A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks
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Abstract (arXiv)
This paper introduces a scale-invariant methodology employing textitFractal 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 textitroughness to deeply understand their dynamics and enhance the quality of textitintrinsic 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 textitintrinsic explainability of connectionist Artificial Intelligence (AI) systems.
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References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
- 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
- 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
- 2022Renormalization in the neural network-quantum field theory correspondencecited by 7
- 2021Scope and Sense of Explainability for AI-Systemscited by 2
- 2024On The Potential of The Fractal Geometry and The CNNs Ability to Encode itcited by 1
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