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Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA.
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Grokking and double descent aren't as mysterious as you think: they're just the push and pull between representation learning and readout calibration.
Forget in-distribution accuracy, the shape of your model's internal representations is a better predictor of real-world generalization.