A learning framework trains neural networks to output context-conditioned Gaussian overbounds with provable conservatism on quantile grids for convolution-based uncertainty propagation.
Enhancing tropospheric z enith wet delay interpolation with gaussian process regression,
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Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation
A learning framework trains neural networks to output context-conditioned Gaussian overbounds with provable conservatism on quantile grids for convolution-based uncertainty propagation.