Mlp, Neural Networks, Machine Learning, Artificial Intelligence, Universal Approximation.
alternatives to MLPs. These networks showcase superior performance in both accuracy and interpretability domains. While MLPs draw inspiration from the universal approximation theorem, KANs take cues from the Kolmogorov-Arnold representation theorem. Unlike MLPs, which rely on fixed activation