Titlebook: Bridging the Gap Between AI and Reality; First International Bernhard Steffen Conference proceedings 2024 The Editor(s) (if applicable) an

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Zur begrenzten Organisierbarkeit von Führunged for easy comprehension and validation by humans. Our approach takes input-output samples from complex machine learning models, such as deep neural networks, and automatically derives interpretable .. A mimic program precisely imitates the behavior of an opaque model over the provided data. We dis

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Managementmoden in der Verwaltungrpret and verify, and they operate in stochastic and unknown environments with high-dimensional state space. These complexities make analyses of the internals of LECs and manual modeling of the environments extremely challenging. Numerous combinations of automata learning with verification technique

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Managementmoden in der Verwaltungsks. Nevertheless, their integration into safety-critical systems, such as in the aerospace or automotive domains, poses a significant challenge due to the threat of .: perturbations in inputs that might cause the DNN to make grievous mistakes. Multiple studies have demonstrated that even modern DNN

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Managementnachwuchs erfolgreich machenmechanical equipment. We provide mathematical formalizations of requirements of the estimator, such as stability and monotonicity, as properties. To efficiently apply existing tools for verification of neural networks, we reduce the verification of global properties to a representative set of local

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https://doi.org/10.1007/978-3-8349-4612-6t the preimage of a polyhedral set is again a union of polyhedral sets and can be effectively computed. We show several applications of computing the preimage for analysis and interpretability of neural networks.

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https://doi.org/10.1007/978-3-642-87406-2d control. LEAS are often safety-critical. The development and integration of trustworthy ML components present new challenges that extend beyond the boundaries of system’s design to the system’s operation in its real environment. This paper introduces the methodology and tools developed within the

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The Inverse Problem for?Neural Networkst the preimage of a polyhedral set is again a union of polyhedral sets and can be effectively computed. We show several applications of computing the preimage for analysis and interpretability of neural networks.