Paper Citation Record · LEDGER

GNN's Uncertainty Quantification using Self-Distillation

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.20046.

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

pith.paper-citation-record.v1
2506.20046 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:03:44.133670Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

27 of 27 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e546b1cd-b23c-4c43-b231-40f5ca9da28a · outbound

This paper cites AIChE Journal68(6), e17696 (2022).

GNN's Uncertainty Quantification using Self-Distillation AIChE Journal68(6), e17696 (2022)

Reference 1

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Observation cab61e49-936c-4831-bbe0-bec410805bbd · outbound

This paper cites On Self-Distilling Graph Neural Network.

GNN's Uncertainty Quantification using Self-Distillation On Self-Distilling Graph Neural Network

Reference 2

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Observation c8305915-7ef5-40cb-87f5-6e4712703fcd · outbound

This paper cites Artificial Intelligence for Medicine pp.

GNN's Uncertainty Quantification using Self-Distillation Artificial Intelligence for Medicine pp

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T23:03:45.340809Z

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Observation 49ec5a93-acf3-4bee-b725-28817ca76916 · outbound

This paper cites Proceedings of the Canadian Conference on Artificial Intelligence (may 27 2022).

GNN's Uncertainty Quantification using Self-Distillation Proceedings of the Canadian Conference on Artificial Intelligence (may 27 2022)

Reference 4

Resolution: verified exact; doi, observed 2026-08-06T23:03:44.958442Z

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Observation a8b01cdb-1e9d-4201-9703-aa357deb4080 · outbound

This paper cites Journal of Machine Learning Research24(43), 1–48 (2023), http://jmlr.org/papers/v24/22-0567.html.

GNN's Uncertainty Quantification using Self-Distillation Journal of Machine Learning Research24(43), 1–48 (2023), http://jmlr.org/papers/v24/22-0567.html

Reference 5

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Observation 10d670b5-75e3-40b8-ac35-e089cc56edc1 · outbound

This paper cites In: ICASSP 2025 - 2025 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP).

GNN's Uncertainty Quantification using Self-Distillation In: ICASSP 2025 - 2025 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 6

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Observation 55b4d246-d8c1-4ffe-8403-050b29202cd0 · outbound

This paper cites In- ternational Journal of Computer Vision129(6), 1789–1819 (Jun 2021).

GNN's Uncertainty Quantification using Self-Distillation In- ternational Journal of Computer Vision129(6), 1789–1819 (Jun 2021)

Reference 7

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Observation 9a840d63-3b81-4d3b-8b15-73b2e6cba3ba · outbound

This paper cites Zargarbashi, S., Antonelli, S., Bojchevski, A.: Conformal prediction sets for graph neural networks.

GNN's Uncertainty Quantification using Self-Distillation Zargarbashi, S., Antonelli, S., Bojchevski, A.: Conformal prediction sets for graph neural networks

Reference 8

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This paper cites In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vish- wanathan, S., Garnett, R.

GNN's Uncertainty Quantification using Self-Distillation In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vish- wanathan, S., Garnett, R

Reference 9

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Observation 7cf31270-26ea-4779-b92a-2725443a0249 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

GNN's Uncertainty Quantification using Self-Distillation Distilling the Knowledge in a Neural Network

Reference 10

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Observation da512511-7339-43f4-aba2-2c3308f8c83a · outbound

This paper cites In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S.

GNN's Uncertainty Quantification using Self-Distillation In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S

Reference 11

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Observation 234f3355-69e1-4f68-8086-2531535a6e11 · outbound

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GNN's Uncertainty Quantification using Self-Distillation Unresolved cited work

Reference 12

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Observation 24cf4c8b-f79c-4b65-a829-ea385bfc084e · outbound

This paper cites In: Proceedings of the AAAI Symposium Series.

GNN's Uncertainty Quantification using Self-Distillation In: Proceedings of the AAAI Symposium Series

Reference 13

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Observation 34c2b53a-8f9b-4c51-b5ce-31ad30fc39bc · outbound

This paper cites In: Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR.

GNN's Uncertainty Quantification using Self-Distillation In: Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR

Reference 14

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Observation ba1e83c9-8259-4a80-a0ed-a09c778aad64 · outbound

This paper cites Journal of Cheminformatics14(1), 2 (1 2022).

GNN's Uncertainty Quantification using Self-Distillation Journal of Cheminformatics14(1), 2 (1 2022)

Reference 15

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Observation 6848db0d-4d17-4ab8-9281-299246ae21df · outbound

This paper cites In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

GNN's Uncertainty Quantification using Self-Distillation In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 16

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Observation f2e5324f-7777-455b-a35d-c27a0451db6a · outbound

This paper cites In: 2020 IEEE International Conference on Big Data (Big Data).

GNN's Uncertainty Quantification using Self-Distillation In: 2020 IEEE International Conference on Big Data (Big Data)

Reference 17

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Observation 3d60d3dd-f4f1-4824-9b9e-439d702d5130 · outbound

This paper cites In: ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020) (2020), www.graphlearning.io.

GNN's Uncertainty Quantification using Self-Distillation In: ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020) (2020), www.graphlearning.io

Reference 18

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GNN's Uncertainty Quantification using Self-Distillation Unresolved cited work

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This paper cites In: Chaudhuri, K., Salakhutdinov, R.

GNN's Uncertainty Quantification using Self-Distillation In: Chaudhuri, K., Salakhutdinov, R

Reference 20

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Observation ea99cea5-dad2-4f3b-a876-7d1ab8eb313b · outbound

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GNN's Uncertainty Quantification using Self-Distillation Unresolved cited work

Reference 21

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GNN's Uncertainty Quantification using Self-Distillation Unresolved cited work

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Observation 6b2ff7ea-267f-41c0-99e7-b151399528af · outbound

This paper cites Uncertