Structured Generative Model

Structured generative models aim to learn and generate data with inherent structure, enabling more accurate and efficient modeling of complex systems. Current research focuses on developing novel algorithms, such as Bayesian methods and score-based approaches, to improve inference and learning within these models, often leveraging hierarchical or graph-based representations to capture relationships within the data. These advancements are proving valuable in diverse applications, including 3D scene understanding, protein conformation prediction, and image inpainting, by offering improved accuracy, data efficiency, and interpretability compared to unstructured methods.