| A geometric analysis of phase retrieval J Sun, Q Qu, J Wright Foundations of Computational Mathematics 18, 1131-1198, 2018 | 626 | 2018 |
| Complete dictionary recovery over the sphere I: Overview and the geometric picture J Sun, Q Qu, J Wright IEEE Transactions on Information Theory 63 (2), 853-884, 2016 | 328 | 2016 |
| When are nonconvex problems not scary? J Sun, Q Qu, J Wright arXiv preprint arXiv:1510.06096, 2015 | 188 | 2015 |
| A geometric analysis of neural collapse with unconstrained features Z Zhu, T Ding, J Zhou, X Li, C You, J Sulam, Q Qu Advances in Neural Information Processing Systems 34, 29820-29834, 2021 | 170 | 2021 |
| Complete dictionary recovery over the sphere ii: Recovery by riemannian trust-region method J Sun, Q Qu, J Wright IEEE Transactions on Information Theory 63 (2), 885-914, 2016 | 157 | 2016 |
| Complete dictionary recovery using nonconvex optimization J Sun, Q Qu, J Wright International Conference on Machine Learning, 2351-2360, 2015 | 144* | 2015 |
| Structured priors for sparse-representation-based hyperspectral image classification X Sun, Q Qu, NM Nasrabadi, TD Tran IEEE geoscience and remote sensing letters 11 (7), 1235-1239, 2013 | 142 | 2013 |
| Finding a sparse vector in a subspace: Linear sparsity using alternating directions Q Qu, J Sun, J Wright Advances in Neural Information Processing Systems 27, 2014 | 127 | 2014 |
| Abundance estimation for bilinear mixture models via joint sparse and low-rank representation Q Qu, NM Nasrabadi, TD Tran IEEE Transactions on Geoscience and Remote Sensing 52 (7), 4404-4423, 2013 | 124 | 2013 |
| Robust training under label noise by over-parameterization S Liu, Z Zhu, Q Qu, C You International Conference on Machine Learning, 14153-14172, 2022 | 109 | 2022 |
| On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features J Zhou, X Li, T Ding, C You, Q Qu, Z Zhu International Conference on Machine Learning, 27179-27202, 2022 | 87 | 2022 |
| Weakly convex optimization over Stiefel manifold using Riemannian subgradient-type methods X Li, S Chen, Z Deng, Q Qu, Z Zhu, AMC So SIAM Journal on Optimization 31 (3), 1605–1634, 2021 | 82* | 2021 |
| Investigating the catastrophic forgetting in multimodal large language model fine-tuning Y Zhai, S Tong, X Li, M Cai, Q Qu, YJ Lee, Y Ma Conference on Parsimony and Learning, 202-227, 2024 | 80* | 2024 |
| From symmetry to geometry: Tractable nonconvex problems Y Zhang, Q Qu, J Wright arXiv preprint arXiv:2007.06753, 2020 | 61 | 2020 |
| Convolutional phase retrieval via gradient descent Q Qu, Y Zhang, YC Eldar, J Wright IEEE Transactions on Information Theory 66 (3), 1785-1821, 2019 | 57* | 2019 |
| Are all losses created equal: A neural collapse perspective J Zhou, C You, X Li, K Liu, S Liu, Q Qu, Z Zhu Advances in Neural Information Processing Systems 35, 31697-31710, 2022 | 52 | 2022 |
| Geometric analysis of nonconvex optimization landscapes for overcomplete learning Q Qu, Y Zhai, X Li, Y Zhang, Z Zhu International Conference on Learning Representations 2020, 2019 | 47* | 2019 |
| A nonconvex approach for exact and efficient multichannel sparse blind deconvolution Q Qu, X Li, Z Zhu Advances in neural information processing systems 32, 2019 | 45* | 2019 |
| Solving inverse problems with latent diffusion models via hard data consistency B Song, SM Kwon, Z Zhang, X Hu, Q Qu, L Shen International Conference on Learning Representations, 2024 | 40 | 2024 |
| Neural collapse with normalized features: A geometric analysis over the riemannian manifold C Yaras, P Wang, Z Zhu, L Balzano, Q Qu Advances in neural information processing systems 35, 11547-11560, 2022 | 40 | 2022 |
Zhihui ZhuAssistant Professor, Ohio State University在 osu.edu 的电子邮件经过验证
