Stochastic configuration networks: Fundamentals and algorithms D Wang, M Li IEEE Transactions on Cybernetics 47 (10), 3466-3479, 2017 | 509 | 2017 |
Insights into randomized algorithms for neural networks: Practical issues and common pitfalls M Li, D Wang Information Sciences 382, 170-178, 2017 | 199 | 2017 |
Robust stochastic configuration networks with kernel density estimation for uncertain data regression D Wang, M Li Information Sciences 412, 210-222, 2017 | 127 | 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 | 104 | 2020 |
Deep stochastic configuration networks with universal approximation property D Wang, M Li International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 86 | 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 | 83 | 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 | 82 | 2021 |
Haar graph pooling YG Wang, M Li, Z Ma, G Montufar, X Zhuang, Y Fan ICML, 9952-9962, 2020 | 82 | 2020 |
Path integral based convolution and pooling for graph neural networks Z Ma, J Xuan, YG Wang, M Li, P Lio NeurIPS, 16421-16433, 2020 | 77 | 2020 |
Fast Haar transforms for graph neural networks M Li, Z Ma, YG Wang, X Zhuang Neural Networks 128, 188-198, 2020 | 73 | 2020 |
2-D stochastic configuration networks for image data analytics M Li, D Wang IEEE Transactions on Cybernetics 51 (1), 359-372, 2021 | 69 | 2021 |
How framelets enhance graph neural networks X Zheng, B Zhou, J Gao, YG Wang, P Lio, M Li, G Montúfar ICML (Spotlight Paper), 12761-12771, 2021 | 66 | 2021 |
Exercise recommendation based on knowledge concept prediction Z Wu, M Li, Y Tang, Q Liang Knowledge-Based Systems 210, 106481, 2020 | 58 | 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 | 54 | 2019 |
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 | 46 | 2023 |
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 | 46 | 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 | 41* | 2022 |
Multi-view graph convolutional networks with attention mechanism K Yao, J Liang, J Liang, M Li, F Cao Artificial Intelligence 307, 103708, 2022 | 36 | 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 | 34 | 2022 |
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 | 30* | 2023 |