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"

50 / 58 papers shown

Title
SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges Ce Ju Reinmar J. Kobler Antoine Collas M. Kawanabe Cuntai Guan Bertrand Thirion 41 0 0 26 Apr 2025
Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median Jakwang Kim Jiyoung Park Anirban Bhattacharya 30 0 0 19 Apr 2025
Riemann $^2$ : Learning Riemannian Submanifolds from Riemannian Data Leonel Rozo Miguel González-Duque Noémie Jaquier Søren Hauberg 52 1 0 07 Mar 2025
Approximation and bounding techniques for the Fisher-Rao distances between parametric statistical models Frank Nielsen 43 3 0 08 Jan 2025
Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport Michael Wilson Tom Needham A. Srivastava OT 29 0 0 15 Nov 2024
BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models Xinyuan Wang Victor Shea-Jay Huang Renmiao Chen Hao Wang C. Pan Lei Sha Minlie Huang AAML 23 2 0 13 Oct 2024
Bounds on the geodesic distances on the Stiefel manifold for a family of Riemannian metrics Simon Mataigne P.-A. Absil Nina Miolane OT 13 2 0 25 Jul 2024
Regularized estimation of Monge-Kantorovich quantiles for spherical data Bernard Bercu Jérémie Bigot Gauthier Thurin OT 31 1 0 02 Jul 2024
Learning from landmarks, curves, surfaces, and shapes in Geomstats Luís F. Pereira Alice Le Brigant Adele Myers Emmanuel Hartman Amil Khan Malik Tuerkoen Trey Dold Mengyang Gu Pablo Suárez-Serrato Nina Miolane AI4CE 27 0 0 14 Jun 2024
Symmetry Discovery Beyond Affine Transformations Ben Shaw A. Magner Kevin R. Moon 35 1 0 05 Jun 2024
A Metric-based Principal Curve Approach for Learning One-dimensional Manifold E. Cui 16 0 0 20 May 2024
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification Dimitris G. Giovanis Dimitrios Loukrezis Ioannis G. Kevrekidis Michael D. Shields 17 3 0 30 Jan 2024
Learning Distributions on Manifolds with Free-form Flows Peter Sorrenson Felix Dräxler Armand Rousselot Sander Hummerich Ullrich Kothe DRL AI4CE 24 2 0 15 Dec 2023
Fast hyperboloid decision tree algorithms Philippe Chlenski Ethan Turok A. Moretti I. Pe’er 16 4 0 20 Oct 2023
Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation Adele Myers Caitlin Taylor Zhehui Huang Gaurav Sukhatme 3DH 19 2 0 28 Sep 2023
A survey of manifold learning and its applications for multimedia Hannes Fassold 34 1 0 08 Sep 2023
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures Anastasis Kratsios Rui Hong Haitz Sáez de Ocáriz Borde 19 4 0 18 Aug 2023
Geometric Neural Diffusion Processes Emile Mathieu Vincent Dutordoir M. Hutchinson Valentin De Bortoli Yee Whye Teh Richard E. Turner DiffM 32 8 0 11 Jul 2023
Principal subbundles for dimension reduction M. Akhøj J. Benn E. Grong Stefan Sommer Xavier Pennec 36 1 0 06 Jul 2023
Geometric Autoencoders -- What You See is What You Decode Philipp Nazari Sebastian Damrich Fred Hamprecht 22 12 0 30 Jun 2023
HypLL: The Hyperbolic Learning Library Max van Spengler Philipp Wirth Pascal Mettes AI4CE 11 5 0 09 Jun 2023
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration Minheng Chen Zhirun Zhang Shuheng Gu Youyong Kong 25 4 0 10 May 2023
Differential geometry with extreme eigenvalues in the positive semidefinite cone Cyrus Mostajeran Nathael Da Costa Graham W. Van Goffrier R. Sepulchre 14 4 0 14 Apr 2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold Yanhong Fei Xian Wei Yingjie Liu Zhengyu Li Mingsong Chen 12 6 0 16 Feb 2023
