Mining Framework

Mining frameworks are computational tools designed to improve the efficiency and effectiveness of machine learning models by strategically selecting and processing data. Current research focuses on developing frameworks that address challenges like identifying crucial data points for long-context modeling in LLMs, improving object detection and segmentation by mining hard-to-classify instances, and mitigating the effects of noisy labels in datasets, particularly for face recognition. These advancements lead to more accurate and robust models across various applications, impacting fields like natural language processing, computer vision, and biometric identification.