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X-WR-CALNAME:Use Cases for Bayesian Deep Learning in the Age of ChatGPT
X-WR-TIMEZONE:America/Los_Angeles
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UID:2023-07-20-vincent-fortuin@cml.ics.uci.edu
DTSTAMP:20230720T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20230720T110000
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SUMMARY:[CML Seminar] Vincent Fortuin: Use Cases for Bayesian Deep Learning
in the Age of ChatGPT
LOCATION:Donald Bren Hall 3011
DESCRIPTION:Vincent Fortuin\, Research group leader in Machine Learning\, H
elmholtz AI\n\nTitle: Use Cases for Bayesian Deep Learning in the Age of C
hatGPT\n\nAbstract: Many researchers have pondered the same existential qu
estions since the release of ChatGPT: Is scale really all you need? Will t
he future of machine learning rely exclusively on foundation models? In th
is talk\, I will try to make the case that the answer should be a convince
d no and that now\, maybe more than ever\, should be the time to focus on
fundamental questions in machine learning again. I will provide evidence b
y presenting three modern use cases of Bayesian deep learning in the areas
of self-supervised learning\, interpretable additive modeling\, and seque
ntial decision making. Together\, these will show that the research field
of Bayesian deep learning is very much alive and thriving.\n\nhttps://cml.
ics.uci.edu/seminars/2023-07-20-vincent-fortuin
X-ALT-DESC;FMTTYPE=text/html:Vincent Fortuin\, Research
group leader in Machine Learning\, Helmholtz AI
Title: Use C
ases for Bayesian Deep Learning in the Age of ChatGPT
Abstract:<
/b> Many researchers have pondered the same existential questions since th
e release of ChatGPT: Is scale really all you need? Will the future of mac
hine learning rely exclusively on foundation models? In this talk\, I will
try to make the case that the answer should be a convinced no and that no
w\, maybe more than ever\, should be the time to focus on fundamental ques
tions in machine learning again. I will provide evidence by presenting thr
ee modern use cases of Bayesian deep learning in the areas of self-supervi
sed learning\, interpretable additive modeling\, and sequential decision m
aking. Together\, these will show that the research field of Bayesian deep
learning is very much alive and thriving.
https://cml.ics.uci.edu/semi
nars/2023-07-20-vincent-fortuin
URL:https://cml.ics.uci.edu/seminars/2023-07-20-vincent-fortuin
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