The Math Behind KAN — Kolmogorov-Arnold Networks
A new alternative to the classic Multi-Layer Perceptron is out. Why is it more accurate and interpretable? Math and Code Deep Dive.
In today’s world of AI, neural networks drive countless innovations and advancements. At the heart of many breakthroughs is the Multi-Layer Perceptron (MLP), a type of neural network known for its ability to approximate complex functions. But as we push the boundaries of what AI can achieve, we must ask: Can we do better than the classic MLP?
Here’s Kolmogorov-Arnold Networks (KANs), a new approach to neural networks inspired by the Kolmogorov-Arnold representation theorem. Unlike traditional MLPs, which use fixed activation functions at each neuron, KANs use learnable activation functions on the edges (weights) of the network. This simple shift opens up new possibilities in accuracy, interpretability, and efficiency.
This article explores why KANs are a revolutionary advancement in neural network design. We’ll dive into their mathematical foundations, highlight the key differences from MLPs, and show how KANs can outperform traditional methods.
