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On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar restricted boltzmann machines

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dc.contributor.advisorErzin, Engin
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorIsabekov, Altynbek
dc.contributor.programElectrical and Electronics Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.coverage.spatialİstanbul
dc.date.accessioned2024-11-09T22:25:45Z
dc.date.issued2018
dc.format.extent65 leaves : illustrations ; 30 cm.
dc.identifier.urihttps://hdl.handle.net/20.500.14288/5375
dc.keywordsArtificial intelligence
dc.keywordsNeural networks (Computer science)
dc.keywordsYapay öğrenme
dc.keywordsGauss-Bernoulli kısıtlı boltzmann makineleri
dc.language.isoeng
dc.publisherKoç University
dc.relation.collectionKU Theses and Dissertations
dc.rightsrestrictedAccess
dc.rights.copyrightsnote© All Rights Reserved. Accessible to Koç University Affiliated Users Only!
dc.subjectMachine learning
dc.subjectComputational learning theory
dc.titleOn the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar restricted boltzmann machines
dc.typeDissertation
dspace.entity.typePublication
local.contributor.kuauthorIsabekov, Altynbek
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