Penalty Based

Penalty-based methods are increasingly used to improve the reliability and performance of various machine learning models, particularly in scenarios requiring high confidence and robustness. Current research focuses on integrating penalty terms into model optimization to address issues like model calibration (e.g., ensuring accurate confidence scores in segmentation networks), handling infeasible inputs (e.g., in text-to-SQL systems), and incorporating prior knowledge (e.g., in clustering and routing problems). These techniques enhance model accuracy, trustworthiness, and the ability to manage uncertainty, leading to improved decision-making in diverse applications such as healthcare, autonomous driving, and database interaction.