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Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety., , , , , , , , , and 31 other author(s). CoRR, (2021)Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities., , , , and . CVPR Workshops, page 56-65. Computer Vision Foundation / IEEE, (2021)DenseHMM: Learning Hidden Markov Models by Learning Dense Representations., , , and . CoRR, (2020)Tailored Uncertainty Estimation for Deep Learning Systems., , , , , , and . CoRR, (2022)A Novel Regression Loss for Non-Parametric Uncertainty Optimization., , , , , and . CoRR, (2021)A Survey on Uncertainty Toolkits for Deep Learning., , , and . CoRR, (2022)Approaching Neural Network Uncertainty Realism., , , , , , , and . CoRR, (2021)Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog., , , , , , , , , and 4 other author(s). CoRR, (2023)On Modeling and Assessing Uncertainty Estimates in Neural Learning Systems. University of Bonn, Germany, (2023)base-search.net (ftunivbonn:oai:bonndoc.ulb.uni-bonn.de:20.500.11811/10778).Wasserstein dropout., , , , , and . Mach. Learn., 113 (5): 3161-3204 (May 2024)