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Improving The Performance Of Deep Neural Networks Using Two Proposed Activation Functions
2021
IEEE Access
In artificial neural networks, activation functions play a significant role in the learning process. Choosing the proper activation function is a major factor in achieving a successful learning performance. Many activation functions are sufficient universal approximators, but their performance is lacking. Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks. However, the learning process involves many challenges,
doi:10.1109/access.2021.3085855
fatcat:ylz2lfz6erculnzrwoji4wkcva