Two Layer

Two-layer neural networks serve as a crucial simplified model for understanding fundamental aspects of deep learning, enabling rigorous theoretical analysis otherwise intractable in deeper architectures. Current research focuses on characterizing the behavior of these networks under various training methods (e.g., contrastive learning, progressive data expansion) and across different architectures (e.g., convolutional, graph convolutional networks, and mixtures-of-experts). This research sheds light on key phenomena like benign overfitting, the impact of mini-batch size, and the role of spurious correlations, ultimately contributing to a deeper understanding of generalization and robustness in more complex deep learning models. These insights have implications for improving training efficiency and model performance in practical applications.