How Does KAN  (Kolmogorov–Arnold Networks) Act As A Better Substitute For Multi-Layer Perceptrons (MLPs)?

Multi-Layer Perceptrons (MLPs), also known as fully-connected feedforward neural networks, have been significant in modern deep learning. Because of the universal approximation theorem’s guarantee of expressive capacity, they are frequently employed to approximate nonlinear functions. MLPs are widely used; however, they have disadvantages like high parameter consumption and poor interpretability in intricate models like transformers. […]

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