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Paper Citation Record · LEDGER

On the Approximation Properties of Random ReLU Features

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1810.04374.

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

pith.paper-citation-record.v1
1810.04374 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:42:25.416049Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T00:04:06.941779Z

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 1fc8a788-d0fd-4ee6-89c3-175cb6a118b0 · inbound

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs cites this paper.

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs On the Approximation Properties of Random ReLU Features

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T17:42:25.416049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:42:25.416049Z digest=sha256:4929f5359e36698fc95129753da46e35935830b814c8d54bc8255b8ea1561278

Observation eb5e9eb8-f7b2-4e09-9606-cc786d5dc77a · inbound

Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations cites this paper.

Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations On the Approximation Properties of Random ReLU Features

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:51:11.200322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T10:52:56.423871Z digest=sha256:84445d2325d0b6921ec334535f33b8bee0a82d3f6c8baef63a7ee97a22e732a1

Observation fee80405-e874-4249-af0e-0eb80761a0fd · inbound

Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees cites this paper.

Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees On the Approximation Properties of Random ReLU Features

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T19:06:11.475620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T12:35:14.766807Z digest=sha256:2c243c3a5b342cc16309217fb3927bf14b374f7bf7c05a9fd3c95731dacc7ac3

Observation 93042ee0-ae36-40c5-9f13-cca0df80f6c9 · inbound

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model cites this paper.

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Approximation Properties of Random ReLU Features

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:39:38.415132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-15T01:39:21.733359Z digest=sha256:117f58480bfee2487012cabbd69bd3500089d6e7dd6d9d32e1718438164be181

Observation 482de40e-1003-4934-af62-c6369dffc765 · inbound

Random Neural Network Expressivity for Non-Linear Partial Differential Equations cites this paper.

Random Neural Network Expressivity for Non-Linear Partial Differential Equations On the Approximation Properties of Random ReLU Features

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-30T00:04:06.943310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T23:47:02.293881Z digest=sha256:8f5b02e6fe8832729cb3fc37cd6b12747a60463d538197dbc9d68455b512fb5b

Observation bab58271-c5a6-419b-84af-8f591db98109 · inbound

On high probability of universal approximation in random basis expansions with non-continuous weight sampling cites this paper.

On high probability of universal approximation in random basis expansions with non-continuous weight sampling On the Approximation Properties of Random ReLU Features

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T20:43:31.357313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T20:43:31.357313Z digest=sha256:fd435bb4189d45a4a3b6d347817cb9d83f9649a98763a94e94f1c3ef42bde7c5