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Creators/Authors contains: "A. Harapanahalli"

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  1. ; ; (, ICML workshop on Formal Verification of Machine Learning (WFVML 2023))
    In this paper, we present a toolbox for interval analysis in numpy, with an application to formal verification of neural network controlled systems. Using the notion of natural inclusion functions, we systematically construct interval bounds for a general class of mappings. The toolbox offers ef- ficient computation of natural inclusion functions using compiled C code, as well as a familiar inter- face in numpy with its canonical features, such as n-dimensional arrays, matrix/vector operations, and vectorization. We then use this toolbox in for- mal verification of dynamical systems with neural network controllers, through the composition of their inclusion functions. 
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