The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A Review of Activation Function for Artificial Neural Network
Abstract
The activation function plays an important role in the training and the performance of an Artificial Neural Network. They provide the necessary non-linear properties to any Artificial Neural Network. In this work, we provide a review of the most common and recent activation functions used in the hidden layer of an artificial Neural Network.
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Cites
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
(2011) · Cited by 6,993
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
Convergence Results for Neural Networks via Electrodynamics
Panigrahy, R., Rahimi, A., Sachdeva, S., & Zhang, Q. (2018) · Leibniz international proceedings in informatics · doi:10.4230/lipics.itcs.2018.22 · Cited by 2,761
Deep Learning using Rectified Linear Units (ReLU)
Agarap, A. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08375 · Cited by 2,399
Searching for activation functions
Ramachandran, P., Zoph, B., & Le, Q. V. (2017) · arXiv · doi:10.48550/arxiv.1710.05941 · Cited by 2,335
Self-Normalizing Neural Networks
Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.02515 · Cited by 2,267
Mish: A Self Regularized Non-Monotonic Neural Activation FunctionPreprint
Misra, D. (2019) · arXiv (Cornell University) · Cited by 478
Cited by
Tran, K., Vu, H.-C., Pham, L., Boudaoud, N., & Nguyen, H.-S.-H. (2024) · Mathematics · doi:10.3390/math12101569 · Cited by 14
Metadata
- Authors
- Rasamoelina, A. D., Adjailia, F., & Sincak, P.
- Venue
- 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
- Publisher
- IEEE
- Published
- 2020-01
- Pages
- 281-286
- OpenAlex
- W3034021666
- Cited by
- 319
- References
- 25
- FWCI
- 10.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rasamoelina, A. D., Adjailia, F., & Sincak, P. (2020). A Review of Activation Function for Artificial Neural Network. 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). https://doi.org/10.1109/sami48414.2020.9108717
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