BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Semi-Supervised Learning with Prediction-Constrained Variation al Autoencoders 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:2023-05-22-gabe-hope@cml.ics.uci.edu DTSTAMP:20230522T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20230522T130000 DTEND;TZID=America/Los_Angeles:20230522T140000 SUMMARY:[CML Seminar] Gabe Hope: Semi-Supervised Learning with Prediction-C onstrained Variational Autoencoders LOCATION:Donald Bren Hall 4011 DESCRIPTION:Gabe Hope\, PhD Student\, Computer Science\, University of Cali fornia\, Irvine\n\nTitle: Semi-Supervised Learning with Prediction-Constra ined Variational Autoencoders\n\nAbstract: Variational autoencoders (VAEs) have proven to be an effective approach to modeling complex data distribu tions while providing compact representations useful for downstream predic tion tasks. In this work we train VAEs with the dual goals of good likelih ood-based generative modeling and good discriminative performance in super vised and semi-supervised prediction tasks. We show that the dominant appr oach to training semi-supervised VAEs has key weaknesses\, and propose a n ovel framework that maximizes generative likelihood subject to prediction quality constraints. To handle sparse labels\, we further enforce a consis tency constraint requiring predictions on reconstructed data to match thos e on the original data. Our experiments show that prediction and consisten cy constraints improve generative samples as well as image classification performance in semi-supervised settings.\n\nhttps://cml.ics.uci.edu/semina rs/2023-05-22-gabe-hope X-ALT-DESC;FMTTYPE=text/html:Gabe Hope\, PhD Student\, C omputer Science\, University of California\, Irvine

Title: S emi-Supervised Learning with Prediction-Constrained Variational Autoencode rs

Abstract: Variational autoencoders (VAEs) have proven to be an effective approach to modeling complex data distributions while prov iding compact representations useful for downstream prediction tasks. In t his work we train VAEs with the dual goals of good likelihood-based genera tive modeling and good discriminative performance in supervised and semi-s upervised prediction tasks. We show that the dominant approach to training semi-supervised VAEs has key weaknesses\, and propose a novel framework t hat maximizes generative likelihood subject to prediction quality constrai nts. To handle sparse labels\, we further enforce a consistency constraint requiring predictions on reconstructed data to match those on the origina l data. Our experiments show that prediction and consistency constraints i mprove generative samples as well as image classification performance in s emi-supervised settings.

https://cml.ics.uci.edu/seminars/2023-05-22-gabe-hop e URL:https://cml.ics.uci.edu/seminars/2023-05-22-gabe-hope END:VEVENT END:VCALENDAR