Paper Citation Record · LEDGER

Distributions In, Distributions Out: The Case for Soft-Label Training

As of 15 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2511.14117.

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

pith.paper-citation-record.v1
2511.14117 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:44:29.052299Z

measured 15 of 15 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:05:42.318273Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:13:15.814617Z

Reference resolution

14 of 14 outbound references displayed

verified exact1
verified fuzzy0
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb6044d0-62b1-43c9-baeb-6146e9c948ce · outbound

This paper cites an unresolved cited work.

Distributions In, Distributions Out: The Case for Soft-Label Training Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:27.986267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f644f69-8ac5-43a0-a22d-36ad0d022ddb · outbound

This paper cites When do annotator demographics matter? measuring the in- fluence of annotator demographics with the POPQUORN dataset.

Distributions In, Distributions Out: The Case for Soft-Label Training When do annotator demographics matter? measuring the in- fluence of annotator demographics with the POPQUORN dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.102557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da1cc068-82f9-43fa-b73e-665837d8c170 · outbound

This paper cites Battleday, Joshua C.

Distributions In, Distributions Out: The Case for Soft-Label Training Battleday, Joshua C

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.262384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 019f915a-efc2-4c97-a9f2-19e2f2c6cc43 · outbound

This paper cites Deep learning from crowds.

Distributions In, Distributions Out: The Case for Soft-Label Training Deep learning from crowds

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.369006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a979dd65-66d0-429c-b168-500e210fc240 · outbound

This paper cites Domain-weighted majority voting for crowdsourcing.IEEE Transactions on Neural Networks and Learning Systems, 30 (1):163–174, 2019.

Distributions In, Distributions Out: The Case for Soft-Label Training Domain-weighted majority voting for crowdsourcing.IEEE Transactions on Neural Networks and Learning Systems, 30 (1):163–174, 2019

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.482351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1586ae5c-37f7-40e0-9d23-1ef25e8bb2c3 · outbound

This paper cites Learning from multiple annotators with varying expertise.Machine learning, 95(3):291–327, 2014.

Distributions In, Distributions Out: The Case for Soft-Label Training Learning from multiple annotators with varying expertise.Machine learning, 95(3):291–327, 2014

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.540632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 464f4ad4-bc92-46ac-b2be-1ae86c9a30b9 · outbound

This paper cites Learning from multi-annotator data: A noise-aware classification framework.ACM Trans.

Distributions In, Distributions Out: The Case for Soft-Label Training Learning from multi-annotator data: A noise-aware classification framework.ACM Trans

Reference 7

Resolution: verified exact; doi, observed 2026-08-03T21:49:02.282941Z

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.

Observation fa4762ee-85cf-4a39-a21d-71db3e73d49c · outbound

This paper cites Distilling the knowledge in a neural network,.

Distributions In, Distributions Out: The Case for Soft-Label Training Distilling the knowledge in a neural network,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.687808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49d932ab-1706-4dba-bf92-eb5615ec68db · outbound

This paper cites Label distribution learning, 2016.

Distributions In, Distributions Out: The Case for Soft-Label Training Label distribution learning, 2016

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.780445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eed605e-e919-469b-99e3-bb0bc28c5f69 · outbound

This paper cites Learn- ing from biased soft labels.

Distributions In, Distributions Out: The Case for Soft-Label Training Learn- ing from biased soft labels

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.842658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6fed57e-a36b-4f63-9254-8d85c492a848 · outbound

This paper cites Learning with confidence: Training better classifiers from soft labels.Machine Learning, 114(238), 2025.

Distributions In, Distributions Out: The Case for Soft-Label Training Learning with confidence: Training better classifiers from soft labels.Machine Learning, 114(238), 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.905388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08f93014-f45a-42b3-a11d-fa59b3721d9e · outbound

This paper cites Don’t waste a single annotation: improving single-label classifiers through soft labels.

Distributions In, Distributions Out: The Case for Soft-Label Training Don’t waste a single annotation: improving single-label classifiers through soft labels

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.970782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b599787-b073-47be-bf87-4ca283b17e8f · outbound

This paper cites A theoretical analysis of soft-label vs hard-label training in neural networks.

Distributions In, Distributions Out: The Case for Soft-Label Training A theoretical analysis of soft-label vs hard-label training in neural networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:29.052299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b94ebfc-b91f-41e3-91fc-9adfbb68b8e6 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Distributions In, Distributions Out: The Case for Soft-Label Training Distilling the Knowledge in a Neural Network

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:28.742241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 695e91d3-16b9-4090-b9a4-70ec8aff6039 · inbound

Metric-Dependent Annotation Saturation for Learning from Label Distributions cites this paper.

Metric-Dependent Annotation Saturation for Learning from Label Distributions Distributions In, Distributions Out: The Case for Soft-Label Training

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-31T02:03:06.445988Z

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.