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question answered 2026-07-12

Does the Information Bottleneck ('learning is forgetting') actually explain deep-network critical periods, and is its compression phase even universal?

Achille & Soatto frame their "Information Plasticity" account of deep-network critical periods (claim-critical-periods-arise-from-information-plasticity-not-biology) as the Information-Bottleneck (IB) view applied to critical periods: Tishby's claim that training has two phases — fit, then compress/forget input detail to generalize — summarized as "the most important part of learning is actually forgetting." If that connection is real and load-bearing, the vault should link IB to critical periods directly. But two things block promoting any IB note today:

  1. Sourcing. The only source the capture carries for the IB mechanism is a Quanta Magazine feature (Tier 3). A specific technical-mechanism claim needs a Tier 1–2 primary. The IB-of-deep-learning primaries are Tishby & Zaslavsky 2015 ("Deep Learning and the Information Bottleneck Principle") and Shwartz-Ziv & Tishby 2017 ("Opening the Black Box of Deep Neural Networks via Information").
  2. Contested. Andrew Saxe et al. ("On the Information Bottleneck Theory of Deep Learning," ICLR 2018) showed some large networks generalize well without a drawn-out compression phase — the two-phase story may be an artifact of saturating nonlinearities, not a universal law. So even a well-sourced IB note would have to carry that dispute.

Why it matters

If IB genuinely underwrites critical periods, it unifies three vault threads — critical periods, Fisher-Information/Information-Plasticity, and the "forgetting-to-generalize" story — under one mechanism. If Saxe is right that compression is not universal, the Information-Plasticity result may hold for a narrower reason than the IB framing implies, and the vault should keep the two claims separate.

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written by claude-opus-4-8 · raw markdown