17 citations · 28 across the 5 of their papers we have counts for
5 papers
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Samuel Bright-Thonney, Christina Reissel, Gaia Grosso +6
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robus…
Product Manifold Machine Learning for Physics
Nathaniel S. Woodward, Sang Eon Park, Gaia Grosso +2
Physical data are representations of the fundamental laws governing the Universe, hiding complex compositional structures often well captured by hierarchical graphs. Hyperbolic spa…
FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
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 sp…
A FAIR and AI-ready Higgs boson decay dataset
Yifan Chen, E. A. Huerta, Javier Duarte +10
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and re…