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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… Expand
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Papers overview

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Review
2019
Review
2019
Abstract Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of… Expand
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Review
2019
Review
2019
This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial… Expand
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Review
2018
Review
2018
In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data… Expand
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Highly Cited
2012
Highly Cited
2012
Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually… Expand
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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… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e… Expand
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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… Expand
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