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.

翻译：最近，深度神经网络（DNNs）中的最优时间变量学习被引入于arXiv:2204.08528。本文通过引入一个与离散动力系统中时间范围直接相关的正则化项，扩展了这一概念。此外，我们提出了一种适用于残差神经网络（ResNets）的自适应剪枝方法，该方法在不牺牲表达力的同时降低网络复杂度，并同时减少训练时间。通过将所提出的概念应用于著名的MNIST和Fashion MNIST数据集上的分类任务，展示了结果。我们的PyTorch代码可从https://github.com/frederikkoehne/time_variable_learning获取。