MDLW02
27 September 2021 to 1 October 2021
Classical research on inverse problems has focused on establishing conditions which guarantee that solutions to ill-posed problems exist and on methods for approximating solutions in a stable way in the presence of noise. Despite being very successful, such a knowledge-driven approach is also associated with some shortcomings. First of all, a mathematical model is never complete. Secondly, most applications will have inputs which do not cover the full space but stem from an unknown subset or obey an unknown stochastic distribution.
For these reasons, recent research in inverse problems seeks to develop a mathematically coherent foundation for combining classical model-driven approaches, with data-driven models, and in particular those based on deep learning. As an application area of deep learning, inverse problems, however, occupy a special role as the inherent and unavoidable instability of inverse problems - which cannot be remedied by preconditioning or any other type of data preprocessing - is reflected by the failure of naively transferring deep learning concepts from image processing directly to inverse problems in tomography, non-destructive testing or monitoring physical-technical processes in general.
So far several concepts with stunning experimental results have been published for combining model-based approaches for inverse problems with DNNs, however, no consistent theory exists. We expect that discussions at and collaborations stemming from this workshop will contribute towards the creation of a mathematically sound theory and they have the potential to shape future mathematical research in this field.
Due to the ongoing COVID-19 pandemic, all non-UK based applicants should attend this event virtually.
Registration Only
The Registration Package includes admission to all seminars, lunches and refreshments on the days that lectures take place (Monday - Friday), wine reception and formal dinner, but does not include other meals or accommodation.
Formal Dinner Only
Participants on the Accommodation Package or Registration Package, including organisers and speakers, are automatically included in this event. For all remaining participants who would like to attend, such as programme participants, the above charge will apply.
Unfortunately we do not have any accommodation to offer so all successful applicants will need to source their own accommodation.
Please see the Hotels Combined website for a list of local hotels and guesthouses.
Lunch Lunch timings and location will be confirmed with timetable.
Evening Meal Participants are free to make their own arrangements for dinner.
Formal Dinner The Formal Dinner location and date is to be confirmed.
Participants on the Accommodation Package or Registration Package, including organisers and speakers, are automatically included in this event.
The Institute kindly requests that any papers published as a result of this programme’s activities are credited as such. Please acknowledge the support of the Institute in your paper using the following text:
The author(s) would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Deep learning and inverse problems, where work on this paper was undertaken. This work was supported by EPSRC grant EP/R014604/1.
Monday 27th September 2021 | |||
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13:00 to 13:50 | Room 1 | ||
13:50 to 14:00 |
Christie Marr Isaac Newton Institute |
Room 1 | |
14:00 to 14:20 |
Simon Arridge University College London |
Room 1 | |
14:20 to 15:10 | Room 1 | ||
15:10 to 16:00 |
Rebecca Willett University of Chicago |
Room 1 | |
16:00 to 16:40 |
Tom Goldstein University of Maryland, College Park |
Room 1 | |
16:40 to 17:20 |
Lorenzo Rosasco University of Genova; Massachusetts Institute of Technology |
Room 1 | |
17:20 to 18:00 | No Room Required |
Tuesday 28th September 2021 | |||
---|---|---|---|
09:50 to 10:30 |
Jin Keun Seo Yonsei University |
Room 1 | |
10:30 to 11:00 | No Room Required | ||
11:00 to 12:30 |
Chair: Peter Maaß |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
13:30 to 14:10 |
Samuli Siltanen University of Helsinki |
Room 1 | |
14:10 to 14:20 | No Room Required | ||
14:20 to 15:00 |
Andreas Hauptmann University of Oulu |
Room 1 | |
15:00 to 15:30 | No Room Required | ||
15:30 to 16:10 |
Juan Carlos De Los Reyes Escuela Politécnica Nacional de Quito |
Room 1 |
Wednesday 29th September 2021 | |||
---|---|---|---|
09:50 to 10:30 |
Yonina Eldar Weizmann Institute of Science |
Room 1 | |
10:30 to 11:00 | No Room Required | ||
11:00 to 11:40 |
Matthias Ehrhardt University of Bath |
Room 1 | |
11:40 to 11:50 | Room 1 | ||
11:50 to 12:30 |
Thomas Pock Graz University of Technology |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
13:30 to 15:00 | Room 1 | ||
15:00 to 15:30 | No Room Required | ||
15:30 to 17:00 |
Chair: Carola-Bibiane Schönlieb, Simon Arridge |
Room 1 | |
19:30 to 22:00 | No Room Required |
Thursday 30th September 2021 | |||
---|---|---|---|
09:50 to 10:30 |
Silvia Villa Università degli Studi di Genova |
Room 1 | |
10:30 to 11:00 | No Room Required | ||
11:00 to 11:40 |
Cecile Della Valle Université Paris Saclay |
Room 1 | |
11:40 to 11:50 | Room 1 | ||
11:50 to 12:30 |
Martin Benning Queen Mary University of London |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
13:30 to 14:10 |
Marta Betcke University College London |
Room 1 | |
14:10 to 14:20 | Room 1 | ||
14:20 to 15:00 |
Nikola Kovachki CALTECH (California Institute of Technology) |
Room 1 | |
15:00 to 15:30 | No Room Required |
Friday 1st October 2021 | |||
---|---|---|---|
11:00 to 11:40 |
Jonas Adler Google DeepMind Technologies Limited |
Room 1 | |
11:40 to 11:50 | Room 1 | ||
11:50 to 12:30 |
Michael Hintermüller Weierstraß-Institut für Angewandte Analysis und Stochastik; Humboldt-Universität zu Berlin |
Room 1 | |
12:30 to 13:30 | No Room Required |
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