Informed Learning

Informed learning enhances machine learning by integrating prior knowledge into the learning process, aiming to improve accuracy, robustness, and efficiency, particularly when data is scarce or noisy. Current research focuses on developing methods to effectively incorporate diverse forms of prior knowledge, including probabilistic and multi-modal approaches, often within the framework of deep neural networks and continual learning. This field is significant because it addresses limitations of traditional machine learning by leveraging existing domain expertise, leading to more reliable and data-efficient models with applications across various domains, such as autonomous driving and synthetic data generation.