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Titlebook: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning; Wojciech Samek,Grégoire Montavon,Klaus-Robert Müll Book 2019 Sprin

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Towards Reverse-Engineering Black-Box Neural Networks. We further show how the exposed internals can be exploited to strengthen adversarial examples against the model. Our work starts an important discussion on the security implications of diagnosing deployed models with limited accessibility. The code is available at ..
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Book 2019ld of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner...The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecti
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0302-9743 .The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecti978-3-030-28953-9978-3-030-28954-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/978-3-030-28954-6artificial intelligence; computer vision; deep Learning; explainable AI; explanation Methods; fuzzy contr
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Interpretability in Intelligent Systems – A New Concept?mples from this legacy that could enrich current interpretability work: First, . were we point to the rich set of ideas developed in the ‘explainable expert systems’ field and, second, tools for . of high-dimensional feature importance maps which have been developed in the field of computational neuroimaging.
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