Effective Training Data

Effective training data is crucial for the success of machine learning models, driving research into optimizing its quantity, quality, and characteristics for various tasks. Current efforts focus on developing methodologies for creating and evaluating datasets, including exploring the impact of dataset size and composition on model performance across diverse domains like natural language processing, graph classification, and medical imaging, often employing techniques like contrastive learning and transfer learning. These investigations aim to improve model robustness, generalization, and efficiency, ultimately leading to more reliable and impactful machine learning applications.