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Paper Citation Record · LEDGER

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1810.05546.

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

pith.paper-citation-record.v1
1810.05546 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:14:56.607665Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T17:56:06.806973Z

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 2e250d37-282b-4ffb-84aa-282cb52bfbd5 · inbound

Quality of Uncertainty Quantification for Bayesian Neural Network Inference cites this paper.

Quality of Uncertainty Quantification for Bayesian Neural Network Inference Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-25T17:56:06.810306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-25T17:53:20.471215Z digest=sha256:e7058f5916658ca1271239978a6a80ec10c3a3c784755719b70e824dfe81df91

Observation 919d714a-2389-40ee-a6da-c753721d32f7 · inbound

Providing Machine Learning Potentials with High Quality Uncertainty Estimates cites this paper.

Providing Machine Learning Potentials with High Quality Uncertainty Estimates Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T21:18:35.695687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:18:35.695687Z digest=sha256:df3d17044f16ee6f0590e1b5d970b3afc379b7fac2a800c63f08df49bf13cf77

Observation 9497ba89-95ee-4294-ae63-689e095b17bc · inbound

Last-layer committee machines for uncertainty estimations of benthic imagery cites this paper.

Last-layer committee machines for uncertainty estimations of benthic imagery Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-16T11:14:56.607665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:14:56.607665Z digest=sha256:3238d41b4d620ae8b5cf43836baffa36d2e18bfdf8aea6bd7b5255fad6409124