| Latent space oddity: on the curvature of deep generative models G Arvanitidis, LK Hansen, S Hauberg ICLR 2018, 2018 | 299 | 2018 |
| Geometrically enriched latent spaces G Arvanitidis, S Hauberg, B Schölkopf AISTATS 2021, 2020 | 66 | 2020 |
| Variational autoencoders with riemannian brownian motion priors D Kalatzis, D Eklund, G Arvanitidis, S Hauberg ICML 2020, 2020 | 51 | 2020 |
| Fast and robust shortest paths on manifolds learned from data G Arvanitidis, S Hauberg, P Hennig, M Schober AISTATS 2019, 2019 | 50 | 2019 |
| Geodesic clustering in deep generative models T Yang, G Arvanitidis, D Fu, X Li, S Hauberg arXiv preprint arXiv:1809.04747, 2018 | 36 | 2018 |
| A locally adaptive normal distribution G Arvanitidis, LK Hansen, S Hauberg NeurIPS 2016, 2016 | 33 | 2016 |
| Learning riemannian manifolds for geodesic motion skills H Beik-Mohammadi, S Hauberg, G Arvanitidis, G Neumann, L Rozo R:SS 2021, 2021 | 29 | 2021 |
| Exploiting graph embedding in support vector machines G Arvanitidis, A Tefas MLSP 2012, 2012 | 17 | 2012 |
| Pulling back information geometry G Arvanitidis, M González-Duque, A Pouplin, D Kalatzis, S Hauberg AISTATS 2022, 2021 | 15 | 2021 |
| Riemannian Laplace approximations for Bayesian neural networks F Bergamin, P Moreno-Muñoz, S Hauberg, G Arvanitidis NeurIPS 2023, 2023 | 13 | 2023 |
| A prior-based approximate latent riemannian metric G Arvanitidis, B Georgiev, B Schölkopf AISTATS 2022, 2021 | 13 | 2021 |
| Reactive motion generation on learned riemannian manifolds H Beik-Mohammadi, S Hauberg, G Arvanitidis, G Neumann, L Rozo IJRR 2023, 2022 | 11 | 2022 |
| Neural Contractive Dynamical Systems H Beik-Mohammadi, S Hauberg, G Arvanitidis, N Figueroa, G Neumann, ... ICLR 2024, 2024 | 7 | 2024 |
| Maximum likelihood estimation of riemannian metrics from euclidean data G Arvanitidis, LK Hansen, S Hauberg GSI 2017, 2017 | 7 | 2017 |
| On Data Manifolds Entailed by Structural Causal Models R Dominguez-Olmedo, AH Karimi, G Arvanitidis, B Schölkopf ICML 2023, 2023 | 5 | 2023 |
| Bayesian quadrature on Riemannian data manifolds C Fröhlich, A Gessner, P Hennig, B Schölkopf, G Arvanitidis ICML 2021, 2021 | 3 | 2021 |
| On the curvature of the loss landscape A Pouplin, H Roy, SP Singh, G Arvanitidis arXiv preprint arXiv:2307.04719, 2023 | 2 | 2023 |
| Geometrical aspects of manifold learning G Arvanitidis Technical University of Denmark, 2019 | 2 | 2019 |
| Counterfactual explanations via riemannian latent space traversal P Pegios, A Feragen, AA Hansen, G Arvanitidis arXiv preprint arXiv:2411.02259, 2024 | 1 | 2024 |
| On the impact of stable ranks in deep nets B Georgiev, L Franken, M Mukherjee, G Arvanitidis arXiv preprint arXiv:2110.02333, 2021 | 1 | 2021 |
Søren HaubergCognitive Systems, DTU Compute, Technical University of DenmarkEmail yang diverifikasi di dtu.dk