Additive Noise

Additive noise, unwanted signal interference, is a pervasive challenge across diverse scientific fields, impacting data quality and model performance. Current research focuses on developing methods to mitigate or leverage noise in various contexts, including optimization algorithms (like CMA-ES), image processing (using diffusion models and CNNs), and privacy-preserving machine learning (employing differential privacy techniques and noise-infusion strategies). Understanding and effectively managing additive noise is crucial for improving the accuracy, robustness, and privacy of numerous applications, ranging from causal inference and generative AI to signal processing and federated learning.