# On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures Source: [On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:22:05.587Z Authors: Maxim Samarin, Volker Roth, David Belius Published: 2020-06-24T11:40:36.000Z ## Abstract The Neural Tangent Kernel (NTK) is an important milestone in the ongoing effort to build a theory for deep learning. Its prediction that sufficiently wide neural networks behave as kernel methods, or equivalently as random feature models, has been confirmed empirically for certain wide architectures. It remains an open question how well NTK theory models standard neural network architectures of widths common in practice, trained on complex datasets such as ImageNet. We study this question empirically for two well-known convolutional neural network architectures, namely AlexNet and LeNet, and find that their behavior deviates significantly from their finite-width NTK counterparts. For wider versions of these networks, where the number of channels and widths of fully-connected layers are increased, the deviation decreases. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()