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KindShowingImportancePrizesTopicShortcut Learning
Claims about machine-learning models relying on easy-to-learn superficial or spurious features rather than robust task-relevant features, including how that reliance arises and causes failures under distribution shift.
Deep neural networks often rely on shortcut or spurious features that fail under distribution shift.
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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