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Paper Citation Record · LEDGER

Dropout as a Bayesian Approximation: Appendix

As of 24 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 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 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-24T06:31:00.690269+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T00:35:03.194380Z

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 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 2026-07-24T06:31:00.690269+00:00.

source=pdf_text observed=2026-05-24T14:38:10.952106Z digest=sha256:2f4f2c259969b8c1a3007cc39e4fe15bb3691b57932daf8d07eac108257d2724

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 2026-07-24T06:31:00.690269+00:00.

source=pdf_text observed=2026-06-28T00:35:03.194380Z digest=sha256:96a8b5385d31979bcd02d538ae38773befccf327c1572e8722c6bd0849cbba16