Intermediate Language

Intermediate representations are emerging as a crucial area of research across diverse machine learning domains, aiming to improve model performance, interpretability, and efficiency. Current efforts focus on developing intermediate languages for customized sparse data formats in high-performance computing, designing novel training strategies leveraging intermediate representations to enhance the scalability and performance of neural networks (e.g., Graph Neural Networks), and creating intermediate concepts to improve the explainability of black-box models. These advancements have significant implications for various fields, enabling more efficient processing of large datasets, improved model accuracy in tasks requiring complex reasoning, and enhanced understanding of model decision-making processes.