BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks
<|reference_start|>BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks: In this paper, we introduce BSRBF-KAN, a Kolmogorov Arnold Network (KAN) that combines B-splines and radial basis functions (RBFs) to fit input vectors during data training. We perform experiments with BSRBF-KAN, multi-layer perception (MLP), and other popular KANs, including EfficientKAN, FastKAN, FasterKAN, and GottliebKAN over the MNIST and Fashion-MNIST datasets. BSRBF-KAN shows stability in 5 training runs with a competitive average accuracy of 97.55% on MNIST and 89.33% on Fashion-MNIST and obtains convergence better than other networks. We expect BSRBF-KAN to open many combinations of mathematical functions to design KANs. Our repo is publicly available at: https://github.com/hoangthangta/BSRBF_KAN.<|reference_end|>
@article{ta2024bsrbf-kan:,
    title={BSRBF-KAN: A combination of B-splines and Radial Basis Functions in
  Kolmogorov-Arnold Networks},
    author={Hoang-Thang Ta},
    journal={arXiv preprint arXiv:2406.11173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11173},
    primaryClass={cs.CL}
}