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RESTRICTE

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Definitions of restricte in various dictionaries:

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

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Restricte might refer to
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. * RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986, * 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,classification,collaborative filtering, feature learning * and topic modelling. * 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.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.

Restricte might refer to

A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
* RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986,
* 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,classification,collaborative filtering, feature learning
* and topic modelling.
* 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.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.

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