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X-WR-CALNAME:Out-of-Distribution Evaluation: The How\, the Which\, and the
What?!
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UID:2022-05-16-robin-jia@cml.ics.uci.edu
DTSTAMP:20220516T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20220516T130000
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SUMMARY:[CML Seminar] Robin Jia: Out-of-Distribution Evaluation: The How\,
the Which\, and the What?!
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Robin Jia\, Assistant Professor of Computer Science\, Universit
y of Southern California\n\nTitle: Out-of-Distribution Evaluation: The How
\, the Which\, and the What?!\n\nAbstract: NLP models have achieved impres
sive accuracies on in-distribution benchmarks but are unreliable out-of-di
stribution (OOD). In this talk\, I preview my group's ongoing work on eval
uating and improving model performance in OOD settings. First\, I propose
likelihood splits\, a general-purpose way to create challenging non-i.i.d.
benchmarks by measuring generalization to the tail of the data distributi
on. Second\, I describe the advantages of neurosymbolic approaches over en
d-to-end pretrained models for OOD generalization in visual question answe
ring. Finally\, I show how synthesized examples can improve open-set recog
nition.\n\nhttps://cml.ics.uci.edu/seminars/2022-05-16-robin-jia
X-ALT-DESC;FMTTYPE=text/html:Robin Jia\, Assistant Profe
ssor of Computer Science\, University of Southern California
Tit
le: Out-of-Distribution Evaluation: The How\, the Which\, and the What
?!
Abstract: NLP models have achieved impressive accuracies
on in-distribution benchmarks but are unreliable out-of-distribution (OOD)
. In this talk\, I preview my group's ongoing work on evaluating and impro
ving model performance in OOD settings. First\, I propose likelihood split
s\, a general-purpose way to create challenging non-i.i.d. benchmarks by m
easuring generalization to the tail of the data distribution. Second\, I d
escribe the advantages of neurosymbolic approaches over end-to-end pretrai
ned models for OOD generalization in visual question answering. Finally\,
I show how synthesized examples can improve open-set recognition.
<
a href="https://cml.ics.uci.edu/seminars/2022-05-16-robin-jia">https://cml
.ics.uci.edu/seminars/2022-05-16-robin-jia
URL:https://cml.ics.uci.edu/seminars/2022-05-16-robin-jia
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