Kernel Regime

The "kernel regime" in neural networks describes the initial phase of training where network behavior closely resembles that of a kernel method, limiting its representational power and generalization ability. Current research focuses on understanding the transition out of this regime, analyzing the role of gradient descent, and exploring how different architectures (e.g., two-layer networks, transformers) and algorithms (e.g., kernel ridge regression with importance weighting) escape these limitations to achieve better generalization. This research is significant because it helps explain the surprising success of deep learning, bridging the gap between theoretical understanding of kernel methods and the empirical performance of deep neural networks in various applications.