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 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 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

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measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:31.329010Z

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

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Outbound references

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Pith citing papers

Observation 8be635fa-47d5-49f2-a0a0-a2f9a8fb95c8 · inbound

Reliable and scalable variable importance estimation via warm-start and early stopping cites this paper.

Reliable and scalable variable importance estimation via warm-start and early stopping Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.553238Z

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Observation 7ca11418-c230-4f88-bfb2-23b878a7c6a1 · inbound

PINNs for Solving Unsteady Maxwell's Equations: Convergence Issues and Comparative Assessment with Compact Schemes cites this paper.

PINNs for Solving Unsteady Maxwell's Equations: Convergence Issues and Comparative Assessment with Compact Schemes Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:31.329010Z

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Observation 4419049d-6958-4b77-a41d-0d987659dbc9 · inbound

Practical Efficiency of Muon for Pretraining cites this paper.

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

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T01:03:53.170724Z

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Observation d66cbd82-f4f9-4f15-bc38-3ea2e993624b · inbound

Precise gradient descent training dynamics for finite-width multi-layer neural networks cites this paper.

Precise gradient descent training dynamics for finite-width multi-layer neural networks Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:49.850596Z

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Observation 2c5c2bfd-66fd-47a1-bdc5-76a3ab71567e · inbound

New Evidence of the Two-Phase Learning Dynamics of Neural Networks cites this paper.

New Evidence of the Two-Phase Learning Dynamics of Neural Networks Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:13:29.977799Z

Source-reported events for the cited work

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Observation d0170ffb-f7aa-446a-a3f4-7fdff9103954 · inbound

Adversarial Training from Mean Field Perspective cites this paper.

Adversarial Training from Mean Field Perspective Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:21.902600Z

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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

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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

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Observation ea63cfd8-d429-48e1-af50-f8099316e3ec · inbound

Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size cites this paper.

Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:54:42.557483Z

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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

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Observation e5bc9257-f2a5-4fea-9b49-43d03d44e941 · inbound

Asymptotic convexity of wide and shallow neural networks cites this paper.

Asymptotic convexity of wide and shallow neural networks Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:42.888134Z

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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

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Observation b60c682f-1bbe-4f4c-bcc6-0b718a55927a · inbound

Correlation flow governs learning at criticality cites this paper.

Correlation flow governs learning at criticality Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T00:17:57.766785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.