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UQTestFuns: A Python3 Library of Uncertainty Quantification (UQ) Test Functions
UQTestFuns is an open-source Python3 library of test functions commonly used within the applied uncertainty quantification (UQ) community. Specifically, the package provides:... -
UQTestFuns: A Python3 Library of Uncertainty Quantification (UQ) Test Functions
UQTestFuns is an open-source Python3 library of test functions commonly used within the applied uncertainty quantification (UQ) community. Specifically, the package provides:... -
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... -
Inverse design of singlet fission materials with uncertainty-controlled genet...
Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We... -
On double-descent in uncertainty quantification in overparametrized models (c...
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident... -
Expectation consistency for calibration of neural networks (code)
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient... -
Data for the manuscript "DEUCE v1.0: A neural network for probabilistic preci...
File descriptions: verif_inputs.zip and verif_case_inputs.zip Contain folders of raw PGM composites, containing needed input data for verification experiments, that is for the...
