Selecting the most suitable activation function is a critical factor in the effectiveness of deep
learning models, as it influences their learning capacity, stability, and computational …

Selecting the most suitable activation function is a critical factor in the effectiveness of deep
learning models, as it influences their learning capacity, stability, and computational …

Activation functions playa key role in providing remarkable performance in deep neural
networks, and the rectified linear unit (ReLU) is one of the most widely used activation …

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 …

Activation functions shape the outputs of artificial neurons and, therefore, are integral parts
of neural networks in general and deep learning in particular. Some activation functions …

The activation function has a critical influence on whether a convolutional neural network in
deep learning can converge or not; a proper activation function not only makes the …

In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the
hyperbolic tangent (tanh). We point out that, besides the well-known non-zero centered …

The primary neural networks' decision-making units are activation functions. Moreover, they
evaluate the output of networks neural node; thus, they are essential for the performance of …

Deep learning researchers have a keen interest in proposing two new novel activation
functions which can boost network performance. A good choice of activation function can …

In deep learning models, the inputs to the network are processed using activation functions
to generate the output corresponding to these inputs. Deep learning models are of particular …