Kolmogorov, AI, Neural Networks, Machine Learning.
and offer several advantages over MLPs: Expressiveness: KANs can capture more complex functions with fewer parameters, thanks to their ability to learn activation functions on the edges, which are parametrized as splines. Reduced Overfitting: With no linear weights, KANs’ simpler structure aids in