Variational Autoencoders using D-Wave Quantum Annealing

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dc.contributor.authorSleeman, Jennifer
dc.contributor.authorHalem, Milton
dc.date.accessioned2020-07-24T15:24:39Z
dc.date.available2020-07-24T15:24:39Z
dc.date.issued2018-12-13
dc.description.urihttps://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/464627en
dc.format.extent5 pagesen
dc.genrepostersen
dc.identifierdoi:10.13016/m2y1v5-n4qf
dc.identifier.citationJennifer Sleeman and Milton Halem, Variational Autoencoders using D-Wave Quantum Annealing, Walter E Washington Convention Center - eLightning Theater II, https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/464627en
dc.identifier.urihttp://hdl.handle.net/11603/19235
dc.language.isoenen
dc.publisherAGU Pubicationen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2018. American Geophysical Union
dc.subjectUMBC Ebiquity Research Group
dc.subjectdeep learning algorithmsen
dc.subjectD-Wave’s quantum annealeren
dc.subjectRestricted Boltzmann Machines (RBM)en
dc.subjectvariational autoencoderen
dc.titleVariational Autoencoders using D-Wave Quantum Annealingen
dc.typeTexten

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