BoxRL-NNV: Boxed Refinement of Latin Hypercube Samples for Neural Network Verification
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Abstract (arXiv)
BoxRL-NNV is a Python tool for the detection of safety violations in neural networks by computing the bounds of the output variables, given the bounds of the input variables of the network. This is done using global extrema estimation via Latin Hypercube Sampling, and further refinement using L-BFGS-B for local optimization around the initial guess. This paper presents an overview of BoxRL-NNV, as well as our results for a subset of the ACAS Xu benchmark. A complete evaluation of the tool's performance, including benchmark comparisons with state-of-the-art tools, shall be presented at the Sixth International Verification of Neural Networks Competition (VNN-COMP'25).
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References · 8
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2020A Survey on Neural Network Interpretabilitycited by 291
- 2024Marabou 2.0: A Versatile Formal Analyzer of Neural Networkscited by 81
- 2018Deep Neural Decision Treescited by 72
- 2023The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Resultscited by 38
- 2023A DPLL(T) Framework for Verifying Deep Neural Networkscited by 24
- 2022Gaussian Process Surrogate Models for Neural Networkscited by 5
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