Paper Citation Record · LEDGER

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1910.08485.

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

pith.paper-citation-record.v1
1910.08485 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:46:12.279082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T18:23:50.561216Z

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 2d22d087-5d87-4be6-a8a2-c0d61e990f60 · inbound

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach cites this paper.

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach Understanding Deep Networks via Extremal Perturbations and Smooth Masks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T14:46:12.279082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e9c349e-32d6-45fc-a8e2-00e3db8f1616 · inbound

On Spectral Properties of Gradient-based Explanation Methods cites this paper.

On Spectral Properties of Gradient-based Explanation Methods Understanding Deep Networks via Extremal Perturbations and Smooth Masks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T20:35:45.648598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b40f6bb-8a5b-407e-8a04-95cf69d54458 · inbound

Extremal Contours: Gradient-driven contours for compact visual attribution cites this paper.

Extremal Contours: Gradient-driven contours for compact visual attribution Understanding Deep Networks via Extremal Perturbations and Smooth Masks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:25:34.701265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 80b07603-b725-4f93-bc6d-6cee49774e20 · inbound

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data cites this paper.

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data Understanding Deep Networks via Extremal Perturbations and Smooth Masks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:23:50.562808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.