188

Bibliography

[125] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,

and Radu Soricut. Albert: A lite bert for self-supervised learning of language repre-

sentations. arXiv preprint arXiv:1909.11942, 2019.

[126] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,

and Radu Soricut. Albert: A lite bert for self-supervised learning of language repre-

sentations. In ICLR, 2020.

[127] Emanuel Laude, Jan-Hendrik Lange, Jonas Sch pfer, Csaba Domokos, Leal-Taix?

Laura, Frank R. Schmidt, Bjoern Andres, and Daniel Cremers. Discrete-continuous

admm for transductive inference in higher-order mrfs.

In Proceedings of the

IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4539–

4548, 2018.

[128] Cong Leng, Zesheng Dou, Hao Li, Shenghuo Zhu, and Rong Jin. Extremely low bit

neural network: Squeeze the last bit out with admm. In Proceedings of the AAAI

Conference on Artificial Intelligence, pages 3466–3473, 2018.

[129] Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang.

Dn-

detr: Accelerate detr training by introducing query denoising.

In Proceedings of

the IEEE/CVF conference on computer vision and pattern recognition, pages 13619–

13627, 2022.

[130] Fengfu Li, Bo Zhang, and Bin Liu.

Ternary weight networks.

arXiv preprint

arXiv:1605.04711, 2016.

[131] Mu Li, David G Andersen, Alexander J Smola, and Kai Yu. Communication effi-

cient distributed machine learning with the parameter server. Advances in Neural

Information Processing Systems, 27, 2014.

[132] Wei Li, Xiatian Zhu, and Shaogang Gong. Person re-identification by deep joint learn-

ing of multi-loss classification. In Proceedings of the International Joint Conference

on Artificial Intelligence, pages 2194–2200, 2017.

[133] Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou. Factorized bilinear models

for image recognition. In Proc. of ICCV, pages 2079–2087, 2017.

[134] Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen.

Pointcnn: Convolution on x-transformed points. In Proceedings of Advances in Neural

Information Processing Systems, pages 820–830, 2018.

[135] Yanjing Li, Sheng Xu, Xianbin Cao, Li’an Zhuo, Baochang Zhang, Tian Wang, and

Guodong Guo. Dcp–nas: Discrepant child–parent neural architecture search for 1-bit

cnns. International Journal of Computer Vision, pages 1–23, 2023.

[136] Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, and Guodong Guo.

Q-vit: Accurate and fully quantized low-bit vision transformer. In Advances in neural

information processing systems, 2022.

[137] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei

Wang, and Shi Gu. Brecq: Pushing the limit of post-training quantization by block

reconstruction. arXiv preprint arXiv:2102.05426, 2021.

[138] Zefan Li, Bingbing Ni, Wenjun Zhang, Xiaokang Yang, and Wen Gao. Performance

guaranteed network acceleration via high-order residual quantization. In Proceedings

of the IEEE International Conference on Computer Vision, pages 2584–2592, 2017.