Large Scale Machine Learning

Large-scale machine learning focuses on developing and deploying machine learning models with massive datasets and parameter counts, aiming to improve model accuracy and efficiency. Current research emphasizes addressing challenges like data heterogeneity in federated learning, optimizing model training and inference through techniques such as mixed-precision quantization and evolutionary computation, and enhancing model interpretability and robustness. These advancements are crucial for improving the performance and reliability of AI systems across diverse applications, from natural language processing and computer vision to recommendation systems and scientific simulations.