Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2202.07728.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-19T16:06:05.508610Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T16:07:41.051575Z
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 9eaf3925-d6a3-4768-b8f7-b96761bbf5e2 · inbound
From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks cites this paper.
From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:07:41.062745Z
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.