Noise Filtering

Noise filtering aims to remove unwanted noise from data while preserving valuable information, a crucial task across diverse fields like image processing, speech recognition, and machine learning. Current research emphasizes developing model-agnostic and computationally efficient methods, including those leveraging techniques like high-dimensional orthogonality and diffusion models, as well as exploring the impact of different noise types on model training and performance, particularly in large language models. These advancements improve data quality, leading to enhanced model accuracy and robustness in various applications, from improving speech intelligibility for individuals with autism to enabling the detection of weak signals in scientific data.