PAGE

PAGE stands for Parallel Aggregated Gradient Estimation, which is a method used in machine learning to speed up training large models. Instead of calculating the entire gradient (which guides model adjustments) on one machine, PAGE estimates the gradient by combining information from multiple smaller, quick computations done in parallel. This approach reduces computational load and accelerates learning while maintaining accuracy, making it efficient for training complex models on large datasets. Essentially, PAGE optimizes the learning process by leveraging multiple small experiments to approximate the overall direction needed for effective model updates.