Layer NTK

The Neural Tangent Kernel (NTK) approach analyzes the behavior of very wide neural networks by approximating their training dynamics with a kernel method. Current research focuses on understanding the limitations of this approximation, particularly concerning generalization performance and the impact of network architecture (e.g., comparing two-layer versus three-layer models), and exploring scenarios where standard gradient descent surpasses the NTK's predictive power. This work aims to bridge the gap between theoretical understanding of simplified models and the complex behavior of real-world neural networks, potentially leading to improved training algorithms and a deeper understanding of deep learning's success.