Detecting Behavioral and Structural Anomalies in MediaCloud Applications. - Guido Schwenk; Sebastian Bach (CoRR, 2014)

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. - Sebastian Lapuschkin; Alexander Binder; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CVPR, 2016)

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. - Alexander Binder; Grégoire Montavon; Sebastian Lapuschkin; Klaus-Robert Müller; Wojciech Samek (ICANN (2), 2016)

Controlling explanatory heatmap resolution and semantics via decomposition depth. - Sebastian Bach; Alexander Binder; Klaus-Robert Müller; Wojciech Samek (ICIP, 2016)

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation. - Wojciech Samek; Grégoire Montavon; Alexander Binder; Sebastian Lapuschkin; Klaus-Robert Müller (CoRR, 2016)

Interpretable Deep Neural Networks for Single-Trial EEG Classification. - Irene Sturm; Sebastian Bach; Wojciech Samek; Klaus-Robert Müller (CoRR, 2016)

The LRP Toolbox for Artificial Neural Networks. - Sebastian Lapuschkin; Alexander Binder; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (J. Mach. Learn. Res., 2016)

Interpretable human action recognition in compressed domain. - Vignesh Srinivasan; Sebastian Lapuschkin; Cornelius Hellge; Klaus-Robert Müller; Wojciech Samek (ICASSP, 2017)

Understanding and Comparing Deep Neural Networks for Age and Gender Classification. - Wojciech Samek; Alexander Binder; Sebastian Lapuschkin; Klaus-Robert Müller (ICCV Workshops, 2017)

Explaining nonlinear classification decisions with deep Taylor decomposition. - Grégoire Montavon; Sebastian Lapuschkin; Alexander Binder; Wojciech Samek; Klaus-Robert Müller (Pattern Recognit., 2017)

Evaluating the Visualization of What a Deep Neural Network Has Learned. - Wojciech Samek; Alexander Binder; Grégoire Montavon; Sebastian Lapuschkin; Klaus-Robert Müller (IEEE Trans. Neural Networks Learn. Syst., 2017)

Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals. - Sören Becker 001; Marcel Ackermann 001; Sebastian Lapuschkin; Klaus-Robert Müller; Wojciech Samek (CoRR, 2018)

What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis. - Fabian Horst; Sebastian Lapuschkin; Wojciech Samek; Klaus-Robert Müller; Wolfgang Immanuel Schöllhorn (CoRR, 2018)

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. - Sebastian Lapuschkin; Stephan Wäldchen; Alexander Binder; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (CoRR, 2019)

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. - Miriam Hägele; Philipp Seegerer; Sebastian Lapuschkin; Michael Bockmayr; Wojciech Samek; Frederick Klauschen; Klaus-Robert Müller; Alexander Binder (CoRR, 2019)

On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. - Fabian Horst; Djordje Slijepcevic; Sebastian Lapuschkin; Anna-Maria Raberger; Matthias Zeppelzauer; Wojciech Samek; Christian Breiteneder; Wolfgang Immanuel Schöllhorn; Brian Horsak (CoRR, 2019)

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. - Seul-Ki Yeom; Philipp Seegerer; Sebastian Lapuschkin; Simon Wiedemann; Klaus-Robert Müller; Wojciech Samek (CoRR, 2019)

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed. - Christopher J. Anders; Talmaj Marinc; David Neumann; Wojciech Samek; Klaus-Robert Müller; Sebastian Lapuschkin (CoRR, 2019)

iNNvestigate Neural Networks! - Maximilian Alber; Sebastian Lapuschkin; Philipp Seegerer; Miriam Hägele; Kristof T. Schütt; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller; Sven Dähne; Pieter-Jan Kindermans (J. Mach. Learn. Res., 2019)

Opening the machine learning black box with Layer-wise Relevance Propagation. - Sebastian Lapuschkin (2019)

Layer-Wise Relevance Propagation: An Overview. - Grégoire Montavon; Alexander Binder; Sebastian Lapuschkin; Wojciech Samek; Klaus-Robert Müller (Explainable AI, 2019)

ECQ x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. - Daniel Becking; Maximilian Dreyer; Wojciech Samek; Karsten Müller 001; Sebastian Lapuschkin (xxAI@ICML, 2020)

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution. - Gary S. W. Goh; Sebastian Lapuschkin; Leander Weber; Wojciech Samek; Alexander Binder (ICPR, 2020)

