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Paper Citation Record · LEDGER

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.02129.

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

pith.paper-citation-record.v1
2307.02129 v5

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:24:11.841843Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:53:15.453147Z

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 d9ce5ff2-bbab-4095-8848-c2ac83354e34 · inbound

Blink of an eye: a simple theory for feature localization in generative models cites this paper.

Blink of an eye: a simple theory for feature localization in generative models How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-09T17:24:11.841843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:24:11.841843Z digest=sha256:96f5c41cdc663945608f40655f5ddcf2a077a3ea94c1cc36bffd15acc410c0c8

Observation 5bf6fd5a-d3d4-4285-86ca-ca7442b63242 · inbound

Generative models on phase space cites this paper.

Generative models on phase space How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:53:15.454585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T20:52:29.032797Z digest=sha256:ca4e796a4db607f2234128934e11e4a3e33eb3bffbecfc2129563fe430b1ee57