118 citations · 118 across the 2 of their papers we have counts for
2 papers
cs.LG2022
Measurably Stronger Explanation Reliability via Model Canonization
Franz Motzkus, Leander Weber, Sebastian Lapuschkin
While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields ne…
cs.LG2022★ 118 cited
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Dilyara Bareeva +5
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelli…