Author:
Sharma Ochin,Lamba Vikas,Ghatasala G. G. S. Pradeep,Mohapatra Srikanta
Reference22 articles.
1. Rasamoelina, A. D., Adjailia, F., & Sinčák, P. (2020, January). A review of activation function for artificial neural network. In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 281–286). IEEE.
2. Szandała, T. (2021). Review and comparison of commonly used activation functions for deep neural networks. In Bio-inspired neurocomputing (pp. 203–224). Springer, Singapore.
3. Worked upon image data classification and shown to achieve greater results with swish activation function.
4. Zhang, X., Chang, D., Qi, W., & Zhan, Z. (2021). A Study on Different Functionalities and Performances among Different Activation Functions across Different ANNs for Image Classification. In Journal of Physics: ConferenceSeries (Vol. 1732, No. 1, p. 012026). IOP Publishing.
5. Choi, D., Shallue, C. J., Nado, Z., Lee, J., Maddison, C. J., & Dahl, G. E. (2019). On empirical comparisons ofoptimizers for deep learning. arXiv preprint arXiv:1910.05446.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Predictive Modeling of Breast Cancer Outcomes Using Supervised Machine Learning Algorithms;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-08-15