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DTSTAMP:20241006T141632
DTSTART;TZID=America/Detroit:20241122T140000
DTEND;TZID=America/Detroit:20241122T143000
SUMMARY:Exhibition:Tales of the Maya Skies
DESCRIPTION:Tales of the Maya Skies immerses viewers in the wonders of Maya science\, cosmology and myth. This beautifully illustrated story takes us back in time to the jungles of Mexico to discover how Maya scholars developed a sophisticated understanding of astronomy\, architecture\, and mathematics that enabled them to predict solstices\, solar eclipses\, weather patterns and planetary movements.
UID:124089-21861275@events.umich.edu
URL:https://events.umich.edu/event/124089
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Museum,natural history museum,Science,Space
LOCATION:Museum of Natural History
CONTACT:
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DTSTAMP:20241106T153909
DTSTART;TZID=America/Detroit:20241122T150000
DTEND;TZID=America/Detroit:20241122T160000
SUMMARY:Lecture / Discussion:AIM Seminar / MCAIM Colloquium: PDEs and graph-based semi-supervised learning
DESCRIPTION:Abstract:  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. \n\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.\n\nEvent will be in-person and on Zoom: https://umich.zoom.us/j/98734707290\n\nContact:  Selim Esedoglu
UID:121475-21846587@events.umich.edu
URL:https://events.umich.edu/event/121475
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1084
CONTACT:
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