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DTSTART:20190331T030000
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DTSTART:20191027T020000
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BEGIN:VEVENT
DTSTAMP:20260405T161900Z
UID:5b4d8fb087028858162168@ist.ac.at
DTSTART:20190613T160000
DTEND:20190613T180000
DESCRIPTION:Speaker: Gitta Kutyniok\nhosted by Laszlo Erdös\nAbstract: Des
 pite the outstanding success of deep neural networks in real-world applica
 tions\, most of the related research is empirically driven and a mathemati
 cal foundation is almost completely missing. The main goal of a neural net
 work is to approximate a function\, which for instance encodes a classific
 ation task. Thus\, one theoretical approach to derive a fundamental unders
 tanding of deep neural networks focusses on their approximation abilities.
 In this talk we will provide an introduction into this research area. Afte
 r a general overview of mathematics of deep neural networks\, we will disc
 uss theoretical results which prove that not only do (memory-optimal) neur
 al networks have as much approximation power as classical systems such as 
 wavelets or shearlets\, but they are also able to beat the curse of dimens
 ionality. On the numerical side\, we will then show that superior performa
 nce can typically be achieved by combining deep neural networks with class
 ical approaches from approximation theory.
LOCATION:Big Seminar room Ground floor / Office Bldg West (I21.EG.101)\, IS
 TA
ORGANIZER:boosthui@ist.ac.at
SUMMARY:Gitta Kutyniok: The Approximation Power of Deep Neural Networks: Th
 eory and Applications
URL:https://talks-calendar.ista.ac.at/events/1806
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