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A Boltzmann machine, like a Sherrington–Kirkpatrick model, is a network of units with a total "energy" (Hamiltonian) defined for the overall network. Its units produce binary results. Boltzmann machine weights are stochastic. The global energy in a Boltzmann machine is identical in form to that of Hopfield networks and Ising models:
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.
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. It can be considered a composition of simple learning modules. [43]
Energy-based generative neural networks [1] [2] is a class of generative models, which aim to learn explicit probability distributions of data in the form of energy-based models, the energy functions of which are parameterized by modern deep neural networks. Boltzmann machines are a special form of energy-based models with a specific ...
Training of the network involves a pre-training stage accomplished in a greedy layer-wise manner, similar to other deep belief networks. Depending on whether the network is to be used for discrimination or generative tasks, it is then "fine tuned" or trained with either back-propagation or the up–down algorithm (contrastive–divergence ...
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
Geoffrey Hinton developed the deep belief network technique for training many-layered deep autoencoders. His method involves treating each neighboring set of two layers as a restricted Boltzmann machine so that pretraining approximates a good solution, then using backpropagation to fine-tune the results.
The following other wikis use this file: Usage on be.wikipedia.org Размеркаванне Максвела; Usage on da.wikipedia.org Maxwell-Boltzmann-fordelingen