Comment on "Why does deep and cheap learning work so well?" [arXiv:1608.08225]
Not on the map. Why?
Abstract (arXiv)
In a recent paper, "Why does deep and cheap learning work so well?", Lin and Tegmark claim to show that the mapping between deep belief networks and the variational renormalization group derived in [arXiv:1410.3831] is invalid, and present a "counterexample" that claims to show that this mapping does not hold. In this comment, we show that these claims are incorrect and stem from a misunderstanding of the variational RG procedure proposed by Kadanoff. We also explain why the "counterexample" of Lin and Tegmark is compatible with the mapping proposed in [arXiv:1410.3831].
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 2.
3 papers.
- 2021Universality of Winning Tickets: A Renormalization Group Perspectivecited by 8
- 2026Towards Worst-Case Guarantees with Scale-Aware Interpretabilitycited by 3
- 2026Pre-registered tests of solid-state-physics-inspired LLM compression: a cluster-level negative result at small-language-model scalecited by 0
- 2026Pre-registered tests of solid-state-physics-inspired LLM compression: a cluster-level negative result at small-language-model scale28 Sep 2026
- 2026Towards Worst-Case Guarantees with Scale-Aware Interpretability5 Feb 2026
- 2021Universality of Winning Tickets: A Renormalization Group Perspective7 Oct 2021