The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
This work provides theoretical insights into deep learning dynamics, which is incremental but addresses a fundamental gap in understanding when and why empirically observed phenomena occur.
The paper tackles the theoretical understanding of training dynamics in neural networks by analyzing gradient flow on kernel least-squares objectives, showing that under mild conditions, the dynamics undergo three stages characterized by behaviors in Oracle and Empirical Worlds, and formalizing phenomena such as progressive learning of complex functions and the 'deep bootstrap' effect.
To understand how deep learning works, it is crucial to understand the training dynamics of neural networks. Several interesting hypotheses about these dynamics have been made based on empirically observed phenomena, but there exists a limited theoretical understanding of when and why such phenomena occur. In this paper, we consider the training dynamics of gradient flow on kernel least-squares objectives, which is a limiting dynamics of SGD trained neural networks. Using precise high-dimensional asymptotics, we characterize the dynamics of the fitted model in two "worlds": in the Oracle World the model is trained on the population distribution and in the Empirical World the model is trained on a sampled dataset. We show that under mild conditions on the kernel and $L^2$ target regression function the training dynamics undergo three stages characterized by the behaviors of the models in the two worlds. Our theoretical results also mathematically formalize some interesting deep learning phenomena. Specifically, in our setting we show that SGD progressively learns more complex functions and that there is a "deep bootstrap" phenomenon: during the second stage, the test error of both worlds remain close despite the empirical training error being much smaller. Finally, we give a concrete example comparing the dynamics of two different kernels which shows that faster training is not necessary for better generalization.