  Autoencoders have useful applications in high energy physics in anomaly
detection, particularly for jets - collimated showers of particles produced in
collisions such as those at the CERN Large Hadron Collider. We explore the use
of graph-based autoencoders, which operate on jets in their "particle cloud"
representations and can leverage the interdependencies among the particles
within a jet, for such tasks. Additionally, we develop a differentiable
approximation to the energy mover's distance via a graph neural network, which
may subsequently be used as a reconstruction loss function for autoencoders.
