Have you explored any new activation functions for deep learning models?

Activation functions are an essential component of deep learning models, as they introduce non-linearity to the model's output, which allows for the extraction of complex features from the input data. Recently, researchers have proposed several new activation functions that have shown promising results in various applications.

One such activation function is the Swish activation function, which was proposed by Google researchers in 2017. The Swish function is defined as f(x) = x * sigmoid(x), and it has been shown to outperform other popular activation functions like ReLU and its variants in some applications. Another promising activation function is the GeLU function, which is a smooth approximation of the ReLU function. The GeLU function has been shown to improve the performance of neural networks on several benchmark datasets.

Other activation functions that have been proposed recently include the SoftExponential function, the Bent Identity function, and the Gaussian Error Linear Units (GELUs) function. These activation functions have shown to perform well in various deep learning applications.