| dc.date.accessioned | 2021-04-19T09:58:45Z | |
| dc.date.available | 2021-04-19T09:58:45Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | http://publications.mfo.de/handle/mfo/3856 | |
| dc.description.abstract | Machine 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.title | Deep Learning for Inverse Problems (hybrid meeting) | |
| dc.rights.license | Dieses 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.license | This 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.doi | 10.14760/OWR-2021-13 | |
| local.series.id | OWR-2021-13 | |
| local.subject.msc | 65 | |
| local.subject.msc | 94 | |
| local.date-range | 07 Mar - 13 Mar 2021 | |
| local.workshopcode | 2110b | |
| local.workshoptitle | Deep Learning for Inverse Problems (hybrid meeting) | |
| local.organizers | Simon Arridge, London; Peter Maaß, Bremen; Carola-Bibiane Schönlieb, Cambridge UK | |
| local.report-name | Workshop Report 2021,13 | |
| local.opc-photo-id | 2110b | |
| local.publishers-doi | 10.4171/OWR/2021/13 | |
| local.ems-reference | Arridge Simon R., Maaß Peter, Schönlieb Carola-Bibiane: Deep Learning for Inverse Problems. Oberwolfach Rep. 18 (2021), 745-789. doi: 10.4171/OWR/2021/13 | |
