341 citations
- Volkswagen Group (United States)US10 papers
- Technical University of MunichDE7 papers
- Eötvös Loránd UniversityHU5 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE5 papers
- RWTH Aachen UniversityDE5 papers
- Michigan State UniversityUS4 papers
- Technische Universität BraunschweigDE4 papers
- Ludwig-Maximilians-Universität MünchenDE3 papers
- Technische Universität DarmstadtDE3 papers
- Xanadu Quantum Technologies (Canada)CA3 papers
- BMW (Germany)DE2 papers
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7 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…
On Deep Set Learning and the Choice of Aggregations
Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt +1
Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the…
Multi-Source Neural Variational Inference
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference method…
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…