Shedding light on variational autoencoders

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dc.contributor.authorRuiz Vargas, Jose Cupertino [UNESP]
dc.contributor.authorNovaes, Sergio Ferraz [UNESP]
dc.contributor.authorCobe, Raphael [UNESP]
dc.contributor.authorIope, Rogerio [UNESP]
dc.contributor.authorStanzani, Silvio Luiz [UNESP]
dc.contributor.authorTomei, Thiago [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-06T17:18:39Z
dc.date.available2019-10-06T17:18:39Z
dc.date.issued2018-10-01
dc.description.abstractDeep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength.en
dc.description.affiliationSao Paulo State University (Unesp) Center for Scientific Computing (NCC)
dc.description.affiliationUnespSao Paulo State University (Unesp) Center for Scientific Computing (NCC)
dc.format.extent294-298
dc.identifierhttp://dx.doi.org/10.1109/CLEI.2018.00043
dc.identifier.citationProceedings - 2018 44th Latin American Computing Conference, CLEI 2018, p. 294-298.
dc.identifier.doi10.1109/CLEI.2018.00043
dc.identifier.scopus2-s2.0-85071121316
dc.identifier.urihttp://hdl.handle.net/11449/190600
dc.language.isoeng
dc.relation.ispartofProceedings - 2018 44th Latin American Computing Conference, CLEI 2018
dc.rights.accessRightsAcesso restrito
dc.sourceScopus
dc.subjectFresnel diffraction
dc.subjectMachine Learning
dc.subjectTensorflow
dc.subjectVariational Autoencoders
dc.titleShedding light on variational autoencodersen
dc.typeTrabalho apresentado em evento

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Artigos - Instituto de Física Teórica (IFT)