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Paper Citation Record · LEDGER

On the Learnability of Deep Random Networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1904.03866.

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

pith.paper-citation-record.v1
1904.03866 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:27:50.065809Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eaaf8b5a-6b17-4dcf-81ef-5cf8aca37a0f · inbound

High Accuracy and High Fidelity Extraction of Neural Networks cites this paper.

High Accuracy and High Fidelity Extraction of Neural Networks On the Learnability of Deep Random Networks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-14T05:27:50.065809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T05:27:50.065809Z digest=sha256:ec2cf6c999c4d4123b4dccd401b007982c81fdff7b8ccec41427bf0907ed0ade

Observation bbd9b8b4-76eb-453d-b0d5-ff113828a6b6 · inbound

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently On the Learnability of Deep Random Networks

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-04T23:32:21.127608Z

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=arxiv_source observed=2026-05-12T05:27:11.761971Z digest=sha256:69bede3a30e4d6862be11a18346151dc72f5a0c500d4e1a6f91f7781ecbc84db