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Paper Citation Record · LEDGER

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1802.07191.

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

pith.paper-citation-record.v1
1802.07191 v3

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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:03:44.520294Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T01:16:31.718728Z

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 fdd14ff0-b811-48d3-b909-603cc0b009b6 · inbound

EPNAS: Efficient Progressive Neural Architecture Search cites this paper.

EPNAS: Efficient Progressive Neural Architecture Search Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-05-25T01:16:31.720061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-25T01:15:41.635247Z digest=sha256:a398e151c816f3836b139313d597467d524b05c6643f152eaa6116f5698c92d4

Observation 03d91427-7b54-4acb-99a6-5eb5d31d19b5 · inbound

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning cites this paper.

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T11:03:44.520294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:03:44.520294Z digest=sha256:d5b127f69bd86480bad19c6e1b5839b94275f1d8058252be7320ddb227297dcf