about
The Machine Learning Research Lab in Volkswagen Group IT focuses on
fundamental machine learning research for probabilistic inference,
time series modelling, efficient exploration, and control.
Our work starts from probability theory, through neural networks, to applications inside and outside our lab.
This website is to inform you about our work through publications, blog posts, and other public output.
Spatial World Models
Spatial World Models
Latent Matters
10toGO: Virtual thinkathon for Sustainable Development Goals
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Less Suboptimal Learning and Control in Variational POMPDs
A Tale of Gaps
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
Estimating Fingertip Forces, Torques, and Object Curvatures from Fingernail Images
Layerwise learning for quantum neural networks
Learning to Fly
Learning Flat Manifold of VAEs
Approximate Bayesian inference in spatial environments
Learning Hierarchical Priors in VAEs
How to Learn Functions on Sets with Neural Networks
Approximate Geodesics for Deep Generative Models
Network Architecture Optimisation
Deep Variational Bayes Filter
About