Neural networks learn to magnify areas near decision boundaries Jacob A. Zavatone-Veth Sheng Yang Julian Rubinfien C. Pehlevan MLT AI4CE 20 6 0 26 Jan 2023
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems Dimitra Maoutsa 22 1 0 19 Jan 2023
Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation Moise Blanchard Adam Quinn Jaffe 11 3 0 22 Dec 2022
Parametric information geometry with the package Geomstats Alice Le Brigant Jules Deschamps Antoine Collas Nina Miolane 12 4 0 21 Nov 2022
$k$ -Means Clustering for Persistent Homology Yueqi Cao P. Leung Anthea Monod 8 3 0 18 Oct 2022
Rieoptax: Riemannian Optimization in JAX Saiteja Utpala Andi Han Pratik Jawanpuria Bamdev Mishra 16 3 0 10 Oct 2022
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves Adele Myers Nina Miolane 15 3 0 04 Oct 2022
NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning Buyun Liang Tim Mitchell Ju Sun OOD 6 7 0 03 Oct 2022
Small Transformers Compute Universal Metric Embeddings Anastasis Kratsios Valentin Debarnot Ivan Dokmanić 54 11 0 14 Sep 2022
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric Saiteja Utpala Praneeth Vepakomma Nina Miolane 14 7 0 08 Aug 2022
Geometric Learning of Hidden Markov Models via a Method of Moments Algorithm Berlin Chen Cyrus Mostajeran Salem Said 11 2 0 02 Jul 2022
Neural Networks as Paths through the Space of Representations Richard D. Lange Devin Kwok Jordan K Matelsky Xinyue Wang David Rolnick Konrad Paul Kording 27 4 0 22 Jun 2022
Riemannian data-dependent randomized smoothing for neural networks certification Pol Labarbarie H. Hajri M. Arnaudon 23 4 0 21 Jun 2022
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures Martin Bauw Santiago Velasco-Forero Jesús Angulo C. Adnet O. Airiau OODD 19 5 0 12 May 2022
Universal Prototype Transport for Zero-Shot Action Recognition and Localization Pascal Mettes 14 5 0 08 Mar 2022
Riemannian Score-Based Generative Modelling Valentin De Bortoli Emile Mathieu M. Hutchinson James Thornton Yee Whye Teh Arnaud Doucet DiffM 217 164 0 06 Feb 2022
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks Faria Huq Adrish Dey Sahra Yusuf Dena Bazazian Tolga Birdal Nina Miolane 28 1 0 29 Nov 2021
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning Buyun Liang Tim Mitchell Ju Sun 15 3 0 27 Nov 2021
GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition X. Nguyen 3DH 17 33 0 25 Nov 2021
Generalized Shape Metrics on Neural Representations Alex H. Williams Erin M Kunz Simon Kornblith Scott W. Linderman MedIm 4 93 0 27 Oct 2021
Universal Approximation Under Constraints is Possible with Transformers Anastasis Kratsios Behnoosh Zamanlooy Tianlin Liu Ivan Dokmanić 51 26 0 07 Oct 2021
Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes Zhiyuan Liu Jörn Schulz Mohsen Taheri Martin Styner James Damon Stephen M. Pizer J. S. Marron 10 0 0 06 Sep 2021
TensorFlow RiemOpt: a library for optimization on Riemannian manifolds O. Smirnov 8 7 0 27 May 2021
Riemannian Geometry with differentiable ambient space and metric operator Du Nguyen 13 1 0 04 May 2021
Closed-form geodesics and trust-region method to calculate Riemannian logarithms on Stiefel and its quotient manifolds Du Nguyen 23 14 0 12 Mar 2021
Limit Theorems for Fréchet Mean Sets S. Evans Adam Quinn Jaffe 19 5 0 23 Dec 2020