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Deep Learning for Inverse Problems (hybrid meeting)

dc.date.accessioned2021-04-19T09:58:45Z
dc.date.available2021-04-19T09:58:45Z
dc.date.issued2021
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3856
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.titleDeep Learning for Inverse Problems (hybrid meeting)
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-2021-13
local.series.idOWR-2021-13
local.subject.msc65
local.subject.msc94
local.date-range07 Mar - 13 Mar 2021
local.workshopcode2110b
local.workshoptitleDeep Learning for Inverse Problems (hybrid meeting)
local.organizersSimon Arridge, London; Peter Maaß, Bremen; Carola-Bibiane Schönlieb, Cambridge UK
local.report-nameWorkshop Report 2021,13
local.opc-photo-id2110b
local.publishers-doi10.4171/OWR/2021/13
local.ems-referenceArridge Simon R., Maaß Peter, Schönlieb Carola-Bibiane: Deep Learning for Inverse Problems. Oberwolfach Rep. 18 (2021), 745-789. doi: 10.4171/OWR/2021/13


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