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Paper Citation Record · LEDGER

Lower Bounds for Compressed Sensing with Generative Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.02938.

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

pith.paper-citation-record.v1
1912.02938 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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:28:22.116581Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:50:12.521323Z

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 8eb0034e-1e53-4b07-a535-dd0eb1fc2a2a · inbound

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis cites this paper.

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis Lower Bounds for Compressed Sensing with Generative Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-14T13:28:22.116581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:28:22.116581Z digest=sha256:6b4de9dc56b6c7ba806472da0ddc6e7788be0b624013d7b0e117d14256cabe40

Observation 496ec104-e718-4fb9-912e-73619374cfb0 · inbound

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models cites this paper.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Lower Bounds for Compressed Sensing with Generative Models

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:50:12.530292Z

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=2026-08-14T10:50:12.348567Z digest=sha256:157a047e5387b002f0dc78a2e99f45d8e694a706ced88b2d2358c39f622ee23d