Compact Model

Compact models aim to create smaller, faster, and more efficient machine learning models while maintaining or even exceeding the performance of larger counterparts. Current research focuses on techniques like knowledge distillation, weight multiplexing, and the integration of large language models to enhance smaller models' capabilities, particularly in low-data scenarios. These advancements are significant for deploying AI on resource-constrained devices and improving the efficiency of various applications, from natural language processing and image classification to device modeling and time-series analysis. The development of efficient training strategies tailored to compact architectures is also a key area of investigation.