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Paper Citation Record · LEDGER

EENA: Efficient Evolution of Neural Architecture

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.07320.

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

pith.paper-citation-record.v1
1905.07320 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:38:08.284350Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T05:10:55.050289Z

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 abbfaf7f-7339-433b-a130-a40d133bc212 · inbound

AutoML: A Survey of the State-of-the-Art cites this paper.

AutoML: A Survey of the State-of-the-Art EENA: Efficient Evolution of Neural Architecture

Reference 149

Resolution
unresolved
no resolver link, observed 2026-08-14T15:38:08.284350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:38:08.284350Z digest=sha256:292ac9b6d225f1c2d6724396baf348a44e6c4bcdcbf6e00e6fdaf34550e50a4e

Observation c073e86f-0eae-465e-b0db-362a602337d8 · inbound

Rethinking the Number of Channels for the Convolutional Neural Network cites this paper.

Rethinking the Number of Channels for the Convolutional Neural Network EENA: Efficient Evolution of Neural Architecture

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-14T05:10:55.056591Z

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.

source=arxiv_source observed=2026-08-14T05:10:55.018170Z digest=sha256:51f2a54526b24f2b05f3d86511bff83f6766464dda7a6b717e6a39b7132fa9d0