BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Learning Generalized Knowledge for AI with Limited Supervision 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:2024-04-02-chen-wei@cml.ics.uci.edu DTSTAMP:20240402T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20240402T110000 DTEND;TZID=America/Los_Angeles:20240402T120000 SUMMARY:[CML Seminar] Chen Wei: Learning Generalized Knowledge for AI with Limited Supervision LOCATION:Donald Bren Hall 4011 DESCRIPTION:Chen Wei\, PhD Student\, Department of Computer Science\, Johns Hopkins University\n\nTitle: Learning Generalized Knowledge for AI with L imited Supervision\n\nAbstract: As babies\, we begin to grasp the world th rough spontaneous observations\, gradually developing generalized knowledg e about the world. This foundational knowledge enables humans to effortles sly learn new skills without extensive teaching for each task. Can we deve lop a similar paradigm for AI? This talk describes how learning from limit ed supervision can address fundamental challenges in AI such as scalabilit y and generalization while embedding generalized knowledge. I will first t alk about our research in self-supervised learning\, utilizing natural ima ges and videos without human-annotated labels. The second part will descri be how to leverage non-curated image-text pairs\, through which we obtain textual representation of images. This representation comprehensively desc ribes semantic elements in an image and bridges various AI tools such as l arge language models (LLMs)\, enabling diverse vision-language application s.\n\nhttps://cml.ics.uci.edu/seminars/2024-04-02-chen-wei X-ALT-DESC;FMTTYPE=text/html:Chen Wei\, PhD Student\, De partment of Computer Science\, Johns Hopkins University

Title: Learning Generalized Knowledge for AI with Limited Supervision

< b>Abstract: As babies\, we begin to grasp the world through spontaneou s observations\, gradually developing generalized knowledge about the worl d. This foundational knowledge enables humans to effortlessly learn new sk ills without extensive teaching for each task. Can we develop a similar pa radigm for AI? This talk describes how learning from limited supervision c an address fundamental challenges in AI such as scalability and generaliza tion while embedding generalized knowledge. I will first talk about our re search in self-supervised learning\, utilizing natural images and videos w ithout human-annotated labels. The second part will describe how to levera ge non-curated image-text pairs\, through which we obtain textual represen tation of images. This representation comprehensively describes semantic e lements in an image and bridges various AI tools such as large language mo dels (LLMs)\, enabling diverse vision-language applications.

https://cml.ics.u ci.edu/seminars/2024-04-02-chen-wei URL:https://cml.ics.uci.edu/seminars/2024-04-02-chen-wei END:VEVENT END:VCALENDAR