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Paper Citation Record · LEDGER

Asymmetric Loss For Multi-Label Classification

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2009.14119.

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

pith.paper-citation-record.v1
2009.14119 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:24:58.428977Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

64
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ba269440-03a1-4f1b-9ae8-9a5c0c8c6e6c · inbound

Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025 cites this paper.

Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025 Asymmetric Loss For Multi-Label Classification

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-24T08:44:14.295775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-24T08:39:33.303000Z digest=sha256:4d2c1a26d17ebb0f1548d3f4927cae9aa3302daa79dfaa99a2fe9a95b50c9b8e

Observation 4d304ef5-2a6e-40de-a3fd-4c393b1b0fd9 · inbound

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches cites this paper.

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches Asymmetric Loss For Multi-Label Classification

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T12:24:58.428977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:24:58.428977Z digest=sha256:0161fbf4438ec0e4f26a65bbf2e35ec1327de5d5963c38ab37b9870777721412

Observation 295e864a-85e0-40d9-8cac-771b58780c1f · inbound

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease cites this paper.

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease Asymmetric Loss For Multi-Label Classification

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T19:33:20.378169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:33:20.378169Z digest=sha256:edf0e13c429be20b163301d5d1e8a666af27c4d569a42110a8a18cd11e3d8b74

Observation 347ef7a2-0438-458a-af64-9e917e7958a0 · inbound

Depth Jitter: Seeing through the Depth cites this paper.

Depth Jitter: Seeing through the Depth Asymmetric Loss For Multi-Label Classification

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T22:53:05.255324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:53:05.255324Z digest=sha256:436acf8692e81f8d37fba08f87c2ccdc82ad2ff0694fa7d209abb4089cfff3aa

Observation cbb3e358-defd-4bc4-94b0-884752c42c17 · inbound

HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection cites this paper.

HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection Asymmetric Loss For Multi-Label Classification

Reference 42

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T14:04:44.073963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-30T14:00:25.238585Z digest=sha256:77ef9f0aee676aae03a11362e4eac5b8bc6ef0c7cd15e4acb653a852bc36240c