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This work examines both model behavior and internal representations, using the empirical neural tangent kernel (NTK) as the primary diagnostic tool, and develops practical guidance for designing DR schemes, selecting models, and detecting shortcut learning.
Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases that it cannot justify. No Free Lunch (NFL) theorems force any learner that beats chance on some environments to underperform on others. We might hope that past experien...
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