Paper Citation Record · LEDGER

A Survey on Universal Approximation Theorems

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2407.12895.

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

pith.paper-citation-record.v1
2407.12895 v1

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 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:05:45.806017Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

6
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6bd62957-d23d-4cb3-a4cb-007512ddda2d · inbound

SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks cites this paper.

SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks A Survey on Universal Approximation Theorems

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T19:05:45.806017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a42096a7-b352-473d-912d-ec30ec2bf9c5 · inbound

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture cites this paper.

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture A Survey on Universal Approximation Theorems

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T00:22:20.494833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 989bf808-9f64-47de-9143-f8ab1b0052e6 · inbound

Ethics through the Facets of Artificial Intelligence cites this paper.

Ethics through the Facets of Artificial Intelligence A Survey on Universal Approximation Theorems

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:02:20.347145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3323c1b9-c3d9-4c21-9252-b89c3f1bdd76 · inbound

Hybrid Least Squares/Gradient Descent Methods for DeepONets cites this paper.

Hybrid Least Squares/Gradient Descent Methods for DeepONets A Survey on Universal Approximation Theorems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T18:02:28.727675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81eb4841-f600-431e-8962-0c48f6926e83 · inbound

Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks cites this paper.

Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks A Survey on Universal Approximation Theorems

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T13:14:10.598442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f6148a3-d81b-4299-b8bf-524ead36c042 · inbound

A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning cites this paper.

A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning A Survey on Universal Approximation Theorems

Reference 245

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:17:48.528119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb9822bc-c93c-4139-b1e3-2d3de4ca52cd · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions A Survey on Universal Approximation Theorems

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T19:11:10.615255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe111056-c3bd-449e-9796-30d31ac3aaae · inbound

Hybrid Least Squares/Gradient Descent Methods for MIONets cites this paper.

Hybrid Least Squares/Gradient Descent Methods for MIONets A Survey on Universal Approximation Theorems

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:45:49.156566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.