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[N] ‘Deep Learning and Inverse Problems’ Autumn School in Bremen (Germany)


Recent advances in deep learning have had an increasing impact on the field of inverse problems. In order to pass this knowledge on, the Center for Industrial Mathematics at the University of Bremen hosts an ‘autumn school’ between the 4th and 8th of November 2019. In this autumn school, world-renowned experts of the field will be teaching the mathematical foundations of machine learning and deep learning and how these can be applied to a wide range of inverse problems. The autumn school targets young researchers and advanced students in the field of inverse problems.

The registration is now open at until August 15th, 2019.


Asja 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)

A tentative list of topics includes:

Mathematical Foundations of Machine Learning / Deep Learning
Current Computational and Theoretical Questions in Deep Learning
Overview of Inverse Problems
Solving Inverse Problems via Learned Iteration Schemes
Learning Regularizers via Deep Networks
Deep Learning for Inverse Problems arising in Medical Imaging

submitted by /u/cetmann
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.