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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)