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Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods.
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Personalization aims to characterize individual preferences and is widely applied across many fields.
Displacement field calculation of large-scale structures using computer vision with physical constraints
Because of the advantages of easy deployment, low cost and non-contact, computer vision-based structural displacement acquisition technique has received wide attention and research in recent years.
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