Theoretical framework demonstrates fractal dimension analysis in deep neural networks, suggesting improved intrinsic explainability for complex connectionist models.
This paper introduces a scale-invariant methodology employing Fractal Geometry to analyze and explain the nonlinear dynamics of complex connectionist systems. By leveraging architectural self-similarity in Deep Neural Networks (DNNs), we quantify fractal dimensions and roughness to deeply understand their dynamics and enhance the quality of intrinsic explanations. Our approach integrates principles from Chaos Theory to improve visualizations of fractal evolution and utilizes a Graph-Based Neural Network for reconstructing network topology. This strategy aims at advancing the intrinsic explainability of connectionist Artificial Intelligence (AI) systems.
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Moharil et al. (2024) studied this question.
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