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Paper Citation Record · LEDGER

Natural Evolution Strategies

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1106.4487.

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

pith.paper-citation-record.v1
1106.4487 v1

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:14.057331Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:48:53.056924Z

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 49ec47d1-d607-4b4c-ae16-ab26ad23e606 · inbound

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise cites this paper.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Natural Evolution Strategies

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T14:37:14.057331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:37:14.057331Z digest=sha256:f90853260e0ed959049020d411a9d49da5f4c1c90c9926db44bd61ea29576432

Observation 59339402-f08a-4723-bd2c-7434f8185b54 · inbound

How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.

How Should We Meta-Learn Reinforcement Learning Algorithms? Natural Evolution Strategies

Reference 80

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:48:53.127951Z

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=2026-08-06T14:48:52.287410Z digest=sha256:5195b1ed5a2df299bbf95af41b3f3608c19622463d7baad2a80892040f20d776