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2020 – today
- 2024
[c22]Julius Voigt, Sascha Saralajew, Marika Kaden, Katrin Sophie Bohnsack, Lynn V. Reuss, Thomas Villmann:
Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. BIOSTEC (1) 2024: 357-367- [c21]Zhivar Sourati, Darshan Deshpande, Filip Ilievski, Kiril Gashteovski, Sascha Saralajew:
Robust Text Classification: Analyzing Prototype-Based Networks. EMNLP (Findings) 2024: 12736-12757 - [c20]Lukas Ewecker, Lars Ohnemus, Robin Schwager, Stefan Roos, Tim Brühl, Sascha Saralajew:
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night. IV 2024: 1505-1512 - [c19]Lukas Ewecker, Niklas Wagner, Tim Brühl, Robin Schwager, Tin Stribor Sohn, Alexander Engelsberger, Jensun Ravichandran, Hanno Stage, Jacob Langner, Sascha Saralajew:
Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept. IV 2024: 2117-2124 - [c18]Wiem Ben Rim, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Carolin Lawrence, Jürgen Quittek, Sascha Saralajew:
A Human-Centric Assessment of the Usefulness of Attribution Methods in Computer Vision. ECML/PKDD (5) 2024: 20-37 - [i10]Barbara Hammer, Filip Ilievski, Sascha Saralajew, Frank van Harmelen:
Generalization by People and Machines (Dagstuhl Seminar 24192). Dagstuhl Reports 14(5): 1-11 (2024) - 2023
- [j4]Lukas Ewecker, Ebubekir Asan, Lars Ohnemus
, Sascha Saralajew:
Provident vehicle detection at night for advanced driver assistance systems. Auton. Robots 47(3): 313-335 (2023) - [j3]Paulo J. G. Lisboa, Sascha Saralajew, Alfredo Vellido, Ricardo Fernández-Domenech, Thomas Villmann:
The coming of age of interpretable and explainable machine learning models. Neurocomputing 535: 25-39 (2023) - [i9]Zhivar Sourati, Darshan Deshpande, Filip Ilievski, Kiril Gashteovski, Sascha Saralajew:
Robust Text Classification: Analyzing Prototype-Based Networks. CoRR abs/2311.06647 (2023) - 2022
- [c17]Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden:
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. IDA 2022: 354-364 - [i8]Lukas Ewecker, Lars Ohnemus, Robin Schwager, Stefan Roos, Sascha Saralajew:
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night. CoRR abs/2204.11535 (2022) - [i7]Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Wiem Ben Rim, Jürgen Quittek, Carolin Lawrence:
A Human-Centric Assessment Framework for AI. CoRR abs/2205.12749 (2022) - 2021
- [j2]Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Sascha Saralajew, Thomas Villmann:
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. Entropy 23(10): 1357 (2021) - [c16]Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann:
The Coming of Age of Interpretable and Explainable Machine Learning Models. ESANN 2021 - [c15]Christoph Raab, Sascha Saralajew, Frank-Michael Schleif:
Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. ESANN 2021 - [c14]Sascha Saralajew, Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Simon T. Isele, Stefan Roos:
A Dataset for Provident Vehicle Detection at Night. IROS 2021: 9750-9757 - [c13]Simon T. Isele, Marcel P. Schilling, Fabian E. Klein, Sascha Saralajew, J. Marius Zoellner:
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets. VEHITS 2021: 22-33 - [i6]Sascha Saralajew, Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Simon T. Isele, Stefan Roos:
A Dataset for Provident Vehicle Detection at Night. CoRR abs/2105.13236 (2021) - [i5]Lukas Ewecker, Ebubekir Asan, Lars Ohnemus, Sascha Saralajew:
Provident Vehicle Detection at Night for Advanced Driver Assistance Systems. CoRR abs/2107.11302 (2021) - 2020
- [b1]Sascha Saralajew:
New Prototype Concepts in Classification Learning. Bielefeld University, Germany, 2020 - [j1]Jensun Ravichandran, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing 403: 121-132 (2020) - [c12]Emilio Oldenziel, Lars Ohnemus, Sascha Saralajew:
Provident Detection of Vehicles at Night. IV 2020: 472-479 - [c11]Sascha Saralajew, Lars Holdijk, Thomas Villmann:
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. NeurIPS 2020 - [i4]Simon T. Isele, Marcel P. Schilling, Fabian E. Klein, Sascha Saralajew, J. Marius Zoellner:
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets. CoRR abs/2012.01993 (2020) - [i3]Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Stefan Roos, Simon T. Isele, Jakob Ketterer, Leopold Müller, Sascha Saralajew:
Provident Vehicle Detection at Night: The PVDN Dataset. CoRR abs/2012.15376 (2020)
2010 – 2019
- 2019
- [c10]Jensun Ravichandran, Sascha Saralajew, Thomas Villmann:
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. ESANN 2019 - [c9]Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann:
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. NeurIPS 2019: 2788-2799 - [c8]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks. WSOM+ 2019: 189-199 - [i2]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. CoRR abs/1902.00577 (2019) - 2018
- [c7]Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann:
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. ESANN 2018 - [c6]Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann:
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. ICAISC (1) 2018: 724-735 - [i1]Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann:
Prototype-based Neural Network Layers: Incorporating Vector Quantization. CoRR abs/1812.01214 (2018) - 2017
- [c5]Sascha Saralajew, Thomas Villmann:
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning. IJCNN 2017: 1756-1765 - [c4]Thomas Villmann, Michael Biehl, Andrea Villmann, Sascha Saralajew:
Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. WSOM 2017: 69-76 - 2016
- [c3]Sascha Saralajew, David Nebel, Thomas Villmann:
Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning. ICONIP (3) 2016: 362-371 - [c2]Sascha Saralajew, Thomas Villmann:
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning. IJCNN 2016: 2672-2679 - [c1]Thomas Villmann, Marika Kaden, Andrea Bohnsack, J.-M. Villmann, T. Drogies, Sascha Saralajew, Barbara Hammer:
Self-Adjusting Reject Options in Prototype Based Classification. WSOM 2016: 269-279
Coauthor Index
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last updated on 2024-11-28 21:29 CET by the dblp team
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