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Paper Citation Record · LEDGER

Factorization tricks for LSTM networks

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1703.10722.

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

pith.paper-citation-record.v1
1703.10722 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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-24T21:18:24.133880Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-24T21:19:57.103355Z

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 22365b7c-b74b-4fb3-92e8-b07d521b7e91 · inbound

Attention Is All You Need cites this paper.

Attention Is All You Need Factorization tricks for LSTM networks

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-09T03:35:57.943327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-09T03:35:55.867645Z digest=sha256:cd3b363d4d14ce4e3a0ca956b608c1642b18e3c677654dbe3080bed6a16e4d3f

Observation d365640e-a9cf-42dc-bd9d-3da87c396062 · inbound

Separable Convolutional LSTMs for Faster Video Segmentation cites this paper.

Separable Convolutional LSTMs for Faster Video Segmentation Factorization tricks for LSTM networks

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-24T21:19:57.107052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-24T21:18:24.133880Z digest=sha256:d55ef1d085cf75e450ff1c0c55faa052105d9d195a1cab07078385c47d9a7a0e