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Found 5 papers, 5 papers with code
A Singular Woodbury and Pseudo-Determinant Matrix Identities and Application to Gaussian Process Regression
We study a matrix that arises from a singular form of the Woodbury matrix identity.
Noise Estimation in Gaussian Process Regression
We develop a computational procedure to estimate the covariance hyperparameters for semiparametric Gaussian process regression models with additive noise.
Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations
Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases.
A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data.
Interpolating Log-Determinant and Trace of the Powers of Matrix $\mathbf{A} + t \mathbf{B}$
We develop heuristic interpolation methods for the functions $t \mapsto \log \det \left( \mathbf{A} + t \mathbf{B} \right)$ and $t \mapsto \operatorname{trace}\left( (\mathbf{A} + t \mathbf{B})^{p} \right)$ where the matrices $\mathbf{A}$ and $\mathbf{B}$ are Hermitian and positive (semi) definite and $p$ and $t$ are real variables.
