  Autoencoders as tools behind anomaly searches at the LHC have the structural
problem that they only work in one direction, extracting jets with higher
complexity but not the other way around. To address this, we derive classifiers
from the latent space of (variational) autoencoders, specifically in Gaussian
mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves
the problem and improves both the performance and the interpretability of the
networks.
