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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20231101T093405Z
DTSTART:20231115T143000Z
DTEND:20231115T160000Z
SUMMARY:SQUIDS Seminar - How mathematics can inspire novel deep learning 
 architectures: two case studies
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}s27r-lofk9e
 1e-qzt3gw
DESCRIPTION:Schedule:\n- 2:30pm: pretalk\n- 3:05pm: research talk 'How ma
 thematics can inspire novel deep learning architectures: two case studie
 s'\n \nAbstract: Deep learning is revolutionising the modern world\, inc
 luding applied mathematics. Yet\, it has infamously been described as "a
 lchemy"\, and one place that alchemy can arise is in the choice of archi
 tecture. Can we leverage mathematical insights to design deep learning a
 rchitectures in a more principled way? \n \nIn this talk\, I will give t
 wo examples of this from my own research. In the first\, I will talk abo
 ut my recent preprint with Carola-Bibiane Schönlieb\, Subhadip Mukherjee
 \, and Zakhar Shumaylov on learned regularisation for inverse problems i
 n imaging\; how we were inspired by (non)convex analysis to design a nov
 el input weakly convex neural network\, and used this to learn an advers
 arial convex-nonconvex regulariser with provable guarantees and which ov
 ercame the numerical issues of previous adversarial regularisers. In the
  second\, I will talk about ongoing research with Martin Benning\, Jonas
  Latz\, Lisa Kreusser\, and James Rowbottom\, on designing a deep learni
 ng architecture for image segmentation based on the graph Merriman-Bence
 -Osher segmentation method. This method is particularly promising becaus
 e the graph this method learns on the inputted image provides some built
 -in explainability of the outputted segmentation.\n
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:G.114\, Alan Turing Building\, Manchester
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