1 code implementation • 23 Oct 2022 • Elazar Cohen, Yizhak Yisrael Elboher, Clark Barrett, Guy Katz
Recent attempts have demonstrated that abstraction-refinement approaches could play a significant role in mitigating these limitations; but these approaches can often produce networks that are so abstract, that they become unsuitable for verification.
no code implementations • 5 Aug 2022 • Yizhak Yisrael Elboher, Elazar Cohen, Guy Katz
Recent work has proposed enhancing such verification techniques with abstraction-refinement capabilities, which have been shown to boost scalability: instead of verifying a large and complex network, the verifier constructs and then verifies a much smaller network, whose correctness implies the correctness of the original network.
1 code implementation • 31 Oct 2019 • Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
In this paper, we propose a framework that can enhance neural network verification techniques by using over-approximation to reduce the size of the network - thus making it more amenable to verification.
