Proceedings

2024   2023   2022   2021   2020   2019   2018   2017   2016   2015   2014   2010   2009   2008   2005   2004   2002   2000   1999   1998   1997   1996  

2024

Teichmann, M., Larisch, R., Hamker, F.H. (2024)
Robustness of Biologically Grounded Neural Networks Against Image Perturbations
In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning ‐ ICANN 2024. Lecture Notes in Computer Science, vol 15025. Springer, Cham.. doi:10.1007/978-3-031-72359-9_16.

Fietzek, T., Ruff, C., Hamker. F.H. (2024)
A Brain-Inspired Model of Reaching and Adaptation on the iCub Robot
2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE). 1-7. doi:10.1109/ROSE62198.2024.10591174.
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2023

Farahani, A., Atoofi, P., Vitay, J., Hamker, F.H. (2023)
Implicit neural representations for deep drawing and joining experiments
Engineering for a Changing World: 60th ISC. Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08 2023. doi:10.22032/dbt.58849.
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Larisch, R., Berger, L., Hamker, F.H. (2023)
Exploring the Role of Feedback Inhibition for the Robustness Against Corruptions on Event-Based Data
In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning ‐ ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. pp 197‐208. doi:10.1007/978-3-031-44198-1_17.
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2022

Kuske, N., Ragni, M., Röhrbein, F., Vitay, J., Hamker, F.H. (2022)
Demands and potentials of different levels of neuro-cognitive models for human spatial cognition
E. Ferstl, L. Konieczny, & R. Stülpnagel (Eds.) KogWiss2022: the 15th Biannual Conference of the German Society for Cognitive Sciences, Albert-Ludwigs-Universität Freiburg:115-116. doi:10.6094/UNIFR/229611.

Farahani, A., Vitay, J., Hamker, F.H. (2022)
Deep Neural Networks for Geometric Shape Deformation
Bergmann, R., Malburg, L., Rodermund, S.C., Timm, I.J. (eds) KI 2022: Advances in Artificial Intelligence. Lecture Notes in Computer Science(), vol 13404. Springer, Cham. doi:10.1007/978-3-031-15791-2_9.

Fietzek, T., Dinkelbach, H.Ü, Hamker, F.H. (2022)
ANNarchy ‐ iCub: An Interface for Easy Interaction between Neural Network Models and the iCub Robot
IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2022). Chemnitz, 15-17 June 2022. doi:10.1109/CIVEMSA53371.2022.9853699.
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2021

Farahani, A., Vitay, J., Hamker, F.H. (2021)
Geometric Deep Learning and solutions for the industry
Workshop 3D-NordOst 2021. Tagungsband 23. Anwendungsbezogener Workshop zur Erfassung, Modellierung, Verarbeitung und Auswertung von 3D-Daten, Berlin, 02./03.12.2021: 105-113. ISBN: 978-3-942709-27-9.

Atoofi, P., Vitay, J,, Hamker, F.H. (2021)
Geometric Deep Learning: Graph Neural Networks, Challenges, and Breakthroughs
Workshop 3D-NordOst 2021. Tagungsband 23. Anwendungsbezogener Workshop zur Erfassung, Modellierung, Verarbeitung und Auswertung von 3D-Daten, Berlin, 02./03.12.2021:115-124. ISBN: 978-3-942709-27-9.

2020

Forch, V., Vitay, J., Hamker, F.H. (2020)
Recurrent Spatial Attention for Facial Emotion Recognition
LocalizeIT Workshop, Chemnitzer Linux-Tage. Chemnitz, 16.-17. 3. 2019. Chemnitzer Informatik-Berichte 2020:1-8.
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Schröder, E., Braun, S., Mählisch, M., Vitay, J, Hamker, F.H. (2020)
Feature Map Transformation for Fusion of Multi-Sensor Object Detection Networks for Autonomous Driving
Computer Vision Conference (CVC 2019). In: Arai K., Kapoor S. (eds.) Advances in Computer Vision. CVC 2019. AISC 944:118-131. doi:10.1007/978-3-030-17798-0_12.
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2019

