Domain Prior

Domain priors represent the integration of expert knowledge or data-driven insights into machine learning models, primarily to improve performance and robustness, especially in data-scarce or noisy scenarios. Current research focuses on incorporating these priors into various architectures, including Bayesian neural networks and transformers, often using variational inference or regularization techniques to effectively blend prior information with data-driven learning. This approach shows promise in diverse fields like drug discovery, medical image analysis, and dialogue systems, leading to more accurate, reliable, and interpretable models by leveraging domain-specific knowledge to overcome limitations of purely data-driven approaches.