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

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)

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)

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)

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)

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

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

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

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)

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)

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