Schmid, K., Vitay, J., Hamker, F. H. (2019)
Forward Models in the Cerebellum using Reservoirs and Perturbation Learning
Conference on Cognitive Computational Neuroscience. 2019, Berlin, Germany. doi:10.32470/CCN.2019.1139-0.
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Dinkelbach, H.Ü., Vitay, J., Hamker, F.H. (2019)
Scalable simulation of rate-coded and spiking neural networks on shared memory systems
Conference on Cognitive Computational Neuroscience. 13-16 September 2019, Berlin, Germany. doi:10.32470/CCN.2019.1109-0.

Nassour, J., Hamker, F.H. (2019)
Enfolded Textile Actuator for Soft Wearable Robots
2019 IEEE International Conference on Cyborg and Bionic Systems. 18-20 September, 2019. Technical University of Munich, Germany. doi:10.1109/CBS46900.2019.9114425.

Nassour, J., Vaghani, S., Hamker, F.H. (2019)
Design of Soft Exosuit for Elbow Assistance Using Butyl Tubes Rubber and Textile
Wearable Robotics: Challenges and Trends (Werob2018) Springer, Cham. Biosystems & Biorobotics book series (BIOSYSROB) 22:420-424. doi:10.1007/978-3-030-01887-0_81.

Nassour, J., Hamker, F.H. (2019)
Tactile and Proximity Servoing by A Multi-modal Sensory Soft Hand
Wearable Robotics: Challenges and Trends. WeRob 2018. - Springer, Cham. Biosystems & Biorobotics book series (BIOSYSROB) 22:396-400. doi:10.1007/978-3-030-01887-0_76.

2018

Winter, M., Kronfeld, Th., Brunnett, G., Dinkelbach, H.Ü. (2018)
Semi-Automatic Task Planning of Virtual Humans in Digital Factory Settings
Proceedings of CAD'18 CAD Solutions LLC. 283-287. doi:10.14733/cadconfP.2018.283-287.

Larisch, R., Teichmann, M., Hamker, F.H. (2018)
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement
Artificial Neural Networks and Machine Learning - ICANN 2018. - Cham : Springer International Publishing. 253-262. doi:10.1007/978-3-030-01418-6_25.

Schröder, E., Mählisch, M., Vitay, J., Hamker, F.H. (2018)
Fusion of Camera and Lidar Data for Object Detection using Neural Networks
12. Workshop Fahrerassistenzsysteme und automatisiertes Fahren FAS2018. Waiting im Altmühltal, 26.09.-28.09.2018, Uni-DAS e.V., 138-146.
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Makkar, D., Atoofi, P., Hamker, F.H., Nassour, J. (2018)
Motor Program Learning for Humanoid Robot Drawing
2018 IEEE-RAS. 18th IEEE International Conference on Humanoid Robots (HUMANOIDS 2018). Beijing, China, November 6-9 2018, pp. 1-9. doi:10.1109/HUMANOIDS.2018.8624958.

Pan, Y., Hamker, F.H., Nassour, J. (2018)
Humanoid robot grasping with a soft gripper through a learned inverse model of a central pattern generator and tactile servoing
IEEE International Conference on Humanoid Robots (HUMANOIDS 2018). Beijing, China, November 6-9.
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Vaghani, S., Pan, Y., Hamker, F.H., Nassour, J. (2018)
Gait Transition between Simple and Complex Locomotion in Humanoid Robots
International Conference on the Simulation of Adaptive Behavior (SAB2018). Frankfurt, Germany, 26 July. doi:10.1007/978-3-319-97628-0_10.

Nassour, J., Ghadiya, V., Hugel, V., Hamker, F.H. (2018)
Design of New Sensory Soft Hand: Combining Air-Pump Actuation with Superimposed Curvature and Pressure Sensors
The First IEEE-RAS International Conference on Soft Robotics (RoboSoft2018). Livorno, Italy, April 24-28. doi:10.1109/robosoft.2018.8404914.

