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Restricted Boltzmann machine

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

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A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.[1]

Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units).

RBMs were initially proposed under the name Harmonium by Paul Smolensky in 1986,[2] and rose to prominence after Geoffrey Hinton and collaborators used fast learning algorithms for them in the mid-2000s. RBMs have found applications in dimensionality reduction,[3] classification,[4] collaborative filtering,[5] feature learning,[6] topic modelling[7] and even manybody quantum mechanics.[8][9] They can be trained in either supervised or unsupervised ways, depending on the task.[citation needed]

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.[10]

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.[11]

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