| Characterizing possible failure modes in physics-informed neural networks A Krishnapriyan, A Gholami, S Zhe, R Kirby, MW Mahoney Advances in neural information processing systems 34, 26548-26560, 2021 | 808 | 2021 |
| Learning compact recurrent neural networks with block-term tensor decomposition J Ye, L Wang, G Li, D Chen, S Zhe, X Chu, Z Xu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 167 | 2018 |
| Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation Y Yuan, Z Zhang, XT Yang, S Zhe Transportation Research Part B: Methodological 146, 88-110, 2021 | 108 | 2021 |
| SWATShare–A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models MA Rajib, V Merwade, IL Kim, L Zhao, C Song, S Zhe Environmental Modelling & Software 75, 498-512, 2016 | 97 | 2016 |
| A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions M Penwarden, AD Jagtap, S Zhe, GE Karniadakis, RM Kirby Journal of Computational Physics 493, 112464, 2023 | 81 | 2023 |
| Distributed flexible nonlinear tensor factorization S Zhe, K Zhang, P Wang, K Lee, Z Xu, Y Qi, Z Ghahramani Advances in neural information processing systems 29, 2016 | 76 | 2016 |
| Multi-fidelity Bayesian optimization via deep neural networks S Li, W Xing, R Kirby, S Zhe Advances in Neural Information Processing Systems 33, 8521-8531, 2020 | 70 | 2020 |
| Scalable nonparametric multiway data analysis S Zhe, Z Xu, X Chu, Y Qi, Y Park Artificial intelligence and statistics, 1125-1134, 2015 | 58 | 2015 |
| The combinatorial brain surgeon: Pruning weights that cancel one another in neural networks X Yu, T Serra, S Ramalingam, S Zhe International Conference on Machine Learning, 25668-25683, 2022 | 50 | 2022 |
| A metalearning approach for physics-informed neural networks (PINNs): Application to parameterized PDEs M Penwarden, S Zhe, A Narayan, RM Kirby Journal of Computational Physics 477, 111912, 2023 | 47 | 2023 |
| Multifidelity modeling for physics-informed neural networks (PINNs) M Penwarden, S Zhe, A Narayan, RM Kirby Journal of Computational Physics 451, 110844, 2022 | 47 | 2022 |
| Scalable high-order gaussian process regression S Zhe, W Xing, RM Kirby The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 45 | 2019 |
| Probabilistic streaming tensor decomposition Y Du, Y Zheng, K Lee, S Zhe 2018 IEEE International Conference on Data Mining (ICDM), 99-108, 2018 | 44 | 2018 |
| Dintucker: Scaling up gaussian process models on large multidimensional arrays S Zhe, Y Qi, Y Park, Z Xu, I Molloy, S Chari Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 39 | 2016 |
| Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery Z Wang, S Zhe, J Zimmerman, C Morrisey, JE Tonna, V Sharma, ... Scientific reports 12 (1), 1355, 2022 | 36 | 2022 |
| Block-term tensor neural networks J Ye, G Li, D Chen, H Yang, S Zhe, Z Xu Neural Networks 130, 11-21, 2020 | 35 | 2020 |
| Deep multi-fidelity active learning of high-dimensional outputs S Li, RM Kirby, S Zhe arXiv preprint arXiv:2012.00901, 2020 | 32 | 2020 |
| Neuralcp: Bayesian multiway data analysis with neural tensor decomposition B Liu, L He, Y Li, S Zhe, Z Xu Cognitive Computation 10, 1051-1061, 2018 | 32 | 2018 |
| Provably convergent schrödinger bridge with applications to probabilistic time series imputation Y Chen, W Deng, S Fang, F Li, NT Yang, Y Zhang, K Rasul, S Zhe, ... International Conference on Machine Learning, 4485-4513, 2023 | 29 | 2023 |
| Asynchronous distributed variational Gaussian process for regression H Peng, S Zhe, X Zhang, Y Qi International Conference on Machine Learning, 2788-2797, 2017 | 29 | 2017 |
Zenglin XuFudan Universityfudan.edu.cn의 이메일 확인됨