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

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

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

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)

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

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

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

Sparse Coding for Feature Selection on Genome-Wide Association Data. - Ingrid Brænne; Kai Labusch; Amir Madany Mamlouk (ICANN (1), 2010)

Soft-competitive learning of sparse codes and its application to image reconstruction. - Kai Labusch; Erhardt Barth; 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)

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

Soft-competitive learning of sparse data models. - Kai Labusch (2012)

BERT for Named Entity Recognition in Contemporary and Historic German. - Kai Labusch; Clemens Neudecker; David Zellhöfer (KONVENS, 2019)

Named Entity Disambiguation and Linking Historic Newspaper OCR with BERT. - Kai Labusch; Clemens Neudecker (CLEF (Working Notes), 2020)

QURATOR: Innovative Technologies for Content and Data Curation. - Georg Rehm; Peter Bourgonje; Stefanie Hegele; Florian Kintzel; Julián Moreno Schneider; Malte Ostendorff; Karolina Zaczynska; Armin Berger; Stefan Grill; Sören Räuchle; Jens Rauenbusch; Lisa Rutenburg; et al. (Qurator, 2020)

Entity Linking in Multilingual Newspapers and Classical Commentaries with BERT. - Kai Labusch; Clemens Neudecker (CLEF (Working Notes), 2022)

Gauging the Limitations of Natural Language Supervised Text-Image Metrics Learning by Iconclass Visual Concepts. - Kai Labusch; Clemens Neudecker (HIP@ICDAR, 2023)

Document Layout Analysis with Deep Learning and Heuristics. - Vahid Rezanezhad; Konstantin Baierer; Mike Gerber; Kai Labusch; Clemens Neudecker (HIP@ICDAR, 2023)