| Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016 | 438 | 2016 |
| Multi-resolution dynamic mode decomposition for foreground/background separation and object tracking JN Kutz, X Fu, SL Brunton, NB Erichson 2015 IEEE international conference on computer vision workshop (ICCVW), 921-929, 2015 | 71 | 2015 |
| Self-tuning fiber lasers SL Brunton, X Fu, JN Kutz IEEE Journal of Selected Topics in Quantum Electronics 20 (5), 464-471, 2014 | 69 | 2014 |
| Extremum-seeking control of a mode-locked laser SL Brunton, X Fu, JN Kutz IEEE Journal of Quantum Electronics 49 (10), 852-861, 2013 | 69 | 2013 |
| Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation X Fu, SL Brunton, J Nathan Kutz Optics express 22 (7), 8585-8597, 2014 | 59 | 2014 |
| High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm X Fu, JN Kutz Optics express 21 (5), 6526-6537, 2013 | 49 | 2013 |
| Differentially private learning with per-sample adaptive clipping T Xia, S Shen, S Yao, X Fu, K Xu, X Xu, X Fu Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10444 …, 2023 | 12 | 2023 |
| Self-tuning fiber lasers: machine learning applied to optical systems JN Kutz, X Fu, S Brunton Nonlinear Photonics, NTu4A. 7, 2014 | 9 | 2014 |
| Multi-resolution time-scale separation of video content using the dynamic mode decomposition J Kutz, J Grosek, X Fu, S Brunton International Workshop on Video Processing and Quality Metrics for Consumer …, 2015 | 6 | 2015 |
| Machine learning for self-tuning optical systems JN Kutz, SL Brunton Nonlinear Optics, NTh1A. 1, 2019 | 5 | 2019 |
| Adaptive dimensionality-reduction for time-stepping in differential and partial differential equations X Fu, JN Kutz Numerical Mathematics: Theory, Methods and Applications 10 (4), 872-894, 2017 | 5 | 2017 |
| Multi-resolution analysis of dynamical systems using dynamic mode decomposition JN Kutz, S Brunton, X Fu Proceedings of the World Congress on Engineering 1, 1-4, 2015 | 5 | 2015 |
| Using dynamic mode decomposition for real-time background/foreground separation in video JN Kutz, J Grosek, S Brunton, X Fu, S Pendergrass US Patent 9,674,406, 2017 | 4 | 2017 |
| Data driven control of complex optical systems SL Brunton, JN Kutz, X Fu, M Johnson Nonlinear Optics, NW4A. 41, 2015 | 4 | 2015 |
| Multi-aspect heterogeneous graph augmentation Y Zhou, Y Cao, Y Liu, Y Shang, P Zhang, Z Lin, Y Yue, B Wang, X Fu, ... Proceedings of the ACM Web Conference 2023, 39-48, 2023 | 3 | 2023 |
| Tuning multi-input complex dynamic systems using sparse representations of performance and extremum-seeking control JN Kutz, S Brunton, X Fu US Patent 9,972,962, 2018 | 3 | 2018 |
| Clean-image backdoor attacks D Rong, G Yu, S Shen, X Fu, P Qian, J Chen, Q He, X Fu, W Wang International Conference on Artificial Neural Networks, 187-202, 2024 | 2 | 2024 |
| Using dynamic mode decomposition for real-time background/foreground separation in video JN Kutz, J Grosek, S Brunton, X Fu, S Pendergrass Univ. of Washington, Seattle, WA (United States), 2017 | 2 | 2017 |
| Multi-resolution dynamic mode decomposition for foreground/background separation and object tracking J Nathan Kutz, X Fu, SL Brunton, N Benjamin Erichson Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 2 | 2015 |
| Self-supervision meets kernel graph neural models: From architecture to augmentations J Dan, R Wu, Y Liu, B Wang, C Meng, T Liu, T Zhang, N Wang, X Fu, Q Li, ... 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 1076-1083, 2023 | 1 | 2023 |
