Pith. sign in

Paper Citation Record · LEDGER

Bayesian Neural Network Priors Revisited

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.06571.

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

pith.paper-citation-record.v1
2102.06571 v3

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 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:29:36.564492Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

24
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 88c2eaea-341d-44a5-bb2e-5955524723f7 · inbound

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification cites this paper.

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification Bayesian Neural Network Priors Revisited

Reference 76

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:05:29.163886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T14:04:32.279572Z digest=sha256:b85b025fb3955d3bb0a85116cc135f79237f6987fa2b1f6ac2ac685584ea80b5

Observation b7cb4e0e-01b0-46d6-8f04-1fc3794d048c · inbound

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance cites this paper.

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance Bayesian Neural Network Priors Revisited

Reference 26

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:40:08.467177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-25T19:48:28.532570Z digest=sha256:696b886abe34198a96d072c1b111f39cdba4400a92431192f5e1091510f9a801

Observation d7955de9-b409-414f-9d7d-3f6b2c21de71 · inbound

Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation cites this paper.

Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation Bayesian Neural Network Priors Revisited

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T02:14:10.015792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T02:12:45.635294Z digest=sha256:33be4219c075030bda51012e0019ed17278b980bb082ab9727ba49c65c67b281

Observation 0f01a9da-c7e0-4533-ad27-9d21321856e6 · inbound

ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization cites this paper.

ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization Bayesian Neural Network Priors Revisited

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-02T09:29:36.564492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:29:36.564492Z digest=sha256:5814ffbbd95b458ec1f5ae8ff2bd1bca1ee5d8293a47f218149225442393330d

Observation 7b2a0457-fa0c-4bd7-8a7e-9ebc12b8e15c · inbound

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance cites this paper.

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance Bayesian Neural Network Priors Revisited

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-01T02:41:42.663793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T02:41:42.663793Z digest=sha256:eb29e990388ca6e76cfc5bfb5d5a5f7ccfdb2f404db6c87769984d372712ee3c