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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.
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.
In computer science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted Boltzmann machines stacked together. [1]
It uses a restricted Boltzmann machine to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly.
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The Boltzmann machine can be thought of as a noisy Hopfield network. It is one of the first neural networks to demonstrate learning of latent variables (hidden units). Boltzmann machine learning was at first slow to simulate, but the contrastive divergence algorithm speeds up training for Boltzmann machines and Products of Experts.
Deutsch: Restricted Boltzmann Machine (RBM) The zu lernenden Parameter sind rot eingefärbt. Die "visible units" v sind durch ungerichtete Kanten mit den versteckten Einheiten (hidden units) verbunden.
RBM is a three-letter acronym. It refers to: 89.1 Radio Blue Mountains, Radio station in Katoomba, New South Wales; Restricted Boltzmann machine, a type of neural network used in artificial intelligence applications