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Søren Hauberg
Søren Hauberg
Cognitive Systems, DTU Compute, Technical University of Denmark
Verified email at dtu.dk - Homepage
Title
Cited by
Cited by
Year
GNU Octave Manual Version 3: A high-level interactive language for numerical computations
JW Eaton, D Bateman, S Hauberg
Network Theory Limited, ISBN: 0-9546120-6-X, 2008
2607*2008
GNU Octave version 4.0.0 manual: a high-level interactive language for numerical computations. 2015
JW Eaton, D Bateman, S Hauberg, R Wehbring
URL http://www. gnu. org/software/octave/doc/interpreter 8, 13, 2019
3472019
Latent space oddity: on the curvature of deep generative models
G Arvanitidis, LK Hansen, S Hauberg
arXiv preprint arXiv:1710.11379, 2017
2972017
Mapillary street-level sequences: A dataset for lifelong place recognition
F Warburg, S Hauberg, M Lopez-Antequera, P Gargallo, Y Kuang, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
2372020
Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
S Hauberg, O Freifeld, ABL Larsen, J Fisher, L Hansen
Artificial intelligence and statistics, 342-350, 2016
1902016
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
A Feragen, F Lauze, S Hauberg
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
1772014
Learning meaningful representations of protein sequences
NS Detlefsen, S Hauberg, W Boomsma
Nature communications 13 (1), 1914, 2022
1542022
Unscented Kalman filtering on Riemannian manifolds
S Hauberg, F Lauze, KS Pedersen
Journal of mathematical imaging and vision 46, 103-120, 2013
1382013
Manifold valued statistics, exact principal geodesic analysis and the effect of linear approximations
S Sommer, F Lauze, S Hauberg, M Nielsen
Computer Vision–ECCV 2010: 11th European Conference on Computer Vision …, 2010
1332010
A Geometric take on Metric Learning
S Hauberg, O Freifeld, MJ Black
Advances in Neural Information Processing Systems (NIPS), 2012
972012
Reliable training and estimation of variance networks
N Skafte, M Jørgensen, S Hauberg
Advances in Neural Information Processing Systems 32, 2019
93*2019
Hierarchical vaes know what they don’t know
JD Havtorn, J Frellsen, S Hauberg, L Maaløe
International Conference on Machine Learning, 4117-4128, 2021
802021
Grassmann Averages for Scalable Robust PCA
S Hauberg, A Feragen, MJ Black
IEEE Conference on Computer Vision and Pattern Recognition, 2014
782014
Metrics for Probabilistic Geometries
A Tosi, S Hauberg, A Vellido, ND Lawrence
30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), 0
74*
Geometrically enriched latent spaces
G Arvanitidis, S Hauberg, B Schölkopf
arXiv preprint arXiv:2008.00565, 2020
652020
Probabilistic solutions to differential equations and their application to Riemannian statistics
P Hennig, S Hauberg
AISTATS 58 (16), 2013
652013
Deep diffeomorphic transformer networks
NS Detlefsen, O Freifeld, S Hauberg
Proceedings of the IEEE conference on computer vision and pattern …, 2018
622018
Explicit disentanglement of appearance and perspective in generative models
N Skafte, S Hauberg
Advances in Neural Information Processing Systems 32, 2019
552019
Principal curves on Riemannian manifolds
S Hauberg
IEEE transactions on pattern analysis and machine intelligence 38 (9), 1915-1921, 2015
542015
Variational autoencoders with riemannian brownian motion priors
D Kalatzis, D Eklund, G Arvanitidis, S Hauberg
arXiv preprint arXiv:2002.05227, 2020
532020
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