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by cs702
1313 days ago
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Interpolation == extreme overfitting. Double descent phenomenon is what happens after interpolation. -- RESPONDING TO YOUR LAST COMMENT (after reaching thread depth limit): Think of it this way: Why and how does the model's performance continue to improve on previously unseen samples after the model has fully overfit (interpolated between) all training samples? Interpolation is not the end-point in training, but a temporary threshold after which models learn to generalize better, improving on interpolation. How is it that these models improve on interpolation? |
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