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.
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Tullio, L., Rifqi, M. (2025). Extended Boltzmann Machine Generative Model. In: Destercke, S., Martinez, M.V., Sanfilippo, G. (eds) Scalable Uncertainty Management. SUM 2024. Lecture Notes in Computer Science(), vol 15350. Springer, Cham. https://doi.org/10.1007/978-3-031-76235-2_30
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DOI: https://doi.org/10.1007/978-3-031-76235-2_30
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