File:NTK evolution.gif

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English: At initialization, an ensemble of wide neural networks is a zero-mean Gaussian process; during training (gradient descent on mean-square error), the ensemble evolves according to the neural tangent kernel. The converged ensemble is a Gaussian process whose mean is the ridgeless kernel regression estimator and whose variance vanishes on the training points.
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Author Dkarkada

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Date/TimeThumbnailDimensionsUserComment
current17:15, 8 May 2023Thumbnail for version as of 17:15, 8 May 2023900 × 600 (3.96 MB)Dkarkada (talk | contribs)change xaxis label
05:23, 3 May 2023Thumbnail for version as of 05:23, 3 May 2023900 × 600 (3.94 MB)Dkarkada (talk | contribs)clarified NTK curve in the legend
05:14, 3 May 2023Thumbnail for version as of 05:14, 3 May 2023900 × 600 (3.74 MB)Dkarkada (talk | contribs)Uploaded while editing "Neural tangent kernel" on en.wikipedia.org

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