Neural Networks Verification: Perspectives from Formal Method

Neural Networks find applications across diverse domains in Computer Science. Despite their versatility, Neural Networks often demonstrate performance inconsistencies, necessitating the evaluation of their robustness, reliability, and correctness. Traditional Formal Verification techniques, proven effective in other contexts, face challenges when applied to Neural Networks. In this work, we explore the limitations of Formal Verification methods in the context of Neural Networks and further aim to propose a principled method to improve their verification.