Low Rank Structure

Low-rank structure research focuses on exploiting the observation that many large matrices in machine learning, particularly those within deep neural networks (DNNs) and large language models (LLMs), can be effectively approximated by matrices of significantly lower rank. Current research emphasizes developing efficient algorithms that leverage this low-rank property for model compression, faster training, and improved memory efficiency, often employing techniques like singular value decomposition (SVD), tensor decomposition, and various low-rank adaptation methods (e.g., LoRA). This work is crucial for making large-scale models more practical, reducing computational costs, and potentially improving generalization performance in various applications, including natural language processing, computer vision, and recommender systems.