| Weak adversarial networks for high-dimensional partial differential equations Y Zang, G Bao, X Ye, H Zhou Journal of Computational Physics 411, 109409, 2020 | 557 | 2020 |
| Projection onto a simplex Y Chen, X Ye arXiv preprint arXiv:1101.6081, 2011 | 255 | 2011 |
| Wasserstein learning of deep generative point process models S Xiao, M Farajtabar, X Ye, J Yan, L Song, H Zha Advances in neural information processing systems 30, 2017 | 234 | 2017 |
| Fake news mitigation via point process based intervention M Farajtabar, J Yang, X Ye, H Xu, R Trivedi, E Khalil, S Li, L Song, H Zha International conference on machine learning, 1097-1106, 2017 | 231 | 2017 |
| Relevant sparse codes with variational information bottleneck M Chalk, O Marre, G Tkacik Advances in Neural Information Processing Systems 29, 2016 | 221* | 2016 |
| A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data Y Chen, X Ye, F Huang Inverse Problems and Imaging 4 (2), 223-240, 2010 | 111 | 2010 |
| Numerical solution of inverse problems by weak adversarial networks G Bao, X Ye, Y Zang, H Zhou Inverse Problems 36 (11), 115003, 2020 | 109 | 2020 |
| Computational acceleration for MR image reconstruction in partially parallel imaging X Ye, Y Chen, F Huang IEEE transactions on medical imaging 30 (5), 1055-1063, 2010 | 96 | 2010 |
| A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: self‐feeding sparse SENSE F Huang, Y Chen, W Yin, W Lin, X Ye, W Guo, A Reykowski Magnetic Resonance in Medicine 64 (4), 1078-1088, 2010 | 88 | 2010 |
| Bregman operator splitting with variable stepsize for total variation image reconstruction Y Chen, WW Hager, M Yashtini, X Ye, H Zhang Computational optimization and applications 54 (2), 317-342, 2013 | 83 | 2013 |
| Fast algorithms for image reconstruction with application to partially parallel MR imaging Y Chen, W Hager, F Huang, D Phan, X Ye, W Yin SIAM Journal on Imaging Sciences 5 (1), 90-118, 2012 | 83 | 2012 |
| Lyapunov-net: A deep neural network architecture for Lyapunov function approximation N Gaby, F Zhang, X Ye 2022 IEEE 61st Conference on Decision and Control (CDC), 2091-2096, 2022 | 80 | 2022 |
| Machine learning for tomographic imaging G Wang, Y Zhang, X Ye, X Mou IOP Publishing, 2019 | 79 | 2019 |
| Learning to match via inverse optimal transport R Li, X Ye, H Zhou, H Zha Journal of machine learning research 20 (80), 1-37, 2019 | 76 | 2019 |
| Learning deep mean field games for modeling large population behavior J Yang, X Ye, R Trivedi, H Xu, H Zha arXiv preprint arXiv:1711.03156, 2017 | 67 | 2017 |
| Deep mean field games for learning optimal behavior policy of large populations J Yang, X Ye, R Trivedi, H Xu, H Zha International Conference on Learning Representations, 147, 2018 | 55 | 2018 |
| Fast MR Image Reconstruction for Partially Parallel Imaging With Arbitrary k-space trajectories X Ye, Y Chen, W Lin, F Huang Medical Imaging, IEEE Transactions on 30 (3), 575-585, 2011 | 54 | 2011 |
| Decentralized consensus algorithm with delayed and stochastic gradients B Sirb, X Ye SIAM Journal on Optimization 28 (2), 1232-1254, 2018 | 53 | 2018 |
| Sparse classification for computer aided diagnosis using learned dictionaries M Liu, L Lu, X Ye, S Yu, M Salganicoff International Conference on Medical Image Computing and Computer-Assisted …, 2011 | 44 | 2011 |
| Consensus optimization with delayed and stochastic gradients on decentralized networks B Sirb, X Ye 2016 IEEE International Conference on Big Data (Big Data), 76-85, 2016 | 42 | 2016 |
Haomin ZhouSchool of MathematicsVerified email at math.gatech.edu
