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Global optimization of objective functions represented by ReLU networks.

, , , , , , and . Mach. Learn., 112 (10): 3685-3712 (October 2023)

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Global optimization of objective functions represented by ReLU networks., , , , , , and . Mach. Learn., 112 (10): 3685-3712 (October 2023)Parallelization Techniques for Verifying Neural Networks., , , , , , , , , and . FMCAD, page 128-137. IEEE, (2020)Global Optimization of Objective Functions Represented by ReLU Networks., , , , , , and . CoRR, (2020)Efficient Neural Network Analysis with Sum-of-Infeasibilities., , , and . TACAS (1), volume 13243 of Lecture Notes in Computer Science, page 143-163. Springer, (2022)Parallelization Techniques for Verifying Neural Networks., , , , , , , , , and . CoRR, (2020)Interpolating bit-vector formulas using uninterpreted predicates and Presburger arithmetic., , and . Formal Methods Syst. Des., 57 (2): 121-156 (2021)Marabou 2.0: A Versatile Formal Analyzer of Neural Networks., , , , , , , , , and 7 other author(s). CoRR, (2024)Bit-Vector Interpolation and Quantifier Elimination by Lazy Reduction., , and . FMCAD, page 1-10. IEEE, (2018)The Marabou Framework for Verification and Analysis of Deep Neural Networks., , , , , , , , , and 3 other author(s). CAV (1), volume 11561 of Lecture Notes in Computer Science, page 443-452. Springer, (2019)