  There is an increased interest in model agnostic search strategies for
physics beyond the standard model at the Large Hadron Collider. We introduce a
Deep Set Variational Autoencoder and present results on the Dark Machines
Anomaly Score Challenge. We find that the method attains the best anomaly
detection ability when there is no decoding step for the network, and the
anomaly score is based solely on the representation within the encoded latent
space. This method was one of the top-performing models in the Dark Machines
Challenge, both for the open data sets as well as the blinded data sets.
