BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//https://caida.ubc.ca//NONSGML iCalcreator 2.41.92//
CALSCALE:GREGORIAN
METHOD:PUBLISH
UID:38333434-3862-4166-b966-333539303761
X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde
X-WR-TIMEZONE:America/Vancouver
X-WR-CALNAME:Meta-Learning - A Roadmap for Few-Shot Transfer Learning - Hug
 o Larochelle\, Research Scientist\, Google Brain
BEGIN:VTIMEZONE
TZID:America/Vancouver
TZUNTIL:20220313T100000Z
BEGIN:STANDARD
TZNAME:PST
DTSTART:20191103T020000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RDATE:20201101T020000
RDATE:20211107T020000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:PDT
DTSTART:20200308T020000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
RDATE:20210314T020000
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:6d7b4a08-ff49-48bd-ac8a-436bb287e680
DTSTAMP:20260816T131800Z
CLASS:PUBLIC
CREATED:20200806T214932Z
DESCRIPTION:Please register for this event here. Abstract: A lot of the rec
 ent progress on many AI tasks were enabled in part by the availability of 
 large quantities of labeled data for deep learning. Yet\, humans are able 
 to learn new concepts or tasks from as little as a handful of examples. Me
 ta-learning has been a promising framework for addressing the problem of g
 eneralizing from small amounts of data\, known as few-shot learning. In th
 is talk\, I’ll present an overview of the state of this research area. I'l
 l describe Meta-Dataset\, a new benchmark we developed to push further the
  development of few…
DTSTART;TZID=America/Vancouver:20200824T153000
DTEND;TZID=America/Vancouver:20200824T163000
LAST-MODIFIED:20210610T230249Z
LOCATION:Please register to receive the Zoom link
SUMMARY:Meta-Learning - A Roadmap for Few-Shot Transfer Learning - Hugo Lar
 ochelle\, Research Scientist\, Google Brain
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/meta-learning-roadmap-few-shot-transfer-lear
 ning-hugo-larochelle-research-scientist-google
END:VEVENT
END:VCALENDAR