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Thomas Martinetz
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- affiliation: University of Lübeck, Germany
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2020 – today
- 2024
- [c87]Kathleen Anderson, Thomas Martinetz:
Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations. ICANN (1) 2024: 163-177 - [c86]Christoph Linse
, Beatrice Brückner, Thomas Martinetz
Enhancing Generalization in Convolutional Neural Networks Through Regularization with Edge and Line Features. ICANN (1) 2024: 432-446 - [c85]Christoph Linse, Erhardt Barth, Thomas Martinetz:
Leaky ReLUs That Differ in Forward and Backward Pass Facilitate Activation Maximization in Deep Neural Networks. IJCNN 2024: 1-8 - [c84]Julius Martinetz, Christoph Linse, Thomas Martinetz:
Rethinking generalization of classifiers in separable classes scenarios and over-parameterized regimes. IJCNN 2024: 1-10 - 2023
- [c83]Marius Jahrens
Population Coding Can Greatly Improve Performance of Neural Networks: A Comparison. ICANN (5) 2023: 386-398 - [c82]Christoph Linse
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization. ICMLC 2023: 279-283 - [c81]Christoph Linse
Convolutional Neural Networks Do Work with Pre-Defined Filters. IJCNN 2023: 1-8 - 2022
- [j40]Christoph Linse
A walk in the black-box: 3D visualization of large neural networks in virtual reality. Neural Comput. Appl. 34(23): 21237-21252 (2022) - [i11]Christoph Linse, Thomas Martinetz:
Large Neural Networks Learning from Scratch with Very Few Data and without Regularization. CoRR abs/2205.08836 (2022) - [i10]Julius Martinetz, Thomas Martinetz:
Highly over-parameterized classifiers generalize since bad solutions are rare. CoRR abs/2211.03570 (2022) - 2021
- [j39]Hammam A. Alshazly
Towards Explainable Ear Recognition Systems Using Deep Residual Networks. IEEE Access 9: 122254-122273 (2021) - [j38]Shambhavi Mishra
Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering. Comput. Intell. Neurosci. 2021: 6400045:1-6400045:14 (2021) - [j37]Hammam A. Alshazly, Christoph Linse, Mohamed Abdalla
COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Comput. Sci. 7: e655 (2021) - [j36]Hammam A. Alshazly
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning. Sensors 21(2): 455 (2021) - [c80]Dominik Mairhöfer, Manuel Laufer, Paul Martin Simon, Malte Sieren, Arpad Bischof, Thomas Käster, Erhardt Barth, Jörg Barkhausen, Thomas Martinetz:
An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. MIDL 2021: 484-496 - 2020
- [j35]Hammam A. Alshazly
Deep Convolutional Neural Networks for Unconstrained Ear Recognition. IEEE Access 8: 170295-170310 (2020) - [c79]Philipp Grüning
Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks. ICANN (2) 2020: 79-91 - [c78]Marius Jahrens, Thomas Martinetz:
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks. IJCNN 2020: 1-6 - [i9]Marius Jahrens, Thomas Martinetz:
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks. CoRR abs/2003.11608 (2020) - [i8]Philipp Grüning, Thomas Martinetz, Erhardt Barth:
Feature Products Yield Efficient Networks. CoRR abs/2008.07930 (2020) - [i7]Hammam A. Alshazly, Christoph Linse, Erhardt Barth, Thomas Martinetz:
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning. CoRR abs/2011.05317 (2020)
2010 – 2019
- 2019
- [j34]Hammam A. Alshazly
Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition. Sensors 19(19): 4139 (2019) - [j33]Hammam A. Alshazly
Handcrafted versus CNN Features for Ear Recognition. Symmetry 11(12): 1493 (2019) - [c77]Marius Jahrens, Thomas Martinetz:
Multi-layer relation networks for relational reasoning. APPIS 2019: 10:1-10:5 - 2018
- [i6]Marius Jahrens, Thomas Martinetz:
