Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates

We consider optimizing two-layer neural networks in the mean-field regime where the learning dynamics of network weights can be approximated by the evolution in the space of probability measures over the weight parameters associated with the neurons. The mean-field regime is a theoretically attractive alternative to the NTK (lazy training) regime which is only restricted locally in the so-called neural tangent kernel space around specialized initializations. Several prior works (\cite{mei2018mean, chizat2018global}) establish the asymptotic global optimality of the mean-field regime, but it is still challenging to obtain a quantitative convergence rate due to the complicated nonlinearity of the training dynamics. This work establishes a new linear convergence result for two-layer neural networks trained by continuous-time noisy gradient descent in the mean-field regime. Our result relies on a novelty logarithmic Sobolev inequality for two-layer neural networks, and uniform upper bounds on the logarithmic Sobolev constants for a family of measures determined by the evolving distribution of hidden neurons.