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Paper Citation Record · LEDGER

Concise Explanations of Neural Networks using Adversarial Training

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.06583.

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

pith.paper-citation-record.v1
1810.06583 v9

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:41:50.461438Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:41:50.523514Z

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 e08f56a8-7385-49a5-b722-3920e2619faa · inbound

Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods cites this paper.

Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods Concise Explanations of Neural Networks using Adversarial Training

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:41:50.529169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T19:41:50.461438Z digest=sha256:0bb611de17dac22079bc99f62c9fe8dfde6a945638d3ef5ceccc9b508f235102

Observation 710ec529-1e65-4839-a8b7-dac2f503d592 · inbound

A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models cites this paper.

A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models Concise Explanations of Neural Networks using Adversarial Training

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T08:14:21.059567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:14:21.059567Z digest=sha256:c5c949c074b431257a880d000face386df3297916d7ebc1763938322a5ffd711