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Paper Citation Record · LEDGER

Practical Block-wise Neural Network Architecture Generation

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1708.05552.

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

pith.paper-citation-record.v1
1708.05552 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 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T13:28:13.540690Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T13:30:52.928584Z

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 9dda45a7-05d3-490c-ab23-86e09299ad4a · inbound

Searching for Activation Functions cites this paper.

Searching for Activation Functions Practical Block-wise Neural Network Architecture Generation

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:50:54.119659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-12T02:50:53.955004Z digest=sha256:7ac022c45e35e976f0f358d16d4415fca2b177475fcfc4a008f6a748a1b1b099

Observation 8fa6f02a-6a67-4af4-90e6-324823f2d2b3 · inbound

Mise en abyme with artificial intelligence: how to predict the accuracy of NN, applied to hyper-parameter tuning cites this paper.

Mise en abyme with artificial intelligence: how to predict the accuracy of NN, applied to hyper-parameter tuning Practical Block-wise Neural Network Architecture Generation

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-25T13:30:52.931247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-25T13:28:13.540690Z digest=sha256:540a3c4bd9b3ff82599473d451b7e3e97f283183eebf14dba4ccd50923627a42