  A seminal work [Jacot et al., 2018] demonstrated that training a neural
network under specific parameterization is equivalent to performing a
particular kernel method as width goes to infinity. This equivalence opened a
promising direction for applying the results of the rich literature on kernel
methods to neural nets which were much harder to tackle. The present survey
covers key results on kernel convergence as width goes to infinity,
finite-width corrections, applications, and a discussion of the limitations of
the corresponding method.
