  Reliable modeling of conditional densities is important for quantitative
scientific fields such as particle physics. In domains outside physics,
implicit quantile neural networks (IQN) have been shown to provide accurate
models of conditional densities. We present a successful application of IQNs to
jet simulation and correction using the tools and simulated data from the
Compact Muon Solenoid (CMS) Open Data portal.