2017

Jamalian, A., Bergelt, J., Dinkelbach, H.Ü., Hamker, F.H. (2017)
Spatial Attention Improves Object Localization: A Biologically Plausible Neuro-Computational Model for Use in Virtual Reality
In: The IEEE International Conference on Computer Vision (ICCV). 2724-2729. doi:10.1109/ICCVW.2017.320.
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Lötzsch, W., Vitay, J., Hamker, F.H. (2017)
Training a deep policy gradient-based neural network with asynchronous learners on a simulated robotic problem
In: 47. Jahrestagung der Gesellschaft für Informatik e.V. (GI). 2143-2154. doi:10.18420/in2017_214.
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2016

Villagrasa, F., Baladron, J., Hamker, F.H. (2016)
Fast and Slow Learning in a Neuro-Computational Model of Category Acquisition
In: A.E.P. Villa et al. (Eds.): ICANN 2016, Part I, LNCS 9886. 248-255. doi:10.1007/978-3-319-44778-0_29.
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Jamalian, A., Beuth, F., Hamker, F.H. (2016)
The Performance of a Biologically Plausible Model of Visual Attention to Localize Objects in a Virtual Reality
In: A.E.P. Villa et al. (Eds.): ICANN 2016, Part II, LNCS 9887. 447-454. doi:10.1007/978-3-319-44781-0_53.
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Jüngel, S., Beuth, F., Hamker, F. H. (2016)
Entwicklung einer virtuellen Versuchsumgebung zur experimentellen Untersuchung von Raumorientierung und visueller Aufmerksamkeit
In: M. Eibl, M. Gaedke (Eds.), Proc Studierendensymposium Informatik 2016 der TU Chemnitz (TUCSI 2016), 87-98. ISBN: 978-3-944640-85-3.
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Gussev, J., Dinkelbach, H.Ü., Beuth, F., Hamker, F. H. (2016)
Reinforcement Learning with Object Localization in a Virtual Environment
In: M. Eibl, M. Gaedke (Eds.), Proc Studierendensymposium Informatik 2016 der TU Chemnitz (TUCSI 2016), 111-116. ISBN: 978-3-944640-85-3.
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Schwarz, A., Beuth, F., Hamker, F. H. (2016)
Learning of Spatial Invariances for Object-Ground Separation
In: M. Eibl, M. Gaedke (Eds.), Proc Studierendensymposium Informatik 2016 der TU Chemnitz (TUCSI 2016), 127-132. ISBN: 978-3-944640-85-3.
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Jamalian, A., Beuth, F., Hamker, F. H. (2016)
Attentive Robot Vision
In: M. Eibl, M. Gaedke (Eds.), Proc Studierendensymposium Informatik 2016 der TU Chemnitz (TUCSI 2016), 161-164. ISBN: 978-3-944640-85-3.
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2015

Teichmann, M., Hamker, F. H. (2015)
Intrinsic Plasticity: A Simple Mechanism to Stabilize Hebbian Learning in Multilayer Neural Networks
In: T. Villmann, F.-M. Schleif (Eds.), Proc Workshop New Challenges in Neural Computation - NC² 2015, Machine Learning Reports 03/2015, 103-111. ISSN: 1865-3960.
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Beuth, F., Hamker, F. H. (2015)
Attention as cognitive, holistic control of the visual system
In: T. Villmann, F.-M. Schleif (Eds.), Proc Workshop New Challenges in Neural Computation - NC² 2015, Machine Learning Reports 03/2015, 133-140. ISSN: 1865-3960.
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Supplementary Material

