A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks

Autor:	Moharil, Ambarish, Tamburri, Damian, Kumara, Indika, Heuvel, Willem-Jan Van Den, Azarfar, Alireza
Rok vydání:	2024
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.09585 Zobrazit plný text záznamu View this record from Arxiv