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Paper Citation Record · LEDGER

ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1801.00904.

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

pith.paper-citation-record.v1
1801.00904 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:18:31.639194Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T11:18:37.226659Z

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 c7ce0d31-5872-4009-8c5f-b14de9bdd018 · inbound

Error-driven Data-efficient Large Multimodal Model Tuning cites this paper.

Error-driven Data-efficient Large Multimodal Model Tuning ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:18:37.292004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T11:18:31.639194Z digest=sha256:f4f5f643a4fc19d6068e774f687b16bbdb9aff2217671dc8720d5d945a65d0e1

Observation 6428cba5-0440-455f-a9bb-a4c8e1b760d4 · inbound

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction cites this paper.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-02T22:07:06.693896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:07:06.693896Z digest=sha256:f8697619e2ebca438ddd3ed8bf476580d7edd9b8b47a8c8fe1ea1ad48099c292