Noisy Data

Noisy data, ubiquitous in real-world applications, poses a significant challenge to machine learning model accuracy and reliability. Current research focuses on developing robust algorithms and model architectures, such as deep learning networks (including CNNs and Transformers), that can effectively handle various types of noise (e.g., label noise, missing data, sensor noise) across diverse data modalities (e.g., images, text, time series). These advancements are crucial for improving the performance and trustworthiness of machine learning systems in fields ranging from medical imaging and financial prediction to environmental monitoring and natural language processing.