Normalization Process

Normalization processes, crucial for preparing data for machine learning models, are undergoing significant refinement across diverse applications. Current research focuses on improving normalization techniques for numerical data, particularly within language models, and adapting normalization strategies to account for data heterogeneity and burstiness, as seen in financial time series. These advancements leverage various architectures, including recurrent neural networks and Siamese networks, aiming for improved accuracy and efficiency in tasks ranging from text processing and semantic search to financial prediction. The resulting improvements in data quality directly translate to enhanced model performance and broader applicability across numerous fields.