Two Layer Network

Two-layer networks, a fundamental building block in deep learning and network optimization, are the subject of intense research focused on understanding their training dynamics and generalization capabilities. Current studies explore various aspects, including gradient descent algorithms (e.g., large stepsize GD, multi-pass GD), the impact of activation functions and network width, and the role of outliers and simplicity bias in model behavior. These investigations aim to improve training efficiency, enhance generalization performance, and provide a deeper theoretical understanding of neural network learning, with implications for both machine learning algorithms and network optimization in diverse applications.