Deep Learning and Inverse Problems
S. Arridge, M. de Hoop, P. Maass, O. Öktem, C. Schönlieb, M. Unser
Snapshots of Modern Mathematics from Oberwolfach, vol. 15, pp. 1–13, 2019.
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.
@ARTICLE(http://bigwww.epfl.ch/publications/arridge1901.html,
AUTHOR="Arridge, S. and de Hoop, M. and Maass, P. and {\"{O}}ktem, O.
and Sch{\"{o}}nlieb, C. and Unser, M.",
TITLE="Deep Learning and Inverse Problems",
JOURNAL="Snapshots of Modern Mathematics from Oberwolfach",
YEAR="2019",
volume="15",
number="",
pages="1--13",
month="",
note="")
© 2019 The Authors. Personal use of this material is permitted. However, permission to
reprint/republish this material for advertising or promotional purposes or for creating
new collective works for resale or redistribution to servers or lists, or to reuse any
copyrighted component of this work in other works must be obtained from The Authors.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints
invoked by each author's copyright. In most cases, these works may not be reposted without
the explicit permission of the copyright holder.