# Restricted Boltzmann machine

## Class of artificial neural network / From Wikipedia, the free encyclopedia

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A **restricted Boltzmann machine** (**RBM**) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

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RBMs were initially invented under the name **Harmonium** by Paul Smolensky in 1986,[1]
and rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000. RBMs have found applications in dimensionality reduction,[2]
classification,[3]
collaborative filtering,[4] feature learning,[5]
topic modelling[6]
and even many body quantum mechanics.[7][8] They can be trained in either supervised or unsupervised ways, depending on the task.

As their name implies, RBMs are a variant of Boltzmann machines, with the restriction that their neurons must form a bipartite graph:
a pair of nodes from each of the two groups of units (commonly referred to as the "visible" and "hidden" units respectively) may have a symmetric connection between them; and there are no connections between nodes within a group. By contrast, "unrestricted" Boltzmann machines may have connections between hidden units. This restriction allows for more efficient training algorithms than are available for the general class of Boltzmann machines, in particular the gradient-based **contrastive divergence** algorithm.[9]

Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation.[10]