A systematic survey of Lipschitz continuity in deep learning that corrects sigmoid (1/4) and softmax (1/2) Lipschitz constants and proves a sum-over-paths Lipschitz bound for additively-evaluated DAG networks.
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Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness
A systematic survey of Lipschitz continuity in deep learning that corrects sigmoid (1/4) and softmax (1/2) Lipschitz constants and proves a sum-over-paths Lipschitz bound for additively-evaluated DAG networks.