Rate Reduction

Rate reduction, a core concept in information theory and machine learning, aims to minimize data representation size while preserving essential information. Current research focuses on applying rate reduction techniques to improve the efficiency and performance of various models, including spiking neural networks, deep metric learning architectures, and transformers, often leveraging methods like maximal coding rate reduction (MCR²) and sparse rate reduction. These advancements are significant because they address computational bottlenecks in large-scale models, enhance model robustness, and enable efficient deployment in resource-constrained environments, such as mobile robotics and federated learning applications.