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SUMMARY:Deep learning on graphs and manifolds: going beyond Euclidean data
  - Professor Michael Bronstein (Imperial College / Twitter)
DTSTART:20191106T150500Z
DTEND:20191106T155500Z
UID:TALK133756@talks.cam.ac.uk
CONTACT:jo de bono
DESCRIPTION:In the past decade\, deep learning methods have achieved unpre
 cedented performance on a broad range of problems in various fields from c
 omputer vision to speech recognition. So far research has mainly focused o
 n developing deep learning methods for Euclidean-structured data. However\
 , many important applications have to deal with non-Euclidean structured d
 ata\, such as graphs and manifolds. Such data are becoming increasingly im
 portant in computer graphics and 3D vision\, sensor networks\, drug design
 \, biomedicine\, high energy physics\, recommendation systems\, and social
  media analysis. The adoption of deep learning in these fields has been la
 gging behind until recently\, primarily since the non-Euclidean nature of 
 objects dealt with makes the very definition of basic operations used in d
 eep networks rather elusive. In this talk\, I will introduce the emerging 
 field of geometric deep learning on graphs and manifolds\, overview existi
 ng solutions and outline the key difficulties and future research directio
 ns. As examples of applications\, I will show problems from the domains of
  computer vision\, graphics\, medical imaging\, and protein science.
LOCATION:Lecture Theatre 2\, Computer Laboratory
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