The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. - Marika Kaden; Ronny Schubert; Mehrdad Mohannazadeh Bakhtiari; Lucas Schwarz; Thomas Villmann (ESANN, 2021)

Prototype-based One-Class-Classification Learning Using Local Representations. - Daniel Staps; Ronny Schubert; Marika Kaden; Alexander Lampe; Wieland Hermann; Thomas Villmann (IJCNN, 2022)

Variants of Neural Gas for Regression Learning. - Thomas Villmann; Ronny Schubert; Marika Kaden (ESANN, 2023)

A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. - Ronny Schubert; Lynn V. Reuss; Daniel Staps; Marika Kaden; Thomas Villmann; Robert Hasler; Robin Herz; Till Tiemann; Wolfram Richardt (WHISPERS, 2023)

About Vector Quantization and its Privacy in Federated Learning. - Ronny Schubert; Thomas Villmann (ESANN, 2024)

About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach. - Ronny Schubert; Thomas Villmann (WSOM+, 2024)