Geomstats: A Python Package for Riemannian Geometry in Machine Learning

7 April 2020

Paul Chauchat

Papers citing "Geomstats: A Python Package for Riemannian Geometry in Machine Learning"

9 / 9 papers shown

Diffusion Models for Constrained Domains

257

11 Apr 2023

Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation

Moise Blanchard

Adam Quinn Jaffe

153

22 Dec 2022

Parametric information geometry with the package GeomstatsACM Transactions on Mathematical Software (TOMS), 2022

161

21 Nov 2022

$k$-Means Clustering for Persistent Homology

k

-Means Clustering for Persistent HomologyAdvances in Data Analysis and Classification (ADAC), 2022

Yueqi Cao

P. Leung

Anthea Monod

290

18 Oct 2022

Universal Prototype Transport for Zero-Shot Action Recognition and LocalizationInternational Journal of Computer Vision (IJCV), 2022

Pascal Mettes

325

08 Mar 2022

Limit Theorems for Fréchet Mean Sets

S. Evans

Adam Quinn Jaffe

229

23 Dec 2020

Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics

Du Nguyen

160

21 Sep 2020

Riemannian adaptive stochastic gradient algorithms on matrix manifolds

Hiroyuki Kasai

Pratik Jawanpuria

Bamdev Mishra

272

04 Feb 2019

geomstats: a Python Package for Riemannian Geometry in Machine Learning

216

148

21 May 2018