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Paper Citation Record · LEDGER

The Case for Bayesian Deep Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2001.10995.

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

pith.paper-citation-record.v1
2001.10995 v1

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:39:56.405537Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:29:29.681429Z

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 cd44a9c7-5f0f-4300-9b3d-252d0310b318 · inbound

Bayesian Deep Learning for Discrete Choice cites this paper.

Bayesian Deep Learning for Discrete Choice The Case for Bayesian Deep Learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:56.405537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:56.405537Z digest=sha256:149b44b5a24cc6debcb24808c14b9c3c1a4d7d9ca0b15756e0547ee200f0d015

Observation b17c9036-5aeb-4c29-b29d-b0ba4bde49d5 · inbound

Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations cites this paper.

Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations The Case for Bayesian Deep Learning

Reference 83

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:29:29.683561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T18:04:07.755536Z digest=sha256:0aac839504638ade3d2e21953f5b89f7900fce347aac11b69fc4d111436c3254