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A prediction rigidity formalism for low-cost uncertainties in trained neural ...
Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based... -
Prediction rigidities for data-driven chemistry
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures...
