Yann LeCun: Inductive Biases and Regularization Drive Generalization
keunwoochoi · x · 2026-07-17
Yann LeCun points out that inductive biases and regularization are the fundamental drivers of all forms of generalization. Neural networks possess inductive biases inherently through their architecture, alongside explicit or implicit regularization. For example, constraining weight magnitudes ensures that the input-output function satisfies Lipschitz continuity, resulting in a smooth scoring function.
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