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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Restricted Boltzmann Machines achieved notorious popularity in the scientific community in the last decade due to outstanding results in a wide range of applications and also for providing the required mechanisms to build successful deep learning models, i.e., Deep Belief Networks and Deep Boltzmann Machines. However, their main bottleneck is related to the learning step, which is usually time-consuming. In this paper, we introduce a Sigmoid-like family of functions based on the Kaniadakis entropy formulation in the context of the RBM learning procedure. Experiments concerning binary image reconstruction are conducted in four public datasets to evaluate the robustness of the proposed approach. The results suggest that such a family of functions is suitable to increase the convergence rate when compared to standard functions employed by the research community.

UFSCar Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil

UNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, Brazil

UNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, Brazil

CAPES: 001

FAPESP: 2013/07375-0

FAPESP: 2014/12236-1

FAPESP: 2016/06441-7

CNPq: 307066/2017-7