Shandian Zhe
Shandian Zhe
School of Computing, University of Utah
Verifisert e-postadresse på cs.utah.edu - Startside
Sitert av
Sitert av
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
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
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
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
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
Scalable nonparametric multiway data analysis
S Zhe, Z Xu, X Chu, Y Qi, Y Park
Artificial Intelligence and Statistics, 1125-1134, 2015
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
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
Scalable high-order gaussian process regression
S Zhe, W Xing, RM Kirby
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
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
Probabilistic streaming tensor decomposition
Y Du, Y Zheng, K Lee, S Zhe
2018 IEEE International Conference on Data Mining (ICDM), 99-108, 2018
Multifidelity modeling for physics-informed neural networks (pinns)
M Penwarden, S Zhe, A Narayan, RM Kirby
Journal of Computational Physics 451, 110844, 2022
Block-term tensor neural networks
J Ye, G Li, D Chen, H Yang, S Zhe, Z Xu
Neural Networks 130, 11-21, 2020
Asynchronous distributed variational Gaussian process for regression
H Peng, S Zhe, X Zhang, Y Qi
International Conference on Machine Learning, 2788-2797, 2017
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
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
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
Deep multi-fidelity active learning of high-dimensional outputs
S Li, RM Kirby, S Zhe
arXiv preprint arXiv:2012.00901, 2020
Stochastic nonparametric event-tensor decomposition
S Zhe, Y Du
Advances in Neural Information Processing Systems 31, 2018
Bayesian streaming sparse Tucker decomposition
S Fang, RM Kirby, S Zhe
Uncertainty in Artificial Intelligence, 558-567, 2021
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