Training Deep

Training deep neural networks efficiently and effectively remains a central challenge in machine learning. Current research focuses on improving training algorithms (e.g., exploring second-order methods and adaptive gradient normalization), optimizing model architectures (e.g., reversible architectures and sparse mixtures of experts), and reducing computational costs (e.g., through gradient sampling, model compression, and efficient distributed training). These advancements aim to enhance model performance, reduce energy consumption, and enable training on larger datasets or resource-constrained devices, impacting various applications from medical image analysis to financial modeling.