| Design of optimal sparse feedback gains via the alternating direction method of multipliers F Lin, M Fardad, MR Jovanović IEEE Transactions on Automatic Control 58 (9), 2426-2431, 2013 | 550 | 2013 |
| A systematic dnn weight pruning framework using alternating direction method of multipliers T Zhang, S Ye, K Zhang, J Tang, W Wen, M Fardad, Y Wang Proceedings of the European conference on computer vision (ECCV), 184-199, 2018 | 528 | 2018 |
| Optimal control of vehicular formations with nearest neighbor interactions F Lin, M Fardad, MR Jovanovic IEEE Transactions on Automatic Control 57 (9), 2203-2218, 2011 | 224 | 2011 |
| Augmented Lagrangian approach to design of structured optimal state feedback gains F Lin, M Fardad, MR Jovanovic IEEE Transactions on Automatic Control 56 (12), 2923-2929, 2011 | 224 | 2011 |
| Sensor selection for estimation with correlated measurement noise S Liu, SP Chepuri, M Fardad, E Maşazade, G Leus, PK Varshney IEEE Transactions on Signal Processing 64 (13), 3509-3522, 2016 | 210 | 2016 |
| Algorithms for leader selection in stochastically forced consensus networks F Lin, M Fardad, MR Jovanović IEEE Transactions on Automatic Control 59 (7), 1789-1802, 2014 | 164 | 2014 |
| Sparsity-promoting optimal control for a class of distributed systems M Fardad, F Lin, MR Jovanović Proceedings of the 2011 American Control Conference, 2050-2055, 2011 | 153 | 2011 |
| Design of optimal sparse interconnection graphs for synchronization of oscillator networks M Fardad, F Lin, MR Jovanović IEEE Transactions on Automatic Control 59 (9), 2457-2462, 2014 | 101 | 2014 |
| Optimal periodic sensor scheduling in networks of dynamical systems S Liu, M Fardad, E Masazade, PK Varshney IEEE Transactions on Signal Processing 62 (12), 3055-3068, 2014 | 99 | 2014 |
| Sparsity-promoting extended Kalman filtering for target tracking in wireless sensor networks E Masazade, M Fardad, PK Varshney IEEE Signal Processing Letters 19 (12), 845-848, 2012 | 96 | 2012 |
| Adam-admm: A unified, systematic framework of structured weight pruning for dnns T Zhang, K Zhang, S Ye, J Li, J Tang, W Wen, X Lin, M Fardad, Y Wang arXiv preprint arXiv:1807.11091 2 (3), 2018 | 77 | 2018 |
| Sparse feedback synthesis via the alternating direction method of multipliers F Lin, M Fardad, MR Jovanović 2012 American control conference (ACC), 4765-4770, 2012 | 77 | 2012 |
| On the optimal design of structured feedback gains for interconnected systems M Fardad, F Lin, MR Jovanović Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held …, 2009 | 71 | 2009 |
| Algorithms for leader selection in large dynamical networks: Noise-corrupted leaders F Lin, M Fardad, MR Jovanović 2011 50th IEEE Conference on Decision and Control and European Control …, 2011 | 65 | 2011 |
| A memristor-based optimization framework for artificial intelligence applications S Liu, Y Wang, M Fardad, PK Varshney IEEE Circuits and Systems Magazine 18 (1), 29-44, 2018 | 63 | 2018 |
| Sparsity-aware sensor collaboration for linear coherent estimation S Liu, S Kar, M Fardad, PK Varshney IEEE Transactions on Signal Processing 63 (10), 2582-2596, 2015 | 62 | 2015 |
| On the design of optimal structured and sparse feedback gains via sequential convex programming M Fardad, MR Jovanović 2014 American control conference, 2426-2431, 2014 | 58 | 2014 |
| Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ... arXiv preprint arXiv:1903.09769, 2019 | 55 | 2019 |
| Stability in the almost everywhere sense: A linear transfer operator approach R Rajaram, U Vaidya, M Fardad, B Ganapathysubramanian Journal of Mathematical analysis and applications 368 (1), 144-156, 2010 | 55 | 2010 |
| Progressive weight pruning of deep neural networks using ADMM S Ye, T Zhang, K Zhang, J Li, K Xu, Y Yang, F Yu, J Tang, M Fardad, S Liu, ... arXiv preprint arXiv:1810.07378, 2018 | 53 | 2018 |
Mihailo R. JovanovicProfessor of Electrical and Computer Engineering, University of Southern California在 usc.edu 的电子邮件经过验证
