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Paper Citation Record · LEDGER

On the Validity of Bayesian Neural Networks for Uncertainty Estimation

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.01530.

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

pith.paper-citation-record.v1
1912.01530 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:46:17.463672Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T01:34:30.367711Z

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 887be8d0-66c2-46fd-b644-e42173d6e812 · inbound

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models cites this paper.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models On the Validity of Bayesian Neural Networks for Uncertainty Estimation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.463672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.463672Z digest=sha256:e0be5aa45c6142c0e0816dd5acd35f327b6952ae5101f110b9c94bcda5ecfa22

Observation 50d1aced-02dd-4e17-affe-c14e62b9ff0b · inbound

Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins cites this paper.

Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins On the Validity of Bayesian Neural Networks for Uncertainty Estimation

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-22T01:34:30.371452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-22T01:33:17.807572Z digest=sha256:93d71db8919c90d42cd10b51c2274fb057d0d760aa874719af4c7f0e9f1bf961