1. Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (ELUs). 1997, pp 1–13.
arXiv:1511.07289
2. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification.
arXiv:1502.01852
3. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML workshop on deep learning for audio, speech and language processing, vol 28.
http://www.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf
4. Stallkamp J, Schlipsing M, Salmen J, Igel C (2012) Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323–332. doi:
10.1016/j.neunet.2012.02.016
5. Xu B, Wang N, Chen T (2015) Empirical evaluation of rectified activations in convolutional network.
arXiv:1505.00853v2