Redundancy Aware

Redundancy-aware techniques aim to optimize systems and models by intelligently managing and leveraging redundancy, improving efficiency and robustness. Current research focuses on identifying and eliminating redundant components in various contexts, including deep learning models (e.g., through pruning and adapter-based methods), edge computing systems (via optimized resource allocation), and data representations (by reducing feature space collisions). These advancements lead to improved performance, reduced computational costs, and enhanced reliability across diverse applications, from autonomous vehicles to large language models and bioinformatics.