T. Martinetz

发表

Thomas Martinetz, Klaus Schulten, K. Schulten, 1994, Neural Networks.

A new quantitative measure of topology preservation in Kohonen's feature maps

R. Der, T. Villmann, T. Martinetz, 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties.

P. Suganthan, T. Martinetz, K. Kandaswamy, 2011, Journal of theoretical biology.

'Neural-gas' network for vector quantization and its application to time-series prediction

Thomas Martinetz, Klaus Schulten, Stanislav G. Berkovich, 1993, IEEE Trans. Neural Networks.

Three-dimensional neural net for learning visuomotor coordination of a robot arm

Helge J. Ritter, Thomas Martinetz, Klaus Schulten, 1990, IEEE Trans. Neural Networks.

SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes.

P. Suganthan, T. Martinetz, K. Kandaswamy, 2010, Biochemical and biophysical research communications.

Neural computation and self-organizing maps - an introduction

Helge J. Ritter, Thomas Martinetz, Klaus Schulten, 1992, Computation and neural systems series.

Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory

J. Born, T. Martinetz, M. Mölle, 2013, Neuron.

SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information

P. Suganthan, T. Martinetz, K. Kandaswamy, 2010, Journal of biomolecular structure & dynamics.

Simple Method for High-Performance Digit Recognition Based on Sparse Coding

Thomas Martinetz, Erhardt Barth, Kai Labusch, 2008, IEEE Transactions on Neural Networks.

New Challenges in Neural Computation NC 2 – 2010

T. Villmann, T. Martinetz, B. Hammer, 2016 .

Learning Transformation Invariance from Global to Local

T. Martinetz, Jens Hocke, 2016 .

Timing matters: open‐loop stimulation does not improve overnight consolidation of word pairs in humans

L. Marshall, T. Martinetz, M. Mölle, 2016, The European journal of neuroscience.

Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis

Thomas Martinetz, Anke Meyer-Bäse, Dorothee P. Auer, 2004, ESANN.

Learning Transformation Invariance for Object Recognition

T. Martinetz, Jens Hocke, 2016 .

Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition.

Thomas Martinetz, Ganesan Pugalenthi, P N Suganthan, 2010, Protein and peptide letters.

EcmPred: prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection.

Thomas Martinetz, Ganesan Pugalenthi, Krishna Kumar Kandaswamy, 2013, Journal of theoretical biology.

Dynamics of the thalamo-cortical system driven by pulsed sensory stimulation

Michael Schellenberger Costa, L. Marshall, T. Martinetz, 2013, BMC Neuroscience.

A population model of the thalamo-cortical system during deep sleep

Michael Schellenberger Costa, T. Martinetz, J. Claussen, 2013, BMC Neuroscience.

RNAi revised - target mRNA-dependent enhancement of gene silencing

T. Martinetz, J. Claussen, G. Sczakiel, 2015, Nucleic acids research.

Large Neural Networks Learning from Scratch with Very Few Data and without Regularization

T. Martinetz, C. Linse, 2022, 2205.08836.

PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome

Thomas Martinetz, Jiajie Zhang, Suhua Chang, 2011, BMC Bioinformatics.

Computational Matter

L. D. Whitley, C. Torras, H. Schwefel, 2018, Natural Computing Series.

Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

T. Martinetz, S. Borgwardt, C. Andreou, 2021 .

A walk in the black-box: 3D visualization of large neural networks in virtual reality

T. Martinetz, H. Alshazly, C. Linse, 2022, Neural Computing and Applications.

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

T. Martinetz, Shuihua Wang, H. Alshazly, 2022, Frontiers in Public Health.

Closed‐loop acoustic stimulation during an afternoon nap to modulate subsequent encoding

L. Marshall, T. Martinetz, M. Mölle, 2022, Journal of sleep research.

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

H. Handels, T. Martinetz, S. Borgwardt, 2022, Frontiers in Psychiatry.

Kohonens Self-Organizing Maps for Modeling the Formation of the Auditory Cortex of a Bat

Klaus Schulten, Helge Ritter, Thomas Martinetz, 1988 .

Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization

T. Martinetz, C. Linse, 2022, 2205.08836.

Automated assessment of immunofixations with deep neural networks

E. Barth, T. Martinetz, Christian Thiemann, 2022, LaboratoriumsMedizin.

Highly over-parameterized classifiers generalize since bad solutions are rare

T. Martinetz, Julius Martinetz, 2022, 2211.03570.

Differential thalamocortical interactions in slow and fast spindle generation: A computational model

L. Marshall, T. Martinetz, M. Bazhenov, 2022, PloS one.

Learning Efficient Data Representations With Orthogonal Sparse Coding

Thomas Martinetz, Erhardt Barth, Henry Schütze, 2016, IEEE Transactions on Computational Imaging.

Learning orthogonal sparse representations by using geodesic flow optimization

Thomas Martinetz, Erhardt Barth, Henry Schütze, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Application of non-linear transform coding to image processing

Thomas Martinetz, Erhardt Barth, Jens Hocke, 2012, Electronic Imaging.

Sparse Coding Neural Gas Applied to Image Recognition

Thomas Martinetz, Erhardt Barth, Horia Coman, 2012, WSOM.

Multimodal Sparse Features for Object Detection

Thomas Martinetz, Erhardt Barth, Martin Haker, 2009, ICANN.

Sparse Coding Neural Gas: Learning of overcomplete data representations

Thomas Martinetz, Erhardt Barth, Kai Labusch, 2009, Neurocomputing.

Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources

Thomas Martinetz, Erhardt Barth, Kai Labusch, 2008, ICANN.

Learning optimal features for visual pattern recognition

Thomas Martinetz, Erhardt Barth, Kai Labusch, 2007, Electronic Imaging.

Soft-competitive learning of sparse codes and its application to image reconstruction

Thomas Martinetz, Erhardt Barth, Kai Labusch, 2011, Neurocomputing.

Stimulation Augments Spike Sequence Replay and Memory Consolidation during Slow-Wave Sleep

Thomas Martinetz, Maxim Bazhenov, Yina Wei, 2019, The Journal of Neuroscience.

Learning and modeling big data

Haibo He, Thomas Martinetz, Barbara Hammer, 2014, ESANN.

Learning and modelling big data

Thomas Martinetz, Haibo He, Haibo He, 2014 .

Maximum distance minimization for feature weighting

Thomas Martinetz, Jens Hocke, T. Martinetz, 2015, Pattern Recognit. Lett..

Sparse Coding and Selected Applications

Thomas Martinetz, Erhardt Barth, Jens Hocke, 2012, KI - Künstliche Intelligenz.

Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

Nils Daniel Forkert, Heinz Handels, Oskar Maier, 2015, PloS one.

Handcrafted versus CNN Features for Ear Recognition

Thomas Martinetz, Erhardt Barth, Hammam Alshazly, 2019, Symmetry.

AMES: Automated evaluation of sarcomere structures in cardiomyocytes

O. Wolkenhauer, T. Martinetz, R. David, 2021, bioRxiv.

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Thomas Martinetz, Erhardt Barth, Hammam Alshazly, 2020, Sensors.

Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2006, Artificial Intelligence and Applications.

Medical image compression using topology-preserving neural networks

Thomas Martinetz, Anke Meyer-Bäse, Axel Wismüller, 2005, Eng. Appl. Artif. Intell..

Topology-conserving maps for learning visuo-motor-coordination

Klaus Schulten, Helge J. Ritter, Thomas Martinetz, 1989, Neural Networks.

Variability of eye movements when viewing dynamic natural scenes.

Thomas Martinetz, Karl R Gegenfurtner, Michael Dorr, 2010, Journal of vision.

A computational study of suppression of sharp wave ripple complexes by controlling calcium and gap junctions in pyramidal cells

L. Marshall, T. Martinetz, L. Hertel, 2021, Bioengineered.

A software framework for simulating eye trackers

Thomas Martinetz, Erhardt Barth, Martin Böhme, 2008, ETRA.

Topology preservation in self-organizing feature maps: exact definition and measurement

Thomas Villmann, Thomas Martinetz, Ralf Der, 1997, IEEE Trans. Neural Networks.

Solving Raven’s Progressive Matrices with Multi-Layer Relation Networks

Thomas Martinetz, Marius Jahrens, T. Martinetz, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Face Detection Using a Time-of-Flight Camera

Thomas Martinetz, Erhardt Barth, Martin Böhme, 2009, Dyn3D.

Geometric Invariants for Facial Feature Tracking with 3D TOF Cameras

E. Barth, M. Haker, M. Bohme, 2007, 2007 International Symposium on Signals, Circuits and Systems.

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

E. Barth, T. Martinetz, H. Alshazly, 2021, medRxiv.

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

T. Martinetz, A. Jain, H. Alshazly, 2021, Comput. Intell. Neurosci..

Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes

Thomas Martinetz, Erhardt Barth, Michael Dorr, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework

Anita S. Kini, T. Martinetz, Jagendra Singh, 2022, Contrast media & molecular imaging.

Uncertainty propagation for quality assurance in Reinforcement Learning

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

On the Problem of Finding the Least Number of Features by L1-Norm Minimisation

Thomas Martinetz, Sascha Klement, T. Martinetz, 2011, ICANN.

Vector Quantization Algorithm for Time Series Prediction and Visuo-Motor Control of Robots

Thomas Martinetz, Klaus Schulten, Jörg A. Walter, 1991, Wissensbasierte Systeme.

On the dimensions of the olfactory perception space

Thomas Martinetz, Amir Madany Mamlouk, T. Martinetz, 2004, Neurocomputing.

Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells

E. Barth, T. Martinetz, M. Danckwardt, 2012, Clinical & developmental immunology.

EUROPattern Suite technology for computer-aided immunofluorescence microscopy in autoantibody diagnostics

E. Barth, T. Martinetz, M. Danckwardt, 2015, Lupus.

A Single-Camera Remote Eye Tracker

Thomas Martinetz, Erhardt Barth, André Meyer, 2006, PIT.

The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data

Thomas Martinetz, Sascha Klement, Silke Anders, 2013, Neural Computation.

Deep convolutional neural networks as generic feature extractors

Thomas Martinetz, Lars Hertel, Erhardt Barth, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

FP-nets as novel deep networks inspired by vision

E. Barth, T. Martinetz, Philipp Grüning, 2022, Journal of vision.

Towards Explainable Ear Recognition Systems Using Deep Residual Networks

Thomas Martinetz, Erhardt Barth, Hammam Alshazly, 2021, IEEE Access.

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition

Hammam Alshazly, Christoph Linse, Thomas Martinetz, 2019, Sensors.

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Hammam Alshazly, Christoph Linse, Thomas Martinetz, 2020, IEEE Access.

The phase response of the cortical slow oscillation

T. Martinetz, J. Claussen, Arne Weigenand, 2012, Cognitive Neurodynamics.

BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection

Thomas Martinetz, Ganesan Pugalenthi, Mehrnaz Khodam Hazrati, 2011, BMC Bioinformatics.

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

Thomas Martinetz, Jens Christian Claussen, Arne Weigenand, 2014, PLoS Comput. Biol..

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

Thomas Martinetz, Jens Christian Claussen, Jan Born, 2016, PLoS Comput. Biol..

Sensing Forest for Pattern Recognition

Thomas Martinetz, Erhardt Barth, Irina Burciu, 2017, ACIVS.

Feature Products Yield Efficient Networks pdf

Thomas Martinetz, Erhardt Barth, Philipp Grüning, 2020, ArXiv.

Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes

Thomas Martinetz, Erhardt Barth, Michael Dorr, 2011, Cognitive Computation.

Remote Eye Tracking: State of the Art and Directions for Future Development

Thomas Martinetz, Erhardt Barth, André Meyer, 2006 .

Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios

Thomas Martinetz, Sascha Klement, Amir Madany Mamlouk, 2008, ICANN.

Driving Sleep Slow Oscillations by Auditory Closed-Loop Stimulation—A Self-Limiting Process

Thomas Martinetz, Jan Born, Matthias Mölle, 2015, The Journal of Neuroscience.

Guiding the mind's eye: improving communication and vision by external control of the scanpath

Thomas Martinetz, Erhardt Barth, Martin Böhme, 2006, Electronic Imaging.

Eye movement modelling and gaze guidance

Thomas Martinetz, Karl R. Gegenfurtner, Michael Dorr, 2008 .

Guidance of eye movements on a gaze-contingent display

Thomas Martinetz, Karl R. Gegenfurtner, Michael Dorr, 2004 .

Modeling the effect of sleep regulation on a neural mass model

Michael Schellenberger Costa, J. Born, T. Martinetz, 2016, Journal of Computational Neuroscience.

Recursive autoconvolution for unsupervised learning of convolutional neural networks

Thomas Martinetz, Erhardt Barth, Boris Knyazev, 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

Autoconvolution for Unsupervised Feature Learning pdf

Thomas Martinetz, Erhardt Barth, Boris Knyazev, 2016, ArXiv.

Foveated Manifold Sensing for object recognition

Thomas Martinetz, Erhardt Barth, Irina Burciu, 2015, 2015 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom).

Gesture Interfaces with Depth Sensors

Thomas Martinetz, Erhardt Barth, Foti Coleca, 2013, Time-of-Flight and Depth Imaging.

Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations

Thomas Martinetz, Amir Madany Mamlouk, Ulrich G. Hofmann, 2005, Neurocomputing.

Global Metric Learning by Gradient Descent

Thomas Martinetz, Jens Hocke, T. Martinetz, 2014, ICANN.

Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ICANN.

Eye movement predictions on natural videos

Thomas Martinetz, Erhardt Barth, Martin Böhme, 2006, Neurocomputing.

EYE MOVEMENT PREDICTIONS ENHANCED BY SACCADE DETECTION

Thomas Martinetz, Erhardt Barth, Martin Böhme, 2004 .

Brain Inspired Cognitive Systems

E. Barth, T. Martinetz, C. Krause, 2022 .

SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines

Thomas Martinetz, Kai Labusch, Daniel Schneegaß, 2009, IEEE Transactions on Neural Networks.

Fast model selection for MaxMinOver-based training of support vector machines

Thomas Martinetz, Sascha Klement, Fabian Timm, 2008, 2008 19th International Conference on Pattern Recognition.

Simple Incremental One-Class Support Vector Classification

Thomas Martinetz, Kai Labusch, Fabian Timm, 2008, DAGM-Symposium.

Suicide prediction with natural language processing of electronic health records

T. Martinetz, A. Roberts, Xingyi Song, 2023, medRxiv.

Do highly over-parameterized neural networks generalize since bad solutions are rare?

T. Martinetz, Julius Martinetz, 2022, 2211.03570.

A New Approach to Classification with the Least Number of Features

Thomas Martinetz, Sascha Klement, T. Martinetz, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Chaos analysis of the cortical boundary for the recognition of psychosis

H. Handels, T. Martinetz, S. Borgwardt, 2023, Journal of psychiatry & neuroscience : JPN.

A Learned Saliency Predictor for Dynamic Natural Scenes

Thomas Martinetz, Erhardt Barth, Michael Dorr, 2010, ICANN.

Dynamic predictions of tracked gaze

Thomas Martinetz, Erhardt Barth, Jan Drewes, 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

Individual predictions of eye-movements with dynamic scenes

Thomas Martinetz, Erhardt Barth, Jan Drewes, 2003, IS&T/SPIE Electronic Imaging.

Self-organizing maps for hand and full body tracking

Thomas Martinetz, Erhardt Barth, Sascha Klement, 2015, Neurocomputing.

Real-time skeleton tracking for embedded systems

Thomas Martinetz, Erhardt Barth, Sascha Klement, 2013, Electronic Imaging.

Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ESANN.

Perception space analysis: From color vision to odor perception

Thomas Martinetz, Amir Madany Mamlouk, Martin Haker, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation

Thomas Martinetz, Kai Labusch, Daniel Schneegaß, 2006, ICANN.

Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ESANN.