Nov 1, 2016 · We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the ...
Abstract—We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to ...
We propose a probabilistic model discovery method for identifying ordinary differential equations governing the dynamics of observed multivariate data.
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Mar 15, 2021 · The most popular approach for addressing the problem of model uncertainty from a Bayesian perspective lies in the method of Bayesian model averaging (BMA).
Introducing sparsity in neu- ral networks has improved model performance, accelerated convergence, and regularized the network, improving model generalization [ ...
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Aug 6, 2015 · In this effort, we perform in this paper an adaptive hierarchical sparse grid surrogate modeling approach to Bayesian inference of large eddy ...
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This paper presents a novel framework for the uncertainty quantification of inverse problems often encountered in suspended nonstructural systems.
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Quantifying Registration Uncertainty With Sparse Bayesian Modelling by Loic Le Folgoc, Herve Delingette, Antonio Criminisi, Nicholas Ayache published.
Oct 27, 2021 · We explore how to exploit multiple datasets to quantify uncertainty in the learned models, and at the same time draw mechanistic conclusions about the target ...
In this paper, we are the first to introduce an efficient Bayesian image registration uncertainty quantification model that employs a low dimensional Fourier ...