Optimal Encoding

Optimal encoding focuses on efficiently representing information, minimizing redundancy while preserving crucial details for specific tasks. Current research explores this across diverse domains, employing techniques like rate-distortion theory, variational autoencoders (VAEs), and various optimization algorithms (e.g., mixed-integer linear programming) to design efficient encoding schemes for data ranging from images and rankings to quantum states. These advancements have implications for improving data compression, enhancing machine learning model performance, and optimizing resource allocation in various applications, including biomedical data analysis and single-pixel imaging.