Regularization Technique

Regularization techniques in machine learning aim to prevent overfitting and improve model generalization by constraining model complexity or modifying the training process. Current research focuses on developing novel regularization methods tailored to specific model architectures (e.g., convolutional neural networks, language models, variational quantum circuits) and learning paradigms (e.g., reinforcement learning, continual learning), often investigating their impact on model robustness, calibration, and privacy. These advancements are significant because improved generalization and robustness are crucial for deploying reliable and trustworthy machine learning models across diverse applications, from medical imaging to finance.