Sebastian Lapuschkin

ORCID

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 83

Top Venues

Technical University of Berlin, Germany (PHD)

Education History

Technical University of Berlin, Germany

PHD

Publications

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. AAAI (2024)
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. CoRR (2024)
Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller, Sebastian Lapuschkin, Wojciech Samek
AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark. J. Frankl. Inst. 361 (1) (2024)
Galip Ümit Yolcu, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
DualView: Data Attribution from the Dual Perspective. CoRR (2024)
Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers. CoRR (2024)
Florian Bley, Sebastian Lapuschkin, Wojciech Samek, Grégoire Montavon
Explaining Predictive Uncertainty by Exposing Second-Order Effects. CoRR (2024)
Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin
PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits. CoRR (2024)
Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR (2024)
Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov
CoSy: Evaluating Textual Explanations of Neurons. CoRR (2024)
Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. CoRR (2024)
Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek
Explainable AI for time series via Virtual Inspection Layers. Pattern Recognit. 150 (2024)
Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
A Fresh Look at Sanity Checks for Saliency Maps. CoRR (2024)
Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CVPR (2023)
Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. CoRR (2023)
Annika Frommholz, Fabian Seipel, Sebastian Lapuschkin, Wojciech Samek, Johanna Vielhaben
XAI-based Comparison of Audio Event Classifiers with different Input Representations. CBMI (2023)
Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
From attribution maps to human-understandable explanations through Concept Relevance Propagation. Nat. Mac. Intell. 5 (9) (2023)
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. J. Mach. Learn. Res. 24 (2023)
Leander Weber, Jim Berend, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
Layer-wise Feedback Propagation. CoRR (2023)
Daniel G. Krakowczyk, Paul Prasse, David Robert Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. CoRR (2023)
Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin
Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations. CoRR (2023)
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Optimizing Explanations by Network Canonization and Hyperparameter Search. CVPR Workshops (2023)
Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. CoRR (2023)
Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. 2023 (2023)
Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek
Explainable AI for Time Series via Virtual Inspection Layers. CoRR (2023)
Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek
Generative Fractional Diffusion Models. CoRR (2023)
Anna Hedström, Philine Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR (2023)
Annika Frommholz, Fabian Seipel, Sebastian Lapuschkin, Wojciech Samek, Johanna Vielhaben
XAI-based Comparison of Input Representations for Audio Event Classification. CoRR (2023)
Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse
Human-Centered Evaluation of XAI Methods. ICDM (Workshops) (2023)
Leander Weber, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek
Beyond explaining: Opportunities and challenges of XAI-based model improvement. Inf. Fusion 92 (2023)
Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations. CVPR Workshops (2023)
Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse
Human-Centered Evaluation of XAI Methods. CoRR (2023)
Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. MICCAI (2) (2023)
Daniel G. Krakowczyk, Paul Prasse, David Robert Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. ETRA (2023)
Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CoRR (2022)
Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. Inf. Fusion 77 (2022)
Djordje Slijepcevic, Fabian Horst, Marvin Simak, Sebastian Lapuschkin, Anna-Maria Raberger, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer, Brian Horsak
Explaining machine learning models for age classification in human gait analysis. CoRR (2022)
Daniel Krakowczyk, David R. Reich, Paul Prasse, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger
Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification. Gaze Meets ML (2022)
Franz Motzkus, Leander Weber, Sebastian Lapuschkin
Measurably Stronger Explanation Reliability Via Model Canonization. ICIP (2022)
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Optimizing Explanations by Network Canonization and Hyperparameter Search. CoRR (2022)
Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin, Brian Horsak, Anna-Maria Raberger, Andreas Kranzl, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer
Explaining Machine Learning Models for Clinical Gait Analysis. ACM Trans. Comput. Heal. 3 (2) (2022)
Leander Weber, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement. CoRR (2022)
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Alexander Binder
Explain and improve: LRP-inference fine-tuning for image captioning models. Inf. Fusion 77 (2022)
Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation. CoRR (2022)
Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen, Dario Zanca, Wojciech Samek, Sebastian Lapuschkin
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. CoRR (2022)
Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen, Dario Zanca, Wojciech Samek, Sebastian Lapuschkin
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. CD-MAKE (2022)
Michael Gerstenberger, Sebastian Lapuschkin, Peter Eisert, Sebastian Bosse
But that's not why: Inference adjustment by interactive prototype deselection. CoRR (2022)
Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, Anja Veronica Witte
Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NeuroImage 261 (2022)
Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations. CoRR (2022)
Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. CoRR (2022)
Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. CoRR (2022)
Franz Motzkus, Leander Weber, Sebastian Lapuschkin
Measurably Stronger Explanation Reliability via Model Canonization. CoRR (2022)
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Raberger, Sebastian Lapuschkin, Wojciech Samek, Wolfgang Immanuel Schöllhorn, Christian Breiteneder, Brian Horsak
Explaining automated gender classification of human gait. CoRR (2022)
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognit. 115 (2021)
Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. CoRR (2021)
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. Proc. IEEE 109 (3) (2021)
Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin
: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. CoRR (2021)
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Alexander Binder
Understanding Image Captioning Models beyond Visualizing Attention. CoRR (2020)
Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, Alexander Binder
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution. CoRR (2020)
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Towards Best Practice in Explaining Neural Network Decisions with LRP. IJCNN (2020)
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder
Explanation-Guided Training for Cross-Domain Few-Shot Classification. CoRR (2020)
Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, Alexander Binder
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution. ICPR (2020)
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond. CoRR (2020)
Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin
: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. xxAI@ICML (2020)
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder
Explanation-Guided Training for Cross-Domain Few-Shot Classification. ICPR (2020)
Fabian Horst, Djordje Slijepcevic, Sebastian Lapuschkin, Anna-Maria Raberger, Matthias Zeppelzauer, Wojciech Samek, Christian Breiteneder, Wolfgang I. Schöllhorn, Brian Horsak
On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. CoRR (2019)
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. CoRR (2019)
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
iNNvestigate Neural Networks! J. Mach. Learn. Res. 20 (2019)
Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, Alexander Binder
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. CoRR (2019)
Christopher J. Anders, Talmaj Marinc, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed. CoRR (2019)
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. CoRR (2019)
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Towards best practice in explaining neural network decisions with LRP. CoRR (2019)
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
iNNvestigate neural networks! CoRR (2018)
Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, Wolfgang I. Schöllhorn
What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis. CoRR (2018)
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, Wojciech Samek
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals. CoRR (2018)
Vignesh Srinivasan, Sebastian Lapuschkin, Cornelius Hellge, Klaus-Robert Müller, Wojciech Samek
Interpretable human action recognition in compressed domain. ICASSP (2017)
Wojciech Samek, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller
Understanding and Comparing Deep Neural Networks for Age and Gender Classification. ICCV Workshops (2017)
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, Wojciech Samek
Understanding and Comparing Deep Neural Networks for Age and Gender Classification. CoRR (2017)
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, Klaus-Robert Müller
Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65 (2017)
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller
Evaluating the Visualization of What a Deep Neural Network Has Learned. IEEE Trans. Neural Networks Learn. Syst. 28 (11) (2017)
Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. CVPR (2016)
Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation. CoRR (2016)
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, Wojciech Samek
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. ICANN (2) (2016)
Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
The LRP Toolbox for Artificial Neural Networks. J. Mach. Learn. Res. 17 (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Sebastian Lapuschkin