Kolmogorov-Arnold Networks: Exploring Dynamic Weights and Attention Mechanisms

Author(s): Shenggang Li

Originally published on Towards AI.

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A Step-by-Step Guide to KAN, Dynamic Weight Adjustments, and Their Relationship to Attention Mechanisms: Investigating the Attention in KAN and Proposing Attention-KAN Extensions

Photo by Rodion Kutsaiev on Unsplash

Artificial Intelligence constantly introduces new ideas, and one that’s gaining attention is Kolmogorov-Arnold Networks (KAN). What sets KAN apart is its foundation: the Kolmogorov-Arnold theorem, a famous mathematical concept that shapes its unique architecture. Unlike traditional neural networks, KAN provides a fresh perspective rooted in mathematics, making it an exciting topic to explore.

As an AI enthusiast and data scientist, I’m drawn to uncovering the hidden depths of KAN. This paper aims to share my findings and make KAN accessible to others, breaking it down step by step.

First, I’ll introduce KAN with practical examples, making it easy for newcomers to understand. Once the basics are clear, I’ll dive into dynamic weight adjustments in KAN, where coefficients become functions of input data — making the network adaptive and flexible, like a recipe that adjusts itself based on the crowd size.

Next, I’ll explore the surprising connection between KAN and attention mechanisms, showing that KAN can be viewed as a special case of attention. Building on… Read the full blog for free on Medium.

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Published via Towards AI