HcLSH: A Novel Non-Linear Monotonic Activation Function for Deep Learning Methods

Activation functions are essential components in any neural network model; they play a crucial role in determining the network's expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models becau...

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Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Abdel-Nabi, Heba, Al-Naymat, Ghazi, Ali, Mostafa, Awajan, Arafat
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Accuracy Activation analysis Activation Function Adaptation models Artificial neural networks Back propagation networks Biological neural networks Computer architecture Convergence Datasets Deep learning Gradient flow Hyperbolic functions Image classification Image Classification Accuracy Machine learning Monotonicity Neural networks Neurons Optimization Performance enhancement Regularization Saturation Trigonometric functions
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