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A lifted Bregman formulation for the inversion of deep neural networks

dc.contributor.authorWang, Xen_US
dc.contributor.authorBenning, Men_US
dc.date.accessioned2023-06-13T15:23:54Z
dc.date.available2023-05-08en_US
dc.date.issued2023en_US
dc.identifier.otherARTN 1176850en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/88899
dc.relation.ispartofFRONTIERS IN APPLIED MATHEMATICS AND STATISTICSen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectinverse problemsen_US
dc.subjectregularization theoryen_US
dc.subjectlifted network trainingen_US
dc.subjectBregman distanceen_US
dc.subjectperceptronen_US
dc.subjectmulti-layer perceptronen_US
dc.subjectvariational regularizationen_US
dc.subjecttotal variation regularizationen_US
dc.titleA lifted Bregman formulation for the inversion of deep neural networksen_US
dc.typeArticle
dc.identifier.doi10.3389/fams.2023.1176850en_US
pubs.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001017117300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume9en_US


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States