Understanding Kolmogorov-arnold Networks: Possible Successors To Mlps?

This article explores the potential advantages of Kolmogorov-Arnold Networks (KANs) over traditional Multi-Layer Perceptrons (MLPs) in the new generation of Deep Learning. KANs use learnable activation functions on edges, making them more accurate and interpretable, and especially useful for functions with sparse compositional structures. The Kolmogorov-Arnold Representation Theorem (KART) states that any continuous function with multiple inputs can be created by combining simple functions of a single input.