BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Functional Priors for Bayesian Deep Learning X-WR-TIMEZONE:America/Los_Angeles REFRESH-INTERVAL;VALUE=DURATION:PT1H X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2022-05-02-maurizio-filippone-and-ba-hien-tran@cml.ics.uci.edu DTSTAMP:20261023T065725Z LAST-MODIFIED:20261023T065725Z SEQUENCE:59694 DTSTART;TZID=America/Los_Angeles:20220502T130000 DTEND;TZID=America/Los_Angeles:20220502T140000 SUMMARY:[CML Seminar] Maurizio Filippone and Ba-Hien Tran: Functional Prior s for Bayesian Deep Learning LOCATION:Donald Bren Hall 4011 DESCRIPTION:Maurizio Filippone and Ba-Hien Tran\, Associate Professor and P hD Student\, EURECOM\n\nTitle: Functional Priors for Bayesian Deep Learnin g\n\nAbstract: The Bayesian treatment of neural networks dictates that a p rior distribution is specified over their weight and bias parameters\, whi ch is challenging because modern networks have huge numbers of parameters and non-linearities. Gaussian processes instead offer a rigorous non-param etric framework to define priors over the space of functions. In this talk we introduce a robust framework to impose functional priors on modern neu ral networks by minimizing the Wasserstein distance between samples of sto chastic processes\, and extend it to model selection for Bayesian autoenco ders. Coupling these priors with scalable MCMC offers systematically large performance improvements over alternative choices of priors.\n\nhttps://c ml.ics.uci.edu/seminars/2022-05-02-maurizio-filippone-and-ba-hien-tran X-ALT-DESC;FMTTYPE=text/html:Maurizio Filippone and Ba-Hien Tran\, Associate Professor and PhD Student\, EURECOM

Title:< /b> Functional Priors for Bayesian Deep Learning

Abstract: T he Bayesian treatment of neural networks dictates that a prior distributio n is specified over their weight and bias parameters\, which is challengin g because modern networks have huge numbers of parameters and non-linearit ies. Gaussian processes instead offer a rigorous non-parametric framework to define priors over the space of functions. In this talk we introduce a robust framework to impose functional priors on modern neural networks by minimizing the Wasserstein distance between samples of stochastic processe s\, and extend it to model selection for Bayesian autoencoders. Coupling t hese priors with scalable MCMC offers systematically large performance imp rovements over alternative choices of priors.

https ://cml.ics.uci.edu/seminars/2022-05-02-maurizio-filippone-and-ba-hien-tran URL:https://cml.ics.uci.edu/seminars/2022-05-02-maurizio-filippone-and-ba-h ien-tran END:VEVENT END:VCALENDAR