| A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals W Zhang, G Peng, C Li, Y Chen, Z Zhang Sensors 17 (2), 425, 2017 | 1271 | 2017 |
| A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load W Zhang, C Li, G Peng, Y Chen, Z Zhang Mechanical systems and signal processing 100, 439-453, 2018 | 1125 | 2018 |
| ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis Y Chen, G Peng, C Xie, W Zhang, C Li, S Liu Neurocomputing 294, 61-71, 2018 | 113 | 2018 |
| Bearing fault diagnosis using fully-connected winner-take-all autoencoder C Li, WEI Zhang, G Peng, S Liu IEEE Access 6, 6103-6115, 2017 | 112 | 2017 |
| Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input W Zhang, G Peng, C Li MATEC web of conferences 95, 13001, 2017 | 112 | 2017 |
| Rolling element bearings fault intelligent diagnosis based on convolutional neural networks using raw sensing signal W Zhang, G Peng, C Li Advances in Intelligent Information Hiding and Multimedia Signal Processing …, 2017 | 41 | 2017 |
| Asynchronous upper confidence bound algorithms for federated linear bandits C Li, H Wang International Conference on Artificial Intelligence and Statistics, 6529-6553, 2022 | 31 | 2022 |
| Communication efficient distributed learning for kernelized contextual bandits C Li, H Wang, M Wang, H Wang Advances in Neural Information Processing Systems 35, 19773-19785, 2022 | 18 | 2022 |
| Communication efficient federated learning for generalized linear bandits C Li, H Wang Advances in Neural Information Processing Systems 35, 38411-38423, 2022 | 14 | 2022 |
| Unifying clustered and non-stationary bandits C Li, Q Wu, H Wang International Conference on Artificial Intelligence and Statistics, 1063-1071, 2021 | 12 | 2021 |
| Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial? F Yao, C Li, KA Sankararaman, Y Liao, Y Zhu, Q Wang, H Wang, H Xu Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Learning kernelized contextual bandits in a distributed and asynchronous environment C Li, H Wang, M Wang, H Wang International Conference on Learning Representation, 2023 | 6 | 2023 |
| When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution C Li, Q Wu, H Wang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 4 | 2021 |
| Incentivizing exploration in linear bandits under information gap H Wang, H Xu, C Li, Z Liu, H Wang arXiv preprint arXiv:2104.03860, 2021 | 4 | 2021 |
| Incentivized communication for federated bandits Z Wei, C Li, H Xu, H Wang Advances in Neural Information Processing Systems 36, 54399-54420, 2023 | 3 | 2023 |
| Learning the optimal recommendation from explorative users F Yao, C Li, D Nekipelov, H Wang, H Xu Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9457-9465, 2022 | 3 | 2022 |
| Learning from a learning user for optimal recommendations F Yao, C Li, D Nekipelov, H Wang, H Xu International Conference on Machine Learning, 25382-25406, 2022 | 2 | 2022 |
| Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict? F Yao, C Li, D Nekipelov, H Wang, H Xu arXiv preprint arXiv:2402.15467, 2024 | | 2024 |
| Incentivized Truthful Communication for Federated Bandits Z Wei, C Li, T Ren, H Xu, H Wang arXiv preprint arXiv:2402.04485, 2024 | | 2024 |
| Communication-Efficient Federated Non-Linear Bandit Optimization C Li, C Liu, YX Wang arXiv preprint arXiv:2311.01695, 2023 | | 2023 |
Wei ZhangMechanical Engineering, National University of SingaporeVerified email at u.nus.edu
