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SUMMARY:Taejin Paik (Seoul National University)
DTSTART:20230524T090000Z
DTEND:20230524T100000Z
DTSTAMP:20260423T035536Z
UID:CompAlg/15
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/15/"
 >Isometry-Invariant and Subdivision-Invariant Representations of Embedded 
 Simplicial Complexes</a>\nby Taejin Paik (Seoul National University) as pa
 rt of Machine Learning Seminar\n\n\nAbstract\nGeometric objects such as me
 shes and graphs are commonly used in various applications\, but analyzing 
 them can be challenging due to their complex structures. Traditional appro
 aches may not be robust to transformations like subdivision or isometry\, 
 leading to inconsistent results. Here is a novel approach to address these
  limitations by using only topological and geometric data to analyze simpl
 icial complexes in a subdivision-invariant and isometry-invariant way. Thi
 s approach involves using a graph neural network to create an $O(3)$-equiv
 ariant operator and the Euler curve transform to generate sufficient stati
 stics that describe the properties of the object.\n
LOCATION:https://researchseminars.org/talk/CompAlg/15/
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