Statistical-mechanical Study of Deep Boltzmann Machine Given Weight Parameters after Training by Singular Value Decomposition
Year: 2022
Type: article
Abstract: Deep learning methods relying on multi-layered networks have been actively studied in a wide range of fields in recent years, and deep Boltzmann machines(DBMs) is one of them. In this study, a model of DBMs with some properites of weight parameters obtained by learning is studied theoretically by a statistical-mechanical approach based on the replica method. The phases characterizing the role of DBMs as a generator and their phase diagram are derived, depending meaningfully on the numbers of layers and units in each layer. It is found, in particular, that the correlation between the weight parameters in the hidden layers plays an essential role and that an increase in the layer number has a negative effect on DBM's performance as a generator when the correlation is smaller than a certain threshold value.
Authors: Yuma Ichikawa, Koji Hukushima
Institution: The University of Tokyo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4229002239