Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure
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Presents a new ensemble regression hyperoptimisation procedure for PDF fitting that combines multiple statistically equivalent methodologies into one PDF set accounting for hyperparameter variation.
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A linear PDF model for Bayesian inference
Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
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Hyperoptimisation algorithm for the next generation of PDF determinations: ensemble regression with an unbiased selection model
Presents a new ensemble regression hyperoptimisation procedure for PDF fitting that combines multiple statistically equivalent methodologies into one PDF set accounting for hyperparameter variation.