Space Regularization

Space regularization in machine learning aims to improve model generalization and efficiency by imposing constraints on the model's behavior, rather than solely on its parameters. Current research focuses on developing sophisticated regularization techniques, including those employing multiple regularization parameters, function-space approaches (considering the model's output directly), and constrained parameter methods that adapt penalties dynamically across different model components. These advancements address challenges like overfitting, catastrophic forgetting in lifelong learning, and the computational cost of searching optimal model architectures, leading to more robust, efficient, and interpretable models across various applications.