Deep neural networks are revolutionizing the way complex systems are developed. However, these automatically-generated networks are opaque to humans, making it difficult to reason about them and guarantee their correctness. Here, we propose a novel approach for verifying properties of a widespread variant of neural networks, called recurrent neural networks. Recurrent neural networks play a key role in, e.g., speech recognition, and their verification is crucial for guaranteeing the reliability of many critical systems. Our approach is based on the inference of invariants, which allow us to reduce the complex problem of verifying recurrent networks into simpler, non-recurrent problems. Experiments with a proof-of-concept implementation of our approach demonstrate that it performs orders-of-magnitude better than the state of the art.
CITATION STYLE
Jacoby, Y., Barrett, C., & Katz, G. (2020). Verifying Recurrent Neural Networks Using Invariant Inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12302 LNCS, pp. 57–74). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59152-6_3
Mendeley helps you to discover research relevant for your work.