Formal methods and software engineering for DL. Security, safety and productivity for DL systems development [article]

Gaetan J.D.R. Hains and Arvid Jakobsson and Youry Khmelevsky
<span title="2019-01-31">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... f 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.11334v1">arXiv:1901.11334v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fy7zq2r3uve5vcdwrfydxxqvyu">fatcat:fy7zq2r3uve5vcdwrfydxxqvyu</a> </span>
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