Training Algorithm

Training algorithms are crucial for optimizing machine learning models, aiming to achieve high accuracy and efficiency. Current research emphasizes improving training speed and stability across diverse model architectures, including neural networks (e.g., ResNets, Transformers, and unfolded GNNs), and addressing challenges like local minima, data heterogeneity, and the need for formal guarantees in safety-critical applications. These advancements are vital for scaling machine learning to larger datasets and more complex tasks, impacting fields ranging from image recognition and natural language processing to control systems and scientific modeling.