Temperature-Based Deep Boltzmann Machines

Autor(es)

Passos, Jr., Leandro Aparecido ^[1] ; Papa, Joao Paulo ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ, Dept Comp, Bauru - Brazil

• Tipo de documento: Artigo Científico
• Fonte: NEURAL PROCESSING LETTERS; v. 48, n. 1, p. 95-107, AUG 2018.
• Citações Web of Science: 1

Resumo

Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-based user identification. Despite other techniques employed for such purposes, Deep Boltzmann Machines (DBMs) are among the most used ones, which are composed of layers of Restricted Boltzmann Machines stacked on top of each other. In this work, we evaluate the concept of temperature in DBMs, which play a key role in Boltzmann-related distributions, but it has never been considered in this context up to date. Therefore, the main contribution of this paper is to take into account this information, as well as the impact of replacing a standard Sigmoid function by another one and to evaluate their influence in DBMs considering the task of binary image reconstruction. We expect this work can foster future research considering the usage of different temperatures during learning in DBMs. (AU)

• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular