Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks Y Gao, MK Ng Journal of Computational Physics 463, 111270, 2022 | 18 | 2022 |
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks Y Gao, Y Gu, M Ng The 40th International Conference on Machine Learning (ICML 2023), 2023 | 6 | 2023 |
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition Y Gao, KC Cheung, MK Ng 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 | 6 | 2022 |
Approximate secular equations for the cubic regularization subproblem Y Gao, MC Yue, M Ng Advances in Neural Information Processing Systems (NeurIPS) 35, 14250-14260, 2022 | 2 | 2022 |
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks Y Gao, MK Ng, M Zhou SIAM Journal on Mathematics of Data Science 5 (4), 949-976, 2023 | 1 | 2023 |
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization Y Gao, H Liu, MK Ng, M Zhou arXiv preprint arXiv:2205.11030, 2022 | 1 | 2022 |
On the Expressive Power of a Variant of the Looped Transformer Y Gao, C Zheng, E Xie, H Shi, T Hu, Y Li, MK Ng, Z Li, Z Liu arXiv preprint arXiv:2402.13572, 2024 | | 2024 |
Blind Deconvolution for Multiple Observed Images with Missing Values Y Gao, X Lin, MK Ng Pacific Journal of Optimization 19, 69-96, 2023 | | 2023 |
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization Y Gao, MK Ng arXiv preprint arXiv:2210.05987, 2022 | | 2022 |