| A geometric analysis of phase retrieval J Sun, Q Qu, J Wright Foundations of Computational Mathematics 18 (5), 1131-1198, 2018 | 695 | 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 | 360 | 2016 |
| 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 (Spotlight) 34, 29820-29834, 2021 | 285 | 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 | 266* | 2024 |
| When are nonconvex problems not scary? J Sun, Q Qu, J Wright arXiv preprint arXiv:1510.06096, 2015 | 200 | 2015 |
| Robust training under label noise by over-parameterization S Liu, Z Zhu, Q Qu, C You International Conference on Machine Learning, 14153-14172, 2022 | 188 | 2022 |
| 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 (Spotlight), 2024 | 183 | 2024 |
| 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 | 178 | 2016 |
| 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 | 148 | 2022 |
| 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 | 147 | 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 | 140 | 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 | 133 | 2013 |
| 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 | 121* | 2021 |
| Complete dictionary recovery over the sphere J Sun, Q Qu, J Wright 2015 International Conference on Sampling Theory and Applications (SampTA …, 2015 | 108 | 2015 |
| The emergence of reproducibility and consistency in diffusion models H Zhang, J Zhou, Y Lu, M Guo, P Wang, L Shen, Q Qu ⭐ Forty-first International Conference on Machine Learning (Best Paper at …, 2024 | 92* | 2024 |
| 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 | 89 | 2022 |
| From symmetry to geometry: Tractable nonconvex problems Y Zhang, Q Qu, J Wright arXiv preprint arXiv:2007.06753, 2020 | 72 | 2020 |
| 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 | 62 | 2022 |
| Convolutional phase retrieval via gradient descent Q Qu, Y Zhang, YC Eldar, J Wright IEEE Transactions on Information Theory 66 (3), 1785-1821, 2019 | 61* | 2019 |
| Diffusion models learn low-dimensional distributions via subspace clustering P Wang, H Zhang, Z Zhang, S Chen, Y Ma, Q Qu arXiv preprint arXiv:2409.02426, 2024 | 54 | 2024 |
Zhihui ZhuAssistant Professor, Ohio State UniversityVerified email at osu.edu
