Exceptional Singularities in Polynomial Neural Networks

The singularities of a neurovariety have so far always turned out to lie at subnetworks: functions already computed by a smaller architecture of the same kind. This is what makes them readable as a bias of training towards simpler models, and whether they exhaust the singular locus is left open in t