Paper Citation Record · LEDGER

Uncertainty Estimation for the Open-Set Text Classification systems

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2604.08560.

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

pith.paper-citation-record.v1
2604.08560 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T09:59:59.926049Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efd04ac1-51bc-4c69-9375-0625a4783551 · outbound

This paper cites an unresolved cited work.

Uncertainty Estimation for the Open-Set Text Classification systems Unresolved cited work

Reference 1

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Observation fd6d20e7-bcdb-420c-9672-fc8f0ab6619a · outbound

This paper cites Jain Stan Z.

Uncertainty Estimation for the Open-Set Text Classification systems Jain Stan Z

Reference 2

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Observation c27d838a-4b80-4be2-8c07-25d778a3f87f · outbound

This paper cites Open-world machine learning: Applications, challenges, and opportunities.

Uncertainty Estimation for the Open-Set Text Classification systems Open-world machine learning: Applications, challenges, and opportunities

Reference 3

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Observation d0d79236-2721-4f25-a2e6-3fc91ae11617 · outbound

This paper cites A review of uncertainty quantiﬁcation in deep learning: Techniques, applications and challenges.

Uncertainty Estimation for the Open-Set Text Classification systems A review of uncertainty quantiﬁcation in deep learning: Techniques, applications and challenges

Reference 4

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This paper cites G¨ unther, S.

Uncertainty Estimation for the Open-Set Text Classification systems G¨ unther, S

Reference 5

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This paper cites Holistic uncertainty estimation for open-set recognition.

Uncertainty Estimation for the Open-Set Text Classification systems Holistic uncertainty estimation for open-set recognition

Reference 6

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This paper cites Spherical conﬁdence learning for face recognition.

Uncertainty Estimation for the Open-Set Text Classification systems Spherical conﬁdence learning for face recognition

Reference 7

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Observation 1b264bfe-eacc-4b52-9dbc-ff28fce31a92 · outbound

This paper cites Open set text classiﬁcation using convolu- tional neural networks.

Uncertainty Estimation for the Open-Set Text Classification systems Open set text classiﬁcation using convolu- tional neural networks

Reference 8

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Uncertainty Estimation for the Open-Set Text Classification systems Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K

Reference 9

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Uncertainty Estimation for the Open-Set Text Classification systems Deep unknown intent detection with margin loss

Reference 10

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Observation 5ccf7100-890d-4858-84fa-16d82dd64576 · outbound

This paper cites Authorship attribution using text distortion.

Uncertainty Estimation for the Open-Set Text Classification systems Authorship attribution using text distortion

Reference 11

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Reference 12

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Uncertainty Estimation for the Open-Set Text Classification systems Authorship attribution

Reference 13

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Observation f3ed8c40-6d19-4376-a193-91a67287ec41 · outbound

This paper cites Open set authorship attribution toward demystifying victorian periodicals.

Uncertainty Estimation for the Open-Set Text Classification systems Open set authorship attribution toward demystifying victorian periodicals

Reference 14

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Observation 35abdd3a-3fb4-49c2-aab9-71254253496b · outbound

This paper cites Open-set semi-supervised text classiﬁcation with latent outlier softening.

Uncertainty Estimation for the Open-Set Text Classification systems Open-set semi-supervised text classiﬁcation with latent outlier softening

Reference 15

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Observation fba67d65-4a12-45d0-8aed-04f3d7618b25 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Uncertainty Estimation for the Open-Set Text Classification systems Arcface: Additive angular margin loss for deep face recognition

Reference 16

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This paper cites Rudd, and Terrance E.

Uncertainty Estimation for the Open-Set Text Classification systems Rudd, and Terrance E

Reference 17

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Observation 45f9e273-3faf-458c-8b3f-67059b208c07 · outbound

This paper cites VoxBlink2: A 100K+ speaker recognition corpus and the open-set speaker-identiﬁcation benchmark.

Uncertainty Estimation for the Open-Set Text Classification systems VoxBlink2: A 100K+ speaker recognition corpus and the open-set speaker-identiﬁcation benchmark

Reference 18

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Observation f6dbfe7e-086d-4f8f-8434-8247e49ed883 · outbound

This paper cites DOC: Deep open classiﬁcation of text documents.

Uncertainty Estimation for the Open-Set Text Classification systems DOC: Deep open classiﬁcation of text documents

Reference 19

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Uncertainty Estimation for the Open-Set Text Classification systems Towards open set deep networks

Reference 20

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Observation a1618a8e-dd46-4d82-8d0e-d170a0883583 · outbound

This paper cites Breaking the closed world assumption in text classiﬁcation.

Uncertainty Estimation for the Open-Set Text Classification systems Breaking the closed world assumption in text classiﬁcation

Reference 21

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Uncertainty Estimation for the Open-Set Text Classification systems Unresolved cited work

Reference 22

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Observation bf6eee3e-ad54-412b-952d-57c8a507da54 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems , volume 30.

Uncertainty Estimation for the Open-Set Text Classification systems What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems , volume 30

Reference 23

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This paper cites Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning.

Uncertainty Estimation for the Open-Set Text Classification systems Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning

Reference 24

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Observation 59e052ba-2833-47b5-9c24-fd98d2b8f472 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Uncertainty Estimation for the Open-Set Text Classification systems Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 25

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Uncertainty Estimation for the Open-Set Text Classification systems Rethinking the Authorship Verification Experimental Setups

Reference 26

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This paper cites Overview of the cross-domain authorship veriﬁcation task at PAN 2020.

Uncertainty Estimation for the Open-Set Text Classification systems Overview of the cross-domain authorship veriﬁcation task at PAN 2020

Reference 27

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This paper cites Recent advances in open set recognition: A survey.

Uncertainty Estimation for the Open-Set Text Classification systems Recent advances in open set recognition: A survey

Reference 28

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Uncertainty Estimation for the Open-Set Text Classification systems Unresolved cited work

Reference 29

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Uncertainty Estimation for the Open-Set Text Classification systems Selective classiﬁcation for deep neural networks

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Uncertainty Estimation for the Open-Set Text Classification systems Fadeeva, R

Reference 31

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Uncertainty Estimation for the Open-Set Text Classification systems Huber, P

Reference 32

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Uncertainty Estimation for the Open-Set Text Classification systems Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T10:00:25.288885Z

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Observation 1084a306-d6d9-4857-bb64-04df58564496 · outbound

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Uncertainty Estimation for the Open-Set Text Classification systems Fisher, Toby Lewis, and Brian J

Reference 34

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Uncertainty Estimation for the Open-Set Text Classification systems On calibration of modern neural networks

Reference 35

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Uncertainty Estimation for the Open-Set Text Classification systems Hallucination detection in llms with topological divergence on attention graphs

Reference 36

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.