1. Dhar, P.: The carbon impact of artificial intelligence. Nat. Mach. Intell. 2(8), 423–425 (2020)
2. Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. Adv. Neural Inf. Process. Syst. 28 (2015)
3. Hassibi, B., Stork, D.: Second order derivatives for network pruning: optimal brain surgeon. Adv. Neural Inf. Process. Syst. 5 (1992)
4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
5. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., Pineau, J.: Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21(1), 10039–10081 (2020)