GELU

Gaussian Error Linear Unit (GELU) is an activation function used in artificial neural networks, particularly in deep learning models. It was introduced by Hendrycks and Gimpel in 2016 as an alternative to the Rectified Linear Unit (ReLU). The GELU function is defined as:

GELU(x) = 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x^3)))

This function combines the linear and non-linear properties of the input, allowing it to model more complex relationships in the data. GELU has been shown to outperform ReLU in various tasks, including natural language processing and image recognition. It is particularly effective in transformer-based models, such as BERT, where it helps to improve the model's performance by allowing for better gradient flow during training. The GELU function is computationally efficient and can be easily implemented in most deep learning frameworks. Its smooth and continuous nature also helps to mitigate the vanishing gradient problem, which can occur with other activation functions like ReLU. Overall, GELU is a versatile and powerful activation function that has become an important tool in the deep learning toolkit.