Article
Version 1
This version is not peer-reviewed
AI Learning through SUSY Field Theory
Version 1
: Received: 22 October 2024 / Approved: 22 October 2024 / Online: 23 October 2024 (16:42:24 CEST)
How to cite: Garayev, G.; Alili, A. AI Learning through SUSY Field Theory. Preprints 2024, 2024101767. https://doi.org/10.20944/preprints202410.1767.v1 Garayev, G.; Alili, A. AI Learning through SUSY Field Theory. Preprints 2024, 2024101767. https://doi.org/10.20944/preprints202410.1767.v1
Abstract
We propose a SUSY-inspired loss framework to address the generalizationrobustness trade-off in AI. Combining bosonic and fermionic components, the loss ensures smooth learning and robustness against adversarial attacks. Parallel transport stabilizes weight updates across non-Euclidean loss landscapes. Validation on CIFAR-10 demonstrates stable convergence and enhanced performance under FGSM and PGD attacks, confirming the effectiveness of the proposed approach. Keywords: Supersymmetry, Generalization, Robustness, Adversarial Attacks, Parallel Transport, Loss Optimization, CIFAR-10.
Keywords
supersymmetry; generalization; robustness; adversarial attacks; parallel transport; loss optimization; CIFAR-10
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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