Julius Voigt

According to our database, Julius Voigt authored at least 8 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

From annotation to reflection: how participatory AI training enhances critical thinking.

Sassan Gholiagha, Jürgen Neyer, Mitja Sienknecht, Magdalena Wolska, Dora Kiesel, Patrick Riehmann, Julius Voigt, Matti Wiegmann, Irene López García, Katrin Girgensohn, Benno Stein, Bernd Fröhlich

AI Soc., March, 2026

FA(IR)2MA-GLVQ - A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.

Marika Kaden, Ronny Schubert, Julius Voigt, Lynn V. Reuss, Alexander Engelsberger, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann

Neurocomputing, 2026

Enforcing Feature Sparseness for Reliable Classification by Prototype-Based Models.

Marika Kaden, Julius Voigt, Sascha Saralajew, Thomas Villmann

Proceedings of the 34th European Symposium on Artificial Neural Networks, 2026

2025

Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models.

Julius Voigt, Marika Kaden, Lynn V. Reuss, Thomas Villmann

Proceedings of the Artificial Intelligence and Soft Computing, 2025

2024

Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation.

Marika Kaden, Julius Voigt, Katrin Sophie Bohnsack, Mandy Lange-Geisler, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond, 2024

Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge.

Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Sophie Bohnsack, Lynn V. Reuss, Thomas Villmann

Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 2024

2023

Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data.

Katrin Sophie Bohnsack, Julius Voigt, Marika Kaden, Florian Heinke, Thomas Villmann

Neurocomputing, October, 2023

2022

Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features.

Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022