Paper Citation Record · LEDGER

Why Shallow Networks Struggle to Approximate and Learn High Frequencies

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.17301.

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

pith.paper-citation-record.v1
2306.17301 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 2026-08-16T06:30:59.297886+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:41:25.076453Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T16:47:10.354637Z

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 2f01ed4b-7f35-44fc-ad83-982570df4476 · inbound

Orthogonal greedy algorithm for linear operator learning with shallow neural network cites this paper.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Why Shallow Networks Struggle to Approximate and Learn High Frequencies

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.945747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9278439c-a300-46f1-8d3f-e946c8c5cf7b · inbound

High precision PINNs in unbounded domains: application to singularity formulation in PDEs cites this paper.

High precision PINNs in unbounded domains: application to singularity formulation in PDEs Why Shallow Networks Struggle to Approximate and Learn High Frequencies

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:25.076453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae9bdddb-5cfb-483a-9b52-f501687f32d2 · inbound

Structured First-Layer Initialization Pre-Training Techniques to Accelerate Training Process Based on $\varepsilon$-Rank cites this paper.

Structured First-Layer Initialization Pre-Training Techniques to Accelerate Training Process Based on $\varepsilon$-Rank Why Shallow Networks Struggle to Approximate and Learn High Frequencies

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:06:11.162441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13e2f5d3-55a5-485e-813d-a67af71cb148 · inbound

Second-Order Path Kernel Interpolation Formulas in Machine Learning cites this paper.

Second-Order Path Kernel Interpolation Formulas in Machine Learning Why Shallow Networks Struggle to Approximate and Learn High Frequencies

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T16:47:10.355899Z

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.