Structured Model

Structured models aim to represent and learn from data exhibiting inherent relationships or dependencies, improving prediction accuracy and interpretability compared to unstructured approaches. Current research focuses on developing efficient inference methods for large-scale models, incorporating Bayesian techniques to quantify uncertainty, and leveraging architectures like Transformers and energy-based models for improved performance and explainability. These advancements are driving progress in diverse applications, including autonomous driving (through high-definition map construction), natural language processing, and other domains requiring the modeling of complex structured data.