Multi-layer Relation Networks. CoRR abs/1811.01838 (2018) - 2017
- [c76]Irina Burciu
Sensing Forest for Pattern Recognition. ACIVS 2017: 126-137 - [c75]Amir Madany Mamlouk, Martin Haker, Thomas Martinetz:
Perception space analysis: From color vision to odor perception. IJCNN 2017: 689-696 - [c74]Boris Knyazev, Erhardt Barth, Thomas Martinetz:
Recursive autoconvolution for unsupervised learning of convolutional neural networks. IJCNN 2017: 2486-2493 - [i5]Lars Hertel, Erhardt Barth, Thomas Käster, Thomas Martinetz:
Deep Convolutional Neural Networks as Generic Feature Extractors. CoRR abs/1710.02286 (2017) - 2016
- [j32]Michael Schellenberger Costa, Jan Born
Modeling the effect of sleep regulation on a neural mass model. J. Comput. Neurosci. 41(1): 15-28 (2016) - [j31]Michael Schellenberger Costa, Arne Weigenand, Hong-Viet Victor Ngo, Lisa Marshall, Jan Born
A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation. PLoS Comput. Biol. 12(9) (2016) - [j30]Henry Schütze
Learning Efficient Data Representations With Orthogonal Sparse Coding. IEEE Trans. Computational Imaging 2(3): 177-189 (2016) - [c73]Irina Burciu
Hierarchical Manifold Sensing with Foveation and Adaptive Partitioning of the Dataset. HVEI 2016: 1-10 - [i4]Boris Knyazev, Erhardt Barth, Thomas Martinetz:
Autoconvolution for Unsupervised Feature Learning. CoRR abs/1606.00611 (2016) - 2015
- [j29]Foti Coleca, Andreea State, Sascha Klement, Erhardt Barth, Thomas Martinetz:
Self-organizing maps for hand and full body tracking. Neurocomputing 147: 174-184 (2015) - [j28]Jens Hocke, Thomas Martinetz:
Maximum distance minimization for feature weighting. Pattern Recognit. Lett. 52: 48-52 (2015) - [c72]Irina Burciu
Foveated Manifold Sensing for object recognition. BlackSeaCom 2015: 196-200 - [c71]Lars Hertel, Erhardt Barth, Thomas Käster, Thomas Martinetz:
Deep convolutional neural networks as generic feature extractors. IJCNN 2015: 1-4 - [c70]Henry Schütze, Erhardt Barth, Thomas Martinetz:
Learning orthogonal sparse representations by using geodesic flow optimization. IJCNN 2015: 1-8 - 2014
- [j27]Arne Weigenand, Michael Schellenberger Costa, Hong-Viet Victor Ngo, Jens Christian Claussen
Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model. PLoS Comput. Biol. 10(11) (2014) - [c69]Barbara Hammer, Haibo He, Thomas Martinetz:
Learning and modeling big data. ESANN 2014 - [c68]Henry Schütze, Erhardt Barth, Thomas Martinetz:
An adaptive hierarchical sensing scheme for sparse signals. Human Vision and Electronic Imaging 2014: 90140G - [c67]Irina Burciu
Visual manifold sensing. Human Vision and Electronic Imaging 2014: 90141B - [c66]Jens Hocke, Thomas Martinetz:
Global Metric Learning by Gradient Descent. ICANN 2014: 129-135 - [c65]Foti Coleca, Sabrina Zîrnovean, Thomas Käster, Thomas Martinetz, Erhardt Barth:
Key-point Detection with Multi-layer Center-surround Inhibition. VISAPP (1) 2014: 386-393 - [i3]Bogdan Miclut, Thomas Käster, Thomas Martinetz, Erhardt Barth:
Committees of deep feedforward networks trained with few data. CoRR abs/1406.5947 (2014) - 2013
- [j26]Sascha Klement, Silke Anders, Thomas Martinetz:
The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data. Neural Comput. 25(6): 1548-1584 (2013) - [c64]Foti Coleca, Thomas Martinetz, Erhardt Barth:
Gesture Interfaces with Depth Sensors. Time-of-Flight and Depth Imaging 2013: 207-227 - [c63]Foti Coleca, Sascha Klement, Thomas Martinetz, Erhardt Barth:
Real-time skeleton tracking for embedded systems. Multimedia Content and Mobile Devices 2013: 86671X - [c62]Mike Wellner, Thomas Käster, Thomas Martinetz, Erhardt Barth:
