| 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 | 535 | 2018 |
| 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 |
| A unified framework of DNN weight pruning and weight clustering/quantization using ADMM S Ye, T Zhang, K Zhang, J Li, J Xie, Y Liang, S Liu, X Lin, Y Wang arXiv preprint arXiv:1811.01907, 2018 | 67 | 2018 |
| 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 |
| Structadmm: Achieving ultrahigh efficiency in structured pruning for dnns T Zhang, S Ye, X Feng, X Ma, K Zhang, Z Li, J Tang, S Liu, X Lin, Y Liu, ... IEEE transactions on neural networks and learning systems 33 (5), 2259-2273, 2021 | 41 | 2021 |
| StructADMM: A systematic, high-efficiency framework of structured weight pruning for DNNs T Zhang, S Ye, K Zhang, X Ma, N Liu, L Zhang, J Tang, K Ma, X Lin, ... arXiv preprint arXiv:1807.11091, 2018 | 35 | 2018 |
