  Parametric approaches to Learning, such as deep learning (DL), are highly
popular in nonlinear regression, in spite of their extremely difficult training
with their increasing complexity (e.g. number of layers in DL). In this paper,
we present an alternative semi-parametric framework which foregoes the
ordinarily required feedback, by introducing the novel idea of geometric
regularization. We show that certain deep learning techniques such as residual
network (ResNet) architecture are closely related to our approach. Hence, our
technique can be used to analyze these types of deep learning. Moreover, we
present preliminary results which confirm that our approach can be easily
trained to obtain complex structures.
