BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251120T120000
DTEND;TZID=America/New_York:20251120T130000
DTSTAMP:20260601T184935
CREATED:20250828T211507Z
LAST-MODIFIED:20250828T211507Z
UID:20946-1763640000-1763643600@seasevents.nmsdev7.com
SUMMARY:FOLDS seminar: Function Space Perspectives on Neural Networks
DESCRIPTION:Zoom link: https://upenn.zoom.us/j/98220304722 \n  \nThis talk reviews a theory of the functions learned by neural networks with Rectified Linear Unit (ReLU) activations. At its core is the observation that deep ReLU networks can be characterized as solutions to data-fitting problems in certain infinite dimensional function spaces. The solutions are compositions of functions from Banach spaces of second-order bounded variation\, defined in the Radon transform domain. Functions in these spaces exhibit strong smoothness in most directions\, making it a natural setting for adapting to intrinsic low-dimensional structure in data. Moreover\, the norms in these spaces are closely tied to the size of neural network weights\, providing a direct connection between function complexity and network parametrization. In particular\, the total variation norm provides an analytic tool for identifying functions that cannot be realized by shallow networks\, thereby yielding a precise characterization of depth separation. Representer theorems reveal the solutions are sparse in the number of active neurons per layer. Sparsity provides a principled path to network compression\, yet some sparse solutions can suffer from poor generalization. The theory suggests new training strategies to promote solutions that generalize more robustly.
URL:https://seasevents.nmsdev7.com/event/folds-seminar-tba-9/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Colloquium
END:VEVENT
END:VCALENDAR