191 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Volkswagen Group (Germany)DE10 papers
- Technical University of MunichDE6 papers
- Data:Lab Munich (Germany)DE4 papers
- Ludwig-Maximilians-Universität MünchenDE3 papers
- Leiden UniversityNL2 papers
- OTH RegensburgDE2 papers
- Barcelona Institute of Science and TechnologyES1 paper
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE1 paper
- Freie Universität BerlinDE1 paper
- Institute of Photonic SciencesES1 paper
- Institute of Science and Technology1 paper
- Johns Hopkins UniversityUS1 paper
6 papers · 1 filter
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt +1
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages…
Learning Flat Latent Manifolds with VAEs
Nutan Chen, Alexej Klushyn, Francesco Ferroni +2
Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, w…
Learning Hierarchical Priors in VAEs
Alexej Klushyn, Nutan Chen, Richard Kurle +2
We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incenti…
Active Learning based on Data Uncertainty and Model Sensitivity
Nutan Chen, Alexej Klushyn, Alexandros Paraschos +2
Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether th…
Approximate Bayesian inference in spatial environments
Atanas Mirchev, Baris Kayalibay, Maximilian Soelch +2
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems suc…
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl, Maximilian Soelch, Justin Bayer +1
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in…