Explanation-Guided Training for Cross-Domain Few-Shot Classification. - Jiamei Sun; Sebastian Lapuschkin; Wojciech Samek; Yunqing Zhao; Ngai-Man Cheung; Alexander Binder (ICPR, 2020)

Towards Best Practice in Explaining Neural Network Decisions with LRP. - Maximilian Kohlbrenner; Alexander Bauer 001; Shinichi Nakajima; Alexander Binder; Wojciech Samek; Sebastian Lapuschkin (IJCNN, 2020)

Understanding Image Captioning Models beyond Visualizing Attention. - Jiamei Sun; Sebastian Lapuschkin; Wojciech Samek; Alexander Binder (CoRR, 2020)

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond. - Wojciech Samek; Grégoire Montavon; Sebastian Lapuschkin; Christopher J. Anders; Klaus-Robert Müller (CoRR, 2020)

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. - Christopher J. Anders; David Neumann; Wojciech Samek; Klaus-Robert Müller; Sebastian Lapuschkin (CoRR, 2021)

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. - Wojciech Samek; Grégoire Montavon; Sebastian Lapuschkin; Christopher J. Anders; Klaus-Robert Müller (Proc. IEEE, 2021)

Pruning by explaining: A novel criterion for deep neural network pruning. - Seul-Ki Yeom; Philipp Seegerer; Sebastian Lapuschkin; Alexander Binder; Simon Wiedemann; Klaus-Robert Müller; Wojciech Samek (Pattern Recognit., 2021)

Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. - Sami Ede; Serop Baghdadlian; Leander Weber; An Nguyen; Dario Zanca; Wojciech Samek; Sebastian Lapuschkin (CD-MAKE, 2022)

Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification. - Daniel Krakowczyk; David R. Reich; Paul Prasse; Sebastian Lapuschkin; Tobias Scheffer; Lena A. Jäger (Gaze Meets ML, 2022)

Measurably Stronger Explanation Reliability Via Model Canonization. - Franz Motzkus; Leander Weber; Sebastian Lapuschkin (ICIP, 2022)

PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. - Frederik Pahde; Leander Weber; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2022)

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. - Anna Hedström; Leander Weber; Dilyara Bareeva; Franz Motzkus; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (CoRR, 2022)

But that's not why: Inference adjustment by interactive prototype deselection. - Michael Gerstenberger; Sebastian Lapuschkin; Peter Eisert; Sebastian Bosse (CoRR, 2022)

From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation. - Reduan Achtibat; Maximilian Dreyer; Ilona Eisenbraun; Sebastian Bosse; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2022)

Explaining automated gender classification of human gait. - Fabian Horst; Djordje Slijepcevic; Matthias Zeppelzauer; Anna-Maria Raberger; Sebastian Lapuschkin; Wojciech Samek; Wolfgang Immanuel Schöllhorn; Christian Breiteneder; Brian Horsak (CoRR, 2022)

Explaining machine learning models for age classification in human gait analysis. - Djordje Slijepcevic; Fabian Horst; Marvin Simak; Sebastian Lapuschkin; Anna-Maria Raberger; Wojciech Samek; Christian Breiteneder; Wolfgang Immanuel Schöllhorn; Matthias Zeppelzauer; Brian Horsak (CoRR, 2022)

Explaining Machine Learning Models for Clinical Gait Analysis. - Djordje Slijepcevic; Fabian Horst; Sebastian Lapuschkin; Brian Horsak; Anna-Maria Raberger; Andreas Kranzl; Wojciech Samek; Christian Breiteneder; Wolfgang Immanuel Schöllhorn; Matthias Zeppelzauer (ACM Trans. Comput. Heal., 2022)

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. - Christopher J. Anders; Leander Weber; David Neumann; Wojciech Samek; Klaus-Robert Müller; Sebastian Lapuschkin (Inf. Fusion, 2022)

Explain and improve: LRP-inference fine-tuning for image captioning models. - Jiamei Sun; Sebastian Lapuschkin; Wojciech Samek; Alexander Binder (Inf. Fusion, 2022)

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. - Simon M. Hofmann; Frauke Beyer; Sebastian Lapuschkin; Ole Goltermann; Markus Loeffler; Klaus-Robert Müller; Arno Villringer; Wojciech Samek; Anja Veronica Witte (NeuroImage, 2022)

XAI-based Comparison of Audio Event Classifiers with different Input Representations. - Annika Frommholz; Fabian Seipel; Sebastian Lapuschkin; Wojciech Samek; Johanna Vielhaben (CBMI, 2023)

