Correlated Noise

Correlated noise, where noise values exhibit dependencies across time or space, poses significant challenges in various fields, from signal processing and machine learning to quantum computing and control systems. Current research focuses on understanding the impact of correlated noise on model performance and developing algorithms to mitigate its effects, employing techniques like noise correlation modeling, blind-spot networks, and instrumental variable regression. These advancements are crucial for improving the accuracy and reliability of diverse applications, ranging from audio enhancement and image denoising to privacy-preserving machine learning and the development of robust quantum computers.