The aim of this paper is to seek for a new optimization algorithm tailored for training RBMs in the hope of obtaining a faster algorithm than the CD algorithm.
ABSTRACT. Restricted Boltzmann Machines (RBMs) are neural network models for unsupervised learning, but have recently found a.
We propose deriving a new training algorithm based on an auxiliary function approach for RBMs using the reconstruction probability of observations as the ...
The restricted Boltzmann machine is an example of a physics-inspired method that is useful for engineering problems. At a high-level, RBMs are probability ...
Restricted Boltzmann machines (RBMs) are stochastic neural networks that can be used to learn features from raw data. They have attracted particular ...
Presentation, 2014-11-17 17:00 [Poster Presentation] Training Algorithm for Restricted Boltzmann Machines Using Auxiliary Function Approach Norihiro Takamune ( ...
Jun 2, 2023 · Training Boltzmann Machines involves learning the parameters, such as the weights and biases, that minimize the difference between the ...
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We propose an alternative deter- ministic iterative procedure based on an improved mean field method from statis- tical physics known as the Thouless-Anderson- ...
Jul 19, 2024 · In this work, we present a novel representation of the imaginary-time propagator based on restricted Boltzmann machines. We test its accuracy ...
The first is to design an efficient MCMC method to get good representative samples from the model distribution and thereby reduce the variance of the estimated ...