On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint

Autor:	Ling, Zenan, Liao, Zhenyu, Qiu, Robert C.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by Workshop on High-dimensional Learning Dynamics, ICML 2023, Honolulu, Hawaii
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.16425 Zobrazit plný text záznamu View this record from Arxiv