10 citations · 11 across the 3 of their papers we have counts for
3 papers
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
Alexander Binder, Leander Weber, Sebastian Lapuschkin +3
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifical…
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder +1
Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of…
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…