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Paper Citation Record · LEDGER

On the Power and Limitations of Random Features for Understanding Neural Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1904.00687.

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

pith.paper-citation-record.v1
1904.00687 v4

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:17:37.255620Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T19:11:09.711340Z

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 a6100a71-0acb-4cce-9b2a-8d1fe5fbf906 · inbound

Limitations of Lazy Training of Two-layers Neural Networks cites this paper.

Limitations of Lazy Training of Two-layers Neural Networks On the Power and Limitations of Random Features for Understanding Neural Networks

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-25T19:11:09.714949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-25T19:11:02.369698Z digest=sha256:bb0ae85a76c0591de13f8d663802ca0ace73d307e591103451801448b37d16fc

Observation cee354a0-a4a9-4536-9824-796fe7941253 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks On the Power and Limitations of Random Features for Understanding Neural Networks

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T14:17:37.255620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:17:37.255620Z digest=sha256:da7c33a4eabae0236fdb358e551ade789ddc311d03d6b22a027759e757f17906