AI RESEARCH
Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective
arXiv CS.LG
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ArXi:2605.21692v1 Announce Type: new Characterizing precisely the asymptotic generalization error of neural networks using parameters that can be estimated efficiently is a crucial problem in machine learning, which relies heavily on heuristics and practitioners' intuition to make key design choices. In order to mitigate this issue, we