A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
and Kottas, Athanasios and MacEachern, Steven N
3 Pith papers cite this work. Polarity classification is still indexing.
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A framework maps erosion distributions to Wasserstein space, uses basis expansion to create a multivariate random field, and applies local regression plus Kriging to predict distributions and their functionals at new locations, outperforming alternatives in simulations and applied to Shaanxi provinc
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
citing papers explorer
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Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
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Spatial Prediction of Local Soil Erosion Distribution in the Wasserstein Space
A framework maps erosion distributions to Wasserstein space, uses basis expansion to create a multivariate random field, and applies local regression plus Kriging to predict distributions and their functionals at new locations, outperforming alternatives in simulations and applied to Shaanxi provinc
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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.