Topology-conserving maps for learning visuo-motor-coordination. - Helge J. Ritter; Thomas Martinetz; Klaus Schulten (Neural Networks, 1989)

Hierarchical neural net for learning control of a robot's arm and gripper. - Thomas Martinetz; Klaus Schulten (IJCNN, 1990)

Three-dimensional neural net for learning visuomotor coordination of a robot arm. - Thomas Martinetz; Helge J. Ritter; Klaus Schulten (IEEE Trans. Neural Networks, 1990)

Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierter Netzwerke (2. Aufl.). - Helge J. Ritter; Thomas Martinetz; Klaus Schulten (Addison-Wesley, 1991)

Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots. - Stan Berkovitch; Philippe Dalger; Ted Hesselroth; Thomas Martinetz; Benoît Noël; Jörg A. Walter; Klaus Schulten (Wissensbasierte Systeme, 1991)

Neural computation and self-organizing maps - an introduction. - Helge J. Ritter; Thomas Martinetz; Klaus Schulten (Addison-Wesley, 1992)

Selbstorganisierende neuronale Netzwerkmodelle zur Bewegungssteuerung. - Thomas Martinetz (Infix Verlag, St. Augustin, Germany, 1992)

A neural network with Hebbian-like adaptation rules learning visuomotor coordination of a PUMA robot. - Thomas Martinetz; Klaus Schulten (ICNN, 1993)

'Neural-gas' network for vector quantization and its application to time-series prediction. - Thomas Martinetz; Stanislav G. Berkovich; Klaus Schulten (IEEE Trans. Neural Networks, 1993)

Topology Preservation in Self-Organizing Feature Maps: General Definition and Efficient Measurement. - Thomas Villmann; Ralf Der; J. Michael Herrmann; Thomas Martinetz (Fuzzy Days, 1994)

Topology representing networks. - Thomas Martinetz; Klaus Schulten (Neural Networks, 1994)

Neural Network Control for Steel Rolling Mills. - Thomas Martinetz; Peter Protzel; Otto Gramckow; Günter Sörgel (SNN Symposium on Neural Networks, 1995)

Topology preservation in self-organizing feature maps: exact definition and measurement. - Thomas Villmann; Ralf Der; J. Michael Herrmann; Thomas Martinetz (IEEE Trans. Neural Networks, 1997)

Molecular Evolution in Time-Dependent Environments. - Claus O. Wilke; Christopher Ronnewinkel; Thomas Martinetz (ECAL, 1999)

Genetic Algorithms in Time-Dependent Environments - Christopher Ronnewinkel; Claus O. Wilke; Thomas Martinetz (CoRR, 1999)

Team Description for Lucky Lübeck - Evidence-Based World State Estimation. - Daniel Polani; Thomas Martinetz (RoboCup, 2000)

An Information-Theoretic Approach for the Quantification of Relevance. - Daniel Polani; Thomas Martinetz; Jan T. Kim (ECAL, 2001)

On the Effects of Transcription Factor Properties on the Information Content of Binding Sites. - Jan T. Kim; Thomas Martinetz; Daniel Polani (German Conference on Bioinformatics, 2001)

A Method for Incorporation of New Evidence to Improve World State Estimation. - Martin Haker; André Meyer; Daniel Polani; Thomas Martinetz (RoboCup, 2001)

One-Class Classification with Subgaussians. - Amir Madany Mamlouk; Jan T. Kim; Erhardt Barth; Michael Brauckmann; Thomas Martinetz (DAGM-Symposium, 2003)

Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis. - Anke Meyer-Bäse; Thomas D. Otto; Thomas Martinetz; Dorothee Auer; Axel Wismüller (ESANN, 2003)

Individual predictions of eye-movements with dynamic scenes. - Erhardt Barth; Jan Drewes; Thomas Martinetz (Human Vision and Electronic Imaging, 2003)