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. - Alexander Binder; Leander Weber; Sebastian Lapuschkin; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CVPR, 2023)

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations. - Maximilian Dreyer; Reduan Achtibat; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2023)

Optimizing Explanations by Network Canonization and Hyperparameter Search. - Frederik Pahde; Galip Ümit Yolcu; Alexander Binder; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2023)

Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. - Daniel G. Krakowczyk; Paul Prasse; David R. Reich; Sebastian Lapuschkin; Tobias Scheffer; Lena A. Jäger (ETRA, 2023)

Human-Centered Evaluation of XAI Methods. - Karam Dawoud; Wojciech Samek; Peter Eisert; Sebastian Lapuschkin; Sebastian Bosse (ICDM (Workshops), 2023)

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. - Frederik Pahde; Maximilian Dreyer; Wojciech Samek; Sebastian Lapuschkin (MICCAI (2), 2023)

XAI-based Comparison of Input Representations for Audio Event Classification. - Annika Frommholz; Fabian Seipel; Sebastian Lapuschkin; Wojciech Samek; Johanna Vielhaben (CoRR, 2023)

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. - Maximilian Dreyer; Frederik Pahde; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2023)

Layer-wise Feedback Propagation. - Leander Weber; Jim Berend; Alexander Binder; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2023)

Generative Fractional Diffusion Models. - Gabriel Nobis; Marco Aversa; Maximilian Springenberg; Michael Detzel; Stefano Ermon; Shinichi Nakajima; Roderick Murray-Smith; Sebastian Lapuschkin; Christoph Knochenhauer; Luis Oala; Wojciech Samek (CoRR, 2023)

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. - Anna Hedström; Leander Weber; Daniel Krakowczyk; Dilyara Bareeva; Franz Motzkus; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (J. Mach. Learn. Res., 2023)

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. - Anna Hedström; Philine Lou Bommer; Kristoffer Knutsen Wickstrøm; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2023)

Beyond explaining: Opportunities and challenges of XAI-based model improvement. - Leander Weber; Sebastian Lapuschkin; Alexander Binder; Wojciech Samek (Inf. Fusion, 2023)

From attribution maps to human-understandable explanations through Concept Relevance Propagation. - Reduan Achtibat; Maximilian Dreyer; Ilona Eisenbraun; Sebastian Bosse; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (Nat. Mac. Intell., 2023)

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. - Maximilian Dreyer; Frederik Pahde; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (AAAI, 2024)

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. - Dilyara Bareeva; Maximilian Dreyer; Frederik Pahde; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2024)

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations. - Maximilian Dreyer; Reduan Achtibat; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2024)

Pruning by Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers. - Sayed Mohammad Vakilzadeh Hatefi; Maximilian Dreyer; Reduan Achtibat; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (ECCV Workshops (21), 2024)

Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization. - Rohan Reddy Mekala; Frederik Pahde; Simon Baur; Sneha Chandrashekar; Madeline Diep; Markus Wenzel 002; Eric L. Wisotzky; Galip Ümit Yolcu; Sebastian Lapuschkin; Jackie Ma; Peter Eisert; Mikael Lindvall; et al. (ECCV Workshops (20), 2024)

AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers. - Reduan Achtibat; Sayed Mohammad Vakilzadeh Hatefi; Maximilian Dreyer; Aakriti Jain; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek (ICML, 2024)

CoSy: Evaluating Textual Explanations of Neurons. - Laura Kopf; Philine Lou Bommer; Anna Hedström; Sebastian Lapuschkin; Marina M.-C. Höhne; Kirill Bykov (NeurIPS, 2024)

Generative Fractional Diffusion Models. - Gabriel Nobis; Maximilian Springenberg; Marco Aversa; Michael Detzel; Rembert Daems; Roderick Murray-Smith; Shinichi Nakajima; Sebastian Lapuschkin; Stefano Ermon; Tolga Birdal; Manfred Opper; Christoph Knochenhauer; et al. (NeurIPS, 2024)

A Fresh Look at Sanity Checks for Saliency Maps. - Anna Hedström; Leander Weber; Sebastian Lapuschkin; Marina M.-C. Höhne (xAI (1), 2024)

Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification. - Christian Tinauer; Anna Damulina; Maximilian Sackl; Martin Soellradl; Reduan Achtibat; Maximilian Dreyer; Frederik Pahde; Sebastian Lapuschkin; Reinhold Schmidt; Stefan Ropele; Wojciech Samek; Christian Langkammer (xAI (2), 2024)

PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits. - Maximilian Dreyer; Erblina Purelku; Johanna Vielhaben; Wojciech Samek; Sebastian Lapuschkin (XAI4CV, 2024)

Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. - Anna Hedström; Leander Weber; Sebastian Lapuschkin; Marina M.-C. Höhne (CoRR, 2024)

DualView: Data Attribution from the Dual Perspective. - Galip Ümit Yolcu; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2024)

PINNfluence: Influence Functions for Physics-Informed Neural Networks. - Jonas R. Naujoks; Aleksander Krasowski; Moritz Weckbecker; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek; René P. Klausen (CoRR, 2024)

Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond. - Dilyara Bareeva; Galip Ümit Yolcu; Anna Hedström; Niklas Schmolenski; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2024)

AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark. - Sören Becker 001; Johanna Vielhaben; Marcel Ackermann 001; Klaus-Robert Müller; Sebastian Lapuschkin; Wojciech Samek (J. Frankl. Inst., 2024)

Explainable AI for time series via Virtual Inspection Layers. - Johanna Vielhaben; Sebastian Lapuschkin; Grégoire Montavon; Wojciech Samek (Pattern Recognit., 2024)

FADE: Why Bad Descriptions Happen to Good Features. - Bruno Puri; Aakriti Jain; Elena Golimblevskaia; Patrick Kahardipraja; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (ACL (Findings), 2025)

Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence. - Frederik Pahde; Maximilian Dreyer; Moritz Weckbecker; Leander Weber; Christopher J. Anders; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (ICLR, 2025)

Explaining predictive uncertainty by exposing second-order effects. - Florian Bley; Sebastian Lapuschkin; Wojciech Samek; Grégoire Montavon (Pattern Recognit., 2025)

Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. - Anna Hedström; Philine Lou Bommer; Thomas F. Burns; Sebastian Lapuschkin; Wojciech Samek; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2025)

Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation. - Leander Weber; Jim Berend; Moritz Weckbecker; Alexander Binder; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (Trans. Mach. Learn. Res., 2025)

Mechanistic understanding and validation of large AI models with SemanticLens. - Maximilian Dreyer; Jim Berend; Tobias Labarta; Johanna Vielhaben; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data. - Frederik Pahde; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

A Close Look at Decomposition-based XAI-Methods for Transformer Language Models. - Leila Arras; Bruno Puri; Patrick Kahardipraja; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations. - Eren Erogullari; Sebastian Lapuschkin; Wojciech Samek; Frederik Pahde (CoRR, 2025)

ASIDE: Architectural Separation of Instructions and Data in Language Models. - Egor Zverev; Evgenii Kortukov; Alexander Panfilov; Soroush Tabesh; Alexandra Volkova; Sebastian Lapuschkin; Wojciech Samek; Christoph H. Lampert (CoRR, 2025)

Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video. - Sonia Joseph; Praneet Suresh; Lorenz Hufe; Edward Stevinson; Robert Graham; Yash Vadi; Danilo Bzdok; Sebastian Lapuschkin; Lee Sharkey; Blake Aaron Richards (CoRR, 2025)

The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation. - Patrick Kahardipraja; Reduan Achtibat; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2025)

From What to How: Attributing CLIP's Latent Components Reveals Unexpected Semantic Reliance. - Maximilian Dreyer; Lorenz Hufe; Jim Berend; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique. - Shreyas Gururaj; Lars Grüne; Wojciech Samek; Sebastian Lapuschkin; Leander Weber (CoRR, 2025)

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection. - Emílio Dolgener Cantú; Rolf Klemens Wittmann; Oliver Abdeen; Patrick Wagner 002; Wojciech Samek; Moritz Baier; Sebastian Lapuschkin (CoRR, 2025)

Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMs. - Sayed Mohammad Vakilzadeh Hatefi; Maximilian Dreyer; Reduan Achtibat; Patrick Kahardipraja; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2025)

See What I Mean? CUE: A Cognitive Model of Understanding Explanations. - Tobias Labarta; Nhi Hoang; Katharina Weitz; Wojciech Samek; Sebastian Lapuschkin; Leander Weber (CoRR, 2025)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks. - Jonas R. Naujoks; Aleksander Krasowski; Moritz Weckbecker; Galip Ümit Yolcu; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek; René P. Klausen (CoRR, 2025)