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Towards rigorous understanding of neural networks via semantics-preserving transformations.

, , , и . Int. J. Softw. Tools Technol. Transf., 25 (3): 301-327 (июня 2023)

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Steffen Schlüter

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Introduction to Symbolic Execution of Neural Networks - Towards Faithful and Explainable Surrogate Models., и . Electron. Commun. Eur. Assoc. Softw. Sci. Technol., (2022)Formal Methods Meet Machine Learning (F3ML)., , , , , и . ISoLA (3), том 13703 из Lecture Notes in Computer Science, стр. 393-405. Springer, (2022)Characteristic invariants in Hennessy-Milner logic., , и . Acta Informatica, 57 (3-5): 671-687 (2020)Forest GUMP: a tool for verification and explanation., , , , и . Int. J. Softw. Tools Technol. Transf., 25 (3): 287-299 (июня 2023)Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations., , , и . CoRR, (2023)Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)., , и . REW, стр. 316-324. IEEE, (2023)The Power of Typed Affine Decision Structures: A Case Study., , , и . CoRR, (2023)Every Component Matters: Generating Parallel Verification Benchmarks with Hardness Guarantees., , , и . ISoLA (4), том 12479 из Lecture Notes in Computer Science, стр. 242-263. Springer, (2020)RERS 2019: Combining Synthesis with Real-World Models., , , , , , , , , и 1 other автор(ы). TACAS (3), том 11429 из Lecture Notes in Computer Science, стр. 101-115. Springer, (2019)