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2020 – today
- 2025
[j3]Jing Gu, Tiangang Zhang, Yihang Gao, Sentao Chen, Yuxin Zhang, Hui Cui, Ping Xuan:
Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction. J. Chem. Inf. Model. 65(1): 435-445 (2025)- 2024
- [c6]Jiquan Ma, Yihang Gao, Haotian Jiang, Jing Gu, Diliara Khairullina, Hui Cui, Geng Chen:
Super-resolved Estimation of White Matter Microstructure via 3D Conditional Latent Diffusion Model. BIBM 2024: 4247-4252 - [i11]Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael K. Ng, Zhenguo Li, Zhaoqiang Liu:
On the Expressive Power of a Variant of the Looped Transformer. CoRR abs/2402.13572 (2024) - [i10]Chuanyang Zheng, Yihang Gao, Han Shi, Minbin Huang, Jingyao Li, Jing Xiong, Xiaozhe Ren, Michael K. Ng, Xin Jiang, Zhenguo Li, Yu Li:
CAPE: Context-Adaptive Positional Encoding for Length Extrapolation. CoRR abs/2405.14722 (2024) - [i9]Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael K. Ng, Xin Jiang, Zhenguo Li, Yu Li:
DAPE V2: Process Attention Score as Feature Map for Length Extrapolation. CoRR abs/2410.04798 (2024) - [i8]Yihang Gao, Vincent Y. F. Tan:
On the Convergence of (Stochastic) Gradient Descent for Kolmogorov-Arnold Networks. CoRR abs/2410.08041 (2024) - [i7]Guoxuan Chen, Han Shi, Jiawei Li, Yihang Gao, Xiaozhe Ren, Yimeng Chen, Xin Jiang, Zhenguo Li, Weiyang Liu, Chao Huang:
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator. CoRR abs/2412.12094 (2024) - 2023
- [j2]Yihang Gao
, Michael K. Ng, Mingjie Zhou:
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks. SIAM J. Math. Data Sci. 5(4): 949-976 (2023) - [c5]Yihang Gao, Yiqi Gu, Michael Ng:
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks. ICML 2023: 10676-10707 - 2022
- [j1]Yihang Gao, Michael K. Ng:
Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks. J. Comput. Phys. 463: 111270 (2022) - [c4]Yihang Gao, Man-Chung Yue, Michael Ng:
Approximate Secular Equations for the Cubic Regularization Subproblem. NeurIPS 2022 - [c3]Yihang Gao, Ka Chun Cheung, Michael K. Ng:
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition. SSCI 2022: 1443-1450 - [i6]Yihang Gao, Huafeng Liu, Michael K. Ng, Mingjie Zhou:
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization. CoRR abs/2205.11030 (2022) - [i5]Yihang Gao, Man-Chung Yue, Michael Kwok-Po Ng:
Approximate Secular Equations for the Cubic Regularization Subproblem. CoRR abs/2209.13268 (2022) - [i4]Yihang Gao, Michael Kwok-Po Ng:
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization. CoRR abs/2210.05987 (2022) - [i3]Yihang Gao, Ka Chun Cheung, Michael K. Ng:
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition. CoRR abs/2211.08760 (2022) - 2021
- [c2]Yihang Gao, Peng Su, Hui Zhao, Meikang Qiu, Meiqin Liu:
Research on Sentiment Dictionary Based on Sentiment Analysis in News Domain. BigDataSecurity 2021: 117-122 - [i2]Yihang Gao, Michael K. Ng:
Approximation for Probability Distributions by Wasserstein GAN. CoRR abs/2103.10060 (2021) - [i1]Yihang Gao, Michael K. Ng:
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks. CoRR abs/2108.13054 (2021) - 2020
- [c1]Yihang Gao, Hui Zhao, Qian Zhou, Meikang Qiu, Meiqin Liu:
An Improved News Recommendation Algorithm Based on Text Similarity. SmartBlock 2020: 132-136
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