Cognitive Modeling

Dr. Sebastian Otte

Cognitive Modeling
Wilhelm Schickard Institute
University of Tübingen

Room C 420
Sand 14
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: Doctoral 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
  • Spiking Neural Networks
  • Pattern Recognition and Data Analysis
  • Evolutionary and Population-based Optimization

Teaching

  • WS18/19: Advanced Neural Networks
  • SS18: Seminar Current Advances in Deep and Recurrent Neural Networks
  • SS18: Informatik II
  • WS17/18: Seminar Dynamic Neural Networks
  • WS17/18: Praktikum Artificial Neural Networks
  • SS17: Informatik II
  • SS17: Praktikum Artificial Neural Networks
  • SS17: Einführung in Neuronale Netze
  • WS16/17: Advanced Neural Networks
  • WS16/17: Seminar Grounded Cognition
  • SS16: Einführung in Neuronale Netze
  • WS15/16: Artificial Intelligence
  • SS15: Advanced Neural Networks
  • SS15: Einführung in Neuronale Netze
  • WS14/15: Artificial Intelligence
  • SS14: Einführung in Neuronale Netze
  • SS14: Proseminar Machine Learning
  • WS13/14: Artificial Intelligence
  • SS13: Einführung in Neuronale Netze
  • SS13: Softwarepraktikum

Supervised theses and student projects

2019

  • Sebastian Penhouët, Locally Embedded Autoencoders (master thesis, work in progress)
  • Marius Hobbhahn, Temporal Gradient-based Module Identification (research project, work in progress)
  • Jakob Stoll, RNN-driven rocket ball motion planning in a dynamically changing environment (bachelor thesis, work in progress)
  • Lea Hofmaier, Generating Locomotion Patterns within Recurrent Sensorimotor Forward Models (master thesis, work in progress)
  • Jonas Einig, Learning Rare Classes (master thesis, in cooperation with Daimler AG, work in progress)
  • Erika Thierer, Learning Non-Maxmimum Suppression (master thesis, in cooperation with Daimler AG, work in progress)
  • Manuel Traub, Feasibility of training Recurrent Spiking Neural (Time Dependent Plasticity, 2019)

2018

  • Jonas Gregor Wiese, Discriminative Learning from Recurrent Generators (bachelor thesis)
  • Marius Hobbhahn, Inverse classification using generative models (bachelor thesis)
  • Sebastian Penhouët, Online Inference of Hyperparameters for Optimization Processes (research project)
  • Matthias Karlbauer, Investigating Noise Suppression for Deep Neural Car Detectors (master thesis, in cooperation with Daimler AG)
  • Florian Martin, Inferring Generating Trajectories from Images of Handwritten Letters with Recurrent Neural Networks (bachelor thesis)
  • Lea Hofmaier, Adding Obstacle-Awareness to a Many-Joint Robot Arm Controlled by a Recurrent Neural Network (lab project)
  • Mitja Nikolaus, Building Compact Generative RNNs For Handwritten Letters (lab project)
  • Patricia Rubisch, A Novel Approach to EEG Data Analysis using Neuro-Evolved Echo State Networks (bachelor thesis)
  • Markus Geike, Supervised Learning in Spiking Neural Networks with Error Feedback Loops (master thesis)
  • Kevin Laube, Reinforcement learning with Differentiable Neural Computers (master thesis)
  • Jonathan Schmidt, Modeling of Spiking Behavior with Gated Recurrent Neural Networks (bachelor thesis)
  • Nils Bultjer, Learning Spectral Representations with Generative Adversarial Networks using Sounds from Instruments and Urban Environment (master thesis)

2017

  • Steffen Schnürer, Event Segmentation mit Hilfe eines Rekurrenten Neuronalen Netzes mit LSTMs (bachelor thesis)
  • Danilo Brajovic, Exploring the informational content of max pooling positions in deep neuronal networks (bachelor thesis)
  • Albert Langensiepen, Systematisierte Generierung von Trainingsszenarien zur Audiosignalseparierung mittels Rekurrenter Neuronaler Netze auf Basis von Ableton (bachelor thesis)
  • Emanuel Gerber, High-Level Action Inference with a Hierarchical RNN Approach (bachelor thesis)
  • Michael Graf, Modellierung der Interaktion von dorsalem und ventralen Pfad mithilfe von Restricted Boltzmann Maschinen (bachelor thesis)
  • Theresa Schmitt, Active Inference with Recurrent Neural Networks (bachelor thesis)
  • Laurenz Grätz, Untersuchung von Pulsing Long Short-Term Memories für BPTT basierte Sequenzmodellierung (bachelor thesis)

