Paper Citation Record · LEDGER

On Lazy Training in Differentiable Programming

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1812.07956.

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

pith.paper-citation-record.v1
1812.07956 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:58:25.948535Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:19:44.387011Z

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 683784e5-2eee-4386-946f-c303bc4d09db · inbound

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

Limitations of Lazy Training of Two-layers Neural Networks On Lazy Training in Differentiable Programming

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:11:09.699675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c788795-92da-4ab6-a283-1c306e49f781 · inbound

Parameter Symmetry Potentially Unifies Deep Learning Theory cites this paper.

Parameter Symmetry Potentially Unifies Deep Learning Theory On Lazy Training in Differentiable Programming

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:25.948535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f65ee60-4932-4c50-b9bc-acb42c536454 · 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 On Lazy Training in Differentiable Programming

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T11:19:06.859986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cd50593-94c5-4dc1-8262-f614001f9e8f · inbound

Weighted quantization using MMD: From mean field to mean shift via gradient flows cites this paper.

Weighted quantization using MMD: From mean field to mean shift via gradient flows On Lazy Training in Differentiable Programming

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.867152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88cde142-5bfc-4994-a575-6740f471615b · inbound

Algorithm Development in Neural Networks: Insights from the Streaming Parity Task cites this paper.

Algorithm Development in Neural Networks: Insights from the Streaming Parity Task On Lazy Training in Differentiable Programming

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:51:44.716758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde4bf20-aa6d-462c-a3f1-84dde0c4d658 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks On Lazy Training in Differentiable Programming

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:17:37.058057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 441e05f8-c612-41d4-95a2-075e36ecd83e · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks On Lazy Training in Differentiable Programming

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:51:05.779803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cb4be57c-6b4a-42da-b488-e19331f49cef · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks On Lazy Training in Differentiable Programming

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:54.549032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f438dc7-7a3c-4cdb-a969-5db640d4c255 · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks On Lazy Training in Differentiable Programming

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:45:12.063633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02e38879-314e-42b8-aeb0-d08e87f9e003 · inbound

Channel Location Constrains the Auditability of Subliminal Learning cites this paper.

Channel Location Constrains the Auditability of Subliminal Learning On Lazy Training in Differentiable Programming

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.388437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.