Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1710.00486.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T05:01:18.269176Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-14T04:41:21.000907Z
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 3fa2e218-5b7c-4b47-bbca-3aa0f82018ce · inbound
Detecting Deep Neural Network Defects with Data Flow Analysis cites this paper.
Detecting Deep Neural Network Defects with Data Flow Analysis DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:18.269176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c61cbfd3-e102-4e76-b507-aa8c44f8fe51 · inbound
Data Sanity Check for Deep Learning Systems via Learnt Assertions cites this paper.
Data Sanity Check for Deep Learning Systems via Learnt Assertions DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T04:41:21.024749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 08f4f099-c403-44de-9500-8ae4a3612e1c · inbound
Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations cites this paper.
Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:46:34.844066Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.