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 ...

The Rectified Linear Unit (ReLU) is a popular activation function in neural networks. It's simple yet effective, helping to solve the vanishing gradient problem. In this presentation, we'll build a ...

Activation functions facilitate deep neural networks by introducing non-linearity to the learning process. The non-linearity feature gives the neural network the ability to learn complex patterns.

We propose programmable low-power-consumption optical ReLU activation function for the fully optical neural network. The optical-to-optical nonlinearity is realized using characteristics of ...

Threshold Relu (TRelu) came about as a result of a week long comparison testing of a variety of activation functions (Relu, General Relu, FTSwish, LiSHT and enhancements of these). As a result of ...