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