Pith. sign in

Paper Citation Record · LEDGER

A Primer on Bayesian Neural Networks: Review and Debates

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.16314.

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

pith.paper-citation-record.v1
2309.16314 v1

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

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:20:19.652116Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:29:00.690047Z

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 d9f7bc9e-abeb-4926-84c9-d115c100fa23 · inbound

Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows cites this paper.

Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows A Primer on Bayesian Neural Networks: Review and Debates

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T10:20:19.652116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:20:19.652116Z digest=sha256:0370385e978b360b85c2b183a6aff3b5c9a98a6be6f6fe6cce7a19101e17b1f2

Observation 53d4c8e9-cb92-40bb-9cb2-edd54826b21e · inbound

Last Layer Hamiltonian Monte Carlo cites this paper.

Last Layer Hamiltonian Monte Carlo A Primer on Bayesian Neural Networks: Review and Debates

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:50.361368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:50.361368Z digest=sha256:25c693aa5eb502645373806f19a87d8989118d17ed23c6118d4c26071952f2ca

Observation 5454adf2-2216-462d-bb4a-1bfe4a24a805 · inbound

Minimaxity and Admissibility of Bayesian Neural Networks cites this paper.

Minimaxity and Admissibility of Bayesian Neural Networks A Primer on Bayesian Neural Networks: Review and Debates

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:45:50.634344Z

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=2026-05-10T19:34:41.269341Z digest=sha256:593909eeb86abe8627a9337d7beb5195b1b2bda2ba97aa96a911df27fcf12e69

Observation 78d16ac1-761b-4e02-8ee4-ea8771022436 · inbound

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks cites this paper.

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks A Primer on Bayesian Neural Networks: Review and Debates

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T22:29:00.691562Z

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=arxiv_source observed=2026-06-26T23:33:16.496226Z digest=sha256:b67e1daaa20524b7e236fd642970912cf99c9692acc906452449590de453793e

Observation d1d8c4b4-6ad6-4617-9218-c6c372d8a319 · inbound

Uncertainty quantification for trustworthy deep learning: Methods and measures cites this paper.

Uncertainty quantification for trustworthy deep learning: Methods and measures A Primer on Bayesian Neural Networks: Review and Debates

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-31T13:23:56.740296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T13:23:56.740296Z digest=sha256:79a275ac7cdf4fe3a76f2341080cdc288fbec0f78fcbdad640113e66b5b75286