| Pairwise adversarial training for unsupervised class-imbalanced domain adaptation W Shi, R Zhu, S Li Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 21 | 2022 |
| Application of artificial intelligence to the monitoring of medication adherence for tuberculosis treatment in Africa: algorithm development and validation JN Sekandi, W Shi, R Zhu, P Kaggwa, E Mwebaze, S Li JMIR AI 2 (1), e40167, 2023 | 14 | 2023 |
| Aging contrast: A contrastive learning framework for fish re-identification across seasons and years W Shi, Z Zhou, BH Letcher, N Hitt, Y Kanno, R Futamura, O Kishida, ... Australasian Joint Conference on Artificial Intelligence, 252-264, 2023 | 4 | 2023 |
| Pigmentation-based visual learning for salvelinus fontinalis individual re-identification Z Zhou, NP Hitt, BH Letcher, W Shi, S Li 2022 IEEE International Conference on Big Data (Big Data), 6850-6852, 2022 | 4 | 2022 |
| Calibrate graph neural networks under out-of-distribution nodes via deep q-learning W Shi, X Yang, X Zhao, H Chen, Z Tao, S Li Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 3 | 2023 |
| Improving robustness of vision transformers via data-augmented virtual adversarial training W Shi, S Li 2022 IEEE International Conference on Big Data (Big Data), 135-140, 2022 | 2 | 2022 |
| A survey of deep graph learning under distribution shifts: from graph out-of-distribution generalization to adaptation K Zhang, S Liu, S Wang, W Shi, C Chen, P Li, S Li, J Li, K Ding arXiv preprint arXiv:2410.19265, 2024 | 1 | 2024 |
| Mixup Virtual Adversarial Training for Robust Vision Transformers W Shi, S Li IEEE Transactions on Big Data, 2024 | | 2024 |
| Unsupervised Class-Imbalanced Domain Adaptation With Pairwise Adversarial Training and Semantic Alignment W Shi, R Zhu, S Li IEEE Transactions on Circuits and Systems for Video Technology, 2024 | | 2024 |
| Dual-windowed Vision Transformer with Angular Self-Attention W Shi, S Li Transactions on Machine Learning Research, 2024 | | 2024 |
| Node-wise Calibration of Graph Neural Networks under Out-of-Distribution Nodes via Reinforcement Learning W Shi, X Yang, X Zhao, H Chen, Z TAO, S Li | | |
| Application of Artificial Intelligence to Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: A Pilot Study JN Sekandi, W Shi, R Zhu, PE Kaggwa, E Mwebaze, S Li Available at SSRN 4074672, 0 | | |
Sheng LiQuantitative Foundation Associate Professor of Data Science, University of VirginiaEmail yang diverifikasi di virginia.edu
