Ddp: Diffusion model for dense visual prediction Y Ji, Z Chen, E Xie, L Hong, X Liu, Z Liu, T Lu, Z Li, P Luo IEEE International Conference on Computer Vision (ICCV), 2023 | 42 | 2023 |
Information-theoretic lower bounds for compressive sensing with generative models Z Liu, J Scarlett IEEE Journal on Selected Areas in Information Theory 1 (1), 292-303, 2020 | 40 | 2020 |
Sample complexity bounds for 1-bit compressive sensing and binary stable embeddings with generative priors Z Liu, S Gomes, A Tiwari, J Scarlett International Conference on Machine Learning (ICML), 2020 | 24 | 2020 |
Rank-one NMF-based initialization for NMF and relative error bounds under a geometric assumption Z Liu, VYF Tan IEEE Transactions on Signal Processing 65 (18), 4717-4731, 2017 | 22 | 2017 |
Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China C Cai, M Fan, J Yao, L Zhou, Y Wang, X Liang, Z Liu, S Chen Ecological Informatics 77, 102164, 2023 | 20 | 2023 |
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning E Xie, L Yao, H Shi, Z Liu, D Zhou, Z Liu, J Li, Z Li IEEE International Conference on Computer Vision (ICCV), 2023 | 19 | 2023 |
Towards sample-optimal compressive phase retrieval with sparse and generative priors Z Liu, S Ghosh, J Scarlett Conference on Neural Information Processing Systems (NeurIPS), 2021 | 18 | 2021 |
Generative principal component analysis Z Liu, J Liu, S Ghosh, J Han, J Scarlett International Conference on Learning Representations (ICLR), 2022 | 15 | 2022 |
The generalized Lasso with nonlinear observations and generative priors Z Liu, J Scarlett Conference on Neural Information Processing Systems (NeurIPS), 2020 | 15 | 2020 |
Data completion for power load analysis considering the low-rank property C Zhuang, J An, Z Liu, R Zeng CSEE Journal of Power and Energy Systems 8 (6), 1751-1759, 2020 | 12* | 2020 |
Non-iterative recovery from nonlinear observations using generative models J Liu, Z Liu Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 11 | 2022 |
The informativeness of -means for learning mixture models Z Liu, VYF Tan IEEE Transactions on Information Theory 65 (11), 7460-7479, 2019 | 7* | 2019 |
Projected gradient descent algorithms for solving nonlinear inverse problems with generative priors Z Liu, J Han International Joint Conference on Artificial Intelligence (IJCAI), 2022 | 5 | 2022 |
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing J Chen, J Scarlett, MK Ng, Z Liu Conference on Neural Information Processing Systems (NeurIPS), 2023 | 4 | 2023 |
Robust 1-bit compressive sensing with partial Gaussian circulant matrices and generative priors Z Liu, S Ghosh, J Scarlett IEEE Information Theory Workshop (ITW), 2021 | 4 | 2021 |
Model selection for nonnegative matrix factorization by support union recovery Z Liu ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 3 | 2019 |
Misspecified Phase Retrieval with Generative Priors Z Liu, X Wang, J Liu Conference on Neural Information Processing Systems (NeurIPS), 2022 | 2 | 2022 |
Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors J Chen, S Huang, MK Ng, Z Liu arXiv preprint arXiv:2309.09032, 2023 | 1 | 2023 |
Accelerating Diffusion Sampling with Optimized Time Steps S Xue, Z Liu, F Chen, S Zhang, T Hu, E Xie, Z Li Conference on Computer Vision and Pattern Recognition (CVPR), 2024 | | 2024 |
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling J Ma, S Xue, T Hu, W Wang, Z Liu, Z Li, ZM Ma, K Kawaguchi International Conference on Machine Learning (ICML), 2024 | | 2024 |