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.

翻译：隐式神经网络已在各类任务中展现出显著成功。然而，关于隐式网络与显式网络之间联系与差异的理论分析仍较为匮乏。本文研究了高维隐式神经网络，并给出了相应共轭核与神经正切核的高维等价形式。在此基础上，我们建立了隐式网络与显式网络在高维空间中的等价性。