Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows

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Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows

2 October 2024

Willem Diepeveen

Georgios Batzolis

Zakhar Shumaylov

Carola-Bibiane Schönlieb

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Papers citing "Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows"

17 / 17 papers shown

Title
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models Louis Bethune David Vigouroux Yilun Du Rufin VanRullen Thomas Serre Victor Boutin DiffM 73 0 0 23 May 2025
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators N. Gabriel N. Johnson George Em Karniadakis AI4CE 119 0 0 21 Feb 2025
Learning Distances from Data with Normalizing Flows and Score Matching Peter Sorrenson Daniel Behrend-Uriarte Christoph Schnörr Ullrich Kothe 114 2 0 12 Jul 2024
Metric Flow Matching for Smooth Interpolations on the Data Manifold Kacper Kapusniak Peter Potaptchik Teodora Reu Leo Zhang Alexander Tong Michael M. Bronstein A. Bose Francesco Di Giovanni 82 22 0 23 May 2024
Pulling back symmetric Riemannian geometry for data analysis W. Diepeveen 77 3 0 11 Mar 2024
Score-based generative models learn manifold-like structures with constrained mixing Wenliang Kevin Li Ben Moran DiffM 96 8 0 16 Nov 2023
Generalization in diffusion models arises from geometry-adaptive harmonic representations Zahra Kadkhodaie Florentin Guth Eero P. Simoncelli Stéphane Mallat AI4CE DiffM 138 84 0 04 Oct 2023
Your diffusion model secretly knows the dimension of the data manifold Jan Stanczuk Georgios Batzolis Teo Deveney Carola-Bibiane Schönlieb DiffM 94 29 0 23 Dec 2022
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood Piotr Tempczyk Rafał Michaluk Łukasz Garncarek Przemysław Spurek Jacek Tabor Adam Goliñski 110 28 0 29 Jun 2022
Riemannian Metric Learning via Optimal Transport Christopher Scarvelis Justin Solomon OT 84 13 0 18 May 2022
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 510 6,599 0 26 Nov 2020
NVAE: A Deep Hierarchical Variational Autoencoder Arash Vahdat Jan Kautz BDL 132 919 0 08 Jul 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 757 10,591 0 17 Feb 2020
Neural Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios DRL 233 778 0 10 Jun 2019
A Locally Adaptive Normal Distribution Georgios Arvanitidis Lars Kai Hansen Søren Hauberg 91 29 0 08 Jun 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 279 3,730 0 26 May 2016
Testing the Manifold Hypothesis Charles Fefferman S. Mitter Hariharan Narayanan 175 537 0 01 Oct 2013