A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Leandro Aparecido Passos,
João Paulo Papa
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… Show more
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