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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.
CMBFAST is a computer code, developed by Uroš Seljak and Matias Zaldarriaga (based on a Boltzmann code written by Edmund Bertschinger, Chung-Pei Ma and Paul Bode) for computing the power spectrum of the cosmic microwave background anisotropy. It is the first efficient program to do so, reducing the time taken to compute the anisotropy from ...
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.
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.
OpenLB is an object-oriented implementation of the lattice Boltzmann methods (LBM). It is the first implementation of a generic platform for LBM programming, which is shared with the open source community . [2]
[2] Let x 1 and x 2 be the vector positions of the two bodies, and m 1 and m 2 be their masses. The goal is to determine the trajectories x 1 (t) and x 2 (t) for all times t, given the initial positions x 1 (t = 0) and x 2 (t = 0) and the initial velocities v 1 (t = 0) and v 2 (t = 0). When applied to the two masses, Newton's second law states that
This can reduce the time required to train a deep restricted Boltzmann machine, and provide a richer and more comprehensive framework for deep learning than classical computing. [69] The same quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well-known classical ...
The exact formulation is as follows. Consider two systems, 1 and 2, in thermal contact, with respective energies E 1 and E 2. We assume E 1 + E 2 = some constant E. The number of microstates of each system will be denoted by Ω 1 and Ω 2. Under our assumptions Ω i depends only on E i.