arXiv Machine Learning September 18, 2026 By Kaitlin Zareno, Jarett Dewbury, Siamak K. Sorooshyari, Hossein Mobahi, Loza F. Tadesse

Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics

Read the original on arXiv Machine Learning →

Sharpness-Aware Minimization (SAM) is applied to improve the generalization of machine learning models for classifying bacterial Raman spectra, a technique that could enable rapid, portable diagnostics for antimicrobial resistance. The study shows that SAM can increase classification accuracy by up to 10.5% on a single data split and by an average of 2.7% across multiple splits compared to the traditional Adam optimizer. These gains demonstrate SAM’s potential to enhance the clinical utility of AI-powered Raman spectroscopy tools.

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