Qing Qu

According to our database, Qing Qu authored at least 59 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Understanding and Improving Transfer Learning of Deep Models via Neural Collapse.

Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu

Trans. Mach. Learn. Res., 2024

Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering.

Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu

CoRR, 2024

Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing.

Siyi Chen, Huijie Zhang, Minzhe Guo, Yifu Lu, Peng Wang, Qing Qu

CoRR, 2024

Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation.

Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu

CoRR, 2024

Decoupled Data Consistency with Diffusion Purification for Image Restoration.

Xiang Li, Soo Min Kwon, Ismail R. Alkhouri, Saiprasad Ravishankar, Qing Qu

CoRR, 2024

Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction.

Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang, Saiprasad Ravishankar

CoRR, 2024

The Emergence of Reproducibility and Consistency in Diffusion Models.

Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation.

Can Yaras, Peng Wang, Laura Balzano, Qing Qu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Symmetric Matrix Completion with ReLU Sampling.

Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Neural Collapse in Multi-label Learning with Pick-all-label Loss.

Pengyu Li, Xiao Li, Yutong Wang, Qing Qu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Generalized Neural Collapse for a Large Number of Classes.

Jiachen Jiang, Jinxin Zhou, Peng Wang, Qing Qu, Dustin G. Mixon, Chong You, Zhihui Zhu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Eye Surgeon: Finding image priors through sparse generators at initialization.

Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Global Geometric Analysis of Maximal Coding Rate Reduction.

Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency.

Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Diffusion-Based Adversarial Purification for Robust Deep Mri Reconstruction.

Ismail Alkhouri, Shijun Liang, Rongrong Wang, Qing Qu, Saiprasad Ravishankar

Proceedings of the IEEE International Conference on Acoustics, 2024

Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture.

Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Efficient Low-Dimensional Compression of Overparameterized Models.

Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures.

Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu

CoRR, 2023

Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics.

Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu

CoRR, 2023

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination.

Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu

CoRR, 2023

The Emergence of Reproducibility and Consistency in Diffusion Models.

Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Liyue Shen, Qing Qu

CoRR, 2023

Investigating the Catastrophic Forgetting in Multimodal Large Language Models.

Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, Yi Ma

CoRR, 2023

The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks.

Can Yaras, Peng Wang, Wei Hu, Zhihui Zhu, Laura Balzano, Qing Qu

CoRR, 2023

Robust Self-Guided Deep Image Prior.

Evan Bell, Shijun Liang, Qing Qu, Saiprasad Ravishankar

Proceedings of the IEEE International Conference on Acoustics, 2023

Robust Deep Image Recovery from Sparsely Corrupted and Sub-Sampled Measurements.

Xiang Li, Saiprasad Ravishankar, Qing Qu

Proceedings of the 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2023

2022

Principled and Efficient Transfer Learning of Deep Models via Neural Collapse.

Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu

CoRR, 2022

Are All Losses Created Equal: A Neural Collapse Perspective.

Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold.

Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features.

Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu

Proceedings of the International Conference on Machine Learning, 2022

Robust Training under Label Noise by Over-parameterization.

Sheng Liu, Zhihui Zhu, Qing Qu, Chong You

Proceedings of the International Conference on Machine Learning, 2022

2021

Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods.

Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So

SIAM J. Optim., 2021

A Geometric Analysis of Neural Collapse with Unconstrained Features.

Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training.

Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery.

Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Convolutional Phase Retrieval via Gradient Descent.

Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright

IEEE Trans. Inf. Theory, 2020

Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient Descent.

Qing Qu, Xiao Li, Zhihui Zhu

SIAM J. Imaging Sci., 2020

From Symmetry to Geometry: Tractable Nonconvex Problems.

Yuqian Zhang, Qing Qu, John Wright

CoRR, 2020

Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications.

Qing Qu, Zhihui Zhu, Xiao Li, Manolis C. Tsakiris, John Wright, René Vidal

CoRR, 2020

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization.

Chong You, Zhihui Zhu, Qing Qu, Yi Ma

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning.

Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu

Proceedings of the 8th International Conference on Learning Representations, 2020

Short and Sparse Deconvolution - A Geometric Approach.

Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Analysis of the Optimization Landscapes for Overcomplete Representation Learning.

Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu

CoRR, 2019

Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods.

Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So

CoRR, 2019

A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution.

Qing Qu, Xiao Li, Zhihui Zhu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exact and Efficient Multi-Channel Sparse Blind Deconvolution - A Nonconvex Approach.

Qing Qu, Xiao Li, Zhihui Zhu

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

A Geometric Analysis of Phase Retrieval.

Ju Sun, Qing Qu, John Wright

Found. Comput. Math., 2018

2017

Complete Dictionary Recovery Over the Sphere II: Recovery by Riemannian Trust-Region Method.

Ju Sun, Qing Qu, John Wright

IEEE Trans. Inf. Theory, 2017

Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture.

Ju Sun, Qing Qu, John Wright

IEEE Trans. Inf. Theory, 2017

Convolutional Phase Retrieval.

Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions.

Qing Qu, Ju Sun, John Wright

IEEE Trans. Inf. Theory, 2016

2015

Subspace Vertex Pursuit: A Fast and Robust Near-Separable Nonnegative Matrix Factorization Method for Hyperspectral Unmixing.

Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

IEEE J. Sel. Top. Signal Process., 2015

When Are Nonconvex Problems Not Scary?

Ju Sun, Qing Qu, John Wright

CoRR, 2015

Complete Dictionary Recovery over the Sphere.

Ju Sun, Qing Qu, John Wright

CoRR, 2015

Complete Dictionary Recovery Using Nonconvex Optimization.

Ju Sun, Qing Qu, John Wright

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation.

Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

IEEE Trans. Geosci. Remote. Sens., 2014

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification.

Xiaoxia Sun, Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

IEEE Geosci. Remote. Sens. Lett., 2014

Subspace vertex pursuit for separable non-negative matrix factorization in hyperspectral unmixing.

Qing Qu, Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran

Proceedings of the IEEE International Conference on Acoustics, 2014

2013

Low rank representation for bilinear abundance estimation problem.

Qing Qu, Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran

Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2013

Hyperspectral abundance estimation for the generalized bilinear model with joint sparsity constraint.

Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

Proceedings of the IEEE International Conference on Acoustics, 2013