Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks

Nandi Schoots, Mattia Jacopo Villani, Niels uit de Bos

Abstract: 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.