Paper Citation Record · LEDGER

Credal Bayesian Deep Learning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.09656.

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

pith.paper-citation-record.v1
2302.09656 v5

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 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:21:13.218461Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:54:01.197095Z

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 e32132e4-49f6-4d0e-9ac2-c4f518c7a656 · inbound

A Unified Evaluation Framework for Epistemic Predictions cites this paper.

A Unified Evaluation Framework for Epistemic Predictions Credal Bayesian Deep Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T05:47:07.311288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aea8afb1-c22c-4336-b5b0-2c596b3c75d7 · inbound

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' cites this paper.

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' Credal Bayesian Deep Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:21:13.218461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06115b3f-35d0-41a9-92d9-a41461efc11c · inbound

Statistical Multicriteria Evaluation of LLM-Generated Text cites this paper.

Statistical Multicriteria Evaluation of LLM-Generated Text Credal Bayesian Deep Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:01:22.668977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fec17322-a5c7-4196-86fa-e2c6d43a3295 · inbound

Quantification of Credal Uncertainty: A Distance-Based Approach cites this paper.

Quantification of Credal Uncertainty: A Distance-Based Approach Credal Bayesian Deep Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a721b563-a0e6-4aaa-a299-7e4d7ce267b1 · inbound

Conformalised imprecise inference for robust extrapolation under limited data cites this paper.

Conformalised imprecise inference for robust extrapolation under limited data Credal Bayesian Deep Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:54:01.198719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.