research
∙
08/31/2023
Learning to Taste: A Multimodal Wine Dataset
We present WineSensed, a large multimodal wine dataset for studying the ...
share
research
∙
07/20/2023
Variational Point Encoding Deformation for Dental Modeling
Digital dentistry has made significant advancements in recent years, yet...
research
∙
06/12/2023
Riemannian Laplace approximations for Bayesian neural networks
Bayesian neural networks often approximate the weight-posterior with a G...
research
∙
06/01/2023
On Masked Pre-training and the Marginal Likelihood
Masked pre-training removes random input dimensions and learns a model t...
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...
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...
research
∙
12/20/2022
Identifying latent distances with Finslerian geometry
Riemannian geometry provides powerful tools to explore the latent space ...
research
∙
11/10/2022
Probabilistic thermal stability prediction through sparsity promoting transformer representation
Pre-trained protein language models have demonstrated significant applic...
research
∙
09/10/2022
Revisiting Active Sets for Gaussian Process Decoders
Decoders built on Gaussian processes (GPs) are enticing due to the margi...
research
∙
06/30/2022
Laplacian Autoencoders for Learning Stochastic Representations
Established methods for unsupervised representation learning such as var...
research
∙
06/17/2022
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
This paper presents the computational challenge on differential geometry...
research
∙
06/03/2022
Is an encoder within reach?
The encoder network of an autoencoder is an approximation of the nearest...
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...
research
∙
03/17/2022
Visualizing Riemannian data with Rie-SNE
Faithful visualizations of data residing on manifolds must take the unde...
research
∙
03/15/2022
Reactive Motion Generation on Learned Riemannian Manifolds
In recent decades, advancements in motion learning have enabled robots t...
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...
research
∙
02/22/2022
Benchmarking Generative Latent Variable Models for Speech
Stochastic latent variable models (LVMs) achieve state-of-the-art perfor...
research
∙
02/22/2022
Adaptive Cholesky Gaussian Processes
We present a method to fit exact Gaussian process models to large datase...
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...
research
∙
01/15/2022
Robust uncertainty estimates with out-of-distribution pseudo-inputs training
Probabilistic models often use neural networks to control their predicti...
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...
research
∙
06/09/2021
Pulling back information geometry
Latent space geometry has shown itself to provide a rich and rigorous fr...
research
∙
06/08/2021
Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environm...
research
∙
06/07/2021
Multi-chart flows
We present Multi-chart flows, a flow-based model for concurrently learni...
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...
research
∙
11/25/2020
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Uncertainty quantification in image retrieval is crucial for downstream ...
research
∙
08/12/2020
Reparametrization Invariance in non-parametric Causal Discovery
Causal discovery estimates the underlying physical process that generate...
research
∙
08/02/2020
Geometrically Enriched Latent Spaces
A common assumption in generative models is that the generator immerses ...
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...
research
∙
04/07/2020
Probabilistic Spatial Transformers for Bayesian Data Augmentation
High-capacity models require vast amounts of data, and data augmentation...
research
∙
02/12/2020
Variational Autoencoders with Riemannian Brownian Motion Priors
Variational Autoencoders (VAEs) represent the given data in a low-dimens...
research
∙
08/20/2019
Expected path length on random manifolds
Manifold learning seeks a low dimensional representation that faithfully...
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...
research
∙
09/13/2018
Geodesic Clustering in Deep Generative Models
Deep generative models are tremendously successful in learning low-dimen...
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...
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...
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 ...
research
∙
02/03/2017
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Principal Component Analysis (PCA) is a fundamental method for estimatin...
research
∙
06/08/2016
A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance ...
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. ...
research
∙
11/27/2014
Metrics for Probabilistic Geometries
We investigate the geometrical structure of probabilistic generative dim...
research
∙
11/02/2014
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
We consider kernel methods on general geodesic metric spaces and provide...
research
∙
06/03/2013
