Differential Encoding

Differential encoding is a technique that improves data representation by focusing on changes or differences between data points rather than the absolute values. Current research explores its application in diverse fields, including graph representation learning (using message-passing and attention mechanisms), time series prediction (like flight trajectory forecasting with multi-scale networks), and reinforcement learning (compressing observation spaces for efficient training). This approach offers significant advantages by reducing data redundancy, improving model efficiency, and enhancing the accuracy and scalability of various machine learning models across different domains.