Scaling Factor

Scaling factors are parameters used to adjust the magnitude or range of data in various machine learning and signal processing applications, aiming to improve model performance, stability, or efficiency. Current research focuses on optimizing scaling factors within deep neural networks (like ResNets and Vision Transformers), decentralized data fusion algorithms, and quantization techniques for low-bit precision models. Effective scaling factor selection is crucial for addressing issues such as generalization ability, training instability (e.g., weight oscillation), and computational efficiency, ultimately impacting the accuracy and robustness of diverse machine learning models and data analysis methods.