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Paper Citation Record · LEDGER

Anomaly Detection using One-Class Neural Networks

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1802.06360.

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

pith.paper-citation-record.v1
1802.06360 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-24T22:50:15.989553Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-24T22:55:03.037751Z

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 ca8b8039-ac48-4015-b941-2d9010cbc10c · inbound

AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data cites this paper.

AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data Anomaly Detection using One-Class Neural Networks

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-05-24T22:55:03.040280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-24T22:50:15.989553Z digest=sha256:457f1fc0e41bd60027b1c309561ae38a039aae10613f015446cc289395b20cb9

Observation 466be41b-b52f-4ed1-8fab-ba3495b00272 · inbound

Better Protein Function Prediction by Modeling Survivorship Bias cites this paper.

Better Protein Function Prediction by Modeling Survivorship Bias Anomaly Detection using One-Class Neural Networks

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-11T01:40:52.245634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-11T01:34:14.101643Z digest=sha256:495120c47f824c70f808dc0bac6722a93ab0764fd3922a6119eecf6b18849e7a