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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.
In (Hinton, Salakhutdinov, 2006), [29] deep belief networks were developed. These train a pair restricted Boltzmann machines as encoder-decoder pairs, then train another pair on the latent representation of the first pair, and so on. [30] The first applications of AE date to early 1990s.
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 ...
Some research groups have recently explored the use of quantum annealing hardware for training Boltzmann machines and deep neural networks. [62] [63] [64] The standard approach to training Boltzmann machines relies on the computation of certain averages that can be estimated by standard sampling techniques, such as Markov chain Monte Carlo ...
Stochastic - Boltzmann Machine, Cauchy Machine; It is to be noted that though these learning rules might appear to be based on similar ideas, they do have subtle differences, as they are a generalisation or application over the previous rule, and hence it makes sense to study them separately based on their origins and intents.
[46] [83] Some research groups have recently explored the use of quantum annealing hardware for training Boltzmann machines and deep neural networks. [84] [85] [86] Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like ...
Lattice Boltzmann models can be operated on a number of different lattices, both cubic and triangular, and with or without rest particles in the discrete distribution function. A popular way of classifying the different methods by lattice is the D n Q m scheme.