Mean Squared Error

Mean Squared Error (MSE) is a widely used metric for evaluating the accuracy of predictive models, aiming to minimize the average squared difference between predicted and actual values. Current research focuses on refining MSE's application in diverse fields, including generative models (like Generative Flow Networks), image restoration (using methods like Posterior-Mean Rectified Flow), and reinforcement learning, often addressing limitations like overestimation bias and sensitivity to data scale. These advancements improve model performance and efficiency across various applications, from image processing and financial forecasting to clinical trials and resource allocation in distributed systems.