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Peek into the Black-Box: Interpretable Neural Network using SAT Equations in Side-Channel Analysis., , , and . IACR Cryptol. ePrint Arch., (2022)Neural Network-Based Rule Models with Truth Tables., , , and . ECAI, volume 372 of Frontiers in Artificial Intelligence and Applications, page 223-230. IOS Press, (2023)Truth-Table Net: A New Convolutional Architecture Encodable by Design into SAT Formulas., , and . ECCV Workshops (1), volume 13801 of Lecture Notes in Computer Science, page 483-500. Springer, (2022)A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making., , and . CoRR, (2023)Semi-supervised learning and graph neural networks for fake news detection., , , , , and . ASONAM, page 568-569. ACM, (2019)NeuroGIFT: Using a Machine Learning Based Sat Solver for Cryptanalysis., , , and . CSCML, volume 12161 of Lecture Notes in Computer Science, page 62-84. Springer, (2020)Peek into the Black-Box: Interpretable Neural Network using SAT Equations in Side-Channel Analysis., , , and . IACR Trans. Cryptogr. Hardw. Embed. Syst., 2023 (2): 24-53 (2023)TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture., , , and . CoRR, (2023)A Deeper Look at Machine Learning-Based Cryptanalysis., , , and . EUROCRYPT (1), volume 12696 of Lecture Notes in Computer Science, page 805-835. Springer, (2021)