5 citations · 5 across the 1 of their papers we have counts for
4 papers
Graph Neural Networks in Particle Physics
Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs---set…
Efficiency Parameterization with Neural Networks
C. Badiali, F. A. Di Bello, G. Frattari +4
Multidimensional efficiency maps are commonly used in high energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned mul…
Towards a Computer Vision Particle Flow
Francesco Armando Di Bello, Sanmay Ganguly, Eilam Gross +4
In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produc…
Set2Graph: Learning Graphs From Sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi +4
Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clusterin…