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CheckINN: Wide Range Neural Network Verification in Imandra

Published: 20 September 2022 Publication History

Abstract

Neural networks are increasingly relied upon as components of complex safety-critical systems such as autonomous vehicles. There is high demand for tools and methods that embed neural network verification in a larger verification cycle. However, neural network verification is difficult due to a wide range of verification properties of interest, each typically only amenable to verification in specialised solvers. In this paper, we show how Imandra, a functional programming language and a theorem prover originally designed for verification, validation and simulation of financial infrastructure can offer a holistic infrastructure for neural network verification. We develop a novel library CheckINN that formalises neural networks in Imandra, and covers different important facets of neural network verification.

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  • (2024)Towards the Formal Verification of SysML v2 ModelsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687820(1086-1095)Online publication date: 22-Sep-2024

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cover image ACM Other conferences
PPDP '22: Proceedings of the 24th International Symposium on Principles and Practice of Declarative Programming
September 2022
187 pages
ISBN:9781450397032
DOI:10.1145/3551357
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Published: 20 September 2022

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  1. Boyer-Moore Provers
  2. Neural Networks
  3. Robustness
  4. Verification

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  • (2024)Towards the Formal Verification of SysML v2 ModelsProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687820(1086-1095)Online publication date: 22-Sep-2024

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