Sebastian Lapuschkin
Sebastian Lapuschkin
Sonstige NamenSebastian Bach
Head of Explainable AI, Fraunhofer Heinrich Hertz Institute
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation
S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek
PLOS ONE 10 (7), e0130140, 2015
Explaining nonlinear classification decisions with deep taylor decomposition
G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
Pattern recognition 65, 211-222, 2017
Evaluating the visualization of what a deep neural network has learned
W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller
IEEE transactions on neural networks and learning systems 28 (11), 2660-2673, 2016
Unmasking Clever Hans predictors and assessing what machines really learn
S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller
Nature communications 10 (1), 1096, 2019
Explaining deep neural networks and beyond: A review of methods and applications
W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller
Proceedings of the IEEE 109 (3), 247-278, 2021
Layer-wise relevance propagation: an overview
G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller
Explainable AI: interpreting, explaining and visualizing deep learning, 193-209, 2019
Layer-wise relevance propagation for neural networks with local renormalization layers
A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek
Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016
Interpretable deep neural networks for single-trial EEG classification
I Sturm, S Lapuschkin, W Samek, KR Müller
Journal of neuroscience methods 274, 141-145, 2016
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
The Journal of Machine Learning Research 20 (93), 1-8, 2019
Analyzing classifiers: Fisher vectors and deep neural networks
S Lapuschkin, A Binder, G Montavon, KR Muller, W Samek
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
Explaining the unique nature of individual gait patterns with deep learning
F Horst, S Lapuschkin, W Samek, KR Müller, WI Schöllhorn
Scientific reports 9 (1), 2391, 2019
AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark
S Becker, J Vielhaben, M Ackermann, KR Müller, S Lapuschkin, W Samek
Journal of the Franklin Institute 361 (1), 418-428, 2024
Pruning by explaining: A novel criterion for deep neural network pruning
SK Yeom, P Seegerer, S Lapuschkin, A Binder, S Wiedemann, KR Müller, ...
Pattern Recognition 115, 107899, 2021
Layer-wise relevance propagation for deep neural network architectures
A Binder, S Bach, G Montavon, KR Müller, W Samek
Information Science and Applications (ICISA) 2016, LNEE 6679, 913-922, 2016
Understanding and comparing deep neural networks for age and gender classification
S Lapuschkin, A Binder, KR Muller, W Samek
Proceedings of the IEEE international conference on computer vision …, 2017
The LRP toolbox for artificial neural networks
S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek
Journal of Machine Learning Research 17 (114), 1-5, 2016
Towards best practice in explaining neural network decisions with LRP
M Kohlbrenner, A Bauer, S Nakajima, A Binder, W Samek, S Lapuschkin
2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
M Hägele, P Seegerer, S Lapuschkin, M Bockmayr, W Samek, ...
Scientific reports 10 (1), 6423, 2020
Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
A Hedström, L Weber, D Krakowczyk, D Bareeva, F Motzkus, W Samek, ...
Journal of Machine Learning Research 24 (34), 1-11, 2023
Finding and removing clever hans: Using explanation methods to debug and improve deep models
CJ Anders, L Weber, D Neumann, W Samek, KR Müller, S Lapuschkin
Information Fusion 77, 261-295, 2022
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