Papers citing 'Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)'

Title
Riemannian-Geometric Fingerprints of Generative Models Hae Jin Song Laurent Itti 140 6 0 28 Jun 2025
Connecting Neural Models Latent Geometries with Relative Geodesic Representations Hanlin Yu Berfin Inal Georgios Arvanitidis Soren Hauberg Francesco Locatello Marco Fumero DRL 364 2 0 02 Jun 2025
Learning geometry and topology via multi-chart flows Hanlin Yu Søren Hauberg Marcelo Hartmann Arto Klami Georgios Arvanitidis AI4CE 230 1 0 30 May 2025
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 460 2 0 23 May 2025
Pulling back symmetric Riemannian geometry for data analysis W. Diepeveen 187 6 0 11 Mar 2024
Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders Vivien van Veldhuizen Sharvaree P. Vadgama Onno J. de Boer Sybren Meijer Erik Bekkers DRL 284 0 0 17 Mar 2023
Identifying latent distances with Finslerian geometry Alison Pouplin David Eklund Carl Henrik Ek Søren Hauberg 277 2 0 20 Dec 2022
Adversarial robustness of VAEs through the lens of local geometryInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022 Asif Khan Amos Storkey AAML DRL 256 5 0 08 Aug 2022
Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders Ivo Richert R. Buch 197 1 0 21 Apr 2022
Geometric instability of out of distribution data across autoencoder architecture S. Agarwala Ben Dees Corey Lowman DRL 162 1 0 28 Jan 2022
Robust uncertainty estimates with out-of-distribution pseudo-inputs training Pierre Segonne Yevgen Zainchkovskyy Søren Hauberg UQCV OOD 104 1 0 15 Jan 2022
Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize S. Agarwala Ben Dees A. Gearhart Corey Lowman 136 2 0 13 Jul 2021
Addressing the Topological Defects of Disentanglement via Distributed Operators Diane Bouchacourt Mark Ibrahim Stéphane Deny 128 22 0 10 Feb 2021
What is a meaningful representation of protein sequences?Nature Communications (Nat Commun), 2020 N. Detlefsen Søren Hauberg Wouter Boomsma 451 136 0 28 Nov 2020
Geometry-Aware Hamiltonian Variational Auto-Encoder Clément Chadebec Clément Mantoux S. Allassonnière DRL 170 17 0 22 Oct 2020
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data Martin Jørgensen Søren Hauberg 261 7 0 21 Jun 2020
Manifolds for Unsupervised Visual Anomaly Detection Louise Naud Alexander Lavin DRL 179 7 0 19 Jun 2020
Variational Autoencoders with Riemannian Brownian Motion PriorsInternational Conference on Machine Learning (ICML), 2020 Dimitris Kalatzis David Eklund Georgios Arvanitidis Søren Hauberg BDL DRL 300 54 0 12 Feb 2020
No Representation without Transformation Giorgio Giannone Saeed Saremi Jonathan Masci Christian Osendorfer BDL DRL 136 2 0 09 Dec 2019
Expected path length on random manifolds David Eklund Søren Hauberg DRL 220 15 0 20 Aug 2019
Reliable training and estimation of variance networksNeural Information Processing Systems (NeurIPS), 2019 N. Detlefsen Martin Jørgensen Søren Hauberg UQCV 258 96 0 04 Jun 2019
Intrinsic Universal Measurements of Non-linear Embeddings Ke Sun 132 0 0 05 Nov 2018