ReLUga

ReLUga is a term that refers to a modification of the Rectified Linear Unit (ReLU) activation function. Standard ReLU is defined as f(x) = max(0, x), meaning it outputs the input directly if it's positive and zero otherwise. ReLUga, however, introduces a small, non-zero slope for negative inputs, rather than a flat zero. The exact mathematical formulation can vary, but a common representation is f(x) = max(epsilon * x, x), where epsilon is a small positive constant. This allows a small gradient to flow back through the network even for negative activations, which can help mitigate the "dying ReLU" problem. The dying ReLU problem occurs when a neuron gets stuck in a state where its output is always zero for any input, effectively becoming inactive during training. By providing a small slope for negative values, ReLUga ensures that neurons can still learn and update their weights, even if they are currently producing negative outputs. This can lead to improved performance and faster convergence in certain deep learning models, particularly in complex neural network architectures.