2016

  • Erika Thierer, Object Detection with Deep Convolutional Neural Networks on RGB-Images (bachelor thesis)
  • Sindy Löwe, Semantic Segmentation of RGB-Images with Deep Convolutional Neural Networks (bachelor thesis)
  • Johannes Reisser, Pedestrian Segmentation with Deep Neural Networks (bachelor thesis)
  • Paul Stöckle, Audiosignal-Separierung mittels Rekurrenter Neuronaler Netze (bachelor thesis)
  • Lorand Madai-Tahy, Investigating Deep Neural Networks for RGB-D Based Object Recognition (bachelor thesis)

2015

  • Michael Schramm, Atembewegungsvorhersage mittels Rekurrenter Neuronaler Netze (bachelor thesis)
  • Marcel Binz, Pattern Recognition in Electroencephalography Signals with Recurrent Neural Networks (bachelor thesis)
  • Tobias Scherer, Terrain Classification based on Acceleration Sensor Data using Recurrent Neural Networks (bachelor thesis)

2014

  • Philipp Leutz, Liganden basiertes Virtual High Troughput Screening mit Künstlichen Neuronalen Netzen (bachelor thesis)
  • Oliver Obenland, Untersuchung populationsbasierter Optimierungsalgorithmen als alternative Trainingsverfahren für Deep Neural Networks (bachelor thesis)
  • Nils Bultjer, GP-GPU Implementation of an Experimental Suite for Deep Neural Networks (bachelor thesis)
  • Michaela Richter, Steuerung eines Micro Aerial Vehicles mit einem Rekurrenten Neuronalen Netz (bachelor thesis)

Publications

2019

  • S. Otte, P. Rubisch, and M. V. Butz, “Gradient-Based Learning of Compositional Dynamics with Modular RNNs”, in Artificial Neural Networks and Machine Learning – ICANN 2019, 2019, pp. 484–496. Best paper award.
  • S. Otte, J. Stoll, and M. V. Butz, “Incorporating Adaptive RNN-based Action Inference and Sensory Perception”, in Artificial Neural Networks and Machine Learning – ICANN 2019, 2019, pp. 543–555.
  • M. V. Butz, Menge Tobias, D. Humaidan, and S. Otte, “Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE”, in Artificial Neural Networks and Machine Learning – ICANN 2019, 2019, pp. 639–653.
  • M. V. Butz, D. Bilkey, D. Humaidan, A. Knott, and S. Otte, “Learning, planning, and control in a monolithic neural event inference architecture”, Neural Networks, May 2019.

2018

  • S. Otte, L. Hofmaier, and M. V. Butz, “Integrative Collision Avoidance Within RNN-Driven Many-Joint Robot Arms”, in Artificial Neural Networks and Machine Learning – ICANN 2018, 2018, pp. 748–758.
  • P. Kuhlmann, P. Sanzenbacher, and S. Otte, “Online Carry Mode Detection for Mobile Devices with Compact RNNs”, in Artificial Neural Networks and Machine Learning – ICANN 2018, 2018, pp. 232–241.
  • M. V. Butz, D. Bilkey, A. Knott, and S. Otte, “REPRISE: A Retrospective and Prospective Inference Scheme”, Proceedings of the 40th Annual Meeting of the Cognitive Science Society, pp. 1427–1432, Jul. 2018.
  • A. Zwiener, S. Otte, R. Hanten, and A. Zell, “Configuration Depending Crosstalk Torque Calibration for Robotic Manipulators with Deep Neural Regression Models”, in 15th International Conference on Intelligent Autonomous Systems (IAS), Baden-Baden, Germany, 2018, pp. 361–373.
  • R. Hanten, P. Kuhlmann, S. Otte, and A. Zell, “Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications”, in 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018, pp. 2000–2006.

2017

  • S. Otte and M. V. Butz, “Differentiable Oscillators in Recurrent Neural Networks for Gradient-based Sequence Modeling”, in Artificial Neural Networks and Machine Learning – ICANN 2016, 2017, pp. 745–746.
  • 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, p. 3803.
  • 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, pp. 452–457.

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.