Ming Li
Cited by
Cited by
Stochastic configuration networks: Fundamentals and algorithms
D Wang, M Li
IEEE Transactions on Cybernetics 47 (10), 3466-3479, 2017
Insights into randomized algorithms for neural networks: Practical issues and common pitfalls
M Li, D Wang
Information Sciences 382, 170-178, 2017
Robust stochastic configuration networks with kernel density estimation for uncertain data regression
D Wang, M Li
Information Sciences 412, 210-222, 2017
Investigating the influence of interaction on learning persistence in online settings: Moderation or mediation of academic emotions?
J Yu, C Huang, Z Han, T He, M Li
International Journal of Environmental Research and Public Health 17 (7), 2320, 2020
Haar graph pooling
YG Wang, M Li, Z Ma, G Montufar, X Zhuang, Y Fan
ICML, 9952-9962, 2020
Deep stochastic configuration networks with universal approximation property
D Wang, M Li
International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
Social participation of the elderly in China: The roles of conventional media, digital access and social media engagement
T He, C Huang, M Li, Y Zhou, S Li
Telematics and Informatics 48, 101347, 2020
Fast Haar transforms for graph neural networks
M Li, Z Ma, YG Wang, X Zhuang
Neural Networks 128, 188-198, 2020
Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis
C Huang, Z Han, M Li, X Wang, W Zhao
Australasian Journal of Educational Technology 37 (2), 81-95, 2021
How framelets enhance graph neural networks
X Zheng, B Zhou, J Gao, YG Wang, P Lio, M Li, G Mont˙far
ICML (Spotlight Paper), 12761-12771, 2021
2-D stochastic configuration networks for image data analytics
M Li, D Wang
IEEE Transactions on Cybernetics 51 (1), 359-372, 2021
Path integral based convolution and pooling for graph neural networks
Z Ma, J Xuan, YG Wang, M Li, P Lio
NeurIPS, 16421-16433, 2020
Exercise recommendation based on knowledge concept prediction
Z Wu, M Li, Y Tang, Q Liang
Knowledge-Based Systems 210, 106481, 2020
Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression
M Li, C Huang, D Wang
Information Sciences 473, 73-86, 2019
Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring
Y Liu, W Yu, T Dillon, W Rahayu, M Li
IEEE Transactions on Industrial Informatics 18 (2), 1345-1354, 2021
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer
Y Wang, YG Wang, C Hu, M Li, Y Fan, N Otter, I Sam, H Gou, Y Hu, ...
npj Precision Oncology 6 (45), 2022
Are graph convolutional networks with random weights feasible?
C Huang, M Li, F Cao, H Fujita, Z Li, X Wu
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 2751-2768, 2023
MathNet: Haar-like wavelet multiresolution analysis for graph representation learning
X Zheng, B Zhou, M Li, YG Wang, J Gao
Knowledge-Based Systems 273, 110609, 2023
Multi-view graph convolutional networks with attention mechanism
K Yao, J Liang, J Liang, M Li, F Cao
Artificial Intelligence 307, 103708, 2022
Deep multi-graph neural networks with attention fusion for recommendation
Y Song, H Ye, M Li, F Cao
Expert Systems with Applications 191, 116240, 2022
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