Conformalized Quantum DeepONet Ensembles reduce operator inference from quadratic to linear complexity using QOrthoNNs and SPQCs while delivering distribution-free uncertainty guarantees through ensemble conformal prediction.
Bayesian Neural Networks: An Introduction and Survey
2 Pith papers cite this work, alongside 184 external citations. Polarity classification is still indexing.
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Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
Conformalized Quantum DeepONet Ensembles reduce operator inference from quadratic to linear complexity using QOrthoNNs and SPQCs while delivering distribution-free uncertainty guarantees through ensemble conformal prediction.
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Uncertainty quantification via conformal prediction in data assimilation
Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.