Sigmoid Function

The sigmoid function, an S-shaped curve, is a crucial activation function in neural networks, impacting model performance and interpretability. Current research focuses on improving its application in various contexts, including enhancing optimization algorithms (e.g., through adaptive friction coefficients), tightening its convex relaxations for formal verification, and addressing limitations like activation bottlenecks in recurrent networks and multi-label classification. These advancements are significant for improving the accuracy, robustness, and efficiency of neural networks across diverse applications, from image classification and autonomous driving to control systems and time series forecasting.