FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
2108.02214 , archiveprefix =
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Presents design principles for a unified AI-ready data schema for heterogeneous QCD detector data, applied to simulated Barrel Imaging Calorimeter data in the ePIC detector.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Design Principles for AI-Ready QCD Data with a Barrel Imaging Calorimeter Application
Presents design principles for a unified AI-ready data schema for heterogeneous QCD detector data, applied to simulated Barrel Imaging Calorimeter data in the ePIC detector.