Papers

Our latest publications

  • Variational Tracking and Prediction with Generative Disentangled State-Space Models
    Adnan Akhundov, Maximilian Soelch, Justin Bayer, and Patrick van der Smagt (2019)
    arXiv [www]
  • Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images
    Nutan Chen, Göran Westling, Benoni B. Edin, and Patrick van der Smagt (2019)
    Robotica [www]
  • Early Integration for Movement Modeling in Latent Spaces
    Rachel Hornung, Nutan Chen, and Patrick van der Smagt (2019)
    The Handbook of Multimodal-Multisensor Interfaces, Volume 3: Language Processing, Software, Commercialization, and Emerging Directions [www]
  • Unsupervised real-time control through variational empowerment
    Maximilian Karl, Philip Becker-Ehmck, Maximilian Soelch, Djalel Benbouzid, Patrick van der Smagt, and Justin Bayer (2019)
    International Symposium on Robotics Research (ISRR) [www]
  • Learning Hierarchical Priors in VAEs
    Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, and Patrick van der Smagt (2019)
    Conference on Neural Information Processing Systems (NeurIPS) [www]
  • Switching Linear Dynamics for Variational Bayes Filtering
    Philip Becker-Ehmck, Jan Peters, and Patrick van der Smagt (2019)
    International Conference on Machine Learning (ICML) [www]
  • Approximate bayesian inference in spatial environments
    Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt, and Justin Bayer (2019)
    Robotics: Science and Systems (RSS) [www]
  • On Deep Set Learning and the Choice of Aggregations
    Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, and Justin Bayer (2019)
    International Conference on Artificial Neural Networks (ICANN) [www|blog]
  • Increasing the Generalisation Capacity of Conditional VAEs
    Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, and Patrick van der Smagt (2019)
    International Conference on Artificial Neural Networks (ICANN) [www]
  • Fast approximate geodesics for deep generative models
    Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, and Patrick van der Smagt (2019)
    International Conference on Artificial Neural Networks (ICANN) [www|blog]
  • Bayesian learning of neural network architectures
    Georgi Dikov, Patrick van der Smagt, and Justin Bayer (2019)
    International Conference on Artificial Intelligence and Statistics (AISTATS) [www|blog]
  • Orc—a lightweight, lightning-fast middleware
    Felix Frank, Alexandros Paraschos, and Patrick van der Smagt (2019)
    IEEE International Conference on Robotic Computing (IRC) [www]
  • Multi-source neural variational inference
    Richard Kurle, Stephan Guennemann, and Patrick van der Smagt (2018)
    AAAI Conference on Artficial Intelligence [www]
  • Active learning based on data uncertainty and model sensitivity
    Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, and Patrick van der Smagt (2018)
    International Conference on Intelligent Robots and Systems (IROS) [www|blog]
  • Metrics for deep generative models
    Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick van der Smagt (2018)
    International Conference on Artificial Intelligence and Statistics (AISTATS) [www|blog]
  • CNN-based segmentation of medical imaging data
    Baris Kayalibay, Grady Jensen, and Patrick van der Smagt (2017)
    arXiv [www]
  • Deep variational Bayes filters: unsupervised learning of state space models from raw data
    Maximilian Karl, Maximilian Soelch, Justin Bayer, and Patrick van der Smagt (2017)
    International Conference on Learning Representations (ICLR) [www|blog]
  • Dynamic movement primitives in latent space of time-dependent variational autoencoders
    Nutan Chen, Maximilian Karl, and Patrick van der Smagt (2016)
    International Conference on Humanoid Robots (Humanoids) [www]

Previous publications