The number of successful applications of deep learning in the context of inverse problems rapidly grows. In this autumn school we lay the foundations of both machine learning and inverse problems, as well as cover current research topics in their intersection. The autumn school targets advanced Master students, PhD students and PostDocs working on inverse problems, who are interested in the intersection of deep learning and inverse problems.
LecturersAsja Fischer (Ruhr University Bochum)
Carola-Bibiane Schönlieb (University of Cambridge)
Markus Haltmeier (University of Innsbruck)
Martin Benning (Queen Mary University of London)
Matthias Bethge (Max Planck Institute, Tübingen)
Nihat Ay (Max Planck Institute, Leipzig)
Ozan Öktem (KTH Stockholm)
Simon Arridge (University College London)
Organization committeeChristian Etmann (University of Bremen, Germany)
Daniel Otero Baguer (University of Bremen, Germany)
Jens Behrmann (University of Bremen, Germany)
Sören Dittmer (University of Bremen, Germany)
Dr. Tobias Kluth (University of Bremen, Germany)
Prof. Dr. Dr. h.c. Peter Maass (University of Bremen, Germany)
AcknowledgmentsWe gratefully acknowledge the support of this summer school by the DFG Research Training Group 2224 on Parameter Identification at the Center for Industrial Mathematics, University of Bremen.
Thanks to Matthias Knauer for his help in setting up this website.