Prof. Dr.

Dominik Endres

Prof. Dr. Dominik Endres

Prof. Dr. Dominik Endres

Motivation: how do our brains represent the knowledge that both dogs and birds are animals, or that a car is a special type of vehicle with four wheels and an engine? More generally speaking, how do entities come to have meaning? Answering this fundamental cognitive neuroscience question would have several important applications. For example, it might enable us to design assistive technology for patients with certain degenerative diseases, e.g. semantic dementia (visual associative agnosia, Alzheimer's). On the more technical side, if we understood how the brain represents relational information on different levels of the (visual) cortical hierarchy, we would be able to bride the gap between mostly sensory-driven, bottom-up approaches in computer vision and machine learning on the one hand, and semantic-level, logical AI approaches (such as Markov logic or Bayesian logic programs) on the other hand.

Contact details
Philipps-University Marburg
FB 04 Psychology
Gutenbergstr. 18
35036 Marburg, Germany
+49 (0)6421 28 238 18


  • Junker, M., Endres, D., Sun, Z. P., Dicke, P. W., Giese, M., & Thier, P. (2018). Learning from the past: A reverberation of past errors in the cerebellar climbing fiber signal. PLoS biology, 16(8), e2004344. find paper
  • Chiovetto, E., Curio, C., Endres, D., & Giese, M. (2018). Perceptual integration of kinematic components in the recognition of emotional facial expressions. Journal of vision, 18(4), 13-13. doi:10.1167/18.4.13. find paper
  • Clever, D., Harant, M., Koch, K. H., Mombaur, K. and Endres, D. (2016). A novel approach for the generation of complex humanoid walking sequences based on a combination of optimal control and learning of movement primitives. Robotics and Autonomous Systems, Volume 83, 287–298. find paper
  • Clever, D., Harant, M., Mombaur, K., Naveau, M., Stasse, O. and Endres, D. (2017). COCoMoPL: A Novel Approach for Humanoid Walking Generation Combining Optimal Control, Movement Primitives and Learning and its transfer to the real robot HRP-2. IEEE Robotics and Automation Letters ,2(2):977 – 984. find paper
  • Mukovskiy, A., Taubert, N., Endres, D., Vassallo, C., Naveau, M., Stasse, O., Souères, P. and Giese, M. A. (2017). Modeling of coordinated human body motion by learning of structured dynamic representations. In J.-P. Laumond, N. Mansard, and J.-B. Lasserre, editors, Geometric and Numerical Foundations of Movements, volume 117 of STAR Series, pages 1–26. Springer. find paper
  • Quaglio, P., Yegenoglu, A., Torre, E., Endres, D. M., & Grün, S. (2017). Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience, 11, 41. find paper
  • Schubert, M., & Endres, D. (2018). Empirically Evaluating the Similarity Model of Geist, Lengnink and Wille. In International Conference on Conceptual Structures (pp. 88-95). Springer, Cham. find paper
  • Velychko, D., Endres , D., Taubert, N., and Giese, M. A. (2014). Coupling Gaussian process dynamical models with product-of-experts kernels. In Proceeding of the 24th International Conference on Artificial Neural Networks, LNCS 8681, pages 603–610. Springer. find paper
  • Velychko, D., Knopp, B. and Endres D. (2017). The coupled variational Gaussian process dynamical model. In Proceedings of the 27th International Conference on Artificial Neural Networks,pages 1–9. DOI find paper
  • Velychko, D., Knopp, B. and Endres, D. (2016). The variational coupled Gaussian process dynamical model (Abstract). NIPS Workshop on Neurorobotics. DOI find paper

former project-related publications

  • Endres , D., Christensen, A., Omlor, L., and Giese, M. A. (2011a). Emulating human observers with Bayesian binning: segmentation of action streams. ACM Transactions on Applied Perception (TAP), 8(3):16:1–12. find paper
  • Endres , D., Neumann, H., Kolesnik, M., and Giese, M. A. (2011b). Hooligan detection: the effects of saliency and expert knowledge. In Proceedings of the 4th International Conference for Imaging in Crime Detection and Prevention (ICDP 2011), pages 1–6. IET, ISBN-978-1-84919-565-2. find paper
  • Endres, D., Chiovetto, E. and Giese, M. A. (2013a). Model selection for the extraction of movement primitives. Frontiers in Computational Neuroscience , 7:185. find paper
  • Endres, D., Meirovitch, Y. Flash, T. and Giese M. A. (2013b). Segmenting sign language into motor primitives with Bayesian binning. Frontiers in Computational Neuroscience , 7:68, 2013. find paper
  • Taubert, N., Christensen, A., Endres, D. and Giese, M. A. (2012). Online Simulation of Emotional Interactive Behaviors with Hierarchical Gaussian Process Dynamical Models. Proceedings of the ACM Symposium on Applied Perception (ACM-SAP 2012), pages 25–32. find paper
  • Taubert, N., Löffler, M., Ludolph, N., Christensen, A., Endres, D. and Giese M.A. (2013). A virtual reality setup for controllable, stylized real-time interactions between humans and avatars with sparse Gaussian process dynamical models. Proceedings of the ACM Symposium on Applied Perception (ACM-SAP 2013), pages 41–44, 2013. find paper