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Paper Citation Record · LEDGER

Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1412.3121.

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

pith.paper-citation-record.v1
1412.3121 v2

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:46:22.369091Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-23T21:23:27.898198Z

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 108d603a-b375-49bd-a6ea-a5b681a1a630 · inbound

Recent Advances in Multimodal Affective Computing: An NLP Perspective cites this paper.

Recent Advances in Multimodal Affective Computing: An NLP Perspective Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

Reference 126

Resolution
verified exact
local_arxiv, observed 2026-05-23T21:23:27.901271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T21:19:06.228443Z digest=sha256:1c53178bcdca135934e76a8f224389f7e3adbfa7c2e28258ac1abfcb4bfd5e3f

Observation 9c2c6e6a-6503-40ec-91a9-d6581241142f · inbound

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning cites this paper.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T10:46:22.369091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.369091Z digest=sha256:2ca34cb4543c18e36b47fd20e072649e9bd89413feea31094269dc9d56fce816