Deep learning generalization explained by Occam's razor questioned in new paper

A new paper published on arXiv explores the phenomenon of benign interpolation in deep learning, where models generalize well despite perfectly fitting training data. The authors argue that current explanations, which often invoke Occam's razor or a preference for simplicity among interpolating models, create an explanatory gap. They contend that these new accounts lack a provable connection to generalization, unlike classical statistical learning theory, and that the term "simplicity" is being used to mask an unargued assumption. AI

IMPACT Challenges current theoretical understanding of deep learning generalization, potentially influencing future research directions.

RANK_REASON Academic paper discussing theoretical aspects of deep learning generalization. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• deep learning
• Occam's razor

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