Paper Citation Record · LEDGER

Meta-learning Representations for Learning from Multiple Annotators

As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.10259.

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

pith.paper-citation-record.v1
2506.10259 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:40:26.583626Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact5
verified fuzzy7
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd56c10d-7bda-414b-823e-d9bbc0956985 · outbound

This paper cites The number of classes in each task is four, and the number of support data per class (shot) was one, three, and five.

Meta-learning Representations for Learning from Multiple Annotators The number of classes in each task is four, and the number of support data per class (shot) was one, three, and five

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:40:26.842160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56011f7d-0b2a-4d3d-9573-4c2fdc045ee2 · outbound

This paper cites Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing.

Meta-learning Representations for Learning from Multiple Annotators Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:40:26.883943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8049ab05-a330-4682-8411-c7cb975b7599 · outbound

This paper cites an unresolved cited work.

Meta-learning Representations for Learning from Multiple Annotators Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:40:27.074919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30ecddc0-d2ed-4640-8707-7b468c268b8e · outbound

This paper cites The gray and non-gray nodes represent observe and unobserved variables, respectively.

Meta-learning Representations for Learning from Multiple Annotators The gray and non-gray nodes represent observe and unobserved variables, respectively

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:27.058210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c0a9108-988e-4627-abd9-5189b0c0da3c · outbound

This paper cites The number of classes in each task is ten, and the number of support data per class is one, three, and five.

Meta-learning Representations for Learning from Multiple Annotators The number of classes in each task is ten, and the number of support data per class is one, three, and five

Reference 10

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:40:26.753845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ba16706-4a6d-4c59-b5d8-fe0218ab3076 · outbound

This paper cites Here, methods with the symbol ‘MV’ used majority voting for determining the label of each support example.

Meta-learning Representations for Learning from Multiple Annotators Here, methods with the symbol ‘MV’ used majority voting for determining the label of each support example

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:27.011875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de142032-e78d-4e68-a069-422d5979a942 · outbound

This paper cites We used four-class classification problem: three support examples per class and five annotators.

Meta-learning Representations for Learning from Multiple Annotators We used four-class classification problem: three support examples per class and five annotators

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:26.996234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf7cab80-8158-41db-98f4-e1625a20cf43 · outbound

This paper cites Boldface denotes the best and comparable methods according to the paired t-test (p= 0.05).

Meta-learning Representations for Learning from Multiple Annotators Boldface denotes the best and comparable methods according to the paired t-test (p= 0.05)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:26.967038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de4f777d-5a79-44e9-bfa3-47f2c7cb4d49 · outbound

This paper cites Tables 3 and 4 show the average test accuracy with different numbers of support data and annotators on Omniglot and Miniimagenet, respectively.

Meta-learning Representations for Learning from Multiple Annotators Tables 3 and 4 show the average test accuracy with different numbers of support data and annotators on Omniglot and Miniimagenet, respectively

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:26.981997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50f96ec6-6da4-46b9-87ce-8bc61c9476b6 · outbound

This paper cites Few-shot Learning for Topic Modeling.

Meta-learning Representations for Learning from Multiple Annotators Few-shot Learning for Topic Modeling

Reference 2015

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:40:26.927662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41b904eb-310f-4635-a96f-be0397848f14 · outbound

This paper cites A Survey on Programmatic Weak Supervision.

Meta-learning Representations for Learning from Multiple Annotators A Survey on Programmatic Weak Supervision

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T04:40:25.737399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bd7c64f-e03f-477c-bc49-1c5b1d058774 · outbound

This paper cites an unresolved cited work.

Meta-learning Representations for Learning from Multiple Annotators Unresolved cited work

Reference 2017

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:40:27.091225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7675b3ad-5d49-41c2-b262-bd4643175afb · outbound

This paper cites Crowdsourcing with Meta-Workers: A New Way to Save the Budget.

Meta-learning Representations for Learning from Multiple Annotators Crowdsourcing with Meta-Workers: A New Way to Save the Budget

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:40:26.951101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd7fd594-c9ac-4a40-a730-570326b64d0c · outbound

This paper cites We also evaluated other recent methods (Liang et al., 2022; Gao et al.,.

Meta-learning Representations for Learning from Multiple Annotators We also evaluated other recent methods (Liang et al., 2022; Gao et al.,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:27.027385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2990aa7d-3601-49bc-91c5-70ac0e9e426a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Meta-learning Representations for Learning from Multiple Annotators Adam: A Method for Stochastic Optimization

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T04:40:25.259375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57ef387d-6a95-4a46-a4aa-fd3a16e0c01a · outbound

This paper cites highway”, “inside city.

Meta-learning Representations for Learning from Multiple Annotators highway”, “inside city

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:40:27.043231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.