| dc.date.accessioned | 2022-01-14T10:49:26Z | |
| dc.date.available | 2022-01-14T10:49:26Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | http://publications.mfo.de/handle/mfo/3909 | |
| dc.description.abstract | Data science has become a field of major importance for science and technology
nowadays and poses a large variety of
challenging mathematical questions.
The area
of applied harmonic analysis has a significant impact on such problems by providing methodologies
both for theoretical questions and for a wide range of applications
in signal and image processing and machine learning.
Building on the success of three previous workshops on applied harmonic analysis in 2012, 2015 and 2018,
this workshop focused
on several exciting novel directions such as mathematical theory of
deep learning, but also reported progress on long-standing open problems in the field. | |
| dc.title | Applied Harmonic Analysis and Data Science (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-2021-55 | |
| local.series.id | OWR-2021-55 | |
| local.subject.msc | 65 | |
| local.subject.msc | 94 | |
| local.subject.msc | 15 | |
| local.date-range | 28 Nov - 04 Dec 2021 | |
| local.workshopcode | 2148 | |
| local.workshoptitle | Applied Harmonic Analysis and Data Science (hybrid meeting) | |
| local.organizers | Ingrid Daubechies, Durham; Gitta Kutyniok, München; Holger Rauhut, Aachen; Thomas Strohmer, Davis | |
| local.report-name | Workshop Report 2021,55 | |
| local.opc-photo-id | 2148 | |
| local.publishers-doi | 10.4171/OWR/2021/55 | |
