Better Generalization

Better generalization in machine learning aims to improve a model's ability to perform well on unseen data, a crucial aspect for real-world applications. Current research focuses on techniques like weight decay, sharpness minimization, and various continual learning strategies, often applied to neural networks including transformers and convolutional models, to achieve this goal. These efforts are driven by the need for more robust and reliable AI systems across diverse domains, impacting fields ranging from medical image analysis and natural language processing to climate modeling and robotics. Improved generalization ultimately leads to more trustworthy and effective AI deployments.