
Pulling back information geometry
Latent space geometry has shown itself to provide a rich and rigorous fr...
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Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environm...
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Multichart flows
We present Multichart flows, a flowbased model for concurrently learni...
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Hierarchical VAEs Know What They Don't Know
Deep generative models have shown themselves to be stateoftheart dens...
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Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Uncertainty quantification in image retrieval is crucial for downstream ...
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Reparametrization Invariance in nonparametric Causal Discovery
Causal discovery estimates the underlying physical process that generate...
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Geometrically Enriched Latent Spaces
A common assumption in generative models is that the generator immerses ...
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Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
We propose a fully generative model where the latent variable respects b...
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Probabilistic Spatial Transformers for Bayesian Data Augmentation
Highcapacity models require vast amounts of data, and data augmentation...
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Variational Autoencoders with Riemannian Brownian Motion Priors
Variational Autoencoders (VAEs) represent the given data in a lowdimens...
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Expected path length on random manifolds
Manifold learning seeks a low dimensional representation that faithfully...
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Fast and Robust Shortest Paths on Manifolds Learned from Data
We propose a fast, simple and robust algorithm for computing shortest pa...
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Geodesic Clustering in Deep Generative Models
Deep generative models are tremendously successful in learning lowdimen...
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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...
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Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Latent variable models learn a stochastic embedding from a lowdimension...
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Latent Space Oddity: on the Curvature of Deep Generative Models
Deep generative models provide a systematic way to learn nonlinear data ...
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Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Principal Component Analysis (PCA) is a fundamental method for estimatin...
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A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance ...
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Dreaming More Data: Classdependent Distributions over Diffeomorphisms for Learned Data Augmentation
Data augmentation is a key element in training highdimensional models. ...
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Metrics for Probabilistic Geometries
We investigate the geometrical structure of probabilistic generative dim...
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Geodesic Exponential Kernels: When Curvature and Linearity Conflict
We consider kernel methods on general geodesic metric spaces and provide...
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Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
We study a probabilistic numerical method for the solution of both bound...
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Søren Hauberg
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