Regularization Method

Regularization methods aim to improve the generalization ability and robustness of machine learning models, preventing overfitting and enhancing performance on unseen data. Current research focuses on adapting and improving existing techniques like dropout, weight decay, and label smoothing for various model architectures, including neural networks used in reinforcement learning, open-set recognition, and vision-language models, as well as exploring novel approaches such as quantization and gradient control. These advancements are crucial for building reliable and efficient models across diverse applications, from image classification and natural language processing to solving complex inverse problems and improving forecasting accuracy.