| Neural proximal gradient descent for compressive imaging DD M. Mardani, Q. Sun, V. Papyan, H. Monajemi, Sh. Vasanawala, J. Pauly Advances in Neural Information Processing Systems, 0 | 243* | |
| A PID Controller Approach for Stochastic Optimization of Deep Networks W An, H Wang, Q Sun, J Xu, Q Dai, L Zhang CVPR2018/ Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 150 | 2018 |
| PID controller-based stochastic optimization acceleration for deep neural networks H Wang, Y Luo, W An, Q Sun, J Xu, L Zhang IEEE transactions on neural networks and learning systems 31 (12), 5079-5091, 2020 | 79 | 2020 |
| How to Close Sim-Real Gap? Transfer with Segmentation! M Yan, Q Sun, I Frosio, S Tyree, J Kautz arXiv preprint arXiv:2005.07695, 2020 | 55* | 2020 |
| Grac: Self-guided and self-regularized actor-critic L Shao, Y You, M Yan, S Yuan, Q Sun, J Bohg Conference on Robot Learning, 267-276, 2022 | 27 | 2022 |
| Bayesian opponent exploitation in imperfect-information games S Ganzfried, Q Sun 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2018 | 25 | 2018 |
| A recipe for global convergence guarantee in deep neural networks K Kawaguchi, Q Sun Proceedings of the AAAI conference on artificial intelligence 35 (9), 8074-8082, 2021 | 11 | 2021 |
| Convolutional Imputation of Matrix Networks Q Sun, M Yan, D Donoho, S Boyd ICML2018, http://proceedings.mlr.press/v80/sun18d/, 2018 | 11 | 2018 |
| Degrees of freedom analysis of unrolled neural networks M Mardani, Q Sun, V Papyan, S Vasanawala, J Pauly, D Donoho arXiv preprint arXiv:1906.03742, 2019 | 10 | 2019 |
| Differential inclusions for modeling nonsmooth ADMM variants: A continuous limit theory H Yuan, Y Zhou, CJ Li, Q Sun International Conference on Machine Learning, 7232-7241, 2019 | 10 | 2019 |
| Distributional Robust Kelly Gambling Q Sun, S Boyd | 10 | 2018 |
| Stochastic modified equations for continuous limit of stochastic ADMM X Zhou, H Yuan, CJ Li, Q Sun arXiv preprint arXiv:2003.03532, 2020 | 9 | 2020 |
| Distributional Robust Kelly Gambling: Optimal Strategy under Uncertainty in the Long-Run Q Sun, S Boyd https://arxiv.org/abs/1812.10371, 2021 | 3 | 2021 |
| Convex sparse blind deconvolution Q Sun, D Donoho arXiv preprint arXiv:2106.07053, 2021 | 2 | 2021 |
| OPTIMIZATION-BASED MODELING IN INVESTMENT AND DATA SCIENCE Q Sun Stanford PhD Thesis, 2020 | | 2020 |
| A General Continuous-Time Formulation of Stochastic ADMM and Its Variants X Zhou, H Yuan, Q Sun http://stanford.edu/~qysun/Stochastic_ADMM_SDE.pdf, 2020 | | 2020 |
| Stanford PhD thesis: Optimization-based Modeling in Investment and Data Science Q Sun http://stanford.edu/~qysun/Stanford_University_PhD_Thesis_QingyunSun.pdf, 2020 | | 2020 |
| Convex Sparse Blind Deconvolution: Phase Transition, Observation Sample Complexity, Stability, Robustness Q Sun, D Donoho http://stanford.edu/~qysun/Convex_Sparse_Blind_deconvolution_Journal_draft.pdf, 2020 | | 2020 |
| A Continuous Limit Theory for Nonsmooth ADMM Variants H Yuan, Y Zhou, CJ Li, Q Sun | | 2019 |
| Shared Weights Yield Accurate Image Recovery with Scarce Training Labels: SURE Analysis M Mardani, Q Sun, V Papyan, S Vasanawala, J Pauly, D Donoho http://stanford.edu/~qysun/neuralPGD_ICML.pdf, 2019 | | 2019 |
David DonohoProfessor f Statistics, Stanford UniversityEmail verificata su stanford.edu