Singular Value

Singular value decomposition (SVD) is a fundamental matrix factorization technique with applications across diverse fields, currently experiencing renewed interest in optimizing and analyzing large-scale machine learning models. Research focuses on leveraging SVD to improve parameter-efficient fine-tuning of large language and vision models, developing novel algorithms like LoRA, PiSSA, and SVFit that utilize singular values for faster convergence and enhanced performance. These advancements offer significant potential for reducing computational costs and improving the efficiency and accuracy of deep learning, impacting areas such as natural language processing, computer vision, and medical image analysis.