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ORCIDZhaoqiang Liu
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2020 – today
- 2024
[j5]Junren Chen, Zhaoqiang Liu, Meng Ding, Michael K. Ng:
Uniform Recovery Guarantees for Quantized Corrupted Sensing Using Structured or Generative Priors. SIAM J. Imaging Sci. 17(3): 1909-1977 (2024)- [c21]Junren Chen, Zhaoqiang Liu:
Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors. AAAI 2024: 11346-11354 - [c20]Shuchen Xue, Zhaoqiang Liu, Fei Chen, Shifeng Zhang, Tianyang Hu, Enze Xie, Zhenguo Li:
Accelerating Diffusion Sampling with Optimized Time Steps. CVPR 2024: 8292-8301 - [c19]Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi:
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling. ICML 2024 - [i21]Junren Chen, Zhaoqiang Liu, Meng Ding, Michael K. Ng:
Uniform Recovery Guarantees for Quantized Corrupted Sensing Using Structured or Generative Priors. CoRR abs/2401.08402 (2024) - [i20]Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael K. Ng, Zhenguo Li, Zhaoqiang Liu:
On the Expressive Power of a Variant of the Looped Transformer. CoRR abs/2402.13572 (2024) - [i19]Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi:
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling. CoRR abs/2402.15170 (2024) - [i18]Shuchen Xue, Zhaoqiang Liu, Fei Chen, Shifeng Zhang, Tianyang Hu, Enze Xie, Zhenguo Li:
Accelerating Diffusion Sampling with Optimized Time Steps. CoRR abs/2402.17376 (2024) - [i17]Junren Chen, Zhaoqiang Liu, Michael Kwok-Po Ng, Jonathan Scarlett:
Robust Instance Optimal Phase-Only Compressed Sensing. CoRR abs/2408.06275 (2024) - 2023
- [j4]Can Cai, Min Fan, Jing Yao, Lele Zhou, Yuanzhe Wang, Xiaoying Liang, Zhaoqiang Liu, Shu Chen:
Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China. Ecol. Informatics 77: 102164 (2023) - [c18]Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou
, Zhaoqiang Liu, Jiawei Li, Zhenguo Li:
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning. ICCV 2023: 4207-4216 - [c17]Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, Ping Luo:
DDP: Diffusion Model for Dense Visual Prediction. ICCV 2023: 21684-21695 - [c16]Junren Chen, Jonathan Scarlett, Michael Ng, Zhaoqiang Liu:
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. NeurIPS 2023 - [i16]Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, Ping Luo:
DDP: Diffusion Model for Dense Visual Prediction. CoRR abs/2303.17559 (2023) - [i15]Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, Zhenguo Li:
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning. CoRR abs/2304.06648 (2023) - [i14]Junren Chen, Shuai Huang, Michael K. Ng, Zhaoqiang Liu:
Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors. CoRR abs/2309.09032 (2023) - [i13]Junren Chen, Jonathan Scarlett, Michael Kwok-Po Ng, Zhaoqiang Liu:
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing. CoRR abs/2310.03758 (2023) - 2022
- [c15]Jiulong Liu, Zhaoqiang Liu:
Non-Iterative Recovery from Nonlinear Observations using Generative Models. CVPR 2022: 233-243 - [c14]Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Generative Principal Component Analysis. ICLR 2022 - [c13]Zhaoqiang Liu, Jun Han:
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors. IJCAI 2022: 3271-3277 - [c12]Zhaoqiang Liu, Xinshao Wang, Jiulong Liu:
Misspecified Phase Retrieval with Generative Priors. NeurIPS 2022 - [i12]Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Generative Principal Component Analysis. CoRR abs/2203.09693 (2022) - [i11]Jiulong Liu, Zhaoqiang Liu:
Non-Iterative Recovery from Nonlinear Observations using Generative Models. CoRR abs/2205.15749 (2022) - [i10]Zhaoqiang Liu, Jun Han:
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors. CoRR abs/2209.10093 (2022) - [i9]Zhaoqiang Liu, Xinshao Wang, Jiulong Liu:
Misspecified Phase Retrieval with Generative Priors. CoRR abs/2210.05571 (2022) - 2021
- [c11]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. ITW 2021: 1-6 - [c10]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. NeurIPS 2021: 17656-17668 - [i8]Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett:
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors. CoRR abs/2106.15358 (2021) - [i7]Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett:
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors. CoRR abs/2108.03570 (2021) - 2020
- [j3]Zhaoqiang Liu, Jonathan Scarlett:
Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models. IEEE J. Sel. Areas Inf. Theory 1(1): 292-303 (2020) - [c9]Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett:
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. ICML 2020: 6216-6225 - [c8]Zhaoqiang Liu, Jonathan Scarlett:
The Generalized Lasso with Nonlinear Observations and Generative Priors. NeurIPS 2020 - [c7]Zhaoqiang Liu, Jonathan Scarlett:
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models. SPCOM 2020: 1-5 - [i6]Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett:
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors. CoRR abs/2002.01697 (2020) - [i5]Zhaoqiang Liu, Jonathan Scarlett:
The Generalized Lasso with Nonlinear Observations and Generative Priors. CoRR abs/2006.12415 (2020)
2010 – 2019
- 2019
- [j2]Zhaoqiang Liu, Vincent Y. F. Tan:
The Informativeness of k-Means for Learning Mixture Models. IEEE Trans. Inf. Theory 65(11): 7460-7479 (2019) - [c6]Zhaoqiang Liu:
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery. ICASSP 2019: 3407-3411 - [c5]Zhaoqiang Liu:
Error Bounds for Spectral Clustering over Samples from Spherical Gaussian Mixture Models. ICASSP 2019: 3467-3471 - [i4]Zhaoqiang Liu, Jonathan Scarlett:
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models. CoRR abs/1908.10744 (2019) - 2018
- [c4]Zhaoqiang Liu, Vincent Y. F. Tan:
The Informativeness of k-Means for Learning Mixture Models. ISIT 2018: 26-30 - [c3]Zhaoqiang Liu, Vincent Y. F. Tan:
Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption. ITA 2018: 1-15 - [c2]Mine Alsan, Zhaoqiang Liu, Vincent Y. F. Tan:
Minimax Lower Bounds for Nonnegative Matrix Factorization. SSP 2018: 363-367 - [i3]Zhaoqiang Liu:
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery. CoRR abs/1810.10078 (2018) - 2017
- [j1]Zhaoqiang Liu, Vincent Y. F. Tan:
Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption. IEEE Trans. Signal Process. 65(18): 4717-4731 (2017) - [c1]Zhaoqiang Liu, Vincent Y. F. Tan:
Relative error bounds for nonnegative matrix factorization under a geometric assumption. ICASSP 2017: 2552-2556 - [i2]Zhaoqiang Liu, Vincent Y. F. Tan:
The Informativeness of k-Means for Learning Gaussian Mixture Models. CoRR abs/1703.10534 (2017) - 2016
- [i1]Zhaoqiang Liu, Vincent Y. F. Tan:
Relative Error Bounds for Nonnegative Matrix Factorization under a Geometric Assumption. CoRR abs/1612.08549 (2016)
Coauthor Index
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