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SUMMARY:Thomas Pock (University of Graz)
DTSTART:20210601T101500Z
DTEND:20210601T114500Z
DTSTAMP:20260423T022603Z
UID:MathDeep/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MathDeep/7/"
 >Learning with energy-based models</a>\nby Thomas Pock (University of Graz
 ) as part of Mathematics of Deep Learning\n\n\nAbstract\nIn this talk\, I 
 will show how to use learning techniques to significantly improve energy-b
 ased models. I will start by showing that even for the simplest models suc
 h as total variation\, one can greatly improve the accuracy of the numeric
 al approximation by learning the "best" discretization within a class of c
 onsistent discretizations. Then I will move forward to more expressive mod
 els and show how they can be learned in order to give state-of-the art per
 formance for image reconstruction problems\, such as denoising\, superreso
 lution\, MRI and CT. Finally\, I will show how energy based models for ima
 ge labeling such as Markov random fields can be used in the framework of d
 eep learning.\n
LOCATION:https://researchseminars.org/talk/MathDeep/7/
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