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Mini-Workshop: Deep Learning and Inverse Problems

dc.date.accessioned2019-10-24T15:41:09Z
dc.date.available2019-10-24T15:41:09Z
dc.date.issued2018
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3633
dc.description.abstractMachine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems.
dc.titleMini-Workshop: Deep Learning and Inverse Problems
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWR-2018-11
local.series.idOWR-2018-11
local.subject.msc65
local.subject.msc94
local.sortindex1073
local.date-range04 Mar - 10 Mar 2018
local.workshopcode1810c
local.workshoptitleMini-Workshop: Deep Learning and Inverse Problems
local.organizersSimon Arridge, London; Maarten de Hoop, Houston; Peter Maaß, Bremen; Carola Schönlieb, Cambridge UK
local.report-nameWorkshop Report 2018,11
local.opc-photo-id1810c
local.publishers-doi10.4171/OWR/2018/11
local.ems-referenceArridge Simon, de Hoop Maarten, Maaß Peter, Schönlieb Carola-Bibiane: Mini-Workshop: Deep Learning and Inverse Problems. Oberwolfach Rep. 15 (2018), 559-589. doi: 10.4171/OWR/2018/11


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