Paper Citation Record · LEDGER

Dropout as a Bayesian Approximation: Appendix

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1506.02157.

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

pith.paper-citation-record.v1
1506.02157 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:37:45.568249Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T14:17:03.057948Z

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 0736ae61-c16b-4e4d-832a-b0ca26ae5410 · inbound

Solar image denoising with convolutional neural networks cites this paper.

Solar image denoising with convolutional neural networks Dropout as a Bayesian Approximation: Appendix

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T14:37:45.568249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 762a9845-c5d0-45e5-9840-8f9e61c925e7 · inbound

Bayesian Neural Networks: An Introduction and Survey cites this paper.

Bayesian Neural Networks: An Introduction and Survey Dropout as a Bayesian Approximation: Appendix

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-05-24T14:39:35.240331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c42c17a3-175c-41ef-b3b2-549f3626ed53 · inbound

Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control cites this paper.

Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control Dropout as a Bayesian Approximation: Appendix

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T05:20:18.458563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82579cbe-7734-4f7b-bb67-99afd2ab1075 · inbound

A method for classification of data with uncertainty using hypothesis testing cites this paper.

A method for classification of data with uncertainty using hypothesis testing Dropout as a Bayesian Approximation: Appendix

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:47.935681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ffe8208-aa9b-494b-9838-fb987c842b6e · inbound

Graph Evidential Learning for Anomaly Detection cites this paper.

Graph Evidential Learning for Anomaly Detection Dropout as a Bayesian Approximation: Appendix

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:41.296419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea92b51a-bab1-4eb4-8e38-4290a27bc94c · inbound

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample cites this paper.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Dropout as a Bayesian Approximation: Appendix

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.576156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b52649-da48-4ff5-b1a9-a9c985cc793e · inbound

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design cites this paper.

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design Dropout as a Bayesian Approximation: Appendix

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-07-02T14:17:03.059072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 48eccfff-6a4e-4e10-b31b-d2c236b3111c · inbound

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design cites this paper.

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design Dropout as a Bayesian Approximation: Appendix

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T12:20:14.009699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.