Optimizing depth-of-field based on a range map and a wavelet transform. Multimedia Content and Mobile Devices 2013: 86671U - [c61]Jens Hocke, Thomas Martinetz:
Feature Weighting by Maximum Distance Minimization. ICANN 2013: 420-425 - 2012
- [j25]Thomas Binder, Thomas Martinetz:
On the boundedness of an Iteration involving Points on the Hypersphere. Int. J. Comput. Geom. Appl. 22(6): 499-516 (2012) - [j24]Jens Hocke, Kai Labusch, Erhardt Barth, Thomas Martinetz:
Sparse Coding and Selected Applications. Künstliche Intell. 26(4): 349-355 (2012) - [j23]Eleonora Vig, Michael Dorr
Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes. IEEE Trans. Pattern Anal. Mach. Intell. 34(6): 1080-1091 (2012) - [c60]Jens Hocke, Erhardt Barth, Thomas Martinetz:
Application of non-linear transform coding to image processing. Human Vision and Electronic Imaging 2012: 829105 - [c59]Thomas Binder, Florian Kriener, Christian Wichner, Manuel Wille, Mike Wellner, Thomas Käster, Thomas Martinetz, Erhardt Barth:
How to make a small phone camera shoot like a big DSLR: creating and fusing multi-modal exposure series. Human Vision and Electronic Imaging 2012: 829106 - [c58]Henry Schütze, Thomas Martinetz, Silke Anders, Amir Madany Mamlouk:
A Multivariate Approach to Estimate Complexity of FMRI Time Series. ICANN (2) 2012: 540-547 - [c57]Horia Coman, Erhardt Barth, Thomas Martinetz:
Sparse Coding Neural Gas Applied to Image Recognition. WSOM 2012: 105-114 - [c56]Andreea State, Foti Coleca, Erhardt Barth, Thomas Martinetz:
Hand Tracking with an Extended Self-Organizing Map. WSOM 2012: 115-124 - 2011
- [j22]Jiajie Zhang, Amir Madany Mamlouk, Thomas Martinetz, Suhua Chang, Jing Wang, Rolf Hilgenfeld:
PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome. BMC Bioinform. 12: 248 (2011) - [j21]Krishna Kumar Kandaswamy, Ganesan Pugalenthi
BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and ReliefF feature selection. BMC Bioinform. 12: 345 (2011) - [j20]Eleonora Vig, Michael Dorr
Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes. Cogn. Comput. 3(1): 79-88 (2011) - [j19]John Aldo Lee
Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing 74(9): 1299-1300 (2011) - [j18]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Soft-competitive learning of sparse codes and its application to image reconstruction. Neurocomputing 74(9): 1418-1428 (2011) - [j17]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent. IEEE J. Sel. Top. Signal Process. 5(5): 1048-1060 (2011) - [c55]Sascha Klement, Thomas Martinetz:
On the Problem of Finding the Least Number of Features by L1-Norm Minimisation. ICANN (1) 2011: 315-322 - 2010
- [j16]Martin Böhme, Martin Haker, Thomas Martinetz, Erhardt Barth:
Shading constraint improves accuracy of time-of-flight measurements. Comput. Vis. Image Underst. 114(12): 1329-1335 (2010) - [c54]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Bag of Pursuits and Neural Gas for Improved Sparse Coding. COMPSTAT 2010: 327-336 - [c53]Kai Labusch, Thomas Martinetz:
Learning sparse codes for image reconstruction. ESANN 2010 - [c52]Eleonora Vig, Michael Dorr
A Learned Saliency Predictor for Dynamic Natural Scenes. ICANN (3) 2010: 52-61 - [c51]Sascha Klement, Thomas Martinetz:
The Support Feature Machine for Classifying with the Least Number of Features. ICANN (2) 2010: 88-93 - [c50]Sascha Klement, Thomas Martinetz:
A New Approach to Classification with the Least Number of Features. ICMLA 2010: 141-146 - [c49]Fabian Timm, Thomas Martinetz:
Statistical Fourier Descriptors for Defect Image Classification. ICPR 2010: 4190-4193 - [i2]Thomas Binder, Thomas Martinetz:
On the boundedness of an iteration involving points on the hypersphere. CoRR abs/1001.1624 (2010)
2000 – 2009
- 2009
