Extended Boltzmann Machine Generative Model

Lancelot Tullio and Maria Rifqi ()
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Lancelot Tullio: LEMMA - Laboratoire d'économie mathématique et de microéconomie appliquée - Université Paris-Panthéon-Assas, Université Paris-Panthéon-Assas
Maria Rifqi: LEMMA - Laboratoire d'économie mathématique et de microéconomie appliquée - Université Paris-Panthéon-Assas, Université Paris-Panthéon-Assas

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Abstract: The increase in computing power in recent years has brought generative models and the use of synthetic data back to the fore to solve a variety of previously unsolved problems, in particular when fields are subject to constraints linked to the sensitivity of the information processed. This article proposes a modified version of restricted Boltzmann machines (RBM), known as Bernoulli machines, to improve its ability to handle non-binary data without making the methodology more complex to understand and manipulate. To assess the performance of our algorithm, we compare it with various generative models that are well documented and have repeatedly proven their effectiveness in a variety of contexts. We also chose to use a large number of open source datasets with different types of features and different sizes in order the verify the generalization capacity and sclalability of our approach.

Date: 2025-11-12
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Published in Scalable Uncertainty Management: 16th International Conference, SUM 2024, Nov 2024, Palerme (Italie), Italy. 15350, Springer Nature Switzerland, pp.408-420, 2025, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-76235-2_30⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05007631

DOI: 10.1007/978-3-031-76235-2_30

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