  The generation of accurate neutrino-nucleus cross-section models needed for
neutrino oscillation experiments require simultaneously the description of many
degrees of freedom and precise calculations to model nuclear responses. The
detailed calculation of complete models makes the Monte Carlo generators slow
and impractical. We present Exhaustive Neural Importance Sampling (ENIS), a
method based on normalizing flows to find a suitable proposal density for
rejection sampling automatically and efficiently, and discuss how this
technique solves common issues of the rejection algorithm.
