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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.
The code is written in C++ and is used by application programmers as well as developers, with the ability to implement custom models [3] OpenLB supports complex data structures that allow simulations in complex geometries and parallel execution using MPI, OpenMP and CUDA on high-performance computers.
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]
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 ...
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.
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Here, k ≈ 1.38 × 10 −23 J/K is the Boltzmann constant and kT 0 is the available noise power density (the noise is thermal noise, Johnson noise). As a numerical example: A receiver has a bandwidth of 100 MHz, a noise figure of 1.5 dB and the physical temperature of the system is 290 K.