Paper Citation Record · LEDGER

Data augmentation instead of explicit regularization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1806.03852.

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

pith.paper-citation-record.v1
1806.03852 v5

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 2026-08-07T14:26:58.798231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:10:09.228257Z

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 6ea1f3af-329a-4902-bb67-b42f65015c88 · inbound

Meta-aware Learning in text-to-SQL Large Language Model cites this paper.

Meta-aware Learning in text-to-SQL Large Language Model Data augmentation instead of explicit regularization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:58.798231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bf200ee-35a0-40c3-b072-372e6ea7bf55 · inbound

How does the optimizer implicitly bias the model merging loss landscape? cites this paper.

How does the optimizer implicitly bias the model merging loss landscape? Data augmentation instead of explicit regularization

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:51:13.453709Z

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 5d9c8fb4-b125-4a3f-b739-5d67844a1071 · inbound

Enhancing Adversarial Transferability through Block Stretch and Shrink cites this paper.

Enhancing Adversarial Transferability through Block Stretch and Shrink Data augmentation instead of explicit regularization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T21:01:02.487286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 951d2382-13fa-4f39-807f-cf9a5b98dcb1 · inbound

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? cites this paper.

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? Data augmentation instead of explicit regularization

Reference 149

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:10:09.230143Z

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.