  When training data is scarce, the incorporation of additional prior knowledge
can assist the learning process. While it is common to initialize neural
networks with weights that have been pre-trained on other large data sets,
pre-training on more concise forms of knowledge has rather been overlooked. In
this paper, we propose a novel informed machine learning approach and suggest
to pre-train on prior knowledge. Formal knowledge representations, e.g. graphs
or equations, are first transformed into a small and condensed data set of
knowledge prototypes. We show that informed pre-training on such knowledge
prototypes (i) speeds up the learning processes, (ii) improves generalization
capabilities in the regime where not enough training data is available, and
(iii) increases model robustness. Analyzing which parts of the model are
affected most by the prototypes reveals that improvements come from deeper
layers that typically represent high-level features. This confirms that
informed pre-training can indeed transfer semantic knowledge. This is a novel
effect, which shows that knowledge-based pre-training has additional and
complementary strengths to existing approaches.
