geomstats: a Python Package for Riemannian Geometry in Machine Learning

21 May 2018

Papers citing "geomstats: a Python Package for Riemannian Geometry in Machine Learning"

8 / 58 papers shown

Title
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics Du Nguyen 6 5 0 21 Sep 2020
Exponential-wrapped distributions on symmetric spaces Emmanuel Chevallier Didong Li Yulong Lu David B. Dunson 9 8 0 04 Sep 2020
Geoopt: Riemannian Optimization in PyTorch Max Kochurov R. Karimov Sergei Kozlukov 14 114 0 06 May 2020
Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration Cong Gao Xingtong Liu Wenhao Gu Benjamin Killeen Mehran Armand Russell H. Taylor Mathias Unberath ViT MedIm 9 47 0 24 Mar 2020
Escaping from saddle points on Riemannian manifolds Yue Sun Nicolas Flammarion Maryam Fazel 23 71 0 18 Jun 2019
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry Benjamin Hou Nina Miolane Bishesh Khanal M. J. Lee A. Alansary Steven G. McDonagh Joseph V. Hajnal Daniel Rueckert Ben Glocker Bernhard Kainz MedIm 9 29 0 02 May 2018
Differential geometry and stochastic dynamics with deep learning numerics Line Kühnel Alexis Arnaudon Stefan Sommer AI4CE 16 25 0 22 Dec 2017
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 238 3,236 0 24 Nov 2016