Mlp, Neural Networks, Machine Learning, Artificial Intelligence, Universal Approximation.
superior parameter efficiency, with the width-10 KAN requiring only 10² parameters, whereas the width-100 MLP demands 10⁴ parameters, making KANs a hundred times more parameter efficient. Overall, extensive empirical evaluations underscore the remarkable efficacy of KANs over MLPs, emphasizing