  Recent work has established an alternative to traditional multi-layer
perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The
general KAN framework uses learnable activation functions on the edges of the
computational graph followed by summation on nodes. The learnable edge
activation functions in the original implementation are basis spline functions
(B-Spline). Here, we present a model in which learnable grids of B-Spline
activation functions are replaced by grids of re-weighted sine functions. We
show that this leads to better or comparable numerical performance to B-Spline
KAN models on the MNIST benchmark, while also providing a substantial speed
increase on the order of 4-8 times.
