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Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear ApproximationsGaussian-Like Spatial Priors for Articulated TrackingUnscented Kalman Filtering on Riemannian ManifoldsPredicting Articulated Human Motion from Spatial ProcessesData-driven forward model inference for EEG brain imagingUnscented Kalman Filtering for Articulated Human TrackingA Locally Adaptive Normal DistributionSpatial Models of Human MotionProbabilistic Solutions to Differential Equations and their Application to Riemannian StatisticsMetrics for Probabilistic GeometriesTransformations Based on Continuous Piecewise-Affine Velocity Fields.Scalable Robust Principal Component Analysis using Grassmann Averages.Geodesic exponential kernels: When curvature and linearity conflictPrincipal Curves on Riemannian Manifolds.Latent Space Oddity: on the Curvature of Deep Generative ModelsIntrinsic Grassmann Averages for Online Linear and Robust Subspace LearningDeep Diffeomorphic Transformer NetworksMaximum Likelihood Estimation of Riemannian Metrics from Euclidean DataModel Transport: Towards Scalable Transfer Learning on ManifoldsA Random Riemannian Metric for Probabilistic Shortest-Path TractographyGPU Accelerated Likelihoods for Stereo-Based Articulated TrackingThree Dimensional Monocular Human Motion Analysis in End-Effector SpaceBrownian Warps for Non-Rigid RegistrationFast and Robust Shortest Paths on Manifolds Learned from DataDirectional Statistics with the Spherical Normal DistributionGrassmann Averages for Scalable Robust PCAProbabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE SolversNatural metrics and least-committed priors for articulated trackingAn Empirical Study on the Performance of Spectral Manifold Learning TechniquesData-Driven Importance Distributions for Articulated TrackingStick It! Articulated Tracking Using Spatial Rigid Object PriorsMeans in spaces of tree-like shapesExplicit Disentanglement of Appearance and Perspective in Generative ModelsReliable training and estimation of variance networksA Geometric take on Metric LearningDreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data AugmentationIntrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace LearningVariational Autoencoders with Riemannian Brownian Motion Priors
P50
description
dansk maskinlæringsforsker
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researcher, machine learning, Technical University of Denmark
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wetenschapper
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Søren Hauberg
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P1416
P1053
L-2104-2016
P106
P1416
P1960
M1fMGOMAAAAJ
P21
P2456
P31
P3829
P496
0000-0001-7223-877X
P512
P569
2000-01-01T00:00:00Z