A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1901.11334v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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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<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>
more »... 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.
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