Paper Citation Record · LEDGER

Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2212.13881.

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

pith.paper-citation-record.v1
2212.13881 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:19:06.974626Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:23:30.973455Z

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 271e0afb-0925-436c-b665-82c946573226 · inbound

Adaptive kernel predictors from feature-learning infinite limits of neural networks cites this paper.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T11:19:06.974626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d642a020-2fb7-4cc8-8141-684029e43550 · inbound

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems cites this paper.

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:49.634000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7add6fa-6caa-45f2-856d-13bf6aefd0cb · inbound

Steering Autoregressive Music Generation with Recursive Feature Machines cites this paper.

Steering Autoregressive Music Generation with Recursive Feature Machines Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T05:10:54.507767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d9a6768-f600-4836-86ff-a0a9db9a5f0e · inbound

AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark cites this paper.

AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:03:43.708017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 83df945e-52ba-44a1-a89e-727bb022f8d7 · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 148

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:55.910302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea09f00b-8b15-4ee9-8a99-abee78c0d8ac · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Reference 148

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.770002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe206ce3-4a95-4bde-8350-3b2306a2554f · inbound

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization cites this paper.

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:23:30.975617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.