  Recently, optimal time variable learning in deep neural networks (DNNs) was
introduced in arXiv:2204.08528. In this manuscript we extend the concept by
introducing a regularization term that directly relates to the time horizon in
discrete dynamical systems. Furthermore, we propose an adaptive pruning
approach for Residual Neural Networks (ResNets), which reduces network
complexity without compromising expressiveness, while simultaneously decreasing
training time. The results are illustrated by applying the proposed concepts to
classification tasks on the well known MNIST and Fashion MNIST data sets. Our
PyTorch code is available on
https://github.com/frederikkoehne/time_variable_learning.
