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• W2912587325 abstract "Deep Learning (DL) techniques are now widespread and being integrated into many important systems. Their classification and recognition abilities ensure their relevance for multiple application domains. As machine-learning that relies on training instead of algorithm programming, they offer a high degree of productivity. But they can be vulnerable to attacks and the verification of their correctness is only just emerging as a scientific and engineering possibility. This paper is a major update of a previously-published survey, attempting to cover all recent publications in this area. It also covers an even more recent trend, namely the design of domain-specific languages for producing and training neural nets." @default.
• W2912587325 created "2019-02-21" @default.
• W2912587325 creator A5006607311 @default.
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• W2912587325 date "2019-01-31" @default.
• W2912587325 modified "2023-09-27" @default.
• W2912587325 title "Formal methods and software engineering for DL. Security, safety and productivity for DL systems development." @default.
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• W2912587325 hasPublicationYear "2019" @default.
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