Shallow Neural Network

Shallow neural networks, characterized by a single hidden layer, are a focus of research aiming to understand fundamental aspects of neural network learning and approximation capabilities. Current research investigates their optimization dynamics, exploring various training algorithms like gradient flow and its variants, and analyzing the impact of network architecture (e.g., activation functions, width) and data properties on performance and generalization. This research is significant because it provides crucial insights into the theoretical underpinnings of deep learning, informing the design of more efficient and robust models for applications ranging from function approximation to signal processing and time series analysis.