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arXiv · 2101.05795

A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines

Leandro Aparecido Passos·João Paulo Papa
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Abstract

Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memory- and evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results.

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Leandro Aparecido Passos, João Paulo Papa. 2021-01-14. A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines. https://doi.org/10.1016/j.asoc.2019.105717

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