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Paper Citation Record · LEDGER

Variational Inference to Measure Model Uncertainty in Deep Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1902.10189.

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

pith.paper-citation-record.v1
1902.10189 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:37:55.024781Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:33:40.328751Z

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 8294d29b-6f71-4c13-991f-e7dc1b16a8e1 · inbound

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions cites this paper.

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions Variational Inference to Measure Model Uncertainty in Deep Neural Networks

Reference 166

Resolution
unresolved
no resolver link, observed 2026-08-11T20:37:55.024781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:37:55.024781Z digest=sha256:2bb18a731fa7f07a4f0ff02f58d569c579cd5d3e490d3457a1dbd14449ef7720

Observation 9b748e9e-e3ee-432b-9ca8-ba89153c5cdd · inbound

SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis cites this paper.

SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis Variational Inference to Measure Model Uncertainty in Deep Neural Networks

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:33:40.467491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T15:33:37.871620Z digest=sha256:2bd02c88035e2e935f8bc254d08d1145af52f612b91a23a3f2b80420d5e4d162