A fast learning algorithm for deep belief nets
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...Deep belief networks (DBNs) [16] are hybrid models containing a single undirected layer and several directed layers....
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...An alternative to directed graphical models with latent variables are undirected graphical models with latent variables, such as restricted Boltzmann machines (RBMs) [27, 16], deep Boltzmann machines (DBMs) [26] and their numerous variants....
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Cites methods from "A fast learning algorithm for deep ..."
...2 Learning Dropout RBMs Learning algorithms developed for RBMs such as Contrastive Divergence (Hinton et al., 2006) can be directly applied for learning Dropout RBMs....
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References
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"A fast learning algorithm for deep ..." refers background in this paper
...Marks and Movellan (2001) describe a way of using contrastive divergence to perform factor analysis and Welling, Rosen-Zvi, and Hinton (2005) show that a network with logistic, binary visible units and linear, gaussian hidden units can be used for rapid document retrieval....
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...Marks and Movellan (2001) describe a way of using contrastive divergence to perform factor analysis and Welling, Rosen-Zvi, and Hinton (2005) show that a network with logistic, binary visible units and linear, gaussian hidden units can be used for rapid document retrieval....
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