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 …

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 …

Artificial neural networks (ANN), typically referred to as neural networks, are a class of
Machine Learning algorithms and have achieved widespread success, having been …

Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular
allowing for end-to-end learning and reducing the requirement for manual design decisions …

Deep Neural Networks have become the tool of choice for Machine Learning practitioners
today. They have been successfully applied for solving a large class of learning problems …

In recent years, various deep neural networks with different learning paradigms have been
widely employed in various applications, including medical diagnosis, image analysis, self …

Rectified activation units make an important contribution to the success of deep neural
networks in many computer vision tasks. In this paper, we propose a Parametric Deformable …

The scope of research in the domain of activation functions remains limited and centered
around improving the ease of optimization or generalization quality of neural networks …

Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …

Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …