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Berlin State Library: Berlin, Berlin, DE

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Kai Labusch

Works (24)

Document Layout Analysis with Deep Learning and Heuristics

Proceedings of the 7th International Workshop on Historical Document Imaging and Processing
2023-08-25 | Conference paper | Author
Contributors: Vahid Rezanezhad; Konstantin Baierer; Mike Gerber; Kai Labusch; Clemens Neudecker
Source: Self-asserted source
Kai Labusch

Gauging the Limitations of Natural Language Supervised Text-Image Metrics Learning by Iconclass Visual Concepts

Proceedings of the 7th International Workshop on Historical Document Imaging and Processing
2023-08-25 | Conference paper | Author
Contributors: Kai Labusch; Clemens Neudecker
Source: Self-asserted source
Kai Labusch

Entity Linking in Multilingual Newspapers and Classical Commentaries with BERT

CLEF 2022: CLEF 2022 working notes
2022 | Conference paper
Contributors: Labusch, Kai; Neudecker, Clemens
Source: Self-asserted source
Kai Labusch

Named Entity Linking mit Wikidata und GND--Das Potenzial handkuratierter und strukturierter Datenquellen für die semantische Anreicherung von Volltexten

Qualität in der Inhaltserschließung
2021 | Journal article
Contributors: Menzel, Sina; Schnaitter, Hannes; Zinck, Josefine; Petras, Vivien; Neudecker, Clemens; Labusch, Kai; Leitner, Elena; Rehm, Georg
Source: Self-asserted source
Kai Labusch

Named Entity Disambiguation and Linking on Historic Newspaper OCR with BERT.

Conference and Labs of the Evaluation Forum (CLEF 2020)
2020 | Conference paper
Contributors: Labusch, Kai; Neudecker, Clemens
Source: Self-asserted source
Kai Labusch

QURATOR: innovative technologies for content and data curation

arXiv preprint arXiv:2004.12195
2020 | Journal article
Contributors: Rehm, Georg; Bourgonje, Peter; Hegele, Stefanie; Kintzel, Florian; Schneider, Julián Moreno; Ostendorff, Malte; Zaczynska, Karolina; Berger, Armin; Grill, Stefan; Räuchle, Sören et al.
Source: Self-asserted source
Kai Labusch

BERT for named entity recognition in contemporary and historical German

Proceedings of the 15th conference on natural language processing, Erlangen, Germany
2019 | Conference paper
Contributors: Labusch, Kai; Kulturbesitz, Preußischer; Neudecker, Clemens; Zellhöfer, David
Source: Self-asserted source
Kai Labusch

Sparse coding and selected applications

KI-Künstliche Intelligenz
2012 | Journal article
Contributors: Hocke, Jens; Labusch, Kai; Barth, Erhardt; Martinetz, Thomas
Source: Self-asserted source
Kai Labusch

Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent

IEEE Transactions on Selected Topics in Signal Processing
2011 | Journal article
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz
Source: Self-asserted source
Kai Labusch

Soft-competitive Learning of Sparse Codes and its Application to Image Reconstruction

Neurocomputing
2011 | Journal article
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz
Source: Self-asserted source
Kai Labusch

Bag of Pursuits and Neural Gas for Improved Sparse Coding

Proceedings of the 19th International Conference on Computational Statistics
2010 | Conference paper
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz; Gilbert Saporta
Source: Self-asserted source
Kai Labusch

Learning Sparse Codes for Image Reconstruction

Proceedings of the 18th European Symposium on Artificial Neural Networks
2010 | Conference paper
Contributors: Kai Labusch; Thomas Martinetz; Michel Verleysen
Source: Self-asserted source
Kai Labusch

Sparse coding for feature selection on genome-wide association data

Artificial Neural Networks--ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I 20
2010 | Conference paper
Contributors: Brænne, Ingrid; Labusch, Kai; Madany Mamlouk, Amir
Source: Self-asserted source
Kai Labusch

Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas

Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009
2009 | Conference paper
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz; J.C. Principe; R. Miikkulainen
Source: Self-asserted source
Kai Labusch

Demixing Jazz-Music: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources

Neural Network World
2009 | Journal article
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz
Source: Self-asserted source
Kai Labusch

SoftDoubleMaxMinOver: Perceptron-like Training of Support Vector Machines

IEEE Transactions on Neural Networks
2009 | Journal article
Contributors: Thomas Martinetz; Kai Labusch; Daniel Schneegaß
Source: Self-asserted source
Kai Labusch

Sparse Coding Neural Gas: Learning of Overcomplete Data Representations

Neurocomputing
2009 | Journal article
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz
Source: Self-asserted source
Kai Labusch

Learning data representations with Sparse Coding Neural Gas

Proceedings of the 16th European Symposium on Artificial Neural Networks
2008 | Conference paper
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz; Michel Verleysen
Source: Self-asserted source
Kai Labusch

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

IEEE Transactions on Neural Networks
2008 | Journal article
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz
Source: Self-asserted source
Kai Labusch

Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources

Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II
2008 | Conference paper
Contributors: Kai Labusch; Erhardt Barth; Thomas Martinetz; Vera Kurková; Roman Neruda; Jan Koutník
Source: Self-asserted source
Kai Labusch

Learning optimal features for visual pattern recognition

Human Vision and Electronic Imaging XII
2007 | Conference paper
Contributors: Kai Labusch; Udo Siewert; Thomas Martinetz; Erhardt Barth; Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly
Source: Self-asserted source
Kai Labusch

MaxMinOver regression: A simple incremental approach for support vector function approximation

Artificial Neural Networks--ICANN 2006: 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part I 16
2006 | Conference paper
Contributors: Schneegaß, Daniel; Labusch, Kai; Martinetz, Thomas
Source: Self-asserted source
Kai Labusch

SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification.

Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II
2005 | Conference paper
Contributors: Thomas Martinetz; Kai Labusch; Daniel Schneegaß; Wlodzislaw Duch; Janusz Kacprzyk; Erkki Oja; Slawomir Zadrozny
Source: Self-asserted source
Kai Labusch

Sensor Evolution In A Homeokinetic System

Proceedings of the Fifth German Workshop on Artificial Life
2002 | Conference paper
Contributors: Labusch, Kai; Polani, Daniel
Source: Self-asserted source
Kai Labusch