Inner Structure

Research on inner structure focuses on understanding and leveraging the inherent organizational patterns within various data types, aiming to improve model performance, interpretability, and efficiency. Current efforts concentrate on developing novel algorithms and architectures, such as graph neural networks, transformers, and recurrent neural networks, to effectively capture and utilize structural information in diverse domains, including image processing, natural language processing, and knowledge graph completion. These advancements have significant implications for various fields, enabling improved data analysis, more accurate predictions, and the development of more robust and explainable AI systems.