Qing Qu

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Indice H	26	26
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Zhihui ZhuAssistant Professor, Ohio State UniversityEmail verificata su osu.edu
John WrightElectrical Engineering, Columbia UniversityEmail verificata su columbia.edu
Ju SunAssistant Professor, Computer Science & Engineering, University of Minnesota at Twin CitiesEmail verificata su umn.edu
Xiao Li (李虓)Ph.D. candidate at University of MichiganEmail verificata su umich.edu
PENG WANGPostdoc Fellow, Department of Electrical Engineering and Computer Science, University of MichiganEmail verificata su umich.edu
Chong YouGoogleEmail verificata su google.com
Laura BalzanoUniversity of Michigan, Ann ArborEmail verificata su umich.edu
Trac D. TranProsessor of Electrical and Computer Engineering, Johns Hopkins UniversityEmail verificata su jhu.edu
Nasser M NasrabadiProfessor, CSEE-WVU, ECE-JHUEmail verificata su jhu.edu
Jinxin ZhouOhio State UniversityEmail verificata su osu.edu
Xiao LiThe Chinese University of Hong Kong, ShenzhenEmail verificata su cuhk.edu.cn
Can YarasPhD Student, University of MichiganEmail verificata su umich.edu
Sheng LiuStanford UniversityEmail verificata su stanford.edu
Yuqian ZhangAssistant Professor, Rutgers UniversityEmail verificata su rutgers.edu
Saiprasad RavishankarMichigan State UniversityEmail verificata su msu.edu
Yuexiang ZhaiUC Berkeley | Google DeepMindEmail verificata su berkeley.edu
Huijie ZhangGraduate Student, University of Michigan, Ann ArborEmail verificata su umich.edu
Tianyu Ding, Ph.D.Principal Researcher, Microsoft | Johns Hopkins UniversityEmail verificata su microsoft.com
Ismail R. AlkhouriPostdoc, Michigan State University & University of Michigan, Ann ArborEmail verificata su msu.edu
Soo Min KwonPhD Student, University of MichiganEmail verificata su umich.edu

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Qing Qu

Assistant Professor, Dept. of EECS, University of Michigan

Email verificata su umich.edu - Home page

Machine Learning Nonconvex Optimization High Dimensional Data Analysis Deep Learning Theory


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
A geometric analysis of phase retrieval J Sun, Q Qu, J Wright Foundations of Computational Mathematics 18, 1131-1198, 2018	633	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	330	2016
When are nonconvex problems not scary? J Sun, Q Qu, J Wright arXiv preprint arXiv:1510.06096, 2015	192	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	187	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	160	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	129	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	125	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	119	2022
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	101*	2024
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	98	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	87*	2021
From symmetry to geometry: Tractable nonconvex problems Y Zhang, Q Qu, J Wright arXiv preprint arXiv:2007.06753, 2020	62	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	55	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, 2024	52	2024
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	48*	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
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	43	2022

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