1. Hoefler T, Alistarh D, Ben-Nun T, Dryden N, Peste A. Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks. J Mach Learn Res. 2021;22.
2. Wu H, Judd P, Zhang X, Isaev M, Micikevicius P. Integer quantization for deep learning inference: principles and empirical evaluation. 2020:1–20.
3. Allen-Zhu Z, Li Y. Towards understanding ensemble, knowledge distillation and self-distillation in deep learning. 2020.
4. Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K, DenseNet. Implement efficient ConvNet descr pyramids. 2014:1–11.
5. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. 2016:1–13.