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Geomstats: A Python Package for Riemannian Geometry in Machine Learning

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

7 April 2020
Nina Miolane
Alice Le Brigant
Johan Mathe
Benjamin Hou
N. Guigui
Yann Thanwerdas
S. Heyder
Olivier Peltre
Niklas Koep
Hadi Zaatiti
H. Hajri
Yann Cabanes
Thomas Gerald
Paul Chauchat
Christian Shewmake
Bernhard Kainz
Claire Donnat
S. Holmes
Xavier Pennec
    AI4CE
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Papers citing "Geomstats: A Python Package for Riemannian Geometry in Machine Learning"

6 / 6 papers shown
Title
Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation
Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation
Moise Blanchard
Adam Quinn Jaffe
17
3
0
22 Dec 2022
Parametric information geometry with the package Geomstats
Parametric information geometry with the package Geomstats
Alice Le Brigant
Jules Deschamps
Antoine Collas
Nina Miolane
16
4
0
21 Nov 2022
$k$-Means Clustering for Persistent Homology
kkk-Means Clustering for Persistent Homology
Yueqi Cao
P. Leung
Anthea Monod
18
3
0
18 Oct 2022
Operator-valued formulas for Riemannian Gradient and Hessian and
  families of tractable metrics
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics
Du Nguyen
16
5
0
21 Sep 2020
geomstats: a Python Package for Riemannian Geometry in Machine Learning
geomstats: a Python Package for Riemannian Geometry in Machine Learning
Nina Miolane
Johan Mathe
Claire Donnat
Mikael Jorda
Xavier Pennec
AI4CE
37
123
0
21 May 2018
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
264
3,246
0
24 Nov 2016
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