BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Mathematical Sciences - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Mathematical Sciences
X-ORIGINAL-URL:/math
X-WR-CALDESC:Events for Mathematical Sciences
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251024T140000
DTEND;TZID=America/Chicago:20251024T150000
DTSTAMP:20260417T122552
CREATED:20251013T164539Z
LAST-MODIFIED:20251013T164539Z
UID:10016251-1761314400-1761318000@uwm.edu
SUMMARY:Colloquium: Prof. Greg Ongie
DESCRIPTION:A Function Space View of Neural Networks\nProf. Greg Ongie\nAssistant Professor\nMarquette University \nMany mathematical analyses of deep learning focus on how neural network (NN) parameters evolve during training. A complementary perspective is to view NN training as fitting a function belonging to a function space implicitly defined by the architecture and training procedure. In particular\, when parameter norms are explicitly or implicitly constrained\, NNs exhibit a bias toward functions with low “representation cost\,” defined as the minimal parameter norm required to realize the function with a given NN architecture. This talk surveys recent results that characterize representation cost of shallow NN architectures in terms of Banach space norms\, and through non-linear notions of function rank for deeper NN architectures. Finally\, we discuss how bias towards low representation cost functions helps to explain generalization in various applications.
URL:/math/event/colloquium-prof-greg-ongie/
LOCATION:EMS Building\, E495\, 3200 N Cramer St\, Milwaukee\, WI\, United States
CATEGORIES:Colloquia
X-TRIBE-STATUS:
END:VEVENT
END:VCALENDAR