Normalization Technique

Normalization techniques in machine learning aim to standardize data distributions, improving model training, stability, and generalization performance across diverse datasets and hardware. Current research focuses on developing novel normalization methods tailored to specific data types (e.g., time series, images, and neural network weights) and addressing limitations of existing techniques like batch normalization, particularly in scenarios with varying batch sizes or distribution shifts. These advancements are crucial for enhancing the reliability and efficiency of machine learning models in various applications, from medical image analysis and time series forecasting to resource-constrained settings and large-scale knowledge graph processing.