Annotated KAN: A Deep Dive into Kolmogorov-Arnold Networks
2025-05-22
This post provides a comprehensive explanation of the architecture and training process of Kolmogorov-Arnold Networks (KANs), an alternative to Multi-Layer Perceptrons (MLPs). KANs parameterize activation functions by re-wiring the 'multiplication' in an MLP's weight matrix-vector multiplication into function application. The article details KAN's functionality, including a minimal KAN architecture, B-spline optimizations, regularization techniques, with code examples and visualization results. Applications of KANs, such as on the MNIST dataset, and future research directions like improving KAN efficiency are also explored.
Read more