Training a neural network's tangent kernel with the KARE risk estimate produces a kernel predictor that matches or beats the network itself and its after-training kernel on several benchmarks.
Smoothing noisy data with spline functions: estimating the cor- rect degree of smoothing by the method of generalized cross-validation,
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Training NTK to Generalize with KARE
Training a neural network's tangent kernel with the KARE risk estimate produces a kernel predictor that matches or beats the network itself and its after-training kernel on several benchmarks.