Research papers on activation functions

Research papers on activation functions explore how different mathematical formulas influence the performance of neural networks. Activation functions decide how neurons process and transmit information, affecting a network’s ability to learn complex patterns. Studies compare various functions—like ReLU, sigmoid, or newer variants—to understand which produce better accuracy, efficiency, or stability across tasks. This research helps develop more robust and faster AI systems by identifying optimal activation functions for different applications, advancing the overall capabilities of machine learning models.