DeepAI

# Logarithmic landscape and power-law escape rate of SGD

Stochastic gradient descent (SGD) undergoes complicated multiplicative noise for the mean-square loss. We use this property of the SGD noise to derive a stochastic differential equation (SDE) with simpler additive noise by performing a non-uniform transformation of the time variable. In the SDE, the gradient of the loss is replaced by that of the logarithmized loss. Consequently, we show that, near a local or global minimum, the stationary distribution P_ss(θ) of the network parameters θ follows a power-law with respect to the loss function L(θ), i.e. P_ss(θ)∝ L(θ)^-ϕ with the exponent ϕ specified by the mini-batch size, the learning rate, and the Hessian at the minimum. We obtain the escape rate formula from a local minimum, which is determined not by the loss barrier height Δ L=L(θ^s)-L(θ^*) between a minimum θ^* and a saddle θ^s but by the logarithmized loss barrier height Δlog L=log[L(θ^s)/L(θ^*)]. Our escape-rate formula explains an empirical fact that SGD prefers flat minima with low effective dimensions.

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07/06/2022

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