Our latest publications

  • Richard Kurle, Stephan G√ľnnemann, Patrick van der Smagt (2019)
    Multi-Source Neural Variational Inference
    AAAI Conference on Artificial Intelligence [www]
  • Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt (2018)
    Active Learning based on Data Uncertainty and Model Sensitivity
    International Conference on Intelligent Robots and Systems (IROS) [www]
  • Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer (2018)
    Approximate Bayesian inference in spatial environments
    arXiv [www]
  • Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt (2018)
    Metrics for Deep Generative Models
    International Conference on Artificial Intelligence and Statistics (AISTATS) [www]
  • Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer (2017)
    Unsupervised Real-Time Control through Variational Empowerment
    arXiv [www]
  • Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt (2017)
    Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
    International Conference on Learning Representations (ICLR) [www|bibtex|blog]
  • Baris Kayalibay, Grady Jensen, Patrick van der Smagt (2017)
    CNN-based Segmentation of Medical Imaging Data
    arXiv [www|bibtex]
  • Nutan Chen, Maximilian Karl, Patrick van der Smagt (2016)
    Dynamic Movement Primitives in Latent Space of Time-Dependent Variational Autoencoders
    Proc. 16th IEEE-RAS International Conference on Humanoid Robots [pdf|bibtex]
  • Previous publications