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Claims about how neural networks generalize from training data to unseen examples, including observed generalization despite overparameterization and the adequacy of theoretical explanations. Excludes specific phenomena such as compositional or spectral generalization, which have their own tags.

Naive function-counting arguments predict neural-network overfitting, contrary to observed generalization.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →Machine Learningimportance · minorImportance 0.35, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

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