Time Regularization in Optimal Time Variable Learning

• URL: http://arxiv.org/abs/2306.16111v2
• Date: Wed, 6 Dec 2023 10:52:15 GMT
• Title: Time Regularization in Optimal Time Variable Learning
• Authors: Evelyn Herberg and Roland Herzog and Frederik K\"ohne
• Abstract summary: Recently, optimal time variable learning in deep neural networks (DNNs) was introduced in arXiv:2204.08528. We extend the concept by introducing a regularization term that directly relates to the time horizon in discrete dynamical systems. We propose an adaptive pruning approach for Residual Neural Networks (ResNets) Results are illustrated by applying the proposed concepts to classification tasks on the well known MNIST and Fashion MNIST data sets.
• Score: 0.4490343701046724
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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