BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//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:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T120000
DTEND;TZID=America/New_York:20231129T131500
DTSTAMP:20260403T223005
CREATED:20230928T141936Z
LAST-MODIFIED:20230928T141936Z
UID:9840-1701259200-1701263700@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Inherent Interpretability via Language Model Guided Bottleneck Design" (Mark Yatskar\, Penn)
DESCRIPTION:ABSTRACT: \nAs deep learning systems improve\, their applicability to critical domains is hampered because of a lack of transparency. Post-hoc explanations attempt to address this concern but they provide no guarantee of faithfulness to the model’s computations. Inherently interpretable models are an alternative but such models are often considered to be too simple to perform well. In this talk we challenge this assumption by demonstrating how to create high performance inherently interpretable models. Our methods extend concept bottlenecks\, a class of inherently interpretable models\, by casting their creation as a generation problem for large language models. This allows us to develop search routines for finding high performing bottlenecks. We specialize this general approach to image classification\, text classification\, and visual question answering. In these domains\, language model guided bottleneck models perform competitively to their uninterpretable counterparts and in low-data settings even sometimes outperform them.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-mark-yatskar/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
END:VCALENDAR