Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective

In three linesNew arXiv paper introduces the 'Representation Gap', a metric related to neural network generalization error with better asymptotic dynamics. Authors derive precise asymptotic equivalence governed by task intrinsic dimension, validated on synthetic and realistic datasets.

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