| 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 | 522 | 2018 |
| Multi-animal pose estimation, identification and tracking with DeepLabCut J Lauer, M Zhou, S Ye, W Menegas, S Schneider, T Nath, MM Rahman, ... Nature Methods 19 (4), 496-504, 2022 | 270* | 2022 |
| Admm-nn: An algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers A Ren, T Zhang, S Ye, J Li, W Xu, X Qian, X Lin, Y Wang Proceedings of the Twenty-Fourth International Conference on Architectural …, 2019 | 195 | 2019 |
| Adversarial robustness vs. model compression, or both? S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ... Proceedings of the IEEE/CVF International Conference on Computer Vision, 111-120, 2019 | 177 | 2019 |
| Towards robust vision transformer X Mao, G Qi, Y Chen, X Li, R Duan, S Ye, Y He, H Xue Proceedings of the IEEE/CVF conference on Computer Vision and Pattern …, 2022 | 159 | 2022 |
| 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 | 132* | 2021 |
| 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 | 102* | 2019 |
| Adversarial laser beam: Effective physical-world attack to dnns in a blink R Duan, X Mao, AK Qin, Y Chen, S Ye, Y He, Y Yang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 100 | 2021 |
| Non-structured DNN weight pruning—Is it beneficial in any platform? X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, SH Tan, Z Li, D Fan, X Qian, ... IEEE transactions on neural networks and learning systems 33 (9), 4930-4944, 2021 | 98* | 2021 |
| 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 | 63 | 2018 |
| PIM-prune: Fine-grain DCNN pruning for crossbar-based process-in-memory architecture C Chu, Y Wang, Y Zhao, X Ma, S Ye, Y Hong, X Liang, Y Han, L Jiang 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 51 | 2020 |
| Qair: Practical query-efficient black-box attacks for image retrieval X Li, J Li, Y Chen, S Ye, Y He, S Wang, H Su, H Xue Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 48 | 2021 |
| Light-weight calibrator: a separable component for unsupervised domain adaptation S Ye, K Wu, M Zhou, Y Yang, SH Tan, K Xu, J Song, C Bao, K Ma Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 31 | 2020 |
| Enhance the visual representation via discrete adversarial training X Mao, Y Chen, R Duan, Y Zhu, G Qi, X Li, R Zhang, H Xue Advances in Neural Information Processing Systems 35, 7520-7533, 2022 | 25 | 2022 |
| Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics C Weinreb, J Pearl, S Lin, MAM Osman, L Zhang, S Annapragada, ... BioRxiv, 2023 | 24 | 2023 |
| Pcnn: Pattern-based fine-grained regular pruning towards optimizing cnn accelerators Z Tan, J Song, X Ma, SH Tan, H Chen, Y Miao, Y Wu, S Ye, Y Wang, D Li, ... 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 23 | 2020 |
| SuperAnimal models pretrained for plug-and-play analysis of animal behavior S Ye, A Filippova, J Lauer, M Vidal, S Schneider, T Qiu, A Mathis, ... arXiv preprint arXiv:2203.07436, 2022 | 17* | 2022 |
| AmadeusGPT: a natural language interface for interactive animal behavioral analysis S Ye, J Lauer, M Zhou, A Mathis, MW Mathis Neurips 2023, 2023 | 4 | 2023 |
Xue LinNortheastern UniversityVerified email at northeastern.edu
