AI Learning through SUSY Field Theory

Guman Garayev; Azar Alili

doi:10.20944/preprints202410.1767.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 22 October 2024 / Approved: 22 October 2024 / Online: 23 October 2024 (16:42:24 CEST)

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.

supersymmetry; generalization; robustness; adversarial attacks; parallel transport; loss optimization; CIFAR-10

Computer Science and Mathematics, Applied Mathematics

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AI Learning through SUSY Field Theory

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