| A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis G Hu, H Li, Y Xia, L Luo Computers in Industry 100, 287-296, 2018 | 88 | 2018 |
| Intelligent fault diagnosis for large-scale rotating machines using binarized deep neural networks and random forests H Li, G Hu, J Li, M Zhou IEEE Transactions on Automation Science and Engineering 19 (2), 1109-1119, 2021 | 76 | 2021 |
| Event-triggered communication network with limited-bandwidth constraint for multi-agent reinforcement learning G Hu, Y Zhu, D Zhao, M Zhao, J Hao IEEE Transactions on Neural Networks and Learning Systems 34 (8), 3966-3978, 2021 | 50 | 2021 |
| Event-triggered multi-agent reinforcement learning with communication under limited-bandwidth constraint G Hu, Y Zhu, D Zhao, M Zhao, J Hao arXiv preprint arXiv:2010.04978, 2020 | 13 | 2020 |
| An improved dropout method and its application into DBN-based handwriting recognition G Hu, H Li, L Luo, Y Xia 2017 36th Chinese control conference (CCC), 11145-11149, 2017 | 9 | 2017 |
| NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning L Haoran, L Shasha, M Mingjun, H Guangzheng, C Yaran, Z Dongbin arXiv preprint arXiv:2302.03385, 2023 | 3 | 2023 |
| NeuronsMAE: a novel multi-agent reinforcement learning environment for cooperative and competitive multi-robot tasks G Hu, H Li, S Liu, Y Zhu, D Zhao 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 2 | 2023 |
| A DeepBoltzmann Machineand MultiGgrained Scanning Forest Ensemble Collaborative Method andIts ApplicationtoIndustrialFaultDiagnosis HU Guangzheng, X LIHuifang ComputersinIndustry 100, 287G296, 2018 | 2 | 2018 |
| FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game G Hu, Y Zhu, H Li, D Zhao IEEE Transactions on Emerging Topics in Computational Intelligence, 2024 | | 2024 |
