Data di Pubblicazione:
2022
Citazione:
Product Jacobi-Theta Boltzmann machines with score matching / A. Pasquale, D. Krefl, S. Carrazza, F. Nielsen. ((Intervento presentato al convegno ACAT2022 tenutosi a Bari : 23-28 Ottobre nel 2022.
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.
Tipologia IRIS:
14 - Intervento a convegno non pubblicato
Keywords:
Statistics - Machine Learning; Statistics - Machine Learning; Computer Science - Learning
Elenco autori:
A. Pasquale, D. Krefl, S. Carrazza, F. Nielsen
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