Anton Mallasto
Anton Mallasto
AI Scientist, Silo AI
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Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
A Mallasto, A Feragen
Proceedings of the 31st International Conference on Neural Information …, 2017
Wrapped Gaussian process regression on Riemannian manifolds
A Mallasto, A Feragen
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
How well do WGANs estimate the wasserstein metric?
A Mallasto, G Montúfar, A Gerolin
arXiv preprint arXiv:1910.03875, 2019
Entropy-regularized 2-Wasserstein distance between Gaussian measures
A Mallasto, A Gerolin, HQ Minh
Information Geometry, 1-35, 2021
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
A Mallasto, S Hauberg, A Feragen
arXiv preprint arXiv:1805.09122, 2018
(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
A Mallasto, J Frellsen, W Boomsma, A Feragen
arXiv preprint arXiv:1902.03642, 2019
Simulation of conditioned diffusions on the flat torus
MH Jensen, A Mallasto, S Sommer
International Conference on Geometric Science of Information, 685-694, 2019
Estimating 2-Sinkhorn divergence between Gaussian processes from finite-dimensional marginals
A Mallasto
arXiv preprint arXiv:2102.03267, 2021
A formalization of the natural gradient method for general similarity measures
A Mallasto, TD Haije, A Feragen
International Conference on Geometric Science of Information, 599-607, 2019
Affine Transport for Sim-to-Real Domain Adaptation
A Mallasto, K Arndt, M Heinonen, S Kaski, V Kyrki
arXiv preprint arXiv:2105.11739, 2021
Bayesian Inference for Optimal Transport with Stochastic Cost
A Mallasto, M Heinonen, S Kaski
arXiv preprint arXiv:2010.09327, 2020
On the estimation of the Wasserstein distance in generative models
A Mallasto, J Frellsen, W Boomsma, A Feragen
3.5 Geometry in uncertainty quantification
A Feragen, A Mallasto, S Hauberg
Visualization and Processing of Anisotropy in Imaging, Geometry, and …, 0
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