180 citations · 687 across the 34 of their papers we have counts for
6 papers · 1 filter
On Krylov Methods for Large Scale CBCT Reconstruction
Malena Sabate Landman, Ander Biguri, Sepideh Hatamikia +3
Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularizati…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
Joint Modeling for Geometry and Functionality of Cerebral Cortical Surface Images
Jingjing Zou, Chi-Hua Chen, John A. D. Aston
We propose a framework for jointly modeling the geometry and functionality in high dimensional functional surfaces. The proposed mixed effects model characterizes effects of subjec…
Statistical Depth for Big Functional Data with Application to Neuroimaging
Alicia Nieto-Reyes, John A. D. Aston
Functional depth is the functional data analysis technique that orders a functional data set. Unlike the case of data on the real line, defining this order is non-trivial, and part…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
Testing for Geometric Invariance and Equivariance
Louis G. Christie, John A. D. Aston
Invariant and equivariant models incorporate the symmetry of an object to be estimated (here non-parametric regression functions ). These mo…