Compositional Model

Compositional models aim to represent complex systems as combinations of simpler, interacting components, enabling more interpretable, efficient, and generalizable learning. Current research focuses on developing architectures that facilitate this modularity, including techniques like Gaussian-based representations for 3D scene reconstruction, adapter-based methods for personalized model generation, and compositional approaches for causal inference and few-shot learning. This approach holds significant promise for improving the explainability, robustness, and scalability of AI systems across diverse domains, from computer vision and natural language processing to reinforcement learning and causal inference.