1. Bolukbasi, T., Wang, J., Dekei, O., & Saligrama, V. (2017). Adaptive neural networks for fast test-time prediction. In Proceedings of 34th international conference on machine learning (ICML) (pp. 527–536), Sydney.
2. Carsen, S., Marius, P., Nicholas, A. S., Michael, O., Peter, B., et al. (2016). Inhibitory control of correlated intrinsic variability in cortical networks. Elife.
https://doi.org/10.7554/elife.19695
.
3. Chen, F. C., & Jahanshahi, R. J. (2017). NB-CNN: Deep learning-based crack detection using convolutional neural network and naive Bayes data fusion. IEEE Transactions on Industrial Electronics,65(5), 4392–4400.
4. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1800–1807), Honolulu.
5. Denton, E., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014). Exploiting linear structure within convolutional networks for efficient evaluation. In Proceedings of conference and workshop on neural information processing systems (NIPS), Montreal,
http://papers.nips.cc/paper/5544-exploiting-linear-structure-within-convolutional-networks-for-efficient-evaluation.pdf
.