[41] Hongwu Peng, Shanglin Zhou, Yukui Luo, Nuo Xu, Shijin Duan, Ran Ran, Jiahui Zhao, Chenghong Wang, Tong Geng, Wujie Wen, et al. Pasnet: Polynomial architecture search framework for two-party computation-based secure neural network deployment. In 2023 60th ACM/IEEE Design Automation Conference (DAC), pages 1-6. IEEE, 2023.
[42] Hongwu Peng, Shanglin Zhou, Yukui Luo, Nuo Xu, Shijin Duan, Ran Ran, Jiahui Zhao, Shaoyi Huang, Xi Xie, Chenghong Wang, et al. Rrnet: Towards relu-reduced neural network for two-party computation based private inference. arXiv preprint arXiv:2302.02292, 2023.
[43] Qian Lou, Yilin Shen, Hongxia Jin, and Lei Jiang. Safenet: A secure, accurate and fast neural network inference. In International Conference on Learning Representations, 2021.
[44] Mingbao Lin, Yuxin Zhang, Yuchao Li, Bohong Chen, Fei Chao, Mengdi Wang, Shen Li, Yonghong Tian, and Rongrong Ji. 1xn pattern for pruning convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[45] Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, and Xiang Li. Neuron-level structured pruning using polarization regularizer. Advances in neural information processing systems, 33:9865-9877, 2020.
[46] Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Binarized neural networks. Advances in neural information processing systems, 29, 2016.
[47] Suraj Srinivas, Akshayvarun Subramanya, and R Venkatesh Babu. Training sparse neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 138-145, 2017.
[48] Penghang Yin, Jiancheng Lyu, Shuai Zhang, Stanley Osher, Yingyong Qi, and Jack Xin. Understanding straight-through estimator in training activation quantized neural nets. arXiv preprint arXiv:1903.05662, 2019.
[49] Xia Xiao, Zigeng Wang, and Sanguthevar Rajasekaran. Autoprune: Automatic network pruning by regularizing auxiliary parameters. Advances in neural information processing systems, 32, 2019.
[50] Ramy E Ali, Jinhyun So, and A Salman Avestimehr. On polynomial approximations for privacy-preserving and verifiable relu networks. arXiv preprint arXiv:2011.05530, 2020.
[51] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
[52] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In International Conference on Learning Representations, 2017.
[53] Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, and Yongsoo Song. A full rms variant of approximate homomorphic encryption. In International Conference on Selected Areas in Cryptography, pages 347-368. Springer, 2018.
[54] Microsoft SEAL (release 3.7). https://github.com/Microsoft/SEAL, September 2021. Microsoft Research, Redmond, WA.
[55] Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, and Woosuk Choi. Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions. In International Conference on Machine Learning, pages 12403-12422. PMLR, 2022.
[56] Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[57] Xiao Huang, Jundong Li, and Xia Hu. Label informed attributed network embedding. In Proceedings of the tenth ACM international conference on web search and data mining, pages 731-739, 2017.