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Sascha Saralajew
Sascha Saralajew
Bestätigte E-Mail-Adresse bei neclab.eu
Titel
Zitiert von
Zitiert von
Jahr
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
412023
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
332019
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
252020
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
252016
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
232017
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
222020
Prototype-based neural network layers: incorporating vector quantization
S Saralajew, L Holdijk, M Rees, T Villmann
arXiv preprint arXiv:1812.01214, 2018
202018
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
192016
Learning Vector Quantization Methods for Interpretable Classification Learning and Multilayer Networks.
T Villmann, S Saralajew, A Villmann, M Kaden
IJCCI, 15-21, 2018
162018
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
152020
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
122018
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
122017
Provident detection of vehicles at night
E Oldenziel, L Ohnemus, S Saralajew
2020 IEEE Intelligent Vehicles Symposium (IV), 472-479, 2020
112020
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
102023
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
92016
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
82021
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
62021
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
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
52020
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
42022
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