The exceptional performance of deep neural networks in areas such as perception and natural language processing has made them an integral part of many real-world AI systems, including safety-critical ones such as medical diagnosis and autonomous driving. However, neural networks are inherently opaque, and numerous defects have been found in state-of-the-art networks.

In this lab, we will apply various methods for proving the reliability of deep neural networks. In particular, we will use state-of-the-art tools, such as Crown, ERAN, Marabou, and Planet, and apply them to examples from the neural network verification competition.