| 2026 | ESANN | Enforcing Feature Sparseness for Reliable Classification by Prototype-Based Models. | Marika Kaden, Julius Voigt, Sascha Saralajew, Thomas Villmann |
| 2025 | AAAI | A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. | Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker |
| 2025 | ICDM | Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI. | Ashish Rana, Ammar Shaker, Sascha Saralajew, Takashi Suzuki, Kosuke Yasuda, Shintaro Kato, Toshikazu Wada, Toshiyuki Fujikawa, Toru Kikutsuji |
| 2024 | EMNLP | Robust Text Classification: Analyzing Prototype-Based Networks. | Zhivar Sourati, Darshan Deshpande, Filip Ilievski, Kiril Gashteovski, Sascha Saralajew |
| 2024 | ESANN | Domain Knowledge Integration in Machine Learning Systems - An Introduction. | Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2022 | IDA | 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 |
| 2021 | ESANN | The Coming of Age of Interpretable and Explainable Machine Learning Models. | Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann |
| 2021 | ESANN | Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. | Christoph Raab, Sascha Saralajew, Frank-Michael Schleif |
| 2021 | IROS | A Dataset for Provident Vehicle Detection at Night. | Sascha Saralajew, Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Simon T. Isele, Stefan Roos |
| 2021 | VEHITS | Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets. | Simon T. Isele, Marcel P. Schilling, Fabian E. Klein, Sascha Saralajew, J. Marius Zoellner |
| 2019 | ESANN | DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. | Jensun Ravichandran, Sascha Saralajew, Thomas Villmann |
| 2018 | ESANN | Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann |
| 2018 | ICAISC | Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2017 | IJCNN | Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. | Sascha Saralajew, Thomas Villmann |
| 2016 | ICONIP | Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. | Sascha Saralajew, David Nebel, Thomas Villmann |
| 2016 | IJCNN | Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. | Sascha Saralajew, Thomas Villmann |