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  • IdHAL : alexander-gepperth
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40

Alexander Gepperth


Personal Information

 Family status: married, 2children

 Nationality: German

 

 Experience in academia

 5/2011 – present Tenured professor at „École Nationale Supérieure de Techniques
Avancées“ (Palaiseau, France)

 Assignments: Teaching, research, supervision of theses,
organization of the specialization subject „intelligent vehicles“

 Research focus: large-scale learning in intelligent vehicles

 

Industrial experience

02/2008 – 5/2011 Premature tenure as „Senior Scientist“ at Honda Research
Institute Europe GmbH

10/2006 – 2/2008 Four-year contract as „Senior Scientist“ at Honda Research
Institute Europe GmbH, Offenbach am Main, Germany

 Assignments: Basic research in machine learning for intelligent
vehicles, implementation of prototypes and demonstrations,
communication to management and academic community,
supervision of students

 

PhD thesis

11/2002 – 04/2006 at the university of Bochum, institute for neural computation

Subject: „Neural learning methods for visual object recognition“

Degree: Dr. rer. nat (grade: „very good“)

Assignments: research, teaching, participation in third-party funded projects (Honda Research Institute Europe GmbH, Robert Bosch KG , DFG Sonderforschungsbereich 475)

 

Tertiary education

10/1996 – 01/2002 Studies in physics at Ludwig-Maximilians-Universität Munich

Diploma thesis: „Non-BPS states in string theory” (grade: 1,7)

Degree: diploma (grade: „very good“)

 

Alternate civil service (instead of army service)

8/1995 - 10/1996 at the municipal hospital Pfaffenhofen/Ilm

 

Secondary education

6/1995 at Schyren-Gymnasium Pfaffenhofen/Ilm, grade: 1,8

 

Skills

Computers Programming: C/C++, CUDA, Python, Matlab

Web programming: HTML, CSS, PHP

Operating systems: Windows, Linux

Real-time middleware: ROS

Scientific standard tools: LaTeX, svn, git, doxygen, bash, eclipse, gnuplot, make, cmake, ...

Libraries: OpenCV, Qt, numpy/scipy, matplotlib/pylab

Languages German, Czech: mother tongues

English, French: fluent

Spanish: advanced level

Japanese:basic level

Interests

Tennis, volleyball, bodybuilding, Go, playing the violin, real-time strategy games (Starcraft)

 

 

 

 


Article dans une revue6 documents

  • Alexander Gepperth, Cem Karaoguz. A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems. Cognitive Computation, Springer, 2016, 8, pp.924 - 934. 〈10.1007/s12559-016-9389-5〉. 〈hal-01418123〉
  • Alexander Gepperth, Thomas Hecht, Mandar Gogate. A Generative Learning Approach to Sensor Fusion and Change Detection. Cognitive Computation, Springer, 2016, 8, pp.806 - 817. 〈10.1007/s12559-016-9390-z〉. 〈hal-01418125〉
  • Alexander Gepperth, Michael Garcia Ortiz, Egor Sattarov, Bernd Heisele. Dynamic attention priors: a new and efficient concept for improving object detection. Neurocomputing, Elsevier, 2016, 197, pp.14 - 28. 〈10.1016/j.neucom.2016.01.036〉. 〈hal-01418128〉
  • Alexander Gepperth. Efficient online bootstrapping of sensory representations. Neural Networks, Elsevier, 2012, Special Issue on Autonomous Learning, 〈10.1016/j.neunet.2012.11.002〉. 〈hal-00763660〉
  • Alexander Gepperth, Benjamin Dittes, Michaël Garcia Ortiz. The contribution of context information: a case study of object recognition in an intelligent car. Neurocomputing, Elsevier, 2012, 94, 〈10.1016/j.neucom.2012.03.008〉. 〈hal-00763650〉
  • Alexander Gepperth, Sven Rebhan, Stephan Hasler, Jannik Fritsch. Biased competition in visual processing hierarchies: a learning approach using multiple cues. Cognitive Computation, Springer, 2011, 3 (1). 〈hal-00647809〉