- [j15]Dirk Repsilber, Thomas Martinetz, Mats Björklund:
Adaptive Dynamics of Regulatory Networks: Size Matters. EURASIP J. Bioinform. Syst. Biol. 2009 (2009) - [j14]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Sparse Coding Neural Gas: Learning of overcomplete data representations. Neurocomputing 72(7-9): 1547-1555 (2009) - [j13]Thomas Martinetz, Kai Labusch, Daniel Schneegaß:
SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines. IEEE Trans. Neural Networks 20(7): 1061-1072 (2009) - [c48]Martin Haker, Martin Böhme, Thomas Martinetz, Erhardt Barth:
Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera. Dyn3D 2009: 142-153 - [c47]Martin Böhme, Martin Haker, Kolja Riemer, Thomas Martinetz, Erhardt Barth:
Face Detection Using a Time-of-Flight Camera. Dyn3D 2009: 167-176 - [c46]Martin Haker, Martin Böhme, Thomas Martinetz, Erhardt Barth:
Deictic Gestures with a Time-of-Flight Camera. Gesture Workshop 2009: 110-121 - [c45]Martin Haker, Thomas Martinetz, Erhardt Barth:
Multimodal Sparse Features for Object Detection. ICANN (2) 2009: 923-932 - [c44]Fabian Timm, Sascha Klement, Thomas Martinetz, Erhardt Barth:
Welding Inspection using Novel Specularity Features and a One-class SVM. VISAPP (1) 2009: 146-153 - [c43]Fabian Timm, Thomas Martinetz, Erhardt Barth:
Optical Inspection of Welding Seams. VISIGRAPP (Selected Papers) 2009: 269-282 - [c42]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. WSOM 2009: 145-153 - 2008
- [j12]Martin Böhme, Martin Haker, Thomas Martinetz, Erhardt Barth:
A facial feature tracker for human-computer interaction based on 3D Time-Of-Flight cameras. Int. J. Intell. Syst. Technol. Appl. 5(3/4): 264-273 (2008) - [j11]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Simple Method for High-Performance Digit Recognition Based on Sparse Coding. IEEE Trans. Neural Networks 19(11): 1985-1989 (2008) - [c41]Martin Böhme, Martin Haker, Thomas Martinetz, Erhardt Barth:
Shading constraint improves accuracy of time-of-flight measurements. CVPR Workshops 2008: 1-6 - [c40]Martin Haker, Martin Böhme, Thomas Martinetz, Erhardt Barth:
Scale-invariant range features for time-of-flight camera applications. CVPR Workshops 2008: 1-6 - [c39]Kai Labusch, Fabian Timm, Thomas Martinetz:
Simple Incremental One-Class Support Vector Classification. DAGM-Symposium 2008: 21-30 - [c38]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Learning Data Representations with Sparse Coding Neural Gas. ESANN 2008: 233-238 - [c37]Martin Böhme, Michael Dorr, Mathis Graw, Thomas Martinetz, Erhardt Barth:
A software framework for simulating eye trackers. ETRA 2008: 251-258 - [c36]Sascha Klement, Amir Madany Mamlouk, Thomas Martinetz:
Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios. ICANN (1) 2008: 41-50 - [c35]Kai Labusch, Erhardt Barth, Thomas Martinetz:
Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. ICANN (1) 2008: 788-797 - [c34]Fabian Timm, Sascha Klement, Thomas Martinetz:
Fast model selection for MaxMinOver-based training of support vector machines. ICPR 2008: 1-4 - [c33]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Uncertainty propagation for quality assurance in Reinforcement Learning. IJCNN 2008: 2588-2595 - 2007
- [c32]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. ESANN 2007: 301-306 - [c31]Daniel Schneegaß, Anton Maximilian Schäfer, Thomas Martinetz:
The Intrinsic Recurrent Support Vector Machine. ESANN 2007: 325-330 - [c30]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. ESANN 2007: 337-342 - [c29]Kai Labusch, Udo Siewert, Thomas Martinetz, Erhardt Barth:
Learning optimal features for visual pattern recognition. Human Vision and Electronic Imaging 2007: 64920B - [c28]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. ICANN (1) 2007: 109-118 - 2006
