BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:More from Less: Learning with Limited Annotated Data in Vision and Language X-WR-TIMEZONE:America/Los_Angeles 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:2024-03-11-paola-cascante-bonilla@cml.ics.uci.edu DTSTAMP:20240311T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20240311T110000 DTEND;TZID=America/Los_Angeles:20240311T120000 SUMMARY:[CML Seminar] Paola Cascante-Bonilla: More from Less: Learning with Limited Annotated Data in Vision and Language LOCATION:Donald Bren Hall 4011 DESCRIPTION:Paola Cascante-Bonilla\, Postdoctoral Associate\, University of Maryland Institute for Advanced Computer Studies (UMIACS)\n\nTitle: More from Less: Learning with Limited Annotated Data in Vision and Language\n\n Abstract: Despite the impressive results of deep learning models\, modern large-scale systems are required to be trained using massive amounts of ma nually annotated or freely available data on the Internet. But this data i n the wild is insufficient to learn specific structural patterns of the wo rld\, and existing large-scale models still fail on common sense tasks req uiring compositional inference. This talk will focus on answering three fu ndamental questions: (a) How can we create systems that can learn with lim ited annotated data and adapt to new tasks and novel criteria? (b) How can we create systems able to encode real-world concepts with granularity in a robust manner? (c) Is it possible to create such a system with alternati ve data\, complying with privacy protection principles and avoiding cultur al bias? I will conclude with my future plans to continue exploring hyper- realistic synthetic data generation techniques and the expressiveness of g enerative models to train multimodal systems able to perform well in real- world scenarios.\n\nhttps://cml.ics.uci.edu/seminars/2024-03-11-paola-casc ante-bonilla X-ALT-DESC;FMTTYPE=text/html:Paola Cascante-Bonilla\, Po stdoctoral Associate\, University of Maryland Institute for Advanced Compu ter Studies (UMIACS)

Title: More from Less: Learning with Li mited Annotated Data in Vision and Language

Abstract: Despit e the impressive results of deep learning models\, modern large-scale syst ems are required to be trained using massive amounts of manually annotated or freely available data on the Internet. But this data in the wild is in sufficient to learn specific structural patterns of the world\, and existi ng large-scale models still fail on common sense tasks requiring compositi onal inference. This talk will focus on answering three fundamental questi ons: (a) How can we create systems that can learn with limited annotated d ata and adapt to new tasks and novel criteria? (b) How can we create syste ms able to encode real-world concepts with granularity in a robust manner? (c) Is it possible to create such a system with alternative data\, comply ing with privacy protection principles and avoiding cultural bias? I will conclude with my future plans to continue exploring hyper-realistic synthe tic data generation techniques and the expressiveness of generative models to train multimodal systems able to perform well in real-world scenarios.

https://cml.ics.uci.edu/seminars/2024-03-11-paola-cascante-boni lla URL:https://cml.ics.uci.edu/seminars/2024-03-11-paola-cascante-bonilla END:VEVENT END:VCALENDAR