ReLU Activation

The ReLU (Rectified Linear Unit) activation function, a simple yet powerful element in neural networks, is a central focus of ongoing research aimed at understanding its properties and improving its application. Current research explores ReLU's role in approximation theory, examining its capacity to represent functions of varying complexity within different model architectures, including deep and shallow networks, transformers, and recurrent neural networks. This research is crucial for advancing both theoretical understanding of neural network behavior and practical applications, particularly in improving training efficiency, enhancing model robustness, and optimizing inference speed in large-scale models like LLMs. Furthermore, investigations into ReLU's interaction with other components, such as batch normalization and various optimization algorithms, are actively pursued to address challenges like gradient explosion and improve overall model performance.