| Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review B Chen, Z Zhang, N Langrené, S Zhu arXiv preprint arXiv:2310.14735, 2023 | 254 | 2023 |
| On spectral distribution of kernel matrices related to radial basis functions AJ Wathen, S Zhu Numerical Algorithms 70 (4), 709-726, 2015 | 56 | 2015 |
| Knowledge discovery and recommendation with linear mixed model Z Chen, S Zhu, Q Niu, T Zuo Ieee Access 8, 38304-38317, 2020 | 26 | 2020 |
| Compactly supported radial basis functions: how and why? S Zhu OCCAM report, 2012 | 25 | 2012 |
| Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv B Chen, Z Zhang, N Langrené, S Zhu arXiv preprint arXiv:2310.14735 10, 2023 | 21 | 2023 |
| Minimizing synchronizations in sparse iterative solvers for distributed supercomputers SX Zhu, TX Gu, XP Liu Computers & Mathematics With Applications 67 (1), 199-209, 2014 | 20 | 2014 |
| A rate-dependent phase-field model for dynamic shear band formation in strength-like and toughness-like modes Q Zeng, T Wang, S Zhu, H Chen, D Fang Journal of the Mechanics and Physics of Solids 164, 104914, 2022 | 19 | 2022 |
| Convexity and solvability for compactly supported radial basis functions with different shapes S Zhu, AJ Wathen Journal of Scientific Computing 63 (3), 862-884, 2015 | 17 | 2015 |
| Information splitting for big data analytics S Zhu, T Gu, X Xu, Z Mo IEEE CyberC2016, 2016 | 16 | 2016 |
| Learning with linear mixed model for group recommendation systems B Gao, G Zhan, H Wang, Y Wang, S Zhu Proceedings of the 2019 11th International Conference on Machine Learning …, 2019 | 15 | 2019 |
| Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models S Zhu, AJ Wathen arXiv preprint arXiv:1805.05188, 2018 | 14 | 2018 |
| Solving inverse eigenvalue problems via Householder and rank-one matrices S Zhu, T Gu, X Liu Linear algebra and its applications 430 (1), 318-334, 2009 | 12 | 2009 |
| Unleashing the potential of prompt engineering in large language models: a comprehensive review, 2023 B Chen, Z Zhang, N Langrené, S Zhu arXiv preprint arXiv:2310.14735, 2024 | 11 | 2024 |
| Fast calculation of restricted maximum likelihood methods for unstructured high-throughput data S Zhu 2017 IEEE 2nd international conference on big data analysis, 2017 | 10 | 2017 |
| AIMS: Average information matrix splitting S Zhu, T Gu, X Liu Mathematical Foundations of Computing (MFC), 2016 | 10* | 2016 |
| A hybrid recommender system combing singular value decomposition and linear mixed model T Zuo, S Zhu, J Lu Intelligent Computing. SAI 2020. 1228, 347-362, 2020 | 9 | 2020 |
| Sparse inversion for derivative of log determinant S Zhu, AJ Wathen arXiv:1911.00685 1 (arXiv:1911.00685), arXiv:1911.00685, 2019 | 9* | 2019 |
| Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4 Z Chen, S Zhu, Q Niu, X Lu ICBDA, 32-36, 2019 | 8 | 2019 |
| Business Information Systems: 22nd International Conference, BIS 2019, Seville, Spain, June 26–28, 2019, Proceedings, Part I W Abramowicz, R Corchuelo Springer, 2019 | 7 | 2019 |
| EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation Y Wang, X He, S Zhu arXiv preprint arXiv:2406.02638, 2024 | 6 | 2024 |