Gaussian Sampling

Gaussian sampling is a technique used to generate random numbers following a Gaussian (normal) distribution, finding applications across diverse fields. Current research focuses on improving its efficiency and robustness in areas like deep learning model training (e.g., using Gaussian perturbations to optimize gradient calculations), few-shot learning (transforming data distributions to approximate Gaussians), and active learning (defining sample importance based on model sensitivity). These advancements enhance the speed and accuracy of various algorithms, impacting fields ranging from computer vision and natural language processing to robust statistical estimation.