On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics. - Moritz Weckbecker; Wenkai Xu; Gesine Reinert (CoRR, 2022)

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)

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)

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)