List of works - Søren Hauberg

A Continuous Relaxation for Discrete Bayesian Optimization ⬇️

scientific article published on 26 April 2024

A Geometric take on Metric Learning ⬇️

scientific article published in January 2012

A Locally Adaptive Normal Distribution ⬇️

scientific article (publication date: 23 September 2016)

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences ⬇️

scientific article published on 7 June 2024

Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs

scientific article published on 03 June 2024

An Empirical Study on the Performance of Spectral Manifold Learning Techniques

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval ⬇️

scientific article published on 2 February 2023

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval ⬇️

scientific article

Benchmarking Generative Latent Variable Models for Speech ⬇️

scientific article published on 22 February 2022

Bounds all around: training energy-based models with bidirectional bounds ⬇️

article published in 2021

Brownian Warps for Non-Rigid Registration

Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?

scholarly article by Jeppe Thagaard et al published 2020 in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Data-Driven Importance Distributions for Articulated Tracking

Data-driven forward model inference for EEG brain imaging

scientific article

Deep Diffeomorphic Transformer Networks ⬇️

scholarly article published 2018

Directional Statistics with the Spherical Normal Distribution

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation ⬇️

scholarly article published 2016

Explicit Disentanglement of Appearance and Perspective in Generative Models ⬇️

Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions ⬇️

scientific article published on 18 November 2024

Fast and Robust Shortest Paths on Manifolds Learned from Data ⬇️

GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking

article by Rune Møllegaard Friborg et al published 2012 in Lecture Notes in Computer Science

Gaussian-Like Spatial Priors for Articulated Tracking

scientific article

Geodesic exponential kernels: When curvature and linearity conflict

scientific article published on June 2015

Gradients of Functions of Large Matrices ⬇️

scientific article published on 27 May 2024

Grassmann Averages for Scalable Robust PCA

Hierarchical VAEs Know What They Don't Know ⬇️

scientific article published on 16 February 2021

Improving Adversarial Energy-Based Model via Diffusion Process ⬇️

scientific article published on 4 March 2024

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

scientific article published in July 2017

Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning

scientific article published on 04 May 2020

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data ⬇️

scientific article

Laplacian Autoencoders for Learning Stochastic Representations ⬇️

scientific article published on 30 June 2022

Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification

Latent Space Oddity: on the Curvature of Deep Generative Models ⬇️

scholarly article by Georgios Arvanitidis et al published 31 October 2017 in Seventh International Conference on Learning Representations

Learning to Taste: A Multimodal Wine Dataset ⬇️

scientific article published on 31 August 2023

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations

scientific article

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition ⬇️

Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data

scientific article published in 2017

Means in spaces of tree-like shapes

scientific article published in November 2011

Metrics for Probabilistic Geometries ⬇️

scientific article published on 27 November 2014

Model Transport: Towards Scalable Transfer Learning on Manifolds

scientific article published in June 2014

Model-agnostic out-of-distribution detection using combined statistical tests ⬇️

scientific article published on 2 March 2022

Natural metrics and least-committed priors for articulated tracking

article by Søren Hauberg et al published June 2012 in Image and Vision Computing

Neural Contractive Dynamical Systems ⬇️

scientific article published on 17 January 2024

On Masked Pre-training and the Marginal Likelihood ⬇️

scientific article published in June 2023

Predicting Articulated Human Motion from Spatial Processes

scientific article

Principal Curves on Riemannian Manifolds

scientific article published on 29 October 2015

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

scientific article published on 01 January 2014

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics ⬇️

scientific article published on 12 February 2014

Reactive Motion Generation on Learned Riemannian Manifolds ⬇️

scientific article published on 28 August 2023

Reliable training and estimation of variance networks ⬇️

Reparameterization invariance in approximate Bayesian inference ⬇️

scientific article published on 5 June 2024

Riemannian Laplace approximations for Bayesian neural networks ⬇️

scientific article published on 12 June 2023

Scalable Robust Principal Component Analysis using Grassmann Averages

scientific article published on 23 December 2015

Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information ⬇️

scientific article published on 23 September 2024

Spatial Models of Human Motion ⬇️

2011 doctoral thesis written by Søren Hauberg

Spontaneous Symmetry Breaking in Data Visualization

Stick It! Articulated Tracking Using Spatial Rigid Object Priors

Three Dimensional Monocular Human Motion Analysis in End-Effector Space

article

Transformations Based on Continuous Piecewise-Affine Velocity Fields

scientific article published on 11 January 2017

Unscented Kalman Filtering for Articulated Human Tracking

scientific article (publication date: 2011)

Unscented Kalman Filtering on Riemannian Manifolds

scientific article

List of works by Søren Hauberg

A Continuous Relaxation for Discrete Bayesian Optimization ⬇️

A Geometric take on Metric Learning ⬇️

A Locally Adaptive Normal Distribution ⬇️

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences ⬇️

Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs

An Empirical Study on the Performance of Spectral Manifold Learning Techniques

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval ⬇️

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval ⬇️

Benchmarking Generative Latent Variable Models for Speech ⬇️

Bounds all around: training energy-based models with bidirectional bounds ⬇️

Brownian Warps for Non-Rigid Registration

Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?

Data-Driven Importance Distributions for Articulated Tracking

Data-driven forward model inference for EEG brain imaging

Deep Diffeomorphic Transformer Networks ⬇️

Directional Statistics with the Spherical Normal Distribution

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation ⬇️

Explicit Disentanglement of Appearance and Perspective in Generative Models ⬇️

Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions ⬇️

Fast and Robust Shortest Paths on Manifolds Learned from Data ⬇️

GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking

Gaussian-Like Spatial Priors for Articulated Tracking

Geodesic exponential kernels: When curvature and linearity conflict

Gradients of Functions of Large Matrices ⬇️

Grassmann Averages for Scalable Robust PCA

Hierarchical VAEs Know What They Don't Know ⬇️

Improving Adversarial Energy-Based Model via Diffusion Process ⬇️

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data ⬇️

Laplacian Autoencoders for Learning Stochastic Representations ⬇️

Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification

Latent Space Oddity: on the Curvature of Deep Generative Models ⬇️

Learning to Taste: A Multimodal Wine Dataset ⬇️

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition ⬇️

Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data

Means in spaces of tree-like shapes

Metrics for Probabilistic Geometries ⬇️

Model Transport: Towards Scalable Transfer Learning on Manifolds

Model-agnostic out-of-distribution detection using combined statistical tests ⬇️

Natural metrics and least-committed priors for articulated tracking

Neural Contractive Dynamical Systems ⬇️

On Masked Pre-training and the Marginal Likelihood ⬇️

Predicting Articulated Human Motion from Spatial Processes

Principal Curves on Riemannian Manifolds

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics ⬇️

Reactive Motion Generation on Learned Riemannian Manifolds ⬇️

Reliable training and estimation of variance networks ⬇️

Reparameterization invariance in approximate Bayesian inference ⬇️

Riemannian Laplace approximations for Bayesian neural networks ⬇️

Scalable Robust Principal Component Analysis using Grassmann Averages

Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information ⬇️

Spatial Models of Human Motion ⬇️

Spontaneous Symmetry Breaking in Data Visualization

Stick It! Articulated Tracking Using Spatial Rigid Object Priors

Three Dimensional Monocular Human Motion Analysis in End-Effector Space

Transformations Based on Continuous Piecewise-Affine Velocity Fields

Unscented Kalman Filtering for Articulated Human Tracking

Unscented Kalman Filtering on Riemannian Manifolds

Variational Autoencoders with Riemannian Brownian Motion Priors