UC Berkeley

The increasing use of deep neural networks in a variety of applications, including some safety-critical ones, has brought renewed interest in the topic of verification of neural networks. However, verification is most meaningful when performed with high-quality formal specifications. In this paper, we survey the landscape of formal specification for deep neural networks, and discuss the opportunities and challenges for formal methods for this domain.