21 citations · 25 across the 4 of their papers we have counts for
4 papers
Quantum Maximum Entropy Inference and Hamiltonian Learning
Minbo Gao, Zhengfeng Ji, Fuchao Wei
Maximum entropy inference and learning of graphical models are pivotal tasks in learning theory and optimization. This work extends algorithms for these problems, including general…
Convergence Analysis for Restarted Anderson Mixing and Beyond
Fuchao Wei, Chenglong Bao, Yang Liu +1
Anderson mixing (AM) is a classical method that can accelerate fixed-point iterations by exploring historical information. Despite the successful application of AM in scientific co…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Fuchao Wei, Chenglong Bao, Yang Liu
Anderson mixing (AM) is an acceleration method for fixed-point iterations. Despite its success and wide usage in scientific computing, the convergence theory of AM remains unclear,…