| 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 | 823* | 2018 |
| 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 | 813* | 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 | 716 | 2022 |
| 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 | 535* | 2015 |
| Efficient token mixing 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 | 320* | 2021 |
| Pseudoinverse-guided diffusion models for inverse problems J Song, A Vahdat, M Mardani, J Kautz International Conference on Learning Representations, 2023 | 227 | 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 | 226 | 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 | 170 | 2018 |
| 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 | 169 | 2020 |
| 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 | 165 | 2012 |
| Cooperative and graph signal processing: principles and applications P Djuric, C Richard Academic Press, 2018 | 115 | 2018 |
| Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies M Mardani, G Mateos, GB Giannakis IEEE Transactions on Information Theory 59 (8), 5186-5205, 2013 | 114 | 2013 |
| Uncertainty quantification in deep MRI reconstruction V Edupuganti, M Mardani, S Vasanawala, J Pauly IEEE Transactions on Medical Imaging 40 (1), 239-250, 2020 | 111 | 2020 |
| 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 | 100 | 2015 |
| A variational perspective on solving inverse problems with diffusion models M Mardani, J Song, J Kautz, A Vahdat arXiv preprint arXiv:2305.04391, 2023 | 91 | 2023 |
| Decentralized sparsity-regularized rank minimization: Algorithms and applications M Mardani, G Mateos, GB Giannakis IEEE Transactions on Signal Processing 61 (21), 5374-5388, 2013 | 83 | 2013 |
| Recurrent generative adversarial networks for proximal learning and automated compressive image recovery M Mardani, H Monajemi, V Papyan, S Vasanawala, D Donoho, J Pauly arXiv preprint arXiv:1711.10046, 2017 | 74 | 2017 |
| Estimating traffic and anomaly maps via network tomography M Mardani, GB Giannakis IEEE/ACM transactions on networking 24 (3), 1533-1547, 2015 | 73 | 2015 |
| Wasserstein GANs for MR imaging: from paired to unpaired training K Lei, M Mardani, JM Pauly, SS Vasanawala IEEE transactions on medical imaging 40 (1), 105-115, 2020 | 71 | 2020 |
| 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 | 63 | 2023 |
John PaulyProfessor of Electrical Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
