Papers › CVKAN: Complex-Valued Kolmogorov-Arnold Networks
CVKAN: Complex-Valued Kolmogorov-Arnold Networks
Matthias Wolff, Florian Eilers, Xiaoyi Jiang
In this work we propose CKAN, a complex-valued KAN, to join the intrinsic interpretability of KANs and the advantages of Complex-Valued Neural Networks (CVNNs). We show how to transfer a KAN and the necessary associated mechanisms into the complex domain. To confirm that CKAN meets expectations we conduct experiments on symbolic complex-valued function fitting and physically meaningful formulae as well as on a more realistic dataset from knot theory. Our proposed CKAN is more stable and performs on par or better than real-valued KANs while requiring less parameters and a shallower network architecture, making it more explainable.
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