Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018
type: Item
instance of: scholarly article
title: Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018
zbMATH Open document ID: 1409.00063
DOI: 10.4171/OWR/2018/11
published in: Oberwolfach Reports
publication date: 2019-03-15T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 65J22
Mathematics Subject Classification ID: 65Y20
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68-06
Mathematics Subject Classification ID: 65-06
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62-06
zbMATH DE Number: 7036469
describes a project that uses: Wasserstein GAN
describes a project that uses: TransIT
describes a project that uses: DGM
describes a project that uses: U-Net
describes a project that uses: SphereTransform.py
MaRDI profile type: Publication
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Wikidata QID: Q128649054
description: scientific article; zbMATH DE number 7036469