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- W4379165175 abstract "Recurrent neural networks are increasingly employed in safety-critical applications, such as control in cyber-physical systems, and therefore their verification is crucial for guaranteeing reliability and correctness. We present a novel approach for verifying the dynamic behavior of Long short-term memory networks (LSTMs), a popular type of recurrent neural network (RNN). Our approach employs the satisfiability modulo theories (SMT) solver iSAT solving complex Boolean combinations of linear and non-linear constraint formulas (including transcendental functions), and it therefore is able to verify safety properties of these networks." @default.
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- W4379165175 date "2023-01-01" @default.
- W4379165175 modified "2023-09-25" @default.
- W4379165175 title "Verification of LSTM Neural Networks with Non-linear Activation Functions" @default.
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- W4379165175 doi "https://doi.org/10.1007/978-3-031-33170-1_1" @default.
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