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Effect of the output activation function on the probabilities and errors in medical image segmentation. arXiv:2109.00903. 2021."},{"key":"ref25","series-title":"2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)","first-page":"415","article-title":"Evaluating the number of trainable parameters on deep Maxout and LReLU networks for visual recognition","author":"Castaneda","year":"2020 Dec"},{"key":"ref26","first-page":"1","article-title":"Performance evaluation of swish-based activation functions for multi-layer networks","volume":"5","author":"KO\u00c7AK","year":"2022","journal-title":"Artif Intell Studies"},{"key":"ref27","series-title":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","first-page":"51","article-title":"Combine relu with tanh","author":"Li","year":"2020"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"72727","DOI":"10.1109\/ACCESS.2020.2987829","article-title":"RMAF: ReLU-memristor-like activation function for deep learning","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref29","unstructured":"Evangelista LGC, Giusti R. Short-term effects of weight initialization functions in Deep NeuroEvolution. In: The Leading European Event on Bio-Inspired Computation; 2021 Apr 7\u20139; Online. 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