File:NTK evolution.gif
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Summary
| Description |
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. |
| Date | |
| Source | Own work |
| Author | Dkarkada |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 18:15, 8 May 2023 | | 900 × 600 (3.96 MB) | wikimediacommons>Dkarkada | change xaxis label |
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