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Paper Citation Record · LEDGER

Deep metric learning using Triplet network

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1412.6622.

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

pith.paper-citation-record.v1
1412.6622 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 2026-08-14T15:09:56.717550Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T12:37:54.433775Z

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 6efa552b-840d-4297-920f-3f313c505c9f · inbound

Learning a Unified Embedding for Visual Search at Pinterest cites this paper.

Learning a Unified Embedding for Visual Search at Pinterest Deep metric learning using Triplet network

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T15:09:56.717550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:09:56.717550Z digest=sha256:d160d2f17dcdf0ec34ea276f3691bf5201a06961a29f8c19c9a4bbb060f454c9

Observation f6ca0787-78b0-40f9-9976-df3aaa9098c0 · inbound

A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints cites this paper.

A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints Deep metric learning using Triplet network

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-14T12:37:54.448747Z

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=2026-08-14T12:37:53.998897Z digest=sha256:c9047e85d10b40e42fd6b36e3a16fb56194c5895e2726ee3be8a0eaa7e2234ba