  Implicit neural networks have demonstrated remarkable success in various
tasks. However, there is a lack of theoretical analysis of the connections and
differences between implicit and explicit networks. In this paper, we study
high-dimensional implicit neural networks and provide the high dimensional
equivalents for the corresponding conjugate kernels and neural tangent kernels.
Built upon this, we establish the equivalence between implicit and explicit
networks in high dimensions.
