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On the Convergence of Gradient Flow on Multi-layer Linear Models
Hancheng Min · Rene Vidal · Enrique Mallada

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #713
In this paper, we analyze the convergence of gradient flow on a multi-layer linear model with a loss function of the form $f(W_1W_2\cdots W_L)$. We show that when $f$ satisfies the gradient dominance property, proper weight initialization leads to exponential convergence of the gradient flow to a global minimum of the loss. Moreover, the convergence rate depends on two trajectory-specific quantities that are controlled by the weight initialization: the *imbalance matrices*, which measure the difference between the weights of adjacent layers, and the least singular value of the *weight product* $W=W_1W_2\cdots W_L$. Our analysis exploits the fact that the gradient of the overparameterized loss can be written as the composition of the non-overparametrized gradient with a time-varying (weight-dependent) linear operator whose smallest eigenvalue controls the convergence rate. The key challenge we address is to derive a uniform lower bound for this time-varying eigenvalue that lead to improved rates for several multi-layer network models studied in the literature.

Author Information

Hancheng Min (Johns Hopkins University)
Rene Vidal (University of Pennsylvania)
Enrique Mallada (Johns Hopkins University)

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