Kolmogorov-Arnold Networks are a new family of neural network architectures which holds promise for overcoming the curse of dimensionality and has interpretability benefits (arXiv:2404.19756). In this paper, we explore the connection between Kolmogorov Arnold Networks (KANs) with piecewise linear (univariate real) functions and ReLU networks. We provide completely explicit constructions to convert a piecewise linear KAN into a ReLU network and vice versa.
View on arXiv@article{schoots2025_2503.01702,
title={ Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks },
author={ Nandi Schoots and Mattia Jacopo Villani and Niels uit de Bos },
journal={arXiv preprint arXiv:2503.01702},
year={ 2025 }
}