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.
@article{arxiv.2104.02369,
title = {Classification with Runge-Kutta networks and feature space augmentation},
author = {Elisa Giesecke and Axel Kröner},
journal= {arXiv preprint arXiv:2104.02369},
year = {2021}
}