Paper Citation Record · LEDGER

Theory of Deep Learning III: explaining the non-overfitting puzzle

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1801.00173.

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

pith.paper-citation-record.v1
1801.00173 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:40:07.518385Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T16:27:09.712495Z

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 8f302d72-f1b1-4472-a43b-44949bc5e6c1 · inbound

ORI: O Routing Intelligence cites this paper.

ORI: O Routing Intelligence Theory of Deep Learning III: explaining the non-overfitting puzzle

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T19:40:07.518385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45591153-5370-4639-b4a9-67541276db72 · inbound

The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning cites this paper.

The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning Theory of Deep Learning III: explaining the non-overfitting puzzle

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T15:02:51.402132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad4f6c86-9fb6-4868-9484-b091083cc8dd · inbound

Why Does Agentic Safety Fail to Generalize Across Tasks? cites this paper.

Why Does Agentic Safety Fail to Generalize Across Tasks? Theory of Deep Learning III: explaining the non-overfitting puzzle

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:05:59.982076Z

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 7c9050ec-30e2-4ce3-8f9a-d0e3013a90ab · inbound

Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks cites this paper.

Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks Theory of Deep Learning III: explaining the non-overfitting puzzle

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T16:27:09.715135Z

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.