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How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks., , , , and . CoRR, (2020)Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors., , , and . CoRR, (2023)NoiseGrad: enhancing explanations by introducing stochasticity to model weights., , , and . CoRR, (2021)Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science., , , , and . CoRR, (2023)The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus., , , , , and . CoRR, (2023)Visualizing the diversity of representations learned by Bayesian neural networks., , , and . CoRR, (2022)DORA: Exploring outlier representations in Deep Neural Networks., , , , and . CoRR, (2022)ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model., , , , , , and . NeurIPS, (2022)Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches., , , , and . MRS, page 78-84. IEEE, (2023)Finding Spurious Correlations with Function-Semantic Contrast Analysis., , and . xAI (2), volume 1902 of Communications in Computer and Information Science, page 549-572. Springer, (2023)