Zhaoqiang Liu

According to our database, Zhaoqiang Liu authored at least 28 papers between 2017 and 2024.

Collaborative distances:

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

2024

Accelerating Diffusion Sampling with Optimized Time Steps.

Shuchen Xue, Zhaoqiang Liu, Fei Chen, Shifeng Zhang, Tianyang Hu, Enze Xie, Zhenguo Li

CoRR, 2024

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling.

Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi

CoRR, 2024

On the Expressive Power of a Variant of the Looped Transformer.

Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael K. Ng, Zhenguo Li, Zhaoqiang Liu

CoRR, 2024

Uniform Recovery Guarantees for Quantized Corrupted Sensing Using Structured or Generative Priors.

Junren Chen, Zhaoqiang Liu, Meng Ding, Michael K. Ng

CoRR, 2024

Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors.

Junren Chen, Zhaoqiang Liu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China.

Can Cai, Min Fan, Jing Yao, Lele Zhou, Yuanzhe Wang, Xiaoying Liang, Zhaoqiang Liu, Shu Chen

Ecol. Informatics, November, 2023

Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors.

Junren Chen, Shuai Huang, Michael K. Ng, Zhaoqiang Liu

CoRR, 2023

A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing.

Junren Chen, Jonathan Scarlett, Michael Ng, Zhaoqiang Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning.

Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, Zhenguo Li

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

DDP: Diffusion Model for Dense Visual Prediction.

Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, Ping Luo

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Misspecified Phase Retrieval with Generative Priors.

Zhaoqiang Liu, Xinshao Wang, Jiulong Liu

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

Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors.

Zhaoqiang Liu, Jun Han

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Generative Principal Component Analysis.

Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett

Proceedings of the Tenth International Conference on Learning Representations, 2022

Non-Iterative Recovery from Nonlinear Observations using Generative Models.

Jiulong Liu, Zhaoqiang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors.

Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett

CoRR, 2021

Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors.

Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett

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

Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors.

Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett

Proceedings of the IEEE Information Theory Workshop, 2021

2020

Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models.

Zhaoqiang Liu, Jonathan Scarlett

IEEE J. Sel. Areas Inf. Theory, 2020

Sample Complexity Lower Bounds for Compressive Sensing with Generative Models.

Zhaoqiang Liu, Jonathan Scarlett

Proceedings of the International Conference on Signal Processing and Communications, 2020

The Generalized Lasso with Nonlinear Observations and Generative Priors.

Zhaoqiang Liu, Jonathan Scarlett

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

Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors.

Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The Informativeness of k-Means for Learning Mixture Models.

Zhaoqiang Liu, Vincent Y. F. Tan

IEEE Trans. Inf. Theory, 2019

Error Bounds for Spectral Clustering over Samples from Spherical Gaussian Mixture Models.

Zhaoqiang Liu

Proceedings of the IEEE International Conference on Acoustics, 2019

Model Selection for Nonnegative Matrix Factorization by Support Union Recovery.

Zhaoqiang Liu

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Minimax Lower Bounds for Nonnegative Matrix Factorization.

Mine Alsan, Zhaoqiang Liu, Vincent Y. F. Tan

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018

2017

Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption.

Zhaoqiang Liu, Vincent Y. F. Tan

IEEE Trans. Signal Process., 2017

The Informativeness of k-Means for Learning Gaussian Mixture Models.

Zhaoqiang Liu, Vincent Y. F. Tan

CoRR, 2017

Relative error bounds for nonnegative matrix factorization under a geometric assumption.

Zhaoqiang Liu, Vincent Y. F. Tan

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017