参考

参考资料

[BLC13]

Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. 2013. URL: https://arxiv.org/abs/1308.3432, doi:10.48550/ARXIV.1308.3432.

[CWZZ17]

Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. 2017. URL: https://arxiv.org/abs/1710.09282, doi:10.48550/ARXIV.1710.09282.

[CHS+16]

Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or -1. 2016. URL: https://arxiv.org/abs/1602.02830, doi:10.48550/ARXIV.1602.02830.

[CFH+22]

Matteo Croci, Massimiliano Fasi, Nicholas Higham, Theo Mary, and Mantas Mikaitis. Stochastic rounding: implementation, error analysis and applications. Royal Society Open Science, 9:, 03 2022. doi:10.1098/rsos.211631.

[DT17]

Terrance DeVries and Graham W. Taylor. Improved regularization of convolutional neural networks with cutout. 2017. arXiv:1708.04552.

[HMD15]

Song Han, Huizi Mao, and William J. Dally. Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. 2015. URL: https://arxiv.org/abs/1510.00149, doi:10.48550/ARXIV.1510.00149.

[HWZ+16]

Qinyao He, He Wen, Shuchang Zhou, Yuxin Wu, Cong Yao, Xinyu Zhou, and Yuheng Zou. Effective quantization methods for recurrent neural networks. 2016. URL: https://arxiv.org/abs/1611.10176, doi:10.48550/ARXIV.1611.10176.

[HCS+16]

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Quantized neural networks: training neural networks with low precision weights and activations. 2016. URL: https://arxiv.org/abs/1609.07061, doi:10.48550/ARXIV.1609.07061.

[LBBH98]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. doi:10.1109/5.726791.

[LZL16]

Fengfu Li, Bo Zhang, and Bin Liu. Ternary weight networks. 2016. URL: https://arxiv.org/abs/1605.04711, doi:10.48550/ARXIV.1605.04711.

[NFA+21]

Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Tijmen Blankevoort. A white paper on neural network quantization. CoRR, 2021. URL: https://arxiv.org/abs/2106.08295, arXiv:2106.08295.

[RORF16]

Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: imagenet classification using binary convolutional neural networks. 2016. URL: https://arxiv.org/abs/1603.05279, doi:10.48550/ARXIV.1603.05279.

[YHO+19]

Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: regularization strategy to train strong classifiers with localizable features. 2019. arXiv:1905.04899.

[ZCDLP18]

Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. Mixup: beyond empirical risk minimization. 2018. arXiv:1710.09412.

[ZZK+17]

Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. 2017. arXiv:1708.04896.