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Paper Citation Record · LEDGER

NETT: Solving Inverse Problems with Deep Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1803.00092.

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

pith.paper-citation-record.v1
1803.00092 v3

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:24.026961Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:12:27.440656Z

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 fb69cac0-53cf-41f1-b609-5655f5d266fb · inbound

Few-Shot Test-Time Optimization Without Retraining for Semiconductor Recipe Generation and Beyond cites this paper.

Few-Shot Test-Time Optimization Without Retraining for Semiconductor Recipe Generation and Beyond NETT: Solving Inverse Problems with Deep Neural Networks

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:12:27.522994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T15:12:24.026961Z digest=sha256:e3ca56478ea36c9415776ea236e73dcd28a75fc9bbd42390b62b2ca8a820c359

Observation d8145d99-7da8-4f17-90f8-d2da46e9c17e · inbound

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification cites this paper.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification NETT: Solving Inverse Problems with Deep Neural Networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-03T05:14:55.673883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:14:55.673883Z digest=sha256:63ad71f4712b699a2c4443b31d0463b5c9741cf90f98d4f7649e50a3c00eb64e