multi-layer-perceptrons

This study empirically compares Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks, finding that KANs statistically outperform MLPs but with higher computational cost.

This paper investigates how neural networks maintain high accuracy even when over 90% of input features are corrupted, deriving a centroid-based decision rule in the high-noise limit using a mean-field approach.