Sascha Saralajew
Orcid: 0000-0003-2248-8062
According to our database1,
Sascha Saralajew
authored at least 32 papers
between 2016 and 2024.
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Bibliography
2024
Dagstuhl Reports, 2024
A Human-Centric Assessment of the Usefulness of Attribution Methods in Computer Vision.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024
Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2024
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2024
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 2024
2023
Auton. Robots, March, 2023
Neurocomputing, 2023
2022
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night.
CoRR, 2022
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation.
Proceedings of the Advances in Intelligent Data Analysis XX, 2022
2021
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.
Entropy, 2021
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets.
Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, 2021
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021
2020
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability.
Neurocomputing, 2020
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020
2019
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks.
Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, 2019
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019
2018
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning.
Proceedings of the Artificial Intelligence and Soft Computing, 2018
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018
2017
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning.
Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
2016
Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning.
Proceedings of the Neural Information Processing - 23rd International Conference, 2016