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.
Essential boundedness does not imply pointwise uniform universality
Statement refuted
Assume Countable Choice. Every measurable, locally essentially bounded activation whose discontinuity-set closure is Lebesgue null and which is not polynomial almost everywhere yields single-hidden-layer networks uniformly dense in for every compact .
Statement references
Facts & Assumptions
Given: for and for , and
Every finite single-hidden-layer network is on the line (Single-hidden-layer network realization).
A nonzero real polynomial has only finitely many distinct roots (A nonzero real polynomial of degree has no more than distinct real roots).
Countable subsets of are Lebesgue null under Countable Choice (Every at most countable subset of is Lebesgue null; in particular ).
is the class of measurable, locally essentially bounded activations whose discontinuity-set closure is null (Locally essentially bounded activations with null discontinuity closure).
Countable Choice means every sequence of nonempty sets has a function choosing one element from each (The Axiom of Countable Choice ()).
Counterexample
The spike set is countable and null by [F3]. Thus almost everywhere and is locally essentially bounded. Its discontinuities are exactly , a closed null set: away from this set the step part is locally constant and there are no spikes nearby. Hence the activation satisfies [F4].
Consider a finite network on . Absorb constant affine units into , combine terms with identical affine maps and discard groups with zero combined coefficient. Call a nonconstant affine map active when its zero lies in and it takes positive values arbitrarily close to that zero at points of . If an active group remains, fix its zero and choose, among active groups at with nonzero coefficient, one with minimal ; write its combined coefficient as .
The function is not polynomial almost everywhere. If a polynomial agreed with it almost everywhere, it would vanish almost everywhere on , so [F2] would force it to be the zero polynomial; this contradicts almost everywhere on .
Set , choosing sufficiently large that . The chosen unit has preactivation and contributes . For a unit with a different zero, its preactivation tends to a nonzero value; if the limit is negative the unit is eventually negative, and if positive it lies near only finitely many reciprocals, each attained at most once by the nonconstant affine sequence. Thus it spikes at only finitely many . At the same zero, an active group with opposite sign has negative preactivation on this sequence; one with the same sign and a distinct affine map has slope ratio . If is irrational the equation is impossible; if in lowest terms it requires . Choose the infinite sequence , where is the product of the finitely many such numerators (take if there are none); then none of those other groups spikes. Along a tail of this sequence all other spike terms vanish and all Heaviside contributions remain bounded, so . Thus a bounded network can have no active group with nonzero coefficient.
For a bounded network, every nonconstant group that remains has no positive threshold crossing in . Its Heaviside part is constant on , and its spike set in is finite because its positive preactivation values stay bounded away from zero. Constant units are constant as well. Hence is constant outside a finite subset of .
If is unbounded, it cannot approximate the bounded target uniformly. If it is bounded, choose outside the finite exceptional set with ; then , so at least one of and exceeds . Thus no network approximates on with error below , refuting the pointwise-uniform claim.
Depends on
- Locally essentially bounded activations with null discontinuity closure
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Single-hidden-layer network realization
- Every at most countable subset of $\mathbb{R}^n$ is Lebesgue null; in particular $\lambda_1(\mathbb{Q})=0$
- A nonzero real polynomial of degree $n$ has no more than $n$ distinct real roots
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
24 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
- Leshno, Lin, Pinkus, and Schocken, Multilayer Feedforward Networks With a Non-Polynomial Activation Function Can Approximate Any Function, Definitions 3–5 and Theorem 1 (standard reference, not scraped)
- Yves Moreau, Dynamical Neural Networks and Composition Networks for System Modeling, Chapter 2 §2.3 (standard reference, not scraped)