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Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) 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.
The ICS Architecture builds on Harmony Theory, a formalism for artificial neural networks that introduced the restricted Boltzmann machine architecture. This work, up through the early 2000s, is presented in the two-volume book written with Géraldine Legendre, The Harmonic Mind. [6]
This is not a restricted Boltzmann machine. A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising model), named after Ludwig Boltzmann, is a spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, [1] that is a stochastic Ising model.
A generative model is a statistical model of the joint probability distribution ... Boltzmann machine (e.g. Restricted Boltzmann machine, Deep belief network)
Alternatively, it is a hierarchical generative model for deep learning, which is highly effective in image processing and object recognition, though it has been used in other domains too. [2] The salient features of the model include the fact that it scales well to high-dimensional images and is translation-invariant.
The deep belief network model by Hinton et al. (2006) involves learning the distribution of a high-level representation using successive layers of binary or real-valued latent variables. It uses a restricted Boltzmann machine to model each new layer of higher level features.
A restricted Boltzmann machine (RBM) with fully connected visible and hidden units. Note there are no hidden-hidden or visible-visible connections. A deep belief network (DBN) is a probabilistic, generative model made up of multiple hidden layers.
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