Paper Citation Record · LEDGER

Universal approximations of permutation invariant/equivariant functions by deep neural networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1903.01939.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1903.01939 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:05.372643Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-25T04:55:23.373221Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7794a9b4-6678-4442-968e-16b9b954ceee · inbound

Transformers Are Universally Consistent cites this paper.

Transformers Are Universally Consistent Universal approximations of permutation invariant/equivariant functions by deep neural networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:05.372643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccf669b8-5906-4e79-bdff-dc7f92446039 · inbound

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? cites this paper.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Universal approximations of permutation invariant/equivariant functions by deep neural networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:33.732858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86d80782-4b83-48a7-8411-d9426dc4b8da · inbound

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions cites this paper.

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions Universal approximations of permutation invariant/equivariant functions by deep neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T22:14:23.162051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423053e0-c6df-4002-800e-d27b44f09f80 · inbound

Any-Dimensional Invariant Universality cites this paper.

Any-Dimensional Invariant Universality Universal approximations of permutation invariant/equivariant functions by deep neural networks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:55:23.376646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.