Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks
arXiv:2605.21534v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) approximate multivariate functions using learnable univariate edge functions, typically parameterized by B-spline bases. Although effective, spline-based implementations can be computationally expensive. A modified version of KANs, called FastKAN, improves efficiency by replacing splines with Gaussian radial basis functions (RBFs), but it relies on a fixed kernel and shape parameter. In this work, we extend the RBF-based KAN framework by introducing a broader family of radial basis kernels and by initializing t
The paper builds on recent advancements in Kolmogorov-Arnold Networks (KANs) and addresses their computational limitations, indicating active research in optimizing these novel AI architectures.
Improving the efficiency and flexibility of KANs could lead to more robust and less resource-intensive AI models, potentially impacting the development and deployment of advanced AI systems.
This work suggests a potential shift from computationally expensive spline-based KANs to more efficient RBF-based variants with adaptive parameters, improving their practicality for real-world applications.
- · AI researchers
- · AI developers
- · Cloud computing providers
- · Machine learning hardware manufacturers
- · Developers reliant solely on spline-based KANs
Adaptive RBF-KANs could offer a more efficient alternative to traditional KANs for complex function approximation in AI.
Increased efficiency could accelerate the adoption and application of KANs in various AI domains, potentially leading to new breakthroughs.
More efficient and versatile AI architectures could reduce the computational burden of developing advanced AI, democratizing access and accelerating innovation across the sector.
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