| The coming of age of interpretable and explainable machine learning models PJG Lisboa, S Saralajew, A Vellido, R Fernández-Domenech, T Villmann Neurocomputing 535, 25-39, 2023 | 56 | 2023 |
| Classification-by-components: Probabilistic modeling of reasoning over a set of components S Saralajew, L Holdijk, M Rees, E Asan, T Villmann Advances in Neural Information Processing Systems 32, 2019 | 34 | 2019 |
| Fast adversarial robustness certification of nearest prototype classifiers for arbitrary seminorms S Saralajew, L Holdijk, T Villmann Advances in Neural Information Processing Systems 33, 13635-13650, 2020 | 27 | 2020 |
| Self-adjusting reject options in prototype based classification T Villmann, M Kaden, A Bohnsack, JM Villmann, T Drogies, S Saralajew, ... Advances in Self-Organizing Maps and Learning Vector Quantization …, 2016 | 26 | 2016 |
| Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning T Villmann, M Biehl, A Villmann, S Saralajew 2017 12th international workshop on self-organizing maps and learning vector …, 2017 | 23 | 2017 |
| Robustness of generalized learning vector quantization models against adversarial attacks S Saralajew, L Holdijk, M Rees, T Villmann Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering …, 2020 | 22 | 2020 |
| Prototype-based neural network layers: incorporating vector quantization S Saralajew, L Holdijk, M Rees, T Villmann arXiv preprint arXiv:1812.01214, 2018 | 20 | 2018 |
| Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning S Saralajew, T Villmann 2016 International Joint Conference on Neural Networks (IJCNN), 2672-2679, 2016 | 19 | 2016 |
| Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability J Ravichandran, M Kaden, S Saralajew, T Villmann Neurocomputing 403, 121-132, 2020 | 16 | 2020 |
| Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks. T Villmann, S Saralajew, A Villmann, M Kaden IJCCI, 15-21, 2018 | 16 | 2018 |
| Provident detection of vehicles at night E Oldenziel, L Ohnemus, S Saralajew 2020 IEEE Intelligent Vehicles Symposium (IV), 472-479, 2020 | 12 | 2020 |
| Probabilistic learning vector quantization with cross-entropy for probabilistic class assignments in classification learning A Villmann, M Kaden, S Saralajew, T Villmann Artificial Intelligence and Soft Computing: 17th International Conference …, 2018 | 12 | 2018 |
| Transfer learning in classification based on manifold models and its relation to tangent metric learning S Saralajew, T Villmann 2017 International Joint Conference on Neural Networks (IJCNN), 1756-1765, 2017 | 12 | 2017 |
| Provident vehicle detection at night for advanced driver assistance systems L Ewecker, E Asan, L Ohnemus, S Saralajew Autonomous Robots 47 (3), 313-335, 2023 | 10 | 2023 |
| A dataset for provident vehicle detection at night S Saralajew, L Ohnemus, L Ewecker, E Asan, S Isele, S Roos 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 10 | 2021 |
| Adaptive Hausdorff distances and tangent distance adaptation for transformation invariant classification learning S Saralajew, D Nebel, T Villmann Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 9 | 2016 |
| The resolved mutual information function as a structural fingerprint of biomolecular sequences for interpretable machine learning classifiers KS Bohnsack, M Kaden, J Abel, S Saralajew, T Villmann Entropy 23 (10), 1357, 2021 | 6 | 2021 |
| Adaptive tangent metrics in generalized learning vector quantization for transformation and distortion invariant classification learning S Saralajew, T Villmann Proceedings of the International Joint Conference on Neural networks (IJCNN …, 0 | 6 | |
| A learning vector quantization architecture for transfer learning based classification in case of multiple sources by means of null-space evaluation T Villmann, D Staps, J Ravichandran, S Saralajew, M Biehl, M Kaden International Symposium on Intelligent Data Analysis, 354-364, 2022 | 5 | 2022 |
| Radar artifact labeling framework (RALF): method for plausible radar detections in datasets ST Isele, MP Schilling, FE Klein, S Saralajew, JM Zoellner arXiv preprint arXiv:2012.01993, 2020 | 5 | 2020 |
Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences Mittweida在 hs-mittweida.de 的电子邮件经过验证
