On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 31 Aug 2023.
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7 of 11 references.
References · 7
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
- 2019Deep Equilibrium Modelscited by 615
- 2020Multiscale Deep Equilibrium Modelscited by 151
- 2020Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networkscited by 77
- 2022Global Convergence of Over-parameterized Deep Equilibrium Modelscited by 10
- 2021Random matrices in service of ML footprint: ternary random features with no performance losscited by 7
- 2023Global Convergence Rate of Deep Equilibrium Models with General Activationscited by 4
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