Univariate Symbolic Skeleton

Univariate symbolic skeletons represent a simplified, variable-specific model of complex systems, aiming to isolate and explain the influence of individual factors on an overall outcome. Current research focuses on developing machine learning methods, such as transformer networks and autoencoders, to learn these skeletons from data, often addressing challenges like multivariate data analysis and inconsistent data formats across datasets. This work has implications for diverse fields, including improving the interpretability of complex models in scientific simulations and enabling more accurate and efficient 3D human pose estimation and anatomical reconstruction from limited data.