Paper Citation Record · LEDGER

A statistical framework for efficient out of distribution detection in deep neural networks

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.12967.

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

pith.paper-citation-record.v1
2102.12967 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:43:14.225532Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:27:29.574232Z

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 1c45780c-f582-4d46-815f-516140ec2d46 · inbound

OOD Detection with immature Models cites this paper.

OOD Detection with immature Models A statistical framework for efficient out of distribution detection in deep neural networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:14.225532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccc25084-45ee-4d75-8f67-98af89b7d794 · inbound

Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation cites this paper.

Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation A statistical framework for efficient out of distribution detection in deep neural networks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:51:00.759938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2991dafb-ff18-4bd2-be89-2e7b607dab48 · inbound

Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing cites this paper.

Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing A statistical framework for efficient out of distribution detection in deep neural networks

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:23:39.441504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b14c3b90-4de5-4eb3-b8a6-26173c0906aa · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models A statistical framework for efficient out of distribution detection in deep neural networks

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.576514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8e7161dd-d64d-4070-b387-8a16ea71e343 · inbound

Data Provenance for Image Auto-Regressive Generation cites this paper.

Data Provenance for Image Auto-Regressive Generation A statistical framework for efficient out of distribution detection in deep neural networks

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T10:44:37.323940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.