| CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices C Ding, S Liao, Y Wang, Z Li, N Liu, Y Zhuo, C Wang, X Qian, Y Bai, ... Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 349 | 2017 |
| A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning N Liu, Z Li, J Xu, Z Xu, S Lin, Q Qiu, J Tang, Y Wang 2017 IEEE 37th international conference on distributed computing systems …, 2017 | 345 | 2017 |
| AutoCompress: An automatic dnn structured pruning framework for ultra-high compression rates N Liu, X Ma, Z Xu, Y Wang, J Tang, J Ye Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 2019 | 231 | 2019 |
| Deep reinforcement learning for dynamic treatment regimes on medical registry data Y Liu, B Logan, N Liu, Z Xu, J Tang, Y Wang 2017 IEEE international conference on healthcare informatics (ICHI), 380-385, 2017 | 162 | 2017 |
| REQ-YOLO: A resource-aware, efficient quantization framework for object detection on FPGAs C Ding, S Wang, N Liu, K Xu, Y Wang, Y Liang proceedings of the 2019 ACM/SIGDA international symposium on field …, 2019 | 131 | 2019 |
| VIBNN: Hardware acceleration of Bayesian neural networks R Cai, A Ren, N Liu, C Ding, L Wang, X Qian, M Pedram, Y Wang ACM SIGPLAN Notices 53 (2), 476-488, 2018 | 126 | 2018 |
| MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge G Yuan, X Ma, W Niu, Z Li, Z Kong, N Liu, Y Gong, Z Zhan, C He, Q Jin, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 109 | 2021 |
| Llava-phi: Efficient multi-modal assistant with small language model Y Zhu, M Zhu, N Liu, Z Xu, Y Peng Proceedings of the 1st International Workshop on Efficient Multimedia …, 2024 | 77 | 2024 |
| FFT-based deep learning deployment in embedded systems S Lin, N Liu, M Nazemi, H Li, C Ding, Y Wang, M Pedram 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2018 | 67 | 2018 |
| Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 66 | 2021 |
| A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology R Cai, A Ren, O Chen, N Liu, C Ding, X Qian, J Han, W Luo, N Yoshikawa, ... Proceedings of the 46th International Symposium on Computer Architecture …, 2019 | 53 | 2019 |
| Dais: Automatic channel pruning via differentiable annealing indicator search Y Guan, N Liu, P Zhao, Z Che, K Bian, Y Wang, J Tang IEEE Transactions on Neural Networks and Learning Systems 34 (12), 9847-9858, 2022 | 43 | 2022 |
| Learning the dynamic treatment regimes from medical registry data through deep Q-network N Liu, Y Liu, B Logan, Z Xu, J Tang, Y Wang Scientific reports 9 (1), 1495, 2019 | 43 | 2019 |
| Lottery ticket preserves weight correlation: Is it desirable or not? N Liu, G Yuan, Z Che, X Shen, X Ma, Q Jin, J Ren, J Tang, S Liu, Y Wang International Conference on Machine Learning, 7011-7020, 2021 | 38 | 2021 |
| Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai G Yuan, Z Liao, X Ma, Y Cai, Z Kong, X Shen, J Fu, Z Li, C Zhang, H Peng, ... 2021 22nd International Symposium on Quality Electronic Design (ISQED), 135-141, 2021 | 37 | 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 | 37 | 2018 |
| A majority logic synthesis framework for adiabatic quantum-flux-parametron superconducting circuits R Cai, O Chen, A Ren, N Liu, C Ding, N Yoshikawa, Y Wang Proceedings of the 2019 Great Lakes Symposium on VLSI, 189-194, 2019 | 36 | 2019 |
| Deep reinforcement learning: Algorithm, applications, and ultra-low-power implementation H Li, R Cai, N Liu, X Lin, Y Wang Nano Communication Networks 16, 81-90, 2018 | 34 | 2018 |
| ADMM-based weight pruning for real-time deep learning acceleration on mobile devices H Li, N Liu, X Ma, S Lin, S Ye, T Zhang, X Lin, W Xu, Y Wang Proceedings of the 2019 Great Lakes Symposium on VLSI, 501-506, 2019 | 33 | 2019 |
| CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization W Hu, Z Che, N Liu, M Li, J Tang, C Zhang, J Wang IEEE Transactions on Neural Networks and Learning Systems, 2023 | 30 | 2023 |
Yanzhi WangNortheastern Universityایمیل تأیید شده در northeastern.edu