BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Applied Mathematics and Statistics - ECPv6.5.1.6//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Applied Mathematics and Statistics
X-ORIGINAL-URL:https://engineering.jhu.edu/ams
X-WR-CALDESC:Events for Department of Applied Mathematics and Statistics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240215T133000
DTEND;TZID=America/New_York:20240215T143000
DTSTAMP:20240715T212321
CREATED:20231220T200430Z
LAST-MODIFIED:20240313T145019Z
UID:47145-1708003800-1708007400@engineering.jhu.edu
SUMMARY:AMS Weekly Seminar | Associate Professor Jeff Calder
DESCRIPTION:Location:Olin 305 \nWhen: February 15th at 1:30 p.m. \nTitle: PDEs and graph-based semi-supervised learning \nAbstract: Graph-based semi-supervised learning is a field within machine learning that uses both labeled and unlabeled data with an underlying graph structure for classification and regression tasks. In problems where very little labeled data is available\, the classical Laplacian regularization gives very poor results. This can be explained through its PDE continuum limit\, which is an ill-posed elliptic equation. Much work recently has been focused on designing graph-based learning methods with well-posed continuum limits\, including the p-Laplacian\, higher order Laplacians\, re-weighted Laplacians\, and Poisson equations. \nIn this talk\, we will survey this literature\, and present our recent work on using Poisson equations for semi-supervised learning. We will present theoretical results which establish that learning with Poisson equations is provably well-posed at arbitrarily low label rates\, and experimental results showing that it outperforms existing graph-based semi-supervised learning methods on challenging data sets. We will also present some recent work on applications of Poisson learning to graph-based active learning\, where the goal is to select a training set with the most informative examples\, often in a sequential online setting starting at extremely low label rates. \nZoom link: https://wse.zoom.us/j/94601022340
URL:https://engineering.jhu.edu/ams/event/ams-weekly-seminar-associate-professor-jeff-calder/
LOCATION:Olin 305
CATEGORIES:Seminars and Lectures
END:VEVENT
END:VCALENDAR