Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs
Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）；Data61；CSIRO（联邦科学与工业研究组织）；Tsinghua University（清华大学）；Australian National University（澳大利亚国立大学）
