Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

Score-based generative models (SGMs) are a powerful class of generative models that
exhibit remarkable empirical performance. Score-based generative modelling (SGM) …

Denoising diffusion models have proven to be a flexible and effective paradigm for
generative modelling. Their recent extension to infinite dimensional Euclidean spaces has …

Image-based 2D/3D registration is a critical technique for fluoroscopic guided surgical
interventions. Conventional intensity-based 2D/3D registration approa-ches suffer from a …

In this paper, we propose a novel method for representation and classification of two-person
interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian …

Abstract Symmetric Positive Definite (SPD) matrices are ubiquitous in data analysis under
the form of covariance matrices or correlation matrices. Several O (n)-invariant Riemannian …

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

We propose computationally tractable accelerated first-order methods for Riemannian
optimization, extending the Nesterov accelerated gradient (NAG) method. For both …

Synthesizing complex nanostructures and assemblies in experiments involves careful tuning
of design factors to obtain a suitable set of reaction conditions. In this paper, we study the …

We study representations of data from an arbitrary metric space χ in the space of univariate
Gaussian mixtures equipped with a transport metric (Delon and Desolneux 2020). We prove …