Communication dans un congrès33 documents

  • Thomas Kopinski, Fabian Sachara, Alexander Gepperth, Uwe Handmann. A Deep Learning Approach for Hand Posture Recognition from Depth Data. International Conference on Artificial Neural Networks (ICANN), 2016, Barcelona, Spain. pp.179 - 186, 2016, 〈10.1007/978-3-319-44781-0_22〉. 〈hal-01418137〉
  • Alexander Gepperth, Barbara Hammer. Incremental learning algorithms and applications. European Symposium on Artificial Neural Networks (ESANN), 2016, Bruges, Belgium. 〈hal-01418129〉
  • Alexander Gepperth, Mathieu Lefort. Learning to be attractive: probabilistic computation with dynamic attractor networks. Internal Conference on Development and LEarning (ICDL), 2016, Cergy-Pontoise, France. 〈hal-01418141〉
  • Thomas Hecht, Alexander Gepperth. Towards incremental deep learning: multi-level change detection in a hierarchical recognition architecture. European Symposium on Artificial Neural Networks (ESANN), 2016, Bruges, Belgium. 〈hal-01418132〉
  • Thomas Hecht, Alexander Gepperth. Computational Advantages of Deep Prototype-Based Learning. International Conference on Artificial Neural Networks (ICANN), 2016, Barcelona, Spain. pp.121 - 127, 2016, 〈10.1007/978-3-319-44781-0_15〉. 〈hal-01418135〉
  • Cem Karaoguz, Alexander Gepperth. Incremental Learning for Bootstrapping Object Classifier Models. IEEE International Conference On Intelligent Transportation Systems (ITSC), 2016, Seoul, South Korea. 〈hal-01418160〉
  • Mathieu Lefort, Alexander Gepperth. Learning of local predictable representations in partially learnable environments. The International Joint Conference on Neural Networks (IJCNN), Jul 2015, Killarney, Ireland. 2015. 〈hal-01205611〉
  • Mathieu Lefort, Alexander Gepperth. Active learning of local predictable representations with artificial curiosity. International Conference on Development and Learning and Epigenetic Robotics (ICDL-Epirob), Aug 2015, Providence, United States. 〈hal-01205619〉
  • Alexander Gepperth, Mathieu Lefort, Thomas Hecht, Ursula Körner. Resource-efficient incremental learning in very high dimensions. European Symposium on Artificial Neural Networks (ESANN), Apr 2015, Bruges, Belgium. 〈hal-01251015〉
  • Thomas Hecht, Alexander Gepperth. A generative-discriminative learning model for noisy information fusion. International Conference on Development and Learning (ICDL), Aug 2015, Providence, United States. IEEE International Conference on Development and Learning, ICDL 2015, 〈10.1109/DEVLRN.2015.7346148〉. 〈hal-01250967〉
  • Thomas Kopinski, Stéphane Magand, Alexander Gepperth, Uwe Handmann. A light-weight real-time applicable hand gesture recognition system for automotive applications. IEEE International Symposium on Intelligent Vehicles (IV), Jun 2015, Seoul, South Korea. IEEE Intelligent Vehicles Symposium (IV) 2015, pp.336-342, 2015, 〈10.1109/IVS.2015.7225708〉. 〈hal-01251413〉
  • Thomas Kopinski, Alexander Gepperth, Uwe Handmann. A simple technique for improving multi-class classification with neural networks. European Symposium on artificial neural networks (ESANN), Jun 2015, Bruges, Belgium. 〈hal-01251009〉
  • Alexander Gepperth, Thomas Hecht, Mathieu Lefort, Ursula Körner. Biologically inspired incremental learning for high-dimensional spaces. International Conference on Development and Learning (ICDL), Sep 2015, Providence, United States. Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015 Joint IEEE International Conference on 2015, 〈10.1109/DEVLRN.2015.7346155〉. 〈hal-01250961〉
  • Thomas Hecht, Mathieu Lefort, Alexander Gepperth. Using self-organizing maps for regression: the importance of the output function. European Symposium on Artificial Neural Networks (ESANN), Apr 2015, Bruges, Belgium. 〈hal-01251011〉
  • Alexander Gepperth, Mathieu Lefort. Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies. International Conference on Artificial Neural Networks (ICANN), Sep 2014, Hamburg, Germany. pp.715 - 722, 2014, 〈10.1007/978-3-319-11179-7_90〉. 〈hal-01098699〉
  • Egor Sattarov, Sergio Alberto Rodriguez Florez, Alexander Gepperth, Roger Reynaud. Context-based vector fields for multi-object tracking in application to road traffic. IEEE International Conference On Intelligent Transportation Systems (ITSC), Oct 2014, Qingdao, China. pp.1179 - 1185, 2014, 〈10.1109/ITSC.2014.6957847〉. 〈hal-01098701〉
  • Louis-Charles Caron, David Filliat, Alexander Gepperth. Neural Network Fusion of Color, Depth and Location for Object Instance Recognition on a Mobile Robot. Second Workshop on Assistive Computer Vision and Robotics (ACVR), in conjunction with European Conference on Computer Vision, Sep 2014, Zurich, Switzerland. 〈hal-01087392〉
