Towards a General Theory of Infinite-Width Limits of Neural Classifiers
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Obtaining theoretical guarantees for neural networks training appears to be a hard problem in a general case. Recent research has been focused on studying this problem in the limit of infinite width and two different theories have been developed: mean-field (MF) and kernel limit theories. We propose a general framework that provides a link between these seemingly distinct theories. Our framework out of the box gives rise to a discrete-time MF limit which was not previously explored in the literature. We prove a convergence theorem for it and show that it provides a more reasonable approximation for finite-width nets compared to NTK limit if learning rates are not very small. Also, our analysis suggests that all infinite-width limits of a network with a single hidden layer are covered by either mean-field limit theory or kernel limit theory. We show that for networks with more than two hidden layers RMSProp training has a non-trivial MF limit, but GD training does not have one. Overall, our framework demonstrates that both MF and NTK limits have considerable limitations in approximating finite-sized neural nets, indicating the need for designing more accurate infinite-width approximations for them. Source code to reproduce all the reported results is available on GitHub.
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