Bibliography

The breakthrough of deep learning origins from (Krizhevsky et al., 2017) for computer vision, there is a rich of following up works, such as (He et al., 2016). NLP is catching up as well, the recent work (Devlin et al., 2018) shows significant improvements.

Two keys together (Devlin et al., 2018, He et al., 2016). Single author , two authors Newell and Rosenbloom (1980)

Devlin et al., 2018(1,2)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

He et al., 2016(1,2)

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

Krizhevsky et al., 2017

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.

Newell & Rosenbloom, 1980

Newell, A., & Rosenbloom, P. S. (1980). Mechanisms of skill acquisition and the law of practice. CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE.