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dc.contributor.authorRegenwetter, Lyle
dc.contributor.authorSrivastava, Akash
dc.contributor.authorGutfreund, Dan
dc.contributor.authorAhmed, Faez
dc.date.accessioned2023-10-17T18:00:47Z
dc.date.available2023-10-17T18:00:47Z
dc.date.issued2023-12
dc.identifier.urihttps://hdl.handle.net/1721.1/152444
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionof10.1016/j.cad.2023.103609en_US
dc.rightsCreative Commons Attribution-Noncommercial-NoDerivativesen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceMIT News officeen_US
dc.titleBeyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designen_US
dc.typeArticleen_US
dc.identifier.citationRegenwetter, Lyle, Srivastava, Akash, Gutfreund, Dan and Ahmed, Faez. 2023. "Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design." Computer-Aided Design, 165.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentMIT-IBM Watson AI Lab
dc.relation.journalComputer-Aided Designen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-10-17T17:56:03Z
dspace.orderedauthorsRegenwetter, L; Srivastava, A; Gutfreund, D; Ahmed, Fen_US
dspace.date.submission2023-10-17T17:56:15Z
mit.journal.volume165en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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