Generalization Property

Generalization, a machine learning model's ability to perform well on unseen data, is a central research focus, aiming to understand why and how models generalize beyond their training data. Current research investigates this through various lenses, including analyzing the impact of training schedules, loss landscape sharpness (e.g., using SAM), and model architectures like ResNets and GFlowNets, as well as exploring the role of data variability and the effects of quantization. Improved understanding of generalization properties is crucial for building more reliable and robust machine learning systems across diverse applications, from scientific computing to medical diagnosis.