Constructing a Graph-based Kolmogorov Arnold Network for Analysis #graphtheory

Kolmogorov Arnold Networks (KAN) are gaining popularity as an alternative to Multi-Layer Perceptrons in neural networks. KANs utilize the Kolmogorov-Arnold representation theorem to make activation functions “learnable” and improve them. This tutorial focuses on implementing a Graph Kolmogorov Arnold Neural Network (GKAN) in Google Colab, which is useful for modeling nonlinear dynamical systems. The tutorial is based on implementations from WillHua127.

The GKAN class is defined with an input layer, multiple NaiveFourierKANLayer layers, and a linear output layer. In the forward pass, input features are processed through each NaiveFourierKANLayer to capture complex patterns in the data. The model then outputs normalized log probabilities for classification.

The NaiveFourierKANLayer class implements Fourier features to transform input data, enhancing the model’s ability to capture complex patterns. The layer uses learnable Fourier coefficients to adjust the weights of the transformations, making the activation functions “learnable.”

The tutorial also covers training and evaluation functions, setting up model parameters, and splitting the dataset for training and evaluation. By running the model, you can achieve an accuracy of around 84% in predicting academic paper categories in the Cora dataset.

The tutorial provides a Google Colab Notebook for full implementation and aims to help readers learn how to build KANs, which are not widely covered in existing resources.

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Source link: https://medium.com/@ronantech/how-to-build-a-graph-based-kolmogorov-arnold-network-d5b37303f452?source=rss——artificial_intelligence-5