MLPs vs KANs: Evaluating Performance in Machine Learning, Computer Vision, NLP, and Symbolic Tasks

Multi-layer perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs) were compared across diverse domains, including machine learning, computer vision, and natural language processing. The study found that MLPs generally outperformed KANs in most tasks, particularly in audio and text classification, and computer vision. However, KANs showed superior performance in representing symbolic formulas. Both network types were tested with varied configurations and activation functions under controlled conditions to offer a balanced assessment. The research provides insights for future neural network architecture improvements.