Paper Citation Record · LEDGER

Deep Network Approximation Characterized by Number of Neurons

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1906.05497.

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

pith.paper-citation-record.v1
1906.05497 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:22:06.134775Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:14:57.082537Z

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 fadc2d8e-24e3-4bdb-b6ed-b2fd5a0803b8 · inbound

Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery cites this paper.

Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery Deep Network Approximation Characterized by Number of Neurons

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:57.579746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2982187a-b80e-41de-91fa-c7dd37743f3a · inbound

Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon cites this paper.

Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon Deep Network Approximation Characterized by Number of Neurons

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.080558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2b828041-c78a-4ada-9437-f12dda0dff64 · inbound

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models cites this paper.

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models Deep Network Approximation Characterized by Number of Neurons

Reference 145

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:06.134775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b1a639c-2ed2-4535-abcc-92faa2d45545 · inbound

Calibration Prediction Interval for Non-parametric Regression and Neural Networks cites this paper.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep Network Approximation Characterized by Number of Neurons

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T16:40:43.265938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6072599a-a113-47ed-bb51-55d30d4c498a · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Deep Network Approximation Characterized by Number of Neurons

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:31:13.972364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9424a94-b615-4e59-8d1a-79de0b13d58c · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Deep Network Approximation Characterized by Number of Neurons

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.084144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f7ac491d-49e6-4d00-a3f6-72d7b53d291c · inbound

Learning Sparse Compositional Functions with Norm-Constrained Neural Networks cites this paper.

Learning Sparse Compositional Functions with Norm-Constrained Neural Networks Deep Network Approximation Characterized by Number of Neurons

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:33:58.299170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c40e4fe8-6a1e-403d-8588-1f29fecff3c7 · inbound

Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View cites this paper.

Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View Deep Network Approximation Characterized by Number of Neurons

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T15:14:01.521184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.