Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
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Abstract (arXiv)
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding of the connections and differences between implicit DEQs and explicit neural network models. In this paper, leveraging recent advances in random matrix theory (RMT), we perform an in-depth analysis on the eigenspectra of the conjugate kernel (CK) and neural tangent kernel (NTK) matrices for implicit DEQs, when the input data are drawn from a high-dimensional Gaussian mixture. We prove, in this setting, that the spectral behavior of these Implicit-CKs and NTKs depend on the DEQ activation function and initial weight variances, but only via a system of four nonlinear equations. As a direct consequence of this theoretical result, we demonstrate that a shallow explicit network can be carefully designed to produce the same CK or NTK as a given DEQ. Despite derived here for Gaussian mixture data, empirical results show the proposed theory and design principle also apply to popular real-world datasets.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 57 references that are in the map. First posted on arXiv 5 Feb 2024.
Builds on
35 of 57 references.
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Built on by
Citing papers per year: 2024: 3; 2025: 1; 2026: 2.
6 papers.
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flowscited by 4
- 2024IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networkscited by 3
- 2024Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspectivecited by 3
- 2024On Dissipativity of Cross-Entropy Loss in Training ResNetscited by 1
- 2026Consistency Deep Equilibrium Modelscited by 1
- 2026Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approachcited by 1
- 2026Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approach3 Feb 2026
- 2026Consistency Deep Equilibrium Models3 Feb 2026
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows3 Mar 2025
- 2024Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective30 Oct 2024
- 2024IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks11 Oct 2024
- 2024On Dissipativity of Cross-Entropy Loss in Training ResNets29 May 2024