DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. - Jensun Ravichandran; Sascha Saralajew; Thomas Villmann (ESANN, 2019)

Investigation of Activation Functions for Generalized Learning Vector Quantization. - Thomas Villmann; Jensun Ravichandran; Andrea Villmann; David Nebel; Marika Kaden (WSOM+, 2019)

Quantum-Inspired Learning Vector Quantization for Classification Learning. - Thomas Villmann; Jensun Ravichandran; Alexander Engelsberger; Andrea Villmann; Marika Kaden (ESANN, 2020)

Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. - Jensun Ravichandran; Marika Kaden; Sascha Saralajew; Thomas Villmann (Neurocomputing, 2020)

RecLVQ: Recurrent Learning Vector Quantization. - Jensun Ravichandran; Thomas Villmann; Marika Kaden (ESANN, 2021)

Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. - Danny Möbius; Jensun Ravichandran; Marika Kaden; Thomas Villmann (ICONIP (2), 2022)

A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. - Thomas Villmann; Daniel Staps; Jensun Ravichandran; Sascha Saralajew; Michael Biehl; Marika Kaden (IDA, 2022)

Variants of recurrent learning vector quantization. - Jensun Ravichandran; Marika Kaden; Thomas Villmann (Neurocomputing, 2022)

Quantum-inspired learning vector quantizers for prototype-based classification. - Thomas Villmann; Alexander Engelsberger; Jensun Ravichandran; Andrea Villmann; Marika Kaden (Neural Comput. Appl., 2022)

Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept. - Lukas Ewecker; Niklas Wagner; Tim Brühl; Robin Schwager; Tin Stribor Sohn; Alexander Engelsberger; Jensun Ravichandran; Hanno Stage; Jacob Langner; Sascha Saralajew (IV, 2024)