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This paper explores how piecewise linear functions in Kolmogorov-Arnold Networks relate to ReLU networks, suggesting potential advantages.
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.
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Schoots et al. (2025) studied this question.
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