BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Refresh Web Development//Helios Calendar//EN
X-FROM-URL:https://www.bayareascience.org/calendar/index.php?com=details&eID=37907
X-WR-RELCALID:BayAreaScience.org Theoretical perspectives on modern machine learning paradigms: generative, scientific and out-of-distribution 20250313T160000
X-WR-TIMEZONE:America/New_York
X-WR-CALNAME:BayAreaScience.org
BEGIN:VEVENT
URL;VALUE=URI:https://www.bayareascience.org/calendar/index.php?com=detail&eID=37907
DTSTART:20250313T160000
DTEND:20250313T160000
SUMMARY:Theoretical perspectives on modern machine learning paradigms: generative\, scientific and out-of-distribution
DESCRIPTION:Over the past decade\, machine learning models have grown in scale and complexity. Generative models\, for instance\, have gone through many iterations of model classes (e.g.\, GANs\, diffusion models\,...\n______________________________\nThis Event Downloaded From a Helios Calendar Powered Site
LOCATION:Sutardja Dai Hall - UC Berkeley Room 250\, Berkeley\, CA  94720
CATEGORIES:BayAreaScience.org Events
PRIORITY:0
TRANSP:TRANSPARENT
END:VEVENT
END:VCALENDAR