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Paper Citation Record · LEDGER

Optimization for deep learning: theory and algorithms

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.08957.

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

pith.paper-citation-record.v1
1912.08957 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:00:38.463850Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:43:44.794782Z

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 78d20b1f-17a6-45ad-9640-859b7bedfddf · inbound

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation cites this paper.

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation Optimization for deep learning: theory and algorithms

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T22:00:38.463850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T22:00:38.463850Z digest=sha256:a877942130de9d56eb51a61dd9b87f3de1cba35c9f4c34c75e0804b1cf323e34

Observation 09673eaf-1bd1-4f24-9fed-59568c47a922 · inbound

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs cites this paper.

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs Optimization for deep learning: theory and algorithms

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-07T10:43:44.851511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:43:43.996289Z digest=sha256:0ce021e83dfaffd07d2c66b2c830343c4ca5668c9e5f18b99e734e1bfdbbe328