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Shaokai Ye
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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
5332018
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
299*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
1982019
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
1812019
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
1782022
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
136*2021
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
1102021
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
105*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
104*2019
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
662018
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
552020
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
502021
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
332022
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
322020
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
292023
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
232020
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 e-prints, arXiv: 2203.07436, 2022
22*2022
AmadeusGPT: a natural language interface for interactive animal behavioral analysis
S Ye, J Lauer, M Zhou, A Mathis, MW Mathis
Neurips 2023, 2023
62023
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