BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Carola-Bibiane Schönlieb (DAMTP\, University of Cambridge) DTSTART:20201120T150000Z DTEND:20201120T160000Z DTSTAMP:20260423T003243Z UID:MPML/16 DESCRIPTION:Title: Co mbining knowledge and data driven methods for solving inverse imaging prob lems - getting the best from both worlds\nby Carola-Bibiane Schönlieb (DAMTP\, University of Cambridge) as part of Mathematics\, Physics and Ma chine Learning (IST\, Lisbon)\n\n\nAbstract\nInverse problems in imaging r ange from tomographic reconstruction (CT\, MRI\, etc) to image deconvoluti on\, segmentation\, and classification\, just to name a few. In this talk I will discuss\napproaches to inverse imaging problems which have both a m athematical modelling (knowledge driven) and a machine learning (data-driv en) component. Mathematical modelling is crucial in the presence of ill-po sedness\, making use of information about the imaging data\, for narrowing down the search space. Such an approach results in highly generalizable r econstruction and analysis methods which come with desirable solutions gua rantees. Machine learning on the other hand is a powerful tool for customi sing methods to individual data sets. Highly parametrised models such as d eep neural networks in particular\, are powerful tools for accurately mode lling prior information about solutions. The combination of these two para digms\, getting the best from both of these worlds\, is the topic of this talk\, furnished with examples for image classification under minimal supe rvision and for tomographic image reconstruction.\n LOCATION:https://researchseminars.org/talk/MPML/16/ END:VEVENT END:VCALENDAR