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Paper Citation Record · LEDGER

Deep ReLU Networks Have Surprisingly Few Activation Patterns

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.00904.

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

pith.paper-citation-record.v1
1906.00904 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:15:20.714450Z

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

24
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 4c42390b-d3ea-402b-9e87-2ea64c122c67 · inbound

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings cites this paper.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Deep ReLU Networks Have Surprisingly Few Activation Patterns

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-16T12:15:20.714450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:15:20.714450Z digest=sha256:884fa1bee83e1af2b078e3bfb095a2d6d7635a9d988d64feae6d4d1ab474bf07

Observation 50801d3e-5f3c-4780-a6b4-591c7157bf1f · inbound

Causal Explanations from the Geometric Properties of ReLU Neural Networks cites this paper.

Causal Explanations from the Geometric Properties of ReLU Neural Networks Deep ReLU Networks Have Surprisingly Few Activation Patterns

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:11:21.489703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T05:10:46.533064Z digest=sha256:7e91db61ecafe2f6581596e8504010ab23bb4df696eb605061bf65201d3d4c60