Sebastian Otte

Cognitive Modeling
Wilhelm Schickard Institute
University of Tübingen

Room C 419
Sand 14
D-72076 Tübingen, Germany

Phone: +49 7071 29 70481
Fax: +49 7071 29 5719
Email: sebastian.otte[at]uni-tuebingen.de

 

 


Bio

  • Since Juli 2016: Postdoc researcher, Cognitve modeling group, Universtiy of Tübingen
  • Januar 2013 - Juni 2016: PhD student, Cognitive systems group (supervised by Prof. Dr. Andreas Zell), University of Tübingen
  • October 2009 - May 2012: Master of Computer Science, University of Applied Sciences Wiesbaden
  • March 2007 - August 2009: Bachelor of Computer Science, University of Applied Sciences Wiesbaden



Research interests

  • Recurrent and Deep Neural Networks
  • Pattern Recognition and Data Analysis
  • Evolutionary and Population-based Optimization



Publications

2017

  • S. Otte, T. Schmitt, K. Friston, and M. V. Butz, “Inferring Adaptive Goal-Directed Behavior within Recurrent Neural Networks,” in International Conference on Artificial Neural Networks (ICANN), Alghero, Italy, 2017. Accepted for publication.
  • S. Otte, A. Zwiener, and M. V. Butz, “Inherently Constraint-Aware Control of Many-Joint Robot Arms with Inverse Recurrent Models,” in International Conference on Artificial Neural Networks (ICANN), Alghero, Italy, 2017. Accepted for publication.
  • S. Otte and M. V. Butz, “Differentiable Oscillators in Recurrent Neural Networks for Gradient-based Sequence Modeling,” in International Conference on Artificial Neural Networks (ICANN), Alghero, Italy, 2017. Accepted for publication.
  • S. Otte, T. Schmitt, and M. V. Butz, “Anticipatory Active Inference from Learned Recurrent Neural Forward Models,” in 39th Annual Meeting of the Cognitive Science Society (CogSci), London, United Kingdom, 2017. Accepted for publication.
  • C. Gumbsch, S. Otte, and M. V. Butz, “A Computational Model for the Dynamical Learning of Event Taxonomies,” in 39th Annual Meeting of the Cognitive Science Society (CogSci), London, United Kingdom, 2017. Accepted for publication.

 

2016

  • S. Otte, A. Zwiener, R. Hanten, and A. Zell, “Inverse Recurrent Models – An Application Scenario for Many-Joint Robot Arm Control,” in Artificial Neural Networks and Machine Learning – ICANN 2016, 2016, pp. 149–157.
  • A. Dörr, S. Otte, and A. Zell, “Investigating Recurrent Neural Networks for Feature-Less Computational Drug Design,” in Artificial Neural Networks and Machine Learning – ICANN 2016, 2016, pp. 140–148.
  • S. Otte, M. V. Butz, D. Koryakin, F. Becker, M. Liwicki, and A. Zell, “Optimizing recurrent reservoirs with neuro-evolution,” Neurocomputing, vol. 192, pp. 128–138, Jun. 2016.
  • L. Madai-Tahy, S. Otte, R. Hanten, and A. Zell, “Revisiting Deep Convolutional Neural Networks for RGB-D Based Object Recognition,” in Artificial Neural Networks and Machine Learning – ICANN 2016, 2016, pp. 29–37.
  • S. Otte, C. Weiss, T. Scherer, and A. Zell, “Recurrent Neural Networks for Fast and Robust Vibration-based Ground Classification on Mobile Robots,” in IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016, pp. 5603–5608.
  • R. Hanten, S. Buck, S. Otte, and A. Zell, “Vector-AMCL: Vector based Adaptive Monte Carlo Localization for Indoor Maps,” in 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, China, 2016.

 

2015

  • S. Otte, M. Liwicki, and A. Zell, “An Analysis of Dynamic Cortex Memory Networks,” in International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015, pp. 3338–3345.
  • A. Patel et al., “Confronting the challenge of ‘virtual’ prostate biopsy,” in 8th International Symposium on “Focal Therapy and Imaging in Prostate and Kidney Cancer, 2015.
  • S. Otte, F. Becker, M. V. Butz, M. Liwicki, and A. Zell, “Learning Recurrent Dynamics using Differential Evolution,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2015, pp. 65–70.
  • M. Binz, S. Otte, and A. Zell, “On the Applicability of Recurrent Neural Networks for Pattern Recognition in Electroencephalography Signals,” in Machine Learning Reports 03/2015, Workshop New Challenges in Neural Computation, 2015, pp. 85–92.
  • S. Otte, S. Laible, R. Hanten, M. Liwicki, and A. Zell, “Robust Visual Terrain Classification with Recurrent Neural Networks,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2015, pp. 451–456.

 

2014

  • S. Otte, U. Schwanecke, and A. Zell, “ANTSAC: A Generic RANSAC Variant Using Principles of Ant Colony Algorithms,” in 22nd International Conference on Pattern Recognition (ICPR), 2014, pp. 3558–3563.
  • S. Otte, M. Liwicki, and A. Zell, “Dynamic Cortex Memory: Enhancing Recurrent Neural Networks for Gradient-Based Sequence Learning,” in Artificial Neural Networks and Machine Learning – ICANN 2014, Ed. Springer International Publishing, 2014, pp. 1–8.
  • S. Otte, M. Liwicki, and D. Krechel, “Investigating Long Short-Term Memory Networks for various Pattern Recognition Problems,” in Machine Learning and Data Mining in Pattern Recognition, P. Perner, Ed. Springer International Publishing, 2014, pp. 484–497.
  • C. Otte et al., “Investigating Recurrent Neural Networks for OCT A-Scan based Tissue Analysis,” Methods of Information in Medicine, vol. 53, no. 4, pp. 245–249, 2014.
  • L. Wittig, C. Otte, S. Otte, G. Hüttmann, D. Drömann, and A. Schlaefer, “Tissue analysis of solitary pulmonary nodules using OCT A-Scan imaging needle probe,” European Respiratory Journal, vol. 44, no. Suppl 58, p. P4979, Sep. 2014.

 

2013

  • C. Otte, S. Otte, L. Wittig, G. Hüttmann, D. Drömann, and A. Schlaefer, “Identifizierung von Tumorgewebe in der Lunge mittels optischer Kohärenztomographie,” Lübeck, 2013.
  • S. Otte, D. Krechel, and M. Liwicki, “JANNLab Neural Network Framework for Java,” in Poster Proceedings ofthe International Conference on Machine Learning and Data Mining (MLDM), New York, USA, 2013, pp. 39–46.
  • S. Otte et al., “OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks,” in IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013.

 

2012

  • S. Otte, M. Liwicki, D. Krechel, and A. Dengel, “Local Feature based Online Mode Detection with Recurrent Neural Networks,” in ICFHR 2012: International Conference on Frontiers in Handwriting Recognition, 2012.

 

2011

  • S. Otte, U. Schwanecke, and P. Barth, “Mobile 3D Vision - 3D Scene Reconstruction with a Trifocal View on a Mobile Device,” in 06. Multimediakongress, Wismar, Germany, 2011.