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Paper Citation Record · LEDGER

Algorithms for Verifying Deep Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1903.06758.

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

pith.paper-citation-record.v1
1903.06758 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:50.157809Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T22:17:11.621188Z

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 56d4b4d0-eb24-46c4-b5b5-253ded3e55d9 · inbound

Polyhedral Enclosures: An Efficient Combinatorial Abstraction for Nonlinear Neural Feedback Systems cites this paper.

Polyhedral Enclosures: An Efficient Combinatorial Abstraction for Nonlinear Neural Feedback Systems Algorithms for Verifying Deep Neural Networks

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-22T22:17:11.624481Z

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.

source=pdf_text observed=2026-05-22T22:15:13.245491Z digest=sha256:2fa89d567f9516ac7540c4c3b57276ad020b53d91192e85320e1dc589225d8cf

Observation aeab106f-690f-43dc-bd56-26351dff7755 · inbound

Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks cites this paper.

Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks Algorithms for Verifying Deep Neural Networks

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:50.157809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:50.157809Z digest=sha256:65ec8883b9689d7640844612086e27ece3ef10e020628d6e4289cb2875e64a6e