Memory Sample Selection

Memory sample selection focuses on efficiently managing and utilizing past data ("memory") to improve the performance of machine learning models, particularly in scenarios involving continual learning or handling large datasets. Current research emphasizes developing sophisticated algorithms for selecting diverse and informative subsets of past data, often employing techniques like density-based sampling, variational methods for generating synthetic samples, and reinforcement learning to optimize selection strategies. These advancements are crucial for improving the efficiency and accuracy of models in various applications, such as predictive autoscaling in cloud computing and semantic segmentation in computer vision, by reducing computational costs and mitigating catastrophic forgetting.