Abstract
Clustering techniques are crucial for uncovering hidden structures and patterns within datasets. In this talk, I present a competition-based partitioning algorithm designed to detect latent functional characteristics and effectively group data points. This algorithm forms the foundation of a modular modeling framework, which assigns specialized expert models to each identified partition. I will benchmark the performance of this innovative approach against traditional
single-network models.
As this research progresses through the review process, my focus shifts to exploring the capabilities of Large Language Models (LLMs) in generalizing solutions for complex engineering problems. I will share recent insights and outline future research objectives, including an examination of the potential synergy between LLMs and our partitioning methodology. This integration could open new avenues for enhanced data analysis and model performance.
Talk in the series “Train Your Engineering Network”.
