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Paper Citation Record · LEDGER

A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.03916.

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

pith.paper-citation-record.v1
2204.03916 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 2026-08-06T23:00:46.722928Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 547132fc-21f3-402a-ba52-ae67b0cb261f · inbound

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration cites this paper.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T23:00:46.722928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:00:46.722928Z digest=sha256:f964d1b0771be11ffcce496ccd438ba384c83d3a063e673815665481ae94e7fd

Observation 0d023610-ebe8-4835-b75e-5fe25f6acf05 · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search

Reference 136

Resolution
verified exact
local_arxiv, observed 2026-08-01T16:43:29.251316Z

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=arxiv_source observed=2026-08-01T16:42:19.255015Z digest=sha256:ce45aa8fdcfe11ede935390c1285e8e6d073a5706c9ccc547dc4de48b2d39543