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This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees.
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M. Fazlyab, A. Robey, H. Hassani, M. Morari, and G. J. Pappas, “Efficient and accurate estimation of Lipschitz constants for deep neural networks,” in Adv. Neural Inf. Process. Syst. , 2019, pp. 11 423–11 434
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M. Fazlyab, M. Morari, and G. J. Pappas, “Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,” IEEE Trans. Automat. Contr. , 2020
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P. Pauli, J. Berberich, and F. Allgöwer, “Robustness analysis and training of recurrent neural networks using dissipativity theory,” at-Automatisierungstechnik , vol. 70, no. 8, pp. 730–739, 2022
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