Lie group cohomology and (multi) symplectic integrators: new geometric tools for Lie group machine learning based on Souriau geometric statistical mechanics

F Barbaresco, F Gay-Balmaz - Entropy, 2020 - mdpi.com
In this paper, we describe and exploit a geometric framework for Gibbs probability densities
and the associated concepts in statistical mechanics, which unifies several earlier works on …

Computing CNN loss and gradients for pose estimation with Riemannian geometry

B Hou, N Miolane, B Khanal, MCH Lee… - … Image Computing and …, 2018 - Springer
Pose estimation, ie predicting a 3D rigid transformation with respect to a fixed co-ordinate
frame in, SE (3), is an omnipresent problem in medical image analysis. Deep learning …

Numerical accuracy of ladder schemes for parallel transport on manifolds

N Guigui, X Pennec - Foundations of Computational Mathematics, 2022 - Springer
Parallel transport is a fundamental tool to perform statistics on Riemannian manifolds. Since
closed formulae do not exist in general, practitioners often have to resort to numerical …

Operator-valued formulas for Riemannian gradient and Hessian and families of tractable metrics in Riemannian optimization

D Nguyen - Journal of Optimization Theory and Applications, 2023 - Springer
We provide explicit formulas for the Levi-Civita connection and Riemannian Hessian for a
Riemannian manifold that is a quotient of a manifold embedded in an inner product space …

A bi-invariant statistical model parametrized by mean and covariance on rigid motions

E Chevallier, N Guigui - Entropy, 2020 - mdpi.com
This paper aims to describe a statistical model of wrapped densities for bi-invariant statistics
on the group of rigid motions of a Euclidean space. Probability distributions on the group are …