  This paper presents Mixed Formal Learning, a new architecture that learns
models based on formal mathematical representations of the domain of interest
and exposes latent variables. The second element in the architecture learns a
particular skill, typically by using traditional prediction or classification
mechanisms. Our key findings include that this architecture: (1) Facilitates
transparency by exposing key latent variables based on a learned mathematical
model; (2) Enables Low Shot and Zero Shot training of machine learning without
sacrificing accuracy or recall.
