What is KAN?

Kolmogorov-Arnold Networks (KAN) are a novel type of neural network inspired by the Kolmogorov-Arnold representation theorem, which demonstrates how complex multivariable functions can be decomposed into simpler, univariate ones. Unlike traditional models like Multi-Layer Perceptrons (MLPs), which rely on fixed activation functions at each neuron, KAN introduces learnable activation functions on the edges between neurons. This design not only increases the network's adaptability but also provides enhanced interpretability, making it possible to understand how the network processes and transforms information.

KAN Architecture

KAN excels in tasks requiring high levels of non-linear modeling and dynamic adaptability, such as Reinforcement Learning (RL). Its architecture enables symbolic extraction of learned policies, offering human-readable insights into decision-making processes. By reducing the number of trainable parameters while maintaining or exceeding the performance of traditional models, KAN delivers both efficiency and scalability. These features make KAN a powerful and promising alternative for building next-generation AI systems that are interpretable, efficient, and capable of tackling complex challenges across a wide array of domains.

Kolmogorov–Arnold Theorem

Learn more about this innovative approach in the research paper by Ziming Liu.