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Paper Citation Record · LEDGER

Do Bayesian Neural Networks Need To Be Fully Stochastic?

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2211.06291.

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

pith.paper-citation-record.v1
2211.06291 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:43:40.252286Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T03:42:27.800434Z

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 b0ff43f7-8a1a-43fd-9fbd-4e2653adb477 · inbound

Active and transfer learning with partially Bayesian neural networks for materials and chemicals cites this paper.

Active and transfer learning with partially Bayesian neural networks for materials and chemicals Do Bayesian Neural Networks Need To Be Fully Stochastic?

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-10T22:43:40.252286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:43:40.252286Z digest=sha256:44e7d0741f6333a77f5b507bf4547b1e485988ffa5d51bc6e3920c4cdc541987

Observation 3c6e2498-4fb0-4ee5-a07f-7718fd80d111 · inbound

Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks cites this paper.

Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks Do Bayesian Neural Networks Need To Be Fully Stochastic?

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:42:27.802469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-23T03:39:45.167149Z digest=sha256:74d69687b141f235cf96444ccc8fd1b199b7773c8c5c53c107af13967aecfc5d