Formal methods and software engineering for DL. Security, safety and productivity for DL systems development
It reviews existing research for developers and researchers working on reliable deep learning systems, but is incremental as it updates a previous survey.
This paper provides an updated survey on formal methods and software engineering for deep learning systems, addressing security, safety, and productivity challenges, but does not present new experimental results or concrete numbers.
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.