Product Jacobi-Theta Boltzmann machines with score matching
Abstract
Abstract The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltzmann machine (BM) architecture. In this manuscript, the product Jacobi-Theta Boltzmann machine (pJTBM) is introduced as a restricted version of the Riemann-Theta Boltzmann machine (RTBM) with diagonal hidden sector connection matrix. We show that score matching, based on the Fisher divergence, can be used to fit probability densities with the pJTBM more efficiently than with the original RTBM.
Fields of Study & Subjects
Neural Networks and Applications
Generative Adversarial Networks and Image Synthesis
Statistical Mechanics and Entropy
Publication details
- Journal / source
- Journal of Physics Conference Series
- Publisher
- IOP Publishing
- Volume
- 3206
- Issue
- 1
- Pages
- 012127–012127
- Publication date
- 1 April 2026
- Language
- EN