Figure 1: Graphs of the discussed activation functions. From left to right: Tanh, ReLU, GELU. 𝞍 denotes the standard normal cumulative distribution function. Graphs plotted with WolframAlpha. The ...

The drawback with ReLU function is their fragility, that is, when a large gradient is made to flow through ReLU neuron, it can render the neuron useless and make it unable to fire on any other ...

Since only a certain number of neurons are activated, the ReLU function is far more computationally efficient when compared to the sigmoid and tanh functions. 2. ReLU accelerates the convergence of ...

Activation functions are important for a neural network to learn and understand the complex patterns. The main function of it is to introduce non-linear properties into the network. I'll be explaining ...

Activation functions used in hidden layers directly affect the possibilities for describing nonlinear systems using a feedforward neural network. Furthermore, linear based activation functions are ...

Abstract: Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions ...

The development of Transformer models has significantly advanced artificial intelligence, delivering remarkable performance across diverse tasks. However, these advancements often come with steep ...