Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. - Sascha Saralajew; David Nebel; Thomas Villmann (ICONIP (3), 2016)

Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. - Sascha Saralajew; Thomas Villmann (IJCNN, 2016)

Self-Adjusting Reject Options in Prototype Based Classification. - Thomas Villmann; Marika Kaden; Andrea Bohnsack; J.-M. Villmann; T. Drogies; Sascha Saralajew; Barbara Hammer (WSOM, 2016)

Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. - Sascha Saralajew; Thomas Villmann (IJCNN, 2017)

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. - Thomas Villmann; Michael Biehl; Andrea Villmann; Sascha Saralajew (WSOM, 2017)

Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. - Andrea Villmann; Marika Kaden; Sascha Saralajew; Wieland Hermann; Thomas Villmann (ESANN, 2018)

Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. - Andrea Villmann; Marika Kaden; Sascha Saralajew; Thomas Villmann (ICAISC (1), 2018)

Prototype-based Neural Network Layers: Incorporating Vector Quantization. - Sascha Saralajew; Lars Holdijk; Maike Rees; Thomas Villmann (CoRR, 2018)

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

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. - Sascha Saralajew; Lars Holdijk; Maike Rees; Ebubekir Asan; Thomas Villmann (NeurIPS, 2019)

Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. - Sascha Saralajew; Lars Holdijk; Maike Rees; Thomas Villmann (WSOM+, 2019)

Provident Detection of Vehicles at Night. - Emilio Oldenziel; Lars Ohnemus; Sascha Saralajew (IV, 2020)

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. - Sascha Saralajew; Lars Holdijk; Thomas Villmann (NeurIPS, 2020)

Provident Vehicle Detection at Night: The PVDN Dataset. - Lars Ohnemus; Lukas Ewecker; Ebubekir Asan; Stefan Roos; Simon T. Isele; Jakob Ketterer; Leopold Müller; Sascha Saralajew (CoRR, 2020)

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

New Prototype Concepts in Classification Learning. - Sascha Saralajew (2020)

The Coming of Age of Interpretable and Explainable Machine Learning Models. - Paulo Lisboa; Sascha Saralajew; Alfredo Vellido; Thomas Villmann (ESANN, 2021)

Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. - Christoph Raab; Sascha Saralajew; Frank-Michael Schleif (ESANN, 2021)

A Dataset for Provident Vehicle Detection at Night. - Sascha Saralajew; Lars Ohnemus; Lukas Ewecker; Ebubekir Asan; Simon T. Isele; Stefan Roos (IROS, 2021)

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 (VEHITS, 2021)

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. - Katrin Sophie Bohnsack; Marika Kaden; Julia Abel; Sascha Saralajew; Thomas Villmann (Entropy, 2021)

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)

Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night. - Lukas Ewecker; Lars Ohnemus; Robin Schwager; Stefan Roos; Sascha Saralajew (CoRR, 2022)

A Human-Centric Assessment Framework for AI. - Sascha Saralajew; Ammar Shaker; Zhao Xu 001; Kiril Gashteovski; Bhushan Kotnis; Wiem Ben Rim; Jürgen Quittek; Carolin Lawrence (CoRR, 2022)

The coming of age of interpretable and explainable machine learning models. - Paulo J. G. Lisboa; Sascha Saralajew; Alfredo Vellido; Ricardo Fernández-Domenech; Thomas Villmann (Neurocomputing, 2023)

Provident vehicle detection at night for advanced driver assistance systems. - Lukas Ewecker; Ebubekir Asan; Lars Ohnemus; Sascha Saralajew (Auton. Robots, 2023)

Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. - Julius Voigt; Sascha Saralajew; Marika Kaden; Katrin Sophie Bohnsack; Lynn V. Reuss; Thomas Villmann (BIOSTEC (1), 2024)

Robust Text Classification: Analyzing Prototype-Based Networks. - Zhivar Sourati; Darshan Deshpande; Filip Ilievski; Kiril Gashteovski; Sascha Saralajew (EMNLP (Findings), 2024)

Domain Knowledge Integration in Machine Learning Systems - An Introduction. - Marika Kaden; Sascha Saralajew; Thomas Villmann (ESANN, 2024)

Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night. - Lukas Ewecker; Lars Ohnemus; Robin Schwager; Stefan Roos; Tim Brühl; Sascha Saralajew (IV, 2024)

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)

A Human-Centric Assessment of the Usefulness of Attribution Methods in Computer Vision. - Wiem Ben Rim; Ammar Shaker; Zhao Xu 001; Kiril Gashteovski; Bhushan Kotnis; Carolin Lawrence; Jürgen Quittek; Sascha Saralajew (ECML/PKDD (5), 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

Generalization by People and Machines (Dagstuhl Seminar 24192). - Barbara Hammer; Filip Ilievski; Sascha Saralajew; Frank van Harmelen (Dagstuhl Reports, 2024)

A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations. - Sascha Saralajew; Ashish Rana; Thomas Villmann; Ammar Shaker (AAAI, 2025)