Activation Function

Activation functions are crucial components of neural networks, introducing nonlinearity to enable the learning of complex patterns from data. Current research focuses on developing novel activation functions, including those with learnable parameters, and exploring their impact within various architectures like Kolmogorov-Arnold Networks and transformers. These efforts aim to improve model performance, efficiency, and interpretability across diverse applications, from image classification and generation to solving partial differential equations and formal verification tasks. The ongoing search for optimal activation functions is driving significant advancements in the field of deep learning.