Restricted Boltzmann machine

Known as: Contrastive divergence, RBM 
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of… (More)
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Papers overview

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2016
2016
We present a mathematical construction for the restricted Boltzmann machine (RBM) that does not require specifying the number of… (More)
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Highly Cited
2013
Highly Cited
2013
With the rapid growth and the increasing complexity of network infrastructures and the evolution of attacks, identifying and… (More)
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Highly Cited
2012
Highly Cited
2012
Recent developments have demonstrated the capacity of rest rict d Boltzmann machines (RBM) to be powerful generative models, able… (More)
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Highly Cited
2010
Highly Cited
2010
Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each… (More)
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Highly Cited
2010
Highly Cited
2010
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated… (More)
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Highly Cited
2010
Highly Cited
2010
Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech… (More)
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Highly Cited
2008
Highly Cited
2008
The Temporal Restricted Boltzmann Machine (TRBM) is a proba bilistic model for sequences that is able to successfully model (i.e… (More)
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Highly Cited
2008
Highly Cited
2008
Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems… (More)
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Highly Cited
2007
Highly Cited
2007
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class… (More)
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Highly Cited
2003
Highly Cited
2003
The authors introduce a continuous stochastic generative model that can model continuous data, with a simple and reliable… (More)
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