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SineKAN presents a variant of Kolmogorov-Arnold Networks using sinusoidal activation functions, showing comparable or better performance with significant speed improvements over baseline KAN models on benchmark tasks.
This paper introduces a recurrent sinusoidal architecture for implicit neural representations that achieves higher fidelity with fewer parameters and optimization steps by exploiting harmonic line spectrum enrichment through sinusoidal recurrence.