A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks
<|reference_start|>A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks: 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.<|reference_end|>
@article{moharil2024a,
    title={A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of
  Deep Neural Networks},
    author={Ambarish Moharil, Damian Tamburri, Indika Kumara, Willem-Jan Van Den
  Heuvel, Alireza Azarfar},
    journal={arXiv preprint arXiv:2407.09585},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09585},
    primaryClass={cs.NE cs.AI}
}