34 citations · 49 across the 9 of their papers we have counts for
4 papers · 1 filter
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
Yihang Gao, Ka Chun Cheung, Michael K. Ng
Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of…
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization
Yihang Gao, Michael K. Ng
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global con…
Approximate Secular Equations for the Cubic Regularization Subproblem
Yihang Gao, Man-Chung Yue, Michael K. Ng
The cubic regularization method (CR) is a popular algorithm for unconstrained non-convex optimization. At each iteration, CR solves a cubically regularized quadratic problem, calle…
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Yihang Gao, Huafeng Liu, Michael K. Ng +1
Wide applications of differentiable two-player sequential games (e.g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast…