3 citations · 6 across the 4 of their papers we have counts for
15 papers
A Unifying and Canonical Description of Measure-Preserving Diffusions
Alessandro Barp, So Takao, Michael Betancourt +2
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework. In this paper, we develop a geo…
Semi-supervised classification on graphs using explicit diffusion dynamics
Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. In…
Scale-dependent measure of network centrality from diffusion dynamics
Alexis Arnaudon, Robert L. Peach, Mauricio Barahona
Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection betw…
Irreversible Langevin MCMC on Lie Groups
Alexis Arnaudon, Alessandro Barp, So Takao
It is well-known that irreversible MCMC algorithms converge faster to their stationary distributions than reversible ones. Using the special geometric structure of Lie groups $\mat…
Selective metamorphosis for growth modelling with applications to landmarks
Andreas Bock, Alexis Arnaudon, Colin Cotter
We present a framework for shape matching in computational anatomy allowing users control of the degree to which the matching is diffeomorphic. This control is given as a function…
Stochastic Image Deformation in Frequency Domain and Parameter Estimation using Moment Evolutions
Line Kühnel, Alexis Arnaudon, Tom Fletcher +1
Modelling deformation of anatomical objects observed in medical images can help describe disease progression patterns and variations in anatomy across populations. We apply a stoch…