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dc.contributor.authorAlnegheimish, Sarah
dc.contributor.authorLiu, Dongyu
dc.contributor.authorSala, Carles
dc.contributor.authorBerti-Equille, Laure
dc.contributor.authorVeeramachaneni, Kalyan
dc.date.accessioned2022-11-10T13:04:56Z
dc.date.available2022-11-10T13:04:56Z
dc.date.issued2022-06-10
dc.identifier.isbn978-1-4503-9249-5
dc.identifier.urihttps://hdl.handle.net/1721.1/146277
dc.publisherACM|Proceedings of the 2022 International Conference on Management of Dataen_US
dc.relation.isversionofhttps://doi.org/10.1145/3514221.3517910en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACM|Proceedings of the 2022 International Conference on Management of Dataen_US
dc.titleSintel: A Machine Learning Framework to Extract Insights from Signalsen_US
dc.typeArticleen_US
dc.identifier.citationAlnegheimish, Sarah, Liu, Dongyu, Sala, Carles, Berti-Equille, Laure and Veeramachaneni, Kalyan. 2022. "Sintel: A Machine Learning Framework to Extract Insights from Signals."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-11-03T00:39:02Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-03T00:39:02Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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