AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work develops a bootstrap algorithm on Riemannian manifolds that is both computationally efficient and accurate for hypothesis testing and confidence region construction, and establishes high-order asymptotics under an appropriate coordinate representation induced by a second-order retraction.
A theory is developed that connects a computationally tractable nonconvex procedure directly to statistical inference for nonlinear latent factor models with exponential-family links and partially observed entries with severe missingness, weak low-rank signals, and diminishing local curvature.
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