A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q
3 Pith papers cite this work. Polarity classification is still indexing.
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Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
citing papers explorer
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
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Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.
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Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.