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Paper Citation Record · LEDGER

Variational Dropout via Empirical Bayes

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1811.00596.

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

pith.paper-citation-record.v1
1811.00596 v2

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:38:10.328851Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:45:14.757854Z

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 b4a7f798-c14e-4853-a71a-6c5878a320ce · inbound

BARNN: A Bayesian Autoregressive and Recurrent Neural Network cites this paper.

BARNN: A Bayesian Autoregressive and Recurrent Neural Network Variational Dropout via Empirical Bayes

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-09T23:38:10.328851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T23:38:10.328851Z digest=sha256:b14d019b5d9b50a892ceb4a7796cbf9d059a3733af3a70d442e14d9ec5d24225

Observation 2fea1849-06b6-4990-bba0-cb7966a5221a · inbound

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks cites this paper.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Variational Dropout via Empirical Bayes

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:45:14.818433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T14:45:13.680399Z digest=sha256:bdd40f8ee9b781f2ee7e15b028c6f4d897bbaba7d780a1c02b10776cbe693098