Weighted Average

Weighted averaging is a fundamental technique used to combine multiple data points or model outputs, aiming to improve accuracy, robustness, and efficiency. Current research focuses on optimizing weighting schemes within various contexts, including deep learning model aggregation (e.g., using gradient norms or uncertainty measures), trajectory inference (leveraging Wasserstein space), and ensemble methods (like those employing LASSO regularization). These advancements have significant implications across diverse fields, from improving medical image processing and sugarcane disease classification to enhancing the performance of recommendation systems and federated learning in resource-constrained environments.