- [j10]Martin Böhme, Michael Dorr
Eye movement predictions on natural videos. Neurocomputing 69(16-18): 1996-2004 (2006) - [c27]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. Artificial Intelligence and Applications 2006: 428-433 - [c26]Daniel Schneegaß, Thomas Martinetz, Michael Clausohm:
OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method. ESANN 2006: 575-580 - [c25]Martin Böhme, Michael Dorr, Thomas Martinetz, Erhardt Barth:
Gaze-contingent temporal filtering of video. ETRA 2006: 109-115 - [c24]Erhardt Barth, Michael Dorr
Guiding the mind's eye: improving communication and vision by external control of the scanpath. Human Vision and Electronic Imaging 2006: 60570D - [c23]Daniel Schneegaß, Kai Labusch, Thomas Martinetz:
MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. ICANN (1) 2006: 150-158 - [c22]Erhardt Barth, Michael Dorr
Guiding Eye Movements for Better Communication and Augmented Vision. PIT 2006: 1-8 - [c21]Michael Dorr
Gaze-Contingent Spatio-temporal Filtering in a Head-Mounted Display. PIT 2006: 205-207 - [c20]André Meyer, Martin Böhme, Thomas Martinetz, Erhardt Barth:
A Single-Camera Remote Eye Tracker. PIT 2006: 208-211 - [c19]Thomas Martinetz, Amir Madany Mamlouk, Cicero Mota:
Fast and Easy Computation of Approximate Smallest Enclosing Balls. SIBGRAPI 2006: 163-170 - 2005
- [j9]Anke Meyer-Bäse
Medical image compression using topology-preserving neural networks. Eng. Appl. Artif. Intell. 18(4): 383-392 (2005) - [j8]Amir Madany Mamlouk, Hannah Sharp, Kerstin M. L. Menne, Ulrich G. Hofmann
Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations. Neurocomputing 65-66: 275-282 (2005) - [c18]Michael Dorr, Thomas Martinetz, Martin Böhme, Erhardt Barth:
Visibility of temporal blur on a gaze-contingent display. APGV 2005: 33-36 - [c17]Thomas Martinetz, Kai Labusch, Daniel Schneegaß:
SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification. ICANN (2) 2005: 301-306 - [e1]Uwe Brinkschulte, Jürgen Becker, Dietmar Fey, Christian Hochberger, Thomas Martinetz, Christian Müller-Schloer, Hartmut Schmeck, Theo Ungerer, Rolf P. Würtz:
18th International Conference on Architecture of Computing Systems, Workshops, Innsbruck, Austria, March 2005. VDE Verlag 2005, ISBN 3-8007-2880-X [contents] - 2004
- [j7]Amir Madany Mamlouk, Thomas Martinetz:
On the dimensions of the olfactory perception space. Neurocomputing 58-60: 1019-1025 (2004) - [j6]Jan T. Kim, Jan E. Gewehr, Thomas Martinetz:
Binding Matrix: a Novel Approach for Binding Site Recognition. J. Bioinform. Comput. Biol. 2(2): 289-308 (2004) - [c16]Thomas Martinetz:
MinOver Revisited for Incremental Support-Vector-Classification. DAGM-Symposium 2004: 187-194 - [c15]Martin Böhme, Christopher Krause, Thomas Martinetz, Erhardt Barth:
Saliency Extraction for Gaze-Contingent Displays. GI Jahrestagung (2) 2004: 646-650 - 2003
- [c14]Amir Madany Mamlouk, Jan T. Kim, Erhardt Barth, Michael Brauckmann, Thomas Martinetz:
One-Class Classification with Subgaussians. DAGM-Symposium 2003: 346-353 - [c13]Anke Meyer-Bäse, Thomas D. Otto, Thomas Martinetz, Dorothee Auer, Axel Wismüller:
Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis. ESANN 2003: 509-514 - [c12]Erhardt Barth, Jan Drewes, Thomas Martinetz:
Individual predictions of eye-movements with dynamic scenes. Human Vision and Electronic Imaging 2003: 252-259 - [c11]Erhardt Barth, Jan Drewes, Thomas Martinetz:
Dynamic predictions of tracked gaze. ISSPA (1) 2003: 245-248 - 2001
- [c10]Daniel Polani, Thomas Martinetz, Jan T. Kim:
An Information-Theoretic Approach for the Quantification of Relevance. ECAL 2001: 704-713 - [c9]Jan T. Kim, Thomas Martinetz, Daniel Polani:
On the Effects of Transcription Factor Properties on the Information Content of Binding Sites. German Conference on Bioinformatics 2001: 192-194 - [c8]Martin Haker, André Meyer, Daniel Polani, Thomas Martinetz:
A Method for Incorporation of New Evidence to Improve World State Estimation. RoboCup 2001: 362-367 - 2000
- [c7]Daniel Polani, Thomas Martinetz:
Team Description for Lucky Lübeck - Evidence-Based World State Estimation. RoboCup 2000: 481-484
1990 – 1999
- 1999
- [c6]Claus O. Wilke, Christopher Ronnewinkel, Thomas Martinetz:
Molecular Evolution in Time-Dependent Environments. ECAL 1999: 417-421 - [i1]Christopher Ronnewinkel, Claus O. Wilke, Thomas Martinetz:
Genetic Algorithms in Time-Dependent Environments. CoRR physics/9911006 (1999) - 1997
- [j5]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz:
Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Trans. Neural Networks 8(2): 256-266 (1997) - 1995
- [c5]Thomas Martinetz, Peter Protzel, Otto Gramckow, Günter Sörgel:
Neural Network Control for Steel Rolling Mills. SNN Symposium on Neural Networks 1995: 280-286 - 1994
- [j4]Thomas Martinetz, Klaus Schulten:
Topology representing networks. Neural Networks 7(3): 507-522 (1994) - [c4]Thomas Villmann, Ralf Der, J. Michael Herrmann, Thomas Martinetz:
Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. Fuzzy Days 1994: 159-166 - 1993
- [j3]Thomas Martinetz, Stanislav G. Berkovich, Klaus Schulten:
'Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Networks 4(4): 558-569 (1993) - [c3]Thomas Martinetz, Klaus Schulten:
A neural network with Hebbian-like adaptation rules learning visuomotor coordination of a PUMA robot. ICNN 1993: 820-822 - 1992
- [b3]Helge J. Ritter, Thomas Martinetz, Klaus Schulten:
Neural computation and self-organizing maps - an introduction. Computation and neural systems series, Addison-Wesley 1992, ISBN 978-0-201-55443-4, pp. 1-306 - [b2]Thomas Martinetz:
Selbstorganisierende neuronale Netzwerkmodelle zur Bewegungssteuerung. Technical University Munich, Germany, DISKI 14, Infix Verlag, St. Augustin, Germany 1992, ISBN 3-929037-14-9, pp. 1-167 - 1991
- [b1]Helge J. Ritter, Thomas Martinetz, Klaus Schulten:
Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierter Netzwerke (2. Aufl.). Reihe Künstliche Intelligenz, Addison-Wesley 1991, ISBN 978-3-89319-131-4, pp. 1-325 - [c2]Stan Berkovitch, Philippe Dalger, Ted Hesselroth, Thomas Martinetz, Benoît Noël, Jörg A. Walter, Klaus Schulten:
Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots. Wissensbasierte Systeme 1991: 443-447 - 1990
- [j2]Thomas Martinetz, Helge J. Ritter, Klaus Schulten:
Three-dimensional neural net for learning visuomotor coordination of a robot arm. IEEE Trans. Neural Networks 1(1): 131-136 (1990) - [c1]Thomas Martinetz, Klaus Schulten:
Hierarchical neural net for learning control of a robot's arm and gripper. IJCNN 1990: 747-752
1980 – 1989
- 1989
- [j1]Helge J. Ritter, Thomas Martinetz, Klaus Schulten:
Topology-conserving maps for learning visuo-motor-coordination. Neural Networks 2(3): 159-168 (1989)
Coauthor Index
[c85] [c81] [j39] [j37] [j36] [c80] [j35] [c79] [i8] [i7] [j34] [j33] [c76] [c74] [i5] [j30] [c73] [i4] [j29] [c72] [c71] [c70] [c68] [c67] [c65] [i3] [c64] [c63] [c62] [j24] [j23] [c60] [c59] [c57] [c56] [j20] [j18] [j17] [j16] [c54] [c52] [j14] [c48] [c47] [c46] [c45] [c44] [c43] [c42] [j12] [j11] [c41] [c40] [c38] [c37] [c35] [c29] [j10] [c25] [c24] [c22] [c21] [c20] [c18] [c15] [c14] [c12] [c11]
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last updated on 2024-10-07 21:14 CEST by the dblp team
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