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-655473be4c6fe770aa0fdae2241feba3@cmc.deusto.eus
DTSTART:20211109T091000Z
DTEND:20211109T095000Z
DTSTAMP:20251031T224100Z
CREATED:20251031
LAST-MODIFIED:20251031
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:MLIS 2021: Neural Differential Equations, Control and Machine Learning by E. Zuazua
DESCRIPTION:On November 9th, our Head Enrique Zuazua will be talking about “Neural Differential Equations, Control and Machine Learning” on the 3rd International Conference on Machine Learning and Intelligent Systems will be held online on November 8th. – 11th, 2021 at Xiamen, China, supported by Huaqiao University.\nAsbtract. We discuss Neural Ordinary Differential Equations (NODEs) from a control theoretical perspective to address some of the main challenges in Machine Learning and, in particular, data classification and Universal Approximation. More precisely, we adopt the perspective of the simultaneous control of systems of NODEs. For instance, in the context of classification, each item to be classified corresponds to a different initial datum for the Cauchy problem of the NODE. And all the solutions corresponding the data under consideration need to be driven to the corresponding target by means of the same control. We present a genuinely nonlinear and constructive method, allowing to estimate the complexity of the control strategies we develop. The very nonlinear nature of the activation functions governing the nonlinear dynamics of NODEs under consideration plays a key role. It allows deforming half of the phase space while the other half remains invariant, a property that classical models in mechanics do not fulfill. This very property allows to build elementary controls inducing specific dynamics and transformations whose concatenation, along with properly chosen hyperplanes, allows achieving our goals in finitely many steps. We also present the counterparts in the context of the control of neural transport equations, establishing a link between optimal transport and deep neural networks.\nThis is a joint work with Domènec Ruiz-Balet.\nWHEN?\nNovember 9th., 2021 at 10:10\nWHERE?\nBeijing/Online\nRegistration\nTo attend this event a registration fee is required. See more about registration\n|| See more about this talk at the official page of the event\nCheck the keynote speakers of the MLIS2021\n
URL:https://cmc.deusto.eus/events-calendar/mlis-2021-neural-differential-equations-control-and-machine-learning-by-e-zuazua/
CATEGORIES:EZuazua,Seminar/Talk,Workshop
LOCATION:Worldwide
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/FAU-activities-MLIS-EZuazua-10nov2021.png
END:VEVENT
END:VCALENDAR