Deep Boltzmann machines using adaptive temperatures
| dc.contributor.author | Passos Júnior, Leandro A. | |
| dc.contributor.author | Costa, Kelton A. P. [UNESP] | |
| dc.contributor.author | Papa, João P. [UNESP] | |
| dc.contributor.institution | Universidade Federal de São Carlos (UFSCar) | |
| dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
| dc.date.accessioned | 2018-12-11T17:33:53Z | |
| dc.date.available | 2018-12-11T17:33:53Z | |
| dc.date.issued | 2017-01-01 | |
| dc.description.abstract | Deep learning has been considered a hallmark in a number of applications recently. Among those techniques, the ones based on Restricted Boltzmann Machines have attracted a considerable attention, since they are energy-driven models composed of latent variables that aim at learning the probability distribution of the input data. In a nutshell, the training procedure of such models concerns the minimization of the energy of each training sample in order to increase its probability. Therefore, such optimization process needs to be regularized in order to reach the best trade-off between exploitation and exploration. In this work, we propose an adaptive regularization approach based on temperatures, and we show its advantages considering Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs). The proposed approach is evaluated in the context of binary image reconstruction, thus outperforming temperature-fixed DBNs and DBMs. | en |
| dc.description.affiliation | Department of Computing UFSCar - Federal University of São Carlos | |
| dc.description.affiliation | School of Sciences UNESP - São Paulo State University | |
| dc.description.affiliationUnesp | School of Sciences UNESP - São Paulo State University | |
| dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
| dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description.sponsorshipId | FAPESP: #2014/12236-1 | |
| dc.description.sponsorshipId | FAPESP: #2014/16250-9 | |
| dc.description.sponsorshipId | CNPq: #306166/2014-3 | |
| dc.format.extent | 172-183 | |
| dc.identifier | http://dx.doi.org/10.1007/978-3-319-64689-3_14 | |
| dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10424 LNCS, p. 172-183. | |
| dc.identifier.doi | 10.1007/978-3-319-64689-3_14 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.scopus | 2-s2.0-85028518481 | |
| dc.identifier.uri | http://hdl.handle.net/11449/179135 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
| dc.relation.ispartofsjr | 0,295 | |
| dc.rights.accessRights | Acesso aberto | |
| dc.source | Scopus | |
| dc.title | Deep Boltzmann machines using adaptive temperatures | en |
| dc.type | Trabalho apresentado em evento | |
| unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
| unesp.department | Computação - FC | pt |