| Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators J Pathak, S Subramanian, P Harrington, S Raja, A Chattopadhyay, ... arXiv preprint arXiv:2202.11214, 2022 | 1559* | 2022 |
| ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers Y Balaji, S Nah, X Huang, A Vahdat, J Song, Q Zhang, K Kreis, M Aittala, ... arXiv preprint arXiv:2211.01324, 2022 | 1050 | 2022 |
| Deep generative adversarial neural networks for compressive sensing MRI M Mardani, E Gong, JY Cheng, SS Vasanawala, G Zaharchuk, L Xing, ... IEEE transactions on medical imaging 38 (1), 167-179, 2018 | 987* | 2018 |
| Subspace learning and imputation for streaming big data matrices and tensors M Mardani, G Mateos, GB Giannakis IEEE Transactions on Signal Processing 63 (10), 2663-2677, 2015 | 556* | 2015 |
| Scalable attention for transformers via adaptive fourier neural operators J Guibas, M Mardani, Z Li, A Tao, A Anandkumar, B Catanzaro International conference on learning representations, 2021 | 531* | 2021 |
| Pseudoinverse-guided diffusion models for inverse problems J Song, A Vahdat, M Mardani, J Kautz International Conference on Learning Representations, 2023 | 465 | 2023 |
| Physdiff: Physics-guided human motion diffusion model Y Yuan, J Song, U Iqbal, A Vahdat, J Kautz Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 442 | 2023 |
| Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging CM Sandino, JY Cheng, F Chen, M Mardani, JM Pauly, SS Vasanawala IEEE signal processing magazine 37 (1), 117-127, 2020 | 307* | 2020 |
| A variational perspective on solving inverse problems with diffusion models M Mardani, J Song, J Kautz, A Vahdat arXiv preprint arXiv:2305.04391, 2023 | 210 | 2023 |
| Neural proximal gradient descent for compressive imaging M Mardani, Q Sun, D Donoho, V Papyan, H Monajemi, S Vasanawala, ... Advances in Neural Information Processing Systems 31, 2018 | 189 | 2018 |
| Big Data M Mardani, G Mateos, GB Giannakis Cooperative and Graph Signal Processing, 777-797, 2018 | 181* | 2018 |
| Loss-guided diffusion models for plug-and-play controllable generation J Song, Q Zhang, H Yin, M Mardani, MY Liu, J Kautz, Y Chen, A Vahdat International Conference on Machine Learning, 32483-32498, 2023 | 177 | 2023 |
| Dynamic anomalography: Tracking network anomalies via sparsity and low rank M Mardani, G Mateos, GB Giannakis IEEE Journal of Selected Topics in Signal Processing 7 (1), 50-66, 2012 | 170 | 2012 |
| Residual corrective diffusion modeling for km-scale atmospheric downscaling M Mardani, N Brenowitz, Y Cohen, J Pathak, CY Chen, CC Liu, A Vahdat, ... Nature Communications 6 (1), 124, 2025 | 149* | 2025 |
| Uncertainty quantification in deep MRI reconstruction V Edupuganti, M Mardani, S Vasanawala, J Pauly IEEE Transactions on Medical Imaging 40 (1), 239-250, 2020 | 147 | 2020 |
| Residual denoising diffusion models J Liu, Q Wang, H Fan, Y Wang, Y Tang, L Qu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 146 | 2024 |
| Manifold preserving guided diffusion Y He, N Murata, CH Lai, Y Takida, T Uesaka, D Kim, WH Liao, Y Mitsufuji, ... arXiv preprint arXiv:2311.16424, 2023 | 125 | 2023 |
| Recovery of low-rank plus compressed sparse matrices M Mardani, G Mateos, GB Giannakis IEEE Transactions on Information Theory 59 (8), 5186-5205, 2013 | 117 | 2013 |
| Completing any low-rank matrix, provably Y Chen, S Bhojanapalli, S Sanghavi, R Ward The Journal of Machine Learning Research 16 (1), 2999-3034, 2015 | 110 | 2015 |
| Decentralized sparsity-regularized rank minimization: Algorithms and applications M Mardani, G Mateos, GB Giannakis IEEE Transactions on Signal Processing 61 (21), 5374-5388, 2013 | 84 | 2013 |
John PaulyProfessor of Electrical Engineering, Stanford UniversityVerified email at stanford.edu
