Web: http://arxiv.org/abs/2208.05484

Aug. 16, 2022, 1:11 a.m. | Sang Eon Park, Philip Harris, Bryan Ostdiek

cs.LG updates on arXiv.org arxiv.org

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 analysis pipeline for many applications. Using progressively
more realistic simulated collisions at the Large Hadron Collider, we show that
this embedding approach learns the underlying latent structure. With the notion
of volume in Euclidean spaces, we provide for the …

arxiv data embedding learning manifold physics

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