  • Xiao Hu, Sergio Alberto Rodriguez Florez, Alexander Gepperth. A Multi-Modal System for Road Detection and Segmentation. IEEE Intelligent Vehicles Symposium, Jun 2014, Dearborn, Michigan, United States. pp.1365-1370, 2014. 〈hal-01023615〉
  • Alexander Gepperth, Egor Sattarov, Bernd Heisele, Sergio Alberto Rodriguez Florez. Robust visual pedestrian detection by tight coupling to tracking. IEEE International Conference On Intelligent Transportation Systems (ITSC), Oct 2014, Qingdao, China. pp.1935 - 1940, 2014, 〈10.1109/ITSC.2014.6957989〉. 〈hal-01098703〉
  • Thomas Kopinski, Stefan Geisler, Louis-Charles Caron, Alexander Gepperth, Uwe Handmann. A real-time applicable 3D gesture recognition system for automobile HMI. IEEE International Conference On Intelligent Transportation Systems (ITSC), Oct 2014, Qingdao, China. pp.2616 - 2622, 2014, 〈10.1109/ITSC.2014.6958109〉. 〈hal-01098700〉
  • Thomas Kopinski, Darius Malysiak, Alexander Gepperth, Uwe Handmann. Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition. IEEE International Symposium on Computational Intelligence and Informatics, Nov 2014, Budapest, Hungary. 2014. 〈hal-01098695〉
  • Mathieu Lefort, Alexander Gepperth. Discrimination of visual pedestrians data by combining projection and prediction learning. ESANN - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2014, Bruges, Belgium. 2014. 〈hal-01061654〉
  • Louis-Charles Caron, Yang Song, David Filliat, Alexander Gepperth. Neural network based 2D/3D fusion for robotic object recognition. ESANN, May 2014, Bruges, Belgium. pp.127 - 132, 2014. 〈hal-01012090〉
  • Thomas Kopinski, Alexander Gepperth, Stefan Geisler, Uwe Handmann. Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors. International Conference on Artificial Neural Networks (ICANN), Sep 2014, Hamburg, Germany. pp.233 - 240, 2014, 〈10.1007/978-3-319-11179-7_30〉. 〈hal-01098697〉
  • Mathieu Lefort, Alexander Gepperth. PROPRE: PROjection and PREdiction for multimodal correlations learning. An application to pedestrians visual data discrimination. IJCNN - International Joint Conference on Neural Networks, Jul 2014, Pékin, China. 2014. 〈hal-01061662〉
  • Mathieu Lefort, Thomas Kopinski, Alexander Gepperth. Multimodal space representation driven by self-evaluation of predictability. ICDL-EPIROB - The fourth joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, Oct 2014, Gênes, Italy. 2014. 〈hal-01061668〉
  • Alexander Gepperth. Latency-based probabilistic information processing in a learning feedback hierarchy. International Joint Conference on Neural Networks (IJCNN), Jun 2014, Beijing, China. pp.3031 - 3037, 2014, 〈10.1109/IJCNN.2014.6889919〉. 〈hal-01098704〉
  • Thomas Hecht, Mrinal Mohit, Egor Sattarov, Alexander Gepperth. Scene context is more than a Bayesian prior: Competitive vehicle detection with restricted detectors. IEEE International Symposium on Intelligent Vehicles(IV), May 2014, Detroit, United States. pp.1358 - 1364, 2014, 〈10.1109/IVS.2014.6856542〉. 〈hal-01098707〉
  • Egor Sattarov, Sergio Alberto Rodriguez Florez, Alexander Gepperth, Roger Reynaud. Context-based vector fields for multi-object tracking in application to road traffic. 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Oct 2014, Qingdao, China. International Conference on Intelligent Transportation Systems, 2014. 〈hal-01075777〉
  • Mathieu Dubois, Paola Rozo, Alexander Gepperth, Fabio González O., David Filliat. A Comparison of Geometric and Energy-Based Point Cloud Semantic Segmentation Methods. ECMR'13 - 6th European Conference on Mobile Robotics, Sep 2013, Barcelona, Spain. pp.88-93, 2013, Proceedings of the 6th European Conference on Mobile Robotics (ECMR). 〈10.1109/ECMR.2013.6698825〉. 〈hal-00963863〉
  • Alexander Gepperth. Co-training of context models for real-time object detection. IEEE Symposium on Intelligent Vehicles, Jun 2012, Madrid, Spain. 2012. 〈hal-00763676〉
  • Alexander Gepperth, Louis-Charles Caron. Simultaneous concept formation driven by predictability. International conference on development and learning, Nov 2012, San Diego, United States. 2012. 〈hal-00763671〉
  • David Filliat, Emmanuel Battesti, Stéphane Bazeille, Guillaume Duceux, Alexander Gepperth, et al.. RGBD object recognition and visual texture classification for indoor semantic mapping. Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on, Apr 2012, United States. pp.127 - 132, 2012, 〈10.1109/TePRA.2012.6215666〉. 〈hal-00755295〉

HDR1 document

  • Alexander Gepperth. New learning paradigms for real-world environment perception. Machine Learning [cs.LG]. Université Pierre & Marie Curie, 2016. 〈tel-01418147〉