Explicit Modelling

Explicit modeling in machine learning focuses on incorporating prior knowledge or structural constraints into models to improve performance, interpretability, and efficiency. Current research emphasizes developing models that explicitly represent relationships within data, such as using graph neural networks for structured data or integrating physics-based models into neural networks for tasks like trajectory prediction and materials science. This approach leads to more accurate and efficient models across diverse applications, ranging from materials discovery and natural language processing to robotics and time series forecasting, by leveraging both data-driven and domain-specific knowledge.