A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
URL http://link.springer.com/10.1007/ 978-0-387-21736-9
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Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.
A multimodal registration pipeline models splints as rigid mandible transformations to quantify TMJ configuration changes via error propagation and surface metrics.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Assumed Density Filtering and Smoothing with Neural Network Surrogate Models
Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.
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Assessment of the quantitative impact of occlusal positioning splints on temporomandibular joint conditions
A multimodal registration pipeline models splints as rigid mandible transformations to quantify TMJ configuration changes via error propagation and surface metrics.