| Zeit: | 28. August 2024 |
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Prof. Thomas Schön
Department of Information Technology
Division of Systems and Control
Uppsala University
Uppsala, Sweden
Wednesday 2024-08-28 4 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
Abstract
Sequential Monte Carlo methods (including the particle filters and smoothers) allows us to compute probabilistic representations of the unknown objects in models used to represent for example nonlinear dynamical systems. Physical knowledge of a system can be used in the identification process to improve the predictive performance by restricting the space of possible mappings from the input to the output. Typically, the physical models contain unknown parameters that must be learned from data. Sequential Monte Carlo methods are more general, which opens up for interesting possibilities when it comes to the popular diffusion models which underpins many of the contemporary generative AI algorithms. In this context I will show some work we have done when it comes to improving the diffusion model and how this in turn delivers better image restoration. Here it is interesting to note that the diffusion model is built around a dynamical system, which could open up for interesting connections to systems and control.
Biographical Information
Thomas Schön is the Beijer Professor in Artificial Intelligence in the Department of Information Technology at Uppsala University. He received the PhD degree in Automatic Control in Feb. 2006, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001, the BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile). In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences at Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the Automatica Best Paper Prize in 2014, and in 2013 he received the best PhD thesis award by The European Association for Signal Processing. He received the best teacher award at the Institute of Technology, Linköping University in 2009.