Regularization Function

Regularization functions are mathematical tools added to optimization problems to constrain solutions and improve model generalization, particularly in scenarios with noisy data or high dimensionality. Current research focuses on adapting regularization techniques for diverse applications, including image reconstruction, neural network training, and forecasting, often employing self-supervised learning and neural architecture search to optimize their design and implementation. These advancements are crucial for enhancing the performance and robustness of machine learning models and solving complex inverse problems across various scientific and engineering domains. The development of novel regularization functions, particularly those tailored to specific problem structures, is a key area of ongoing investigation.