BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.32.0//EN
X-ORIGINAL-URL:https://dcn.nat.fau.eu/
X-WR-CALNAME:
X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=4SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-9cae15bbdd73dc7a84ee786318022fc8@dcn.nat.fau.eu
DTSTART;TZID=Europe/Berlin:20230605T110000
DTEND;TZID=Europe/Berlin:20230605T120000
DTSTAMP:20230506T091416Z
CREATED:20230506
LAST-MODIFIED:20230604
PRIORITY:5
SEQUENCE:0
TRANSP:OPAQUE
SUMMARY:Learning of Dynamic Processes
DESCRIPTION:Next Monday June 5, 2023:\nOrganized by: FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nTitle: Learning of Dynamic Processes\nSpeaker: Prof. Dr. Juan-Pablo Ortega\nAffiliation: Nanyang Technological University (Singapore)\nAbstract. The last decade has seen the emergence of learning techniques that use the computational power of dynamical systems for information processing. Some of those paradigms are based on architectures that are partially randomly generated and require a relatively cheap training effort, which makes them ideal in many applications. The need for a mathematical understanding of the working principles underlying this approach, collectively known as Reservoir Computing, has led to the construction of new techniques that put together well-known results in systems theory and dynamics with others coming from approximation and statistical learning theory. This combination has allowed in recent times to elevate Reservoir Computing to the realm of provable machine learning paradigms. As we will see in this talk, it also hints at various connections with kernel maps, structure-preserving algorithms, and physics-inspired learning.\nWHERE?\nOn-site / Online\n[On-site]\nRoom Übung 4. Department Mathematik.\nFAU, Friedrich-Alexander-Universität Erlangen-Nürnberg.\nCauerstraße 11, 91058 Erlangen (Germany)\n[Online]\nZoom meeting link\nMeeting ID: 614 4658 1599 | PIN code: 914397\n
URL:https://dcn.nat.fau.eu/events/learning-of-dynamic-processes/
ORGANIZER;CN=FAU DCN-AvH:MAILTO:
CATEGORIES:FAU DCN-AvH Seminar,Seminar/Talk
LOCATION:FAU - Faculty of Sciences
ATTACH;FMTTYPE=image/png:https://dcn.nat.fau.eu/wp-content/uploads/FAUDCNAvHseminar_jpOrtega_05jun2023.png
END:VEVENT
END:VCALENDAR