Connor Fibich’s scientific contributions

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Publications (1)


Verification of LSTM Neural Networks with Non-linear Activation Functions
  • Chapter

June 2023

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94 Reads

Lecture Notes in Computer Science

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Connor Fibich

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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.KeywordsFormal verificationRecurrent neural networksLSTMSMT solvingiSAT