How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Correctly scaled bounded product approximation
Statement
Fix . For every , there is a ReLU network on that satisfies and . Its depth, number of weights, and number of computation units are at most , where depends only on .
Statement references
Facts & Assumptions
Given: , , and the square networks in Logarithmic-size ReLU approximation of the square.
For every , a ReLU network satisfies , , and has depth, weights, and units (Logarithmic-size ReLU approximation of the square).
Proof
Set and, for , put All three arguments lie in . Replacing by gives , so the absolute error is at most .
Since , substituting makes the first and third terms equal and the second zero; substituting makes the first and second terms equal and the third zero. The formula is globally defined, so this cancellation holds for every real value of the other coordinate. Absolute value is represented by , and the three square networks run in parallel followed by an affine combination, so the depth, weights, and units are , since and .
Depends on
Used by
Dependency tree · two levels
2 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Dmitry Yarotsky, Error bounds for approximations with deep ReLU networks, Proposition 3 (standard reference, not scraped)
- Matus Telgarsky, Deep learning theory lecture notes, Chapter 5 (standard reference, not scraped)