Søren Hauberg

research

∙ 06/01/2023

On Masked Pre-training and the Marginal Likelihood

Masked pre-training removes random input dimensions and learns a model t...

0 Pablo Moreno-Muñoz, et al. ∙

research

∙ 03/23/2023

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty

Out of distribution (OOD) medical images are frequently encountered, e.g...

0 Kilian Zepf, et al. ∙

research

∙ 02/02/2023

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

We propose the first Bayesian encoder for metric learning. Rather than r...

4 Frederik Warburg, et al. ∙

research

∙ 12/20/2022

Identifying latent distances with Finslerian geometry

Riemannian geometry provides powerful tools to explore the latent space ...

0 Alison Pouplin, et al. ∙

research

∙ 11/10/2022

Probabilistic thermal stability prediction through sparsity promoting transformer representation

Pre-trained protein language models have demonstrated significant applic...

0 Yevgen Zainchkovskyy, et al. ∙

research

∙ 09/10/2022

Revisiting Active Sets for Gaussian Process Decoders

Decoders built on Gaussian processes (GPs) are enticing due to the margi...

3 Pablo Moreno-Muñoz, et al. ∙

research

∙ 06/30/2022

Laplacian Autoencoders for Learning Stochastic Representations

Established methods for unsupervised representation learning such as var...

12 Marco Miani, et al. ∙

research

∙ 06/17/2022

ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results

This paper presents the computational challenge on differential geometry...

0 Adele Myers, et al. ∙

research

∙ 06/03/2022

Is an encoder within reach?

The encoder network of an autoencoder is an approximation of the nearest...

0 Helene Hauschultz, et al. ∙

research

∙ 05/31/2022

Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry

Deep generative models can automatically create content of diverse types...

0 Miguel Gonzalez Duque, et al. ∙

research

∙ 03/17/2022

Visualizing Riemannian data with Rie-SNE

Faithful visualizations of data residing on manifolds must take the unde...

0 Andri Bergsson, et al. ∙

research

∙ 03/15/2022

Reactive Motion Generation on Learned Riemannian Manifolds

In recent decades, advancements in motion learning have enabled robots t...

3 Hadi Beik-Mohammadi, et al. ∙

research

∙ 03/02/2022

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

We present simple methods for out-of-distribution detection using a trai...

0 Federico Bergamin, et al. ∙

research

∙ 02/22/2022

Benchmarking Generative Latent Variable Models for Speech

Stochastic latent variable models (LVMs) achieve state-of-the-art perfor...

0 Jakob D. Havtorn, et al. ∙

research

∙ 02/22/2022

Adaptive Cholesky Gaussian Processes

We present a method to fit exact Gaussian process models to large datase...

3 Simon Bartels, et al. ∙

research

∙ 02/03/2022

Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization

Place recognition and visual localization are particularly challenging i...

20 Andrea Vallone, et al. ∙

research

∙ 01/15/2022

Robust uncertainty estimates with out-of-distribution pseudo-inputs training

Probabilistic models often use neural networks to control their predicti...

0 Pierre Segonne, et al. ∙

research

∙ 11/01/2021

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

Energy-based models (EBMs) provide an elegant framework for density esti...

1 Cong Geng, et al. ∙

research

∙ 06/09/2021

Pulling back information geometry

Latent space geometry has shown itself to provide a rich and rigorous fr...

0 Georgios Arvanitidis, et al. ∙

research

∙ 06/08/2021

Learning Riemannian Manifolds for Geodesic Motion Skills

For robots to work alongside humans and perform in unstructured environm...

12 Hadi Beik-Mohammadi, et al. ∙

research

∙ 06/07/2021

Multi-chart flows

We present Multi-chart flows, a flow-based model for concurrently learni...

0 Dimitris Kalatzis, et al. ∙

research

∙ 02/16/2021

Hierarchical VAEs Know What They Don't Know

Deep generative models have shown themselves to be state-of-the-art dens...

0 Jakob D. Havtorn, et al. ∙

research

∙ 11/25/2020

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

Uncertainty quantification in image retrieval is crucial for downstream ...

12 Frederik Warburg, et al. ∙

research

∙ 08/12/2020

Reparametrization Invariance in non-parametric Causal Discovery

Causal discovery estimates the underlying physical process that generate...

0 Martin Jørgensen, et al. ∙

research

∙ 08/02/2020

Geometrically Enriched Latent Spaces

A common assumption in generative models is that the generator immerses ...

20 Georgios Arvanitidis, et al. ∙

research

∙ 06/21/2020

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data

We propose a fully generative model where the latent variable respects b...

0 Martin Jørgensen, et al. ∙

research

∙ 04/07/2020

Probabilistic Spatial Transformers for Bayesian Data Augmentation

High-capacity models require vast amounts of data, and data augmentation...

11 Pola Schwöbel, et al. ∙

research

∙ 02/12/2020

Variational Autoencoders with Riemannian Brownian Motion Priors

Variational Autoencoders (VAEs) represent the given data in a low-dimens...

0 Dimitris Kalatzis, et al. ∙

research

∙ 08/20/2019

Expected path length on random manifolds

Manifold learning seeks a low dimensional representation that faithfully...

0 David Eklund, et al. ∙

research

∙ 01/22/2019

Fast and Robust Shortest Paths on Manifolds Learned from Data

We propose a fast, simple and robust algorithm for computing shortest pa...

0 Georgios Arvanitidis, et al. ∙

research

∙ 09/13/2018

Geodesic Clustering in Deep Generative Models

Deep generative models are tremendously successful in learning low-dimen...

6 Tao Yang, et al. ∙

research

∙ 06/13/2018

Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)

We investigate learning of the differential geometric structure of a dat...

4 Søren Hauberg, et al. ∙

research

∙ 05/23/2018

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

Latent variable models learn a stochastic embedding from a low-dimension...

0 Anton Mallasto, et al. ∙

research

∙ 10/31/2017

Latent Space Oddity: on the Curvature of Deep Generative Models

Deep generative models provide a systematic way to learn nonlinear data ...

0 Georgios Arvanitidis, et al. ∙

research

∙ 02/03/2017

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

Principal Component Analysis (PCA) is a fundamental method for estimatin...

0 Rudrasis Chakraborty, et al. ∙

research

∙ 06/08/2016

A Locally Adaptive Normal Distribution

The multivariate normal density is a monotonic function of the distance ...

0 Georgios Arvanitidis, et al. ∙

research

∙ 10/09/2015

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

Data augmentation is a key element in training high-dimensional models. ...

0 Søren Hauberg, et al. ∙

research

∙ 11/27/2014

Metrics for Probabilistic Geometries

We investigate the geometrical structure of probabilistic generative dim...

0 Alessandra Tosi, et al. ∙

research

∙ 11/02/2014

Geodesic Exponential Kernels: When Curvature and Linearity Conflict

We consider kernel methods on general geodesic metric spaces and provide...

0 Aasa Feragen, et al. ∙

research

∙ 06/03/2013

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

We study a probabilistic numerical method for the solution of both bound...

0 Philipp Hennig, et al. ∙

Søren Hauberg

Featured Co-authors