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A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks
Ambarish Moharil, Damian Tamburri, Indika Kumara, Willem-Jan Van · 2024-07-12 · via cs.NE updates on arXiv.org

This paper introduces a scale-invariant methodology employing \textit{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 \textit{roughness} to deeply understand their dynamics and enhance the quality of \textit{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 \textit{intrinsic} explainability of connectionist Artificial Intelligence (AI) systems.

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