · 2004
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A fast learning algorithm for deep belief nets
Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee Whye · 2006
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Differential sparse coding
Bradley, David M and Bagnell, J Andrew · 2008
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Fast inference in sparse coding algorithms with applications to object recognition
Kavukcuoglu, Koray, Ranzato, Marc’Aurelio, and LeCun, Yann · 2008
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On the quantitative analysis of Deep Belief Networks
Salakhutdinov, Ruslan and Murray, Iain · 2008
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The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
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A neural autoregressive topic model
Larochelle, Hugo and Lauly, Stanislas · 2012
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Variational bayesian inference with stochastic search
Paisley, John William, Blei, David M., and Jordan, Michael I · 2012
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Gregor, Karol, Mnih, Andriy, and Wierstra, Daan · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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Learning fast approximations of sparse coding
Gregor, Karol and LeCun, Yann · 2010
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Salakhutdinov, Ruslan and Larochelle, Hugo · 2010
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Deep boltzmann machines
Salakhutdinov, Ruslan and Hinton, Geoffrey E
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Replicated softmax: an undirected topic model
Salakhutdinov, Ruslan and Hinton, Geoffrey E
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Learning stochastic feedforward neural networks
Tang, Yichuan and Salakhutdinov, Ruslan · 2013
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Stochastic back-propagation and variational inference in deep latent gaussian models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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