Online Memory

Online memory research focuses on developing efficient algorithms and data structures for managing and retrieving information from continuously arriving data streams, crucial for applications like continual learning. Current efforts center on designing novel architectures, such as tree-based structures and information-theoretic samplers, that prioritize the selection and storage of the most informative data points while maintaining efficient access. These advancements aim to improve the performance of machine learning models in scenarios with limited memory or unbounded data influx, impacting fields requiring real-time adaptation and learning from evolving data.