Paper Citation Record · LEDGER

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1902.06720.

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

pith.paper-citation-record.v1
1902.06720 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:02:29.363232Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:47:26.170047Z

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 594230e1-80ae-49d6-b302-f918a27c482b · inbound

ID3 Learns Juntas for Smoothed Product Distributions cites this paper.

ID3 Learns Juntas for Smoothed Product Distributions Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:46:10.453628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 120cf4a5-a5ff-41d6-9330-e56ced565a45 · inbound

Limitations of Lazy Training of Two-layers Neural Networks cites this paper.

Limitations of Lazy Training of Two-layers Neural Networks Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:11:09.720890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a451b1ed-81d9-4611-bc66-04cc96959125 · inbound

Finite size corrections for neural network Gaussian processes cites this paper.

Finite size corrections for neural network Gaussian processes Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T11:02:29.363232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b664bb50-65b6-4ce9-b454-a87a21a16496 · inbound

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence cites this paper.

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-14T10:27:42.116640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 129bb48c-afea-4464-bd2f-fc7a6a007143 · inbound

Scaling Laws for Neural Language Models cites this paper.

Scaling Laws for Neural Language Models Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T04:51:47.655093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f93b6da1-2f8b-48a5-be69-5411989a108a · inbound

Scaling Laws for Autoregressive Generative Modeling cites this paper.

Scaling Laws for Autoregressive Generative Modeling Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:49:43.845507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 211ee424-5b2c-40b0-a697-b066df62bbae · inbound

Assessing Quantum Advantage for Gaussian Process Regression cites this paper.

Assessing Quantum Advantage for Gaussian Process Regression Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:36.947914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b702e3d-ed15-450d-82e6-2ad9ceb3121d · inbound

Quantitative Understanding of PDF Fits and their Uncertainties cites this paper.

Quantitative Understanding of PDF Fits and their Uncertainties Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T13:34:53.578576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a58d14a6-3086-4fb1-b4d5-4e1d85ecc7c9 · inbound

Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization cites this paper.

Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T13:33:27.943370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a73b0343-0c7a-4784-b010-6e2376458c4e · inbound

Some Inverse Problems in Particle Physics cites this paper.

Some Inverse Problems in Particle Physics Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 26

Resolution: malformed identifier; arxiv_id, observed 2026-07-02T22:47:26.171522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d7f499b-3882-4e7a-81e8-47d929529bdc · 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 Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-01T12:55:43.888929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d142f73-3175-44f7-b782-48919a9600dd · 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 Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T09:36:50.510084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b093b9-3943-4bba-846d-131ffef317d6 · inbound

Pre-Strings Lectures on Artificial Intelligence cites this paper.

Pre-Strings Lectures on Artificial Intelligence Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-12T06:14:03.658427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.