Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches

• Autor(a) principal: Passos, Leandro A.
• Data de Publicação: 2018
• Outros Autores: Rodrigues, Douglas R., Papa, Joao P. [UNESP], IEEE
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/186246
• topic: Deep Learning; Deep Boltzmann Machines; Meta-heuristic Optimization

The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine learning techniques often require a considerable human effort in this task. In this paper, we propose to fine-tune Deep Boltzmann Machines using meta-heuristic techniques, which do not require the computation of the gradient of the fitness function, that may be insurmountable in high-dimensional optimization spaces. We demonstrate the validity of the proposed approach against Deep Belief Networks concerning binary image reconstruction.