SORA is an adaptive step-size adversarial training algorithm that formalizes epsilon overfitting, introduces the PertAlign metric to predict catastrophic overfitting, and dynamically adjusts perturbations to achieve state-of-the-art robustness and clean accuracy with fixed hyperparameters.
KKT conditions, first-order and second- order optimization, and distributed optimization: Tuto- rial and survey.arXiv preprint arXiv:2110.01858
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The monograph organizes and derives classical Riemannian geometry structures explicitly in coordinate and matrix form for direct use in optimization algorithms on nonlinear manifolds.
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SORA: Free Second-Order Attacks in Fast Adversarial Training
SORA is an adaptive step-size adversarial training algorithm that formalizes epsilon overfitting, introduces the PertAlign metric to predict catastrophic overfitting, and dynamically adjusts perturbations to achieve state-of-the-art robustness and clean accuracy with fixed hyperparameters.
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Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations
The monograph organizes and derives classical Riemannian geometry structures explicitly in coordinate and matrix form for direct use in optimization algorithms on nonlinear manifolds.