Neural embedding: learning the embedding of the manifold of physics data

Abstract In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data anal...

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Bibliografische gegevens
Hoofdauteurs: Park, Sang E., Harris, Philip, Ostdiek, Bryan
Andere auteurs: Massachusetts Institute of Technology. Department of Physics
Formaat: Artikel
Taal:English
Gepubliceerd in: Springer Berlin Heidelberg 2023
Online toegang:https://hdl.handle.net/1721.1/151120

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