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Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications

Computer Science

Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications

V. Flovik

This intriguing study by Vegard Flovik explores the challenges posed by distribution shifts in machine learning. Using synthetic data generated through the van der Waals equation, the research reveals innovative methods to enhance model adaptation and generalization, specifically highlighting the significance of Mahalanobis distance in improving model robustness and tackling uncertainties.

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