Deep Learning and Inverse Problems

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dc.bibliographicCitation.seriesTitleSnapshots of Modern Mathematics from Oberwolfacheng
dc.bibliographicCitation.volume15/2019
dc.contributor.authorArridge, Simon
dc.contributor.authorde Hoop, Maarten
dc.contributor.authorMaass, Peter
dc.contributor.authorÖktem, Ozan
dc.contributor.authorSchönlieb, Carola
dc.contributor.authorUnser, Michael
dc.date.accessioned2022-08-05T08:00:55Z
dc.date.available2022-08-05T08:00:55Z
dc.date.issued2019
dc.description.abstractBig data and deep learning are modern buzz words which presently infiltrate all fields of science and technology. These new concepts are impressive in terms of the stunning results they achieve for a large variety of applications. However, the theoretical justification for their success is still very limited. In this snapshot, we highlight some of the very recent mathematical results that are the beginnings of a solid theoretical foundation for the subject.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/9918
dc.identifier.urihttp://dx.doi.org/10.34657/8956
dc.language.isoeng
dc.publisherOberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH
dc.relation.doihttps://doi.org/10.14760/SNAP-2019-015-EN
dc.relation.essn2626-1995
dc.rights.licenseCC BY-NC-SA 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/eng
dc.subject.ddc510
dc.subject.otherAnalysiseng
dc.subject.otherNumerics and Scientific Computingeng
dc.titleDeep Learning and Inverse Problemseng
dc.typeReporteng
dc.typeTexteng
dcterms.extent12 S.
tib.accessRightsopenAccess
wgl.contributorMFO
wgl.subjectMathematik
wgl.typeReport / Forschungsbericht / Arbeitspapier
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