most citedDeep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

22 citations · 22 across the 1 of their papers we have counts for

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1 paper

cs.LG2020★ 22 cited

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul +3

In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) machines, whose training dynamics is well-approxi…