34 citations · 62 across the 8 of their papers we have counts for
8 papers
Stability and Generalization of Hypergraph Collaborative Networks
Michael Ng, Hanrui Wu, Andy Yip
Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples. Recently, there have been several successful proposals to generalize…
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…
Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer
William T. Ng, K. Siu, Albert C. Cheung +1
A reliable and efficient representation of multivariate time series is crucial in various downstream machine learning tasks. In multivariate time series forecasting, each variable…
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…
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
Wen Yu, Baiying Lei, Yanyan Shen +5
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for the early stag…
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yihang Gao, Michael K. Ng
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equ…
Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks
Yihang Gao, Michael K. Ng, Mingjie Zhou
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation f…
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
Wen Yu, Baiying Lei, Michael K. Ng +3
It is of great significance to apply deep learning for the early diagnosis of Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to…