2014

Debnath, S.; Nassour, J. (2014)
Extending cortical-basal inspired reinforcement learning model with success-failure experience
Development and Learning and Epigenetic Robotics. Joint IEEE International Conferences, 293-298. doi:10.1109/DEVLRN.2014.6982996.
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Debnath, S.; Nassour, J.; Cheng, G. (2014)
Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot
Humanoid Robots. 14th IEEE-RAS International Conference on. 1016-1021, 18-20. doi:10.1109/HUMANOIDS.2014.7041489.
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Kermani, K., Teichmann, M., Hamker, F. H. (2014)
Role of Competition in Robustness under Loss of Information in Feature Detectors
In: T. Villmann, F.-M. Schleif (Eds.), Proc Workshop New Challenges in Neural Computation - NC² 2014, Machine Learning Reports 02/2014, 16-19. ISSN: 1865-3960.
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Truschzinski, M., Müller, N., Dinkelbach, H.Ü., Protzel, P., Hamker, F.H., Ohler, P. (2014)
Deducing human emotions by robots: Computing basic non-verbal expressions of performed actions during a work task
IEEE Multi-conference on Systems and Control (MSC2014)/International Symposium on Intelligent Control (ISIC).

Beuth, F., Jamalian, A., Hamker, F.H. (2014)
How Visual Attention and Suppression Facilitate Object Recognition?
In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (Eds.), Artificial Neural Networks and Machine Learning - ICANN 2014, 24th International Conference on Artificial Neural Networks, Lecture Notes in Computer Science 8681, Springer Heidelberg, 2014. 459-466.
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2010

Beuth, F, Wiltschut, J, Hamker, F. H. (2010)
Attentive Stereoscopic Object Recognition.
In: T. Villmann, F.-M. Schleif (Eds.), Proc Workshop New Challenges in Neural Computation - NC² 2010, Machine Learning reports 04/2010, AG Computational Intelligence, University of Leipzig, 41-48. ISSN: 1865-3960.
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Fix, J., Schroll, H., Anton-Erxleben, K., Womelsdorf, T., Treue, S., Hamker, F. H. (2010)
Influence of spatial attention on the receptive field shape of neurons in monkey area MT
Proceedings of Neurocomp 2010, 147-152.

2009

Beuth, F., Ritter, M. (2009)
Verschmelzendes Clustering in Artmap.
In: Workshop Audiovisuelle Medien (WAM2009), Chemnitz, Germany. 93-106.
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2008

Vitay, J., Hamker, F. H. (2008)
Binding objects to cognition: A brain-like systems approach to the cognitive control of visual perception. International Conference on Cognitive Systems.
(CogSys 2008), Karlsruhe, Germany.

2005

Hamker, F. H. (2005)
A population-based inference framework for feature-based attention in natural scenes.
In: M. De Gregorio et al. (eds.), International Symposium on Brain Vision & Artificial Intelligence (BV&AI 2005), LNCS 3704. Berlin, Heidelberg: Springer-Verlag, 147 - 156.
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Hamker, F. H. (2005)
Modeling Attention: From computational neuroscience to computer vision.
In: L. Paletta et al. (eds.), Attention and Performance in Computational Vision. Second International workshop on attention and performance in computer vision (WAPCV 2004), LNCS 3368. Berlin, Heidelberg: Springer-Verlag, 118 - 132.
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2004

Hamker, F. H. (2004)
Vision as an anticipatory process.
Invited Contribution. In: H.-M. Gro� et al. (eds.), SOAVE 2004, 3rd Workshop on Self Organization of Adaptive Behavior. Fortschritt-Berichte VDI, Reihe 10, Nr. 743. D�sseldorf: VDI Verlag, 79-93.
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Hamker, F. H., Zirnsak, M., Calow, D., Lappe, M. (2004)
Planned action determines perception: A computational model of saccadic mislocalization.
In: U. Ilg et al. (eds.), Dynamic Perception. Infix Verlag, St. Augustin, 71-76.
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2002

Hamker, F. H., Worcester, J. (2002)
Object detection in natural scenes by feedback.
In: H. H. B�lthoff et al. (eds.), Biologically Motivated Computer Vision. Lecture Notes in Computer Science. Berlin, Heidelberg, New York: Springer Verlag, 398-407.
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Hamker, F. H. (2002)
How does the ventral pathway contribute to spatial attention and the planning of eye movements?
In: R. P. Würtz & M. Lappe (eds.) Dynamic Perception. St. Augustin: Infix Verlag, 83-88.
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Brause, R., Hamker, F., Paetz, J. (2002)
Septic shock diagnosis by neural networks and rule based systems.
In: Schmitt, et al. (eds.), Computational Intelligence Processing in Medical Diagnosis. Springer Verlag, New York, 323-356.

2000

Hamker, F. H. (2000)
Distributed competition in directed attention.
In: G. Baratoff, H. Neumann (eds.) Proceedings in Artificial Intelligence, Vol. 9. Dynamische Perzeption. Berlin: AKA, Akademische Verlagsgesellschaft, 39-44.
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Paetz, J., Hamker, F. H., Th�ne, S. (2000)
About the Analysis of Septic Shock Patient Data.
Medical Data Analysis, Proceedings of the First International Symposium ISMDA 2000. Lecture Notes in Computer Science, vol. 1933. Heidelberg: Springer-Verlag, 130-137.
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1999

Hamker, F. H. (1999)
The role of feedback connections in task-driven visual search.
In: D. Heinke, G. W. Humphreys & A. Olson (eds.) Connectionist Models in Cognitive Neuroscience, Proc. of the 5th Neural Computation and Psychology Workshop (NCPW'98). London: Springer Verlag, 252-261.
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Hamker, F. H. (1999)
Life-long learning in incremental neural networks.
In: D. M. Dubois (eds.) International Journal of Computing Anticipatory Systems. CHAOS, Liege, Belgium, 3:65-74.

1998

Hamker, F. H. (1998)
Lebenslang lernfähige Zellstrukturen: Eine Lösung des Stabilitäts-Plastizitäts-Dilemmas?
In: Proceedings der CoWAN '98, Cottbus 1998. Sharker Verlag, 17-37.

Hamker, F. H., Gross, H.-M. (1998)
A lifelong learning approach for incremental neural networks.
In: Fourteenth European Meeting on Cybernatics and Systems Research (EMCSR'98), Vienna, 599-604..

1997

Hamker, F. H., Gross, H.-M. (1997)
Task-based representation in lifelong learning incremental neural networks.
In: VDI Fortschrittberichte, Reihe 8, Nr. 663, Workshop SOAVE'97 - Selbstorganisation von adaptivem Verhalten, Ilmenau, 99-108..
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Hamker, F., Pomierski, T, Gross, H.-M., Debes, K. (1997)
Ein visuomotorisches Sortiersystem auf der Basis von Farbmerkmalen.
In: VDI Fortschrittberichte, Reihe 8, Nr. 663, Workshop SOAVE'97 - Selbstorganisation von adaptivem Verhalten, Ilmenau, 232-238.
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Hamker, F. H., Gross, H.-M. (1997)
Object selection with dynamic neural maps.
In: Proceedings of the International Conference on Artificial Neural Networks (ICANN'97). Lausanne: Springer-Verlag, 919-924.
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1996

Hamker, F. H., Gross, H.-M. (1996)
Intentionale Aufmerksamkeit: Ein alternatives Konzept f�r technische visuo-motorische Systeme.
In: Proceedings des Workshops der GI-Fachgruppe 1.0.4 Bildverstehen "Aktives Sehen in technischen und biologischen Systemen", Hamburg, 101-108.
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Hamker, F. H., Gross, H.-M. (1996)
Task Relevant Relaxation Network for visuo-motory Systems.
In: Proceedings of the International Conference on Pattern Recognition (ICPR'96), Vienna, 406-410.

Hamker, F. H., Gross, H.-M. (1996)
Region Selection: Segmentation, Classification and Task Relevance in a single Grouping Mechanism.
In: Proceedings of the IEEE International Conference on Neural Networks (ICNN'96), Washington, 1540-1545.