Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1704.03976.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:13.523130Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-25T12:36:57.812520Z
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 a5b6a5e4-d646-4568-88ed-465ae1fa464b · inbound
Fooling a Real Car with Adversarial Traffic Signs cites this paper.
Fooling a Real Car with Adversarial Traffic Signs Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T12:36:57.815266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8cf2d3ad-e693-4314-b858-013d4521ee48 · inbound
Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning cites this paper.
Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:35:13.523130Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe66714f-8629-4b69-945a-f009041781c9 · inbound
Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.
Optimising Language Models for Downstream Tasks: A Post-Training Perspective Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Reference 156
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:44:44.331551Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.