Georgios Arvanitidis
Georgios Arvanitidis
Postdoctoral Researcher
Verified email at - Homepage
Cited by
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Latent space oddity: on the curvature of deep generative models
G Arvanitidis, LK Hansen, S Hauberg
Proceedings of the 6th International Conference on Learning Representations …, 2018
Fast and robust shortest paths on manifolds learned from data
G Arvanitidis, S Hauberg, P Hennig, M Schober
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
A locally adaptive normal distribution
G Arvanitidis, LK Hansen, S Hauberg
arXiv preprint arXiv:1606.02518, 2016
Variational autoencoders with riemannian brownian motion priors
D Kalatzis, D Eklund, G Arvanitidis, S Hauberg
arXiv preprint arXiv:2002.05227, 2020
Geodesic clustering in deep generative models
T Yang, G Arvanitidis, D Fu, X Li, S Hauberg
arXiv preprint arXiv:1809.04747, 2018
Exploiting graph embedding in support vector machines
G Arvanitidis, A Tefas
2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2012
Geometrically enriched latent spaces
G Arvanitidis, S Hauberg, B Schölkopf
arXiv preprint arXiv:2008.00565, 2020
Maximum likelihood estimation of Riemannian metrics from Euclidean data
G Arvanitidis, LK Hansen, S Hauberg
International Conference on Geometric Science of Information, 38-46, 2017
A prior-based approximate latent Riemannian metric
G Arvanitidis, B Georgiev, B Schölkopf
arXiv preprint arXiv:2103.05290, 2021
Pulling back information geometry
G Arvanitidis, M González-Duque, A Pouplin, D Kalatzis, S Hauberg
arXiv preprint arXiv:2106.05367, 2021
Learning Riemannian Manifolds for Geodesic Motion Skills
H Beik-Mohammadi, S Hauberg, G Arvanitidis, G Neumann, L Rozo
arXiv preprint arXiv:2106.04315, 2021
On the Impact of Stable Ranks in Deep Nets
B Georgiev, L Franken, M Mukherjee, G Arvanitidis
arXiv preprint arXiv:2110.02333, 2021
Bayesian Quadrature on Riemannian Data Manifolds
C Fröhlich, A Gessner, P Hennig, B Schölkopf, G Arvanitidis
arXiv preprint arXiv:2102.06645, 2021
Geometrical Aspects of Manifold Learning
G Arvanitidis, S Hauberg, LK Hansen, O Winther, M Girolami, D Pfau
Technical University of Denmark, 2019
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