1. Dropout: a simple way to prevent neural networks from overfitting;Srivastava;J Mach Learn Res,2014
2. Shang W, Sohn K, Almeida D, Lee H. Understanding and improving convolutional neural networks via concatenated rectified linear units. arXiv [cs.LG]. 2016. Available at: https://arxiv.org/abs/1603.05201. Accessed Aug 2, 2017.
3. Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. 2011. p. 275.
4. Clevert D-A, Unterthiner T, Hochreiter S. Fast and accurate deep network learning by exponential linear units (ELUs). arXiv [cs.LG]. 2015. Available at: https://arxiv.org/abs/1511.07289. Accessed Aug 2, 2017.
5. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv [cs.CV]. 2014. Available at: https://arxiv.org/abs/1409.1556. Accessed Aug 29, 2017.