| RF-net: A unified meta-learning framework for RF-enabled one-shot human activity recognition S Ding, Z Chen, T Zheng, J Luo Proceedings of the 18th Conference on Embedded Networked Sensor Systems, 517-530, 2020 | 134 | 2020 |
| Kernel based online learning for imbalance multiclass classification S Ding, B Mirza, Z Lin, J Cao, X Lai, TV Nguyen, J Sepulveda Neurocomputing 277, 139-148, 2018 | 80 | 2018 |
| A Meta-learning Based Multi-modal Neural Network for Multi-step ahead Battery Thermal Runaway Forecasting S Ding, C Dong, T Zhao, LM Koh, X Bai, J Luo IEEE Transactions of Industrial Informatics, 2020 | 50 | 2020 |
| Enhancing RF sensing with deep learning: A layered approach T Zheng, Z Chen, S Ding, J Luo IEEE Communications Magazine 59 (2), 70-76, 2021 | 45 | 2021 |
| Enhancing intrinsic adversarial robustness via feature pyramid decoder G Li, S Ding, J Luo, C Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 23 | 2020 |
| Roaming in connecting light: Practical visible light communication leveraging LED sensing Y Yang, S Ding, J Hao, J Luo Proceedings of the 4th ACM Workshop on Visible Light Communication Systems, 9-14, 2017 | 11 | 2017 |
| Adv-4-Adv: Thwarting changing adversarial perturbations via adversarial domain adaptation T Zheng, Z Chen, S Ding, C Cai, J Luo Neurocomputing 569, 127114, 2024 | 5 | 2024 |
| Large-scale modeling and optimization strategy for multi-energy management systems L Setyawan, J Tan, S Ding, E Raynaud, HW Jing 2018 Asian Conference on Energy, Power and Transportation Electrification …, 2018 | 4 | 2018 |
| System reliability enhancement via deep-driven computer vision S Ding Nanyang Technological University, 2021 | | 2021 |
| Machine learning for balanced and imbalanced data S Ding | | 2016 |
