Computer Science > Machine Learning
[Submitted on 1 Aug 2023 (v1), last revised 9 Aug 2023 (this version, v3)]
Title:An Exact Kernel Equivalence for Finite Classification Models
View PDFAbstract:We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine. We compare our exact representation to the well-known Neural Tangent Kernel (NTK) and discuss approximation error relative to the NTK and other non-exact path kernel formulations. We experimentally demonstrate that the kernel can be computed for realistic networks up to machine precision. We use this exact kernel to show that our theoretical contribution can provide useful insights into the predictions made by neural networks, particularly the way in which they generalize.
Submission history
From: Michael Geyer [view email][v1] Tue, 1 Aug 2023 20:22:53 UTC (9,355 KB)
[v2] Mon, 7 Aug 2023 22:47:33 UTC (9,625 KB)
[v3] Wed, 9 Aug 2023 16:25:24 UTC (9,625 KB)
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