  In this paper we combine an approach based on Runge-Kutta Nets considered in
[\emph{Benning et al., J. Comput. Dynamics, 9, 2019}] and a technique on
augmenting the input space in [\emph{Dupont et al., NeurIPS}, 2019] to obtain
network architectures which show a better numerical performance for deep neural
networks in point classification problems. The approach is illustrated with
several examples implemented in PyTorch.
