Deep Learning and Inverse Problems
| dc.contributor.author | Arridge, Simon | |
| dc.contributor.author | de Hoop, Maarten | |
| dc.contributor.author | Maass, Peter | |
| dc.contributor.author | Öktem, Ozan | |
| dc.contributor.author | Schönlieb, Carola | |
| dc.contributor.author | Unser, Michael | |
| dc.contributor.editor | Munday, Sara | |
| dc.contributor.editor | Jahns, Sophia | |
| dc.date.accessioned | 2019-11-21T15:25:41Z | |
| dc.date.available | 2019-11-21T15:25:41Z | |
| dc.date.issued | 2019-11-21 | |
| dc.identifier.uri | http://publications.mfo.de/handle/mfo/3686 | |
| dc.description.abstract | Big 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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Mathematisches Forschungsinstitut Oberwolfach | en_US |
| dc.relation.ispartofseries | Snapshots of modern mathematics from Oberwolfach;2019,15 | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.title | Deep Learning and Inverse Problems | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.14760/SNAP-2019-015-EN | |
| local.series.id | SNAP-2019-015-EN | en_US |
| local.subject.snapshot | Analysis | en_US |
| local.subject.snapshot | Numerics and Scientific Computing | en_US |
| dc.identifier.urn | urn:nbn:de:101:1-2019112812170558110274 | |
| dc.identifier.ppn | 1685383483 |
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