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Paper Citation Record · LEDGER

Training Machine Learning models at the Edge: A Survey

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.02619.

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

pith.paper-citation-record.v1
2403.02619 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:01:03.356164Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T01:10:33.928290Z

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 ff06b7b7-fb56-4d4c-947b-c4fe8a7b1500 · inbound

Vision-Based Embedded System for Noncontact Monitoring of Preterm Infant Behavior in Low-Resource Care Settings cites this paper.

Vision-Based Embedded System for Noncontact Monitoring of Preterm Infant Behavior in Low-Resource Care Settings Training Machine Learning models at the Edge: A Survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T12:01:03.356164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:01:03.356164Z digest=sha256:d3ec9d026314b7fcc443e55a83fbcc89c2e62f8948f05cf12fa223f43da4c45c

Observation 64de6a6b-8c83-4254-adee-243b1d7365e6 · inbound

Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network cites this paper.

Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network Training Machine Learning models at the Edge: A Survey

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:10:33.931254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-18T01:09:49.902797Z digest=sha256:af125a31bbe8ef991c7486b1fefaab69283c2f0c18fcef15b6b086cbeedb7159

Observation 8bc6cc4d-3cdf-44c2-81e0-6eee66cddce6 · inbound

Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing cites this paper.

Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing Training Machine Learning models at the Edge: A Survey

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:36:08.228852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-08T15:03:20.201082Z digest=sha256:5c5a493ac18c75c19c1d82726eaed11a34852e60398c87445ff03f537be45fe2