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Upgrading from KARN to KANN Federated: A Comprehensive Walkthrough
Preamble
When it comes to the crazy world of machine learning, leveraging more advanced neural network architectures can lead to significant improvements in performance and capability. Recently, I upgraded my Kolmogorov-Arnold Network (KARN) to a more sophisticated Kolmogorov-Arnold Neural Network (KANN). This article walks you through the steps I took, highlighting key changes and explaining the rationale behind them with scientific and mathematical insights.
Introduction
The Kolmogorov-Arnold Network (KARN) is a type of neural network inspired by the Kolmogorov-Arnold representation theorem. This theorem states that any multivariable continuous function can be represented as a superposition of continuous functions of one variable and an addition operation. Leveraging this theorem, KARN comprises an inner layer that processes inputs through a non-linear activation function, followed by an outer layer that generates the final output. However, to harness the full potential of this theorem, I transitioned to a Kolmogorov-Arnold Neural Network (KANN), which provides a more granular approach to processing individual input features.
