The choice of activation functions is crucial for modern deep neural networks. Popular hand-
designed activation functions like Rectified Linear Unit (ReLU) and its variants show …

The choice of activation functions is crucial to deep neural networks. ReLU is a popular
hand-designed activation function. Swish, the automatically searched activation function …

The choice of activation functions in deep networks has a significant effect on the training
dynamics and task performance. Currently, the most successful and widely-used activation …

Activation functions are essential for deep learning methods to learn and perform complex
tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and …

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

The most widely used activation functions in current deep feed-forward neural networks are
rectified linear units (ReLU), and many alternatives have been successfully applied, as well …

Deep learning, at its core, contains functions that are the composition of a linear
transformation with a nonlinear function known as the activation function. In the past few …

Activation functions play a crucial role in neural networks because they are the
nonlinearities which have been attributed to the success story of deep learning. One of the …

Rectified linear activation units are important components for state-of-the-art deep
convolutional networks. In this paper, we propose a novel S-shaped rectifiedlinear activation …

The performance of deep network learning strongly depends on the choice of the non-linear
activation function associated with each neuron. However, deciding on the best activation is …