Capacity Characterization

Capacity characterization focuses on determining the maximum amount of information a system, such as a neural network or communication channel, can reliably store or transmit. Current research emphasizes developing accurate and efficient methods for calculating capacity, particularly for complex architectures like treelike neural networks and using techniques like Random Duality Theory and its variants (partially and fully lifted RDT). These advancements improve our understanding of fundamental limits in information processing and contribute to the design of more efficient and powerful systems in machine learning and communication theory. Furthermore, data-driven approaches are being explored to estimate capacity bounds for systems with unknown characteristics.