Paper Citation Record · LEDGER

Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1902.04760.

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

pith.paper-citation-record.v1
1902.04760 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:57:42.783867Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:47:22.766708Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 cfb6856f-6604-481a-8abb-1ba2473e29a7 · inbound

A ZeNN architecture to avoid the Gaussian trap cites this paper.

A ZeNN architecture to avoid the Gaussian trap Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:57:42.783867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcbc0990-6900-49d8-a00e-16ef11868caf · inbound

Universal Value-Function Uncertainties cites this paper.

Universal Value-Function Uncertainties Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:50:00.918705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 182b4bf2-086e-478b-a7e5-4997fd1f11a4 · inbound

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models cites this paper.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:15.749239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bee39d4-ab59-4dbb-b45a-f55c0cb72b80 · inbound

Viability of perturbative expansion for quantum field theories on neurons cites this paper.

Viability of perturbative expansion for quantum field theories on neurons Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:14:28.124241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5146954e-0f9f-4fb7-b635-82feeb61d8af · inbound

How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences cites this paper.

How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:06:06.144949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87a800be-6b47-4831-aa3f-250587306aa6 · inbound

Function graph transformers universally approximate operators between function spaces cites this paper.

Function graph transformers universally approximate operators between function spaces Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.758972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00e10959-add0-413e-819f-94013d8e3b66 · inbound

Discrete signaling mediates chaotic regularization in recurrent neural networks cites this paper.

Discrete signaling mediates chaotic regularization in recurrent neural networks Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:26:54.925731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27e907ed-961d-4bcc-a5b3-b46ea4c08225 · inbound

How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs cites this paper.

How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:47:22.768393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9593607b-0a96-4c5c-a24e-4d649575b7a9 · inbound

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product cites this paper.

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-01T12:55:43.897049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d71a0b9-d414-4bd7-b5a7-da9014bd65f1 · inbound

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product cites this paper.

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T09:36:50.560914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3589f3d-9464-44e0-b342-9bc4cc55629c · inbound

The Differential Neural Tangent Kernel and Its Positivity cites this paper.

The Differential Neural Tangent Kernel and Its Positivity Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-14T13:34:45.196896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.