Statistical learning for detecting protein-DNA-binding sites. - Thomas Martinetz; Jan E. Gewehr; Jan T. Kim (IJCNN, 2003)

Dynamic predictions of tracked gaze. - Erhardt Barth; Jan Drewes; Thomas Martinetz (ISSPA (1), 2003)

MinOver Revisited for Incremental Support-Vector-Classification. - Thomas Martinetz (DAGM-Symposium, 2004)

Saliency Extraction for Gaze-Contingent Displays. - Martin Böhme; Christopher Krause; Thomas Martinetz; Erhardt Barth (GI Jahrestagung (2), 2004)

MaxMinOver: a simple incremental learning procedure for support vector classification. - Thomas Martinetz (IJCNN, 2004)

On the dimensions of the olfactory perception space. - Amir Madany Mamlouk; Thomas Martinetz (Neurocomputing, 2004)

Binding Matrix: a Novel Approach for Binding Site Recognition. - Jan T. Kim; Jan E. Gewehr; Thomas Martinetz (J. Bioinform. Comput. Biol., 2004)

Visibility of temporal blur on a gaze-contingent display. - Michael Dorr; Thomas Martinetz; Martin Böhme; Erhardt Barth (APGV, 2005)

SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification. - Thomas Martinetz; Kai Labusch; Daniel Schneegaß (ICANN (2), 2005)

Medical image compression using topology-preserving neural networks. - Anke Meyer-Bäse; Karsten Jancke; Axel Wismüller; Simon Foo 001; Thomas Martinetz (Eng. Appl. Artif. Intell., 2005)

Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations. - Amir Madany Mamlouk; Hannah Sharp; Kerstin M. L. Menne; Ulrich G. Hofmann; Thomas Martinetz (Neurocomputing, 2005)

Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (Artificial Intelligence and Applications, 2006)

OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method. - Daniel Schneegaß; Thomas Martinetz; Michael Clausohm (ESANN, 2006)

Gaze-contingent temporal filtering of video. - Martin Böhme; Michael Dorr; Thomas Martinetz; Erhardt Barth (ETRA, 2006)

Guiding the mind's eye: improving communication and vision by external control of the scanpath. - Erhardt Barth; Michael Dorr; Martin Böhme; Karl R. Gegenfurtner; Thomas Martinetz (Human Vision and Electronic Imaging, 2006)

MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. - Daniel Schneegaß; Kai Labusch; Thomas Martinetz (ICANN (1), 2006)

Guiding Eye Movements for Better Communication and Augmented Vision. - Erhardt Barth; Michael Dorr; Martin Böhme; Karl R. Gegenfurtner; Thomas Martinetz (PIT, 2006)

Gaze-Contingent Spatio-temporal Filtering in a Head-Mounted Display. - Michael Dorr; Martin Böhme; Thomas Martinetz; Erhardt Barth (PIT, 2006)

A Single-Camera Remote Eye Tracker. - André Meyer; Martin Böhme; Thomas Martinetz; Erhardt Barth (PIT, 2006)

Fast and Easy Computation of Approximate Smallest Enclosing Balls. - Thomas Martinetz; Amir Madany Mamlouk; Cicero Mota (SIBGRAPI, 2006)

Eye movement predictions on natural videos. - Martin Böhme; Michael Dorr; Christopher Krause; Thomas Martinetz; Erhardt Barth (Neurocomputing, 2006)

The Intrinsic Recurrent Support Vector Machine. - Daniel Schneegaß; Anton Maximilian Schäfer; Thomas Martinetz (ESANN, 2007)

Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ESANN, 2007)

Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ESANN, 2007)

Learning optimal features for visual pattern recognition. - Kai Labusch; Udo Siewert; Thomas Martinetz; Erhardt Barth (Human Vision and Electronic Imaging, 2007)

Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ICANN (1), 2007)

Scale-invariant range features for time-of-flight camera applications. - Martin Haker; Martin Böhme; Thomas Martinetz; Erhardt Barth (CVPR Workshops, 2008)

Simple Incremental One-Class Support Vector Classification. - Kai Labusch; Fabian Timm; Thomas Martinetz (DAGM-Symposium, 2008)

Learning Data Representations with Sparse Coding Neural Gas. - Kai Labusch; Erhardt Barth; Thomas Martinetz (ESANN, 2008)

A software framework for simulating eye trackers. - Martin Böhme; Michael Dorr; Mathis Graw; Thomas Martinetz; Erhardt Barth (ETRA, 2008)

Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios. - Sascha Klement; Amir Madany Mamlouk; Thomas Martinetz (ICANN (1), 2008)

Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. - Kai Labusch; Erhardt Barth; Thomas Martinetz (ICANN (1), 2008)

Fast model selection for MaxMinOver-based training of support vector machines. - Fabian Timm; Sascha Klement; Thomas Martinetz (ICPR, 2008)

Uncertainty propagation for quality assurance in Reinforcement Learning. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (IJCNN, 2008)

A facial feature tracker for human-computer interaction based on 3D Time-Of-Flight cameras. - Martin Böhme; Martin Haker; Thomas Martinetz; Erhardt Barth (Int. J. Intell. Syst. Technol. Appl., 2008)

Simple Method for High-Performance Digit Recognition Based on Sparse Coding. - Kai Labusch; Erhardt Barth; Thomas Martinetz (IEEE Trans. Neural Networks, 2008)

Face Detection Using a Time-of-Flight Camera. - Martin Böhme; Martin Haker; Kolja Riemer; Thomas Martinetz; Erhardt Barth (Dyn3D, 2009)

Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera. - Martin Haker; Martin Böhme; Thomas Martinetz; Erhardt Barth (Dyn3D, 2009)

Deictic Gestures with a Time-of-Flight Camera. - Martin Haker; Martin Böhme; Thomas Martinetz; Erhardt Barth (Gesture Workshop, 2009)

Multimodal Sparse Features for Object Detection. - Martin Haker; Thomas Martinetz; Erhardt Barth (ICANN (2), 2009)

Welding Inspection using Novel Specularity Features and a One-class SVM. - Fabian Timm; Sascha Klement; Thomas Martinetz; Erhardt Barth (VISAPP (1), 2009)

Optical Inspection of Welding Seams. - Fabian Timm; Thomas Martinetz; Erhardt Barth (VISIGRAPP (Selected Papers), 2009)

Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. - Kai Labusch; Erhardt Barth; Thomas Martinetz (WSOM, 2009)

Adaptive Dynamics of Regulatory Networks: Size Matters. - Dirk Repsilber; Thomas Martinetz; Mats Björklund (EURASIP J. Bioinform. Syst. Biol., 2009)

Sparse Coding Neural Gas: Learning of overcomplete data representations. - Kai Labusch; Erhardt Barth; Thomas Martinetz (Neurocomputing, 2009)

SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines. - Thomas Martinetz; Kai Labusch; Daniel Schneegaß (IEEE Trans. Neural Networks, 2009)

Bag of Pursuits and Neural Gas for Improved Sparse Coding. - Kai Labusch; Erhardt Barth; Thomas Martinetz (COMPSTAT, 2010)

Learning sparse codes for image reconstruction. - Kai Labusch; Thomas Martinetz (ESANN, 2010)

The Support Feature Machine for Classifying with the Least Number of Features. - Sascha Klement; Thomas Martinetz (ICANN (2), 2010)

A Learned Saliency Predictor for Dynamic Natural Scenes. - Eleonora Vig; Michael Dorr; Thomas Martinetz; Erhardt Barth (ICANN (3), 2010)

A New Approach to Classification with the Least Number of Features. - Sascha Klement; Thomas Martinetz (ICMLA, 2010)

Statistical Fourier Descriptors for Defect Image Classification. - Fabian Timm; Thomas Martinetz (ICPR, 2010)

Shading constraint improves accuracy of time-of-flight measurements. - Martin Böhme; Martin Haker; Thomas Martinetz; Erhardt Barth (Comput. Vis. Image Underst., 2010)

On the Problem of Finding the Least Number of Features by L1-Norm Minimisation. - Sascha Klement; Thomas Martinetz (ICANN (1), 2011)

BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and ReliefF feature selection. - Krishna Kumar Kandaswamy; Ganesan Pugalenthi; Mehrnaz Khodam Hazrati; Kai-Uwe Kalies; Thomas Martinetz (BMC Bioinform., 2011)

PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome. - Jiajie Zhang; Amir Madany Mamlouk; Thomas Martinetz; Suhua Chang; Jing Wang 003; Rolf Hilgenfeld (BMC Bioinform., 2011)

Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes. - Eleonora Vig; Michael Dorr; Thomas Martinetz; Erhardt Barth (Cogn. Comput., 2011)

Soft-competitive learning of sparse codes and its application to image reconstruction. - Kai Labusch; Erhardt Barth; Thomas Martinetz (Neurocomputing, 2011)

Advances in artificial neural networks, machine learning, and computational intelligence. - John Aldo Lee; Frank-Michael Schleif; Thomas Martinetz (Neurocomputing, 2011)

Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent. - Kai Labusch; Erhardt Barth; Thomas Martinetz (IEEE J. Sel. Top. Signal Process., 2011)

How to make a small phone camera shoot like a big DSLR: creating and fusing multi-modal exposure series. - Thomas Binder; Florian Kriener; Christian Wichner; Manuel Wille; Mike Wellner; Thomas Käster; Thomas Martinetz; Erhardt Barth (Human Vision and Electronic Imaging, 2012)

Application of non-linear transform coding to image processing. - Jens Hocke; Erhardt Barth; Thomas Martinetz (Human Vision and Electronic Imaging, 2012)

A Multivariate Approach to Estimate Complexity of FMRI Time Series. - Henry Schütze; Thomas Martinetz; Silke Anders; Amir Madany Mamlouk (ICANN (2), 2012)

Sparse Coding Neural Gas Applied to Image Recognition. - Horia Coman; Erhardt Barth; Thomas Martinetz (WSOM, 2012)

Hand Tracking with an Extended Self-Organizing Map. - Andreea State; Foti Coleca; Erhardt Barth; Thomas Martinetz (WSOM, 2012)

On the boundedness of an Iteration involving Points on the Hypersphere. - Thomas Binder; Thomas Martinetz (Int. J. Comput. Geom. Appl., 2012)

Sparse Coding and Selected Applications. - Jens Hocke; Kai Labusch; Erhardt Barth; Thomas Martinetz (Künstliche Intell., 2012)

Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes. - Eleonora Vig; Michael Dorr; Thomas Martinetz; Erhardt Barth (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Gesture Interfaces with Depth Sensors. - Foti Coleca; Thomas Martinetz; Erhardt Barth (Time-of-Flight and Depth Imaging, 2013)

Real-time skeleton tracking for embedded systems. - Foti Coleca; Sascha Klement; Thomas Martinetz; Erhardt Barth (Multimedia Content and Mobile Devices, 2013)

Optimizing depth-of-field based on a range map and a wavelet transform. - Mike Wellner; Thomas Käster; Thomas Martinetz; Erhardt Barth (Multimedia Content and Mobile Devices, 2013)

Feature Weighting by Maximum Distance Minimization. - Jens Hocke; Thomas Martinetz (ICANN, 2013)

The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data. - Sascha Klement; Silke Anders; Thomas Martinetz (Neural Comput., 2013)

Learning and modeling big data. - Barbara Hammer; Haibo He; Thomas Martinetz (ESANN, 2014)

Visual manifold sensing. - Irina Burciu; Adrian Ion-Margineanu; Thomas Martinetz; Erhardt Barth (Human Vision and Electronic Imaging, 2014)

An adaptive hierarchical sensing scheme for sparse signals. - Henry Schütze; Erhardt Barth; Thomas Martinetz (Human Vision and Electronic Imaging, 2014)

Global Metric Learning by Gradient Descent. - Jens Hocke; Thomas Martinetz (ICANN, 2014)

Key-point Detection with Multi-layer Center-surround Inhibition. - Foti Coleca; Sabrina Zîrnovean; Thomas Käster; Thomas Martinetz; Erhardt Barth (VISAPP (1), 2014)

Committees of deep feedforward networks trained with few data. - Bogdan Miclut; Thomas Käster; Thomas Martinetz; Erhardt Barth (CoRR, 2014)

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model. - Arne Weigenand; Michael Schellenberger Costa; Hong-Viet Victor Ngo; Jens Christian Claussen; Thomas Martinetz (PLoS Comput. Biol., 2014)

Foveated Manifold Sensing for object recognition. - Irina Burciu; Thomas Martinetz; Erhardt Barth (BlackSeaCom, 2015)

Deep convolutional neural networks as generic feature extractors. - Lars Hertel; Erhardt Barth; Thomas Käster; Thomas Martinetz (IJCNN, 2015)

Learning orthogonal sparse representations by using geodesic flow optimization. - Henry Schütze; Erhardt Barth; Thomas Martinetz (IJCNN, 2015)

Self-organizing maps for hand and full body tracking. - Foti Coleca; Andreea State; Sascha Klement; Erhardt Barth; Thomas Martinetz (Neurocomputing, 2015)

Maximum distance minimization for feature weighting. - Jens Hocke; Thomas Martinetz (Pattern Recognit. Lett., 2015)

Hierarchical Manifold Sensing with Foveation and Adaptive Partitioning of the Dataset. - Irina Burciu; Thomas Martinetz; Erhardt Barth (HVEI, 2016)

Autoconvolution for Unsupervised Feature Learning. - Boris Knyazev 001; Erhardt Barth; Thomas Martinetz (CoRR, 2016)

Modeling the effect of sleep regulation on a neural mass model. - Michael Schellenberger Costa; Jan Born; Jens Christian Claussen; Thomas Martinetz (J. Comput. Neurosci., 2016)

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation. - Michael Schellenberger Costa; Arne Weigenand; Hong-Viet Victor Ngo; Lisa Marshall; Jan Born; Thomas Martinetz; Jens Christian Claussen (PLoS Comput. Biol., 2016)

Learning Efficient Data Representations With Orthogonal Sparse Coding. - Henry Schütze; Erhardt Barth; Thomas Martinetz (IEEE Trans. Computational Imaging, 2016)

Sensing Forest for Pattern Recognition. - Irina Burciu; Thomas Martinetz; Erhardt Barth (ACIVS, 2017)

Recursive autoconvolution for unsupervised learning of convolutional neural networks. - Boris Knyazev 001; Erhardt Barth; Thomas Martinetz (IJCNN, 2017)

Perception space analysis: From color vision to odor perception. - Amir Madany Mamlouk; Martin Haker; Thomas Martinetz (IJCNN, 2017)

Multi-layer Relation Networks. - Marius Jahrens; Thomas Martinetz (CoRR, 2018)

Multi-layer relation networks for relational reasoning. - Marius Jahrens; Thomas Martinetz (APPIS, 2019)

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition. - Hammam A. Alshazly; Christoph Linse; Erhardt Barth; Thomas Martinetz (Sensors, 2019)

Handcrafted versus CNN Features for Ear Recognition. - Hammam A. Alshazly; Christoph Linse; Erhardt Barth; Thomas Martinetz (Symmetry, 2019)

Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks. - Philipp Grüning; Thomas Martinetz; Erhardt Barth (ICANN (2), 2020)

Solving Raven's Progressive Matrices with Multi-Layer Relation Networks. - Marius Jahrens; Thomas Martinetz (IJCNN, 2020)

Deep Convolutional Neural Networks for Unconstrained Ear Recognition. - Hammam A. Alshazly; Christoph Linse; Erhardt Barth; Thomas Martinetz (IEEE Access, 2020)

Feature Products Yield Efficient Networks. - Philipp Grüning; Thomas Martinetz; Erhardt Barth (CoRR, 2020)

An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. - Dominik Mairhöfer; Manuel Laufer; Paul Martin Simon; Malte Sieren; Arpad Bischof; Thomas Käster; Erhardt Barth; Jörg Barkhausen; Thomas Martinetz (MIDL, 2021)

Towards Explainable Ear Recognition Systems Using Deep Residual Networks. - Hammam A. Alshazly; Christoph Linse; Erhardt Barth; Sahar Ahmed Idris; Thomas Martinetz (IEEE Access, 2021)

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering. - Shambhavi Mishra; Tanveer Ahmed 001; Vipul Kumar Mishra; Manjit Kaur; Thomas Martinetz; Amit Kumar Jain; Hammam A. Alshazly (Comput. Intell. Neurosci., 2021)

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. - Hammam A. Alshazly; Christoph Linse; Mohamed Abdalla; Erhardt Barth; Thomas Martinetz (PeerJ Comput. Sci., 2021)

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning. - Hammam A. Alshazly; Christoph Linse; Erhardt Barth; Thomas Martinetz (Sensors, 2021)

Large Neural Networks Learning from Scratch with Very Few Data and without Regularization. - Christoph Linse; Thomas Martinetz (CoRR, 2022)

Highly over-parameterized classifiers generalize since bad solutions are rare. - Julius Martinetz; Thomas Martinetz (CoRR, 2022)

A walk in the black-box: 3D visualization of large neural networks in virtual reality. - Christoph Linse; Hammam A. Alshazly; Thomas Martinetz (Neural Comput. Appl., 2022)

Population Coding Can Greatly Improve Performance of Neural Networks: A Comparison. - Marius Jahrens; Hans-Oliver Hansen; Rebecca Köhler; Thomas Martinetz (ICANN (5), 2023)

Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization. - Christoph Linse; Thomas Martinetz (ICMLC, 2023)

Convolutional Neural Networks Do Work with Pre-Defined Filters. - Christoph Linse; Erhardt Barth; Thomas Martinetz (IJCNN, 2023)

AI-based Collimation Optimization for X-Ray Imaging using Time-of-Flight Cameras. - Dominik Mairhöfer; Manuel Laufer; Lennart Berkel; Arpad Bischof; Erhardt Barth; Jörg Barkhausen; Thomas Martinetz (ESANN, 2024)

Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations. - Kathleen Anderson; Thomas Martinetz (ICANN (1), 2024)

Enhancing Generalization in Convolutional Neural Networks Through Regularization with Edge and Line Features. - Christoph Linse; Beatrice Brückner; Thomas Martinetz (ICANN (1), 2024)

Leaky ReLUs That Differ in Forward and Backward Pass Facilitate Activation Maximization in Deep Neural Networks. - Christoph Linse; Erhardt Barth; Thomas Martinetz (IJCNN, 2024)

Rethinking generalization of classifiers in separable classes scenarios and over-parameterized regimes. - Julius Martinetz; Christoph Linse; Thomas Martinetz (IJCNN, 2024)

Why LLMs Cannot Think and How to Fix It. - Marius Jahrens; Thomas Martinetz (CoRR, 2025)

The Resurrection of the ReLU. - Cosku Can Horuz; Geoffrey Kasenbacher; Saya Higuchi; Sebastian Kairat; Jendrik Stoltz; Moritz Pesl; Bernhard A. Moser; Christoph Linse; Thomas Martinetz; Sebastian Otte (CoRR, 2025)

Do Highly Over-Parameterized Neural Networks Generalize Since Bad Solutions are Rare? - Julius Martinetz; Thomas Martinetz (IEEE Trans. Neural Networks Learn. Syst., 2025)