Kolmogorov Arnold Network

Kolmogorov-Arnold Networks (KANs) are a novel type of neural network architecture that uses learnable activation functions placed on edges, rather than nodes, offering a potential alternative to traditional Multi-Layer Perceptrons (MLPs). Current research focuses on improving KAN efficiency and accuracy through variations like SincKANs and EKANs (Equivariant KANs), exploring their application in diverse fields such as image processing, function approximation, and solving partial differential equations. The significance of KANs lies in their potential for enhanced interpretability and performance in specific tasks, although comparisons with MLPs reveal varying degrees of success depending on the application and dataset characteristics.