1 code implementation • ICLR 2019 • Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.
no code implementations • CVPR 2020 • Nina Miolane, Susan Holmes
One of the challenges that naturally arises consists of finding a lower-dimensional subspace for representing such manifold-valued data.
no code implementations • 19 Nov 2019 • Nina Miolane, Frédéric Poitevin, Yee-Ting Li, Susan Holmes
As such, it opens the door to geometric approaches for unsupervised estimations of orientations and camera parameters, making possible fast cryo-EM biomolecule reconstruction.
no code implementations • 8 Nov 2019 • Claire Donnat, Susan Holmes
Convex clustering is a recent stable alternative to hierarchical clustering.
1 code implementation • 19 Mar 2019 • Christof Seiler, Lisa M. Kronstad, Laura J. Simpson, Mathieu Le Gars, Elena Vendrame, Catherine A. Blish, Susan Holmes
In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles.
Applications
no code implementations • 6 Sep 2016 • Nina Miolane, Susan Holmes, Xavier Pennec
We use tools from geometric statistics to analyze the usual estimation procedure of a template shape.
no code implementations • NeurIPS 2014 • Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
The Jacobi metric introduced in mathematical physics can be used to analyze Hamiltonian Monte Carlo (HMC).
1 code implementation • 4 Jul 2014 • Susan Holmes, Simon Rubinstein-Salzedo, Christof Seiler
In this article, we analyze Hamiltonian Monte Carlo (HMC) by placing it in the setting of Riemannian geometry using the Jacobi metric, so that each step corresponds to a geodesic on a suitable Riemannian manifold.
Probability Differential Geometry Statistics Theory Statistics Theory
1 code implementation • 1 Oct 2013 • Paul J. McMurdie, Susan Holmes
We use statistical theory, extensive simulations, and empirical data to show that variance stabilizing normalization using a mixture model like the negative binomial is appropriate for microbiome count data.