Derivations of the Enhanced Gradient for the Boltzmann Machine

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Ilin, Alexander
dc.contributor.author Cho, KyungHyun
dc.contributor.author Raiko, Tapani
dc.date.accessioned 2017-04-25T09:00:39Z
dc.date.available 2017-04-25T09:00:39Z
dc.date.issued 2011
dc.identifier.isbn 978-952-60-4295-4
dc.identifier.issn 1799-490X
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25235
dc.description.abstract This technical report is extends the conference paper [1] and the abstract [2] with detailed derivations and proofs. First we recap notation that we use on the Boltzmann machine and its learning. Then we define transformations for the machine where some of its bits are flipped for all samples, and show the equivalence of the transformed model to the original one. Then we show that traditional update rules are not invariant to the transformations, propose a new update rule called theenhanced gradient, and finally show its invariance to the transformations. en
dc.format.extent 9
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series SCIENCE+TECHNOLOGY en
dc.relation.ispartofseries 20/2011
dc.subject.other Computer science en
dc.title Derivations of the Enhanced Gradient for the Boltzmann Machine en
dc.type D4 Julkaistu kehittämis- tai tutkimusraportti tai -selvitys fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Tietotekniikan laitos fi
dc.contributor.department Department of Computer Science en
dc.subject.keyword enhanced gradient en
dc.subject.keyword boltzmann machine en
dc.subject.keyword sample index en
dc.identifier.urn URN:NBN:fi:aalto-201704243665
dc.type.dcmitype text en


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