1. Agostinelli, F., Hoffman, M., Sadowski, P., & Baldi, P. (2015). Learning activation functions to improve deep neural networks. International Conference on Learning Representations (ICLR) 2015, arXiv:1412.6830v3.
2. SegNet: A deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017
3. Linearized sigmoidal activation: A novel activation function with tractable non-linear characteristics to boost representation capability;Bawa;Expert Systems with Applications,2019
4. Bai, S., Kolter, J.Z., & Koltun, V. (2019). Trellis networks for sequence modeling. International Conference on Learning Representations (ICLR) 2019, arXiv:1810.06682v2. https://openreview.net/forum?id=HyeVtoRqtQ.
5. Bergstra, J., Desjardins, G., Lamblin, P., & Bengio, Y. (2009). Quadratic polynomials learn better image features. Technical report, 1337.