BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv6.5.6//EN
X-ORIGINAL-URL:https://cmc.deusto.eus/
X-WR-CALNAME:cmc.deusto.eus
X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-eba7fcab4a6903ad17ca2e5aa70d6f6c@cmc.deusto.eus
DTSTART:20201209T131500Z
DTEND:20201209T150000Z
DTSTAMP:20201208T165700Z
CREATED:20201208
LAST-MODIFIED:20210527
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:The interplay of Deep Learning and Control Theory
DESCRIPTION:On December 09th our team member Borjan Geshkovski PhD student at DyCon ERC Project ( https://cmc.deusto.eus/erc-dycon/ ) from UAM, will give a talk at “AG Mathematics of Deep Learning” by Friedrich-Alexander Universität Erlangen-Nürnberg (Germany) about The interplay of deep learning and control theory.\nAbstract.It is by now well-known that practical deep supervised learning may roughly be cast as an optimal control problem for a specific discrete-time, nonlinear dynamical system called an artificial neural network. In this talk, we consider the continuous-time formulation of the deep supervised learning problem. We will present, using an analytical approach, this problem’s behavior when the final time horizon increases, a fact that can be interpreted as increasing the number of layers in the neural network setting. We show qualitative and quantitative estimates of the convergence to the zero training error regime depending on the functional to be minimised.\nJoin this Zoom session\nMeeting ID: 995 4572 0602 | PIN code: 607726 \n
URL:https://cmc.deusto.eus/events-calendar/the-interplay-of-deep-learning-and-control-theory/
ORGANIZER;CN=Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU):MAILTO:
CATEGORIES:Events Calendar,Events Calendar Past
LOCATION:Worldwide
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/2020/12/seminar-BorjanG-09dec2020.png
END:VEVENT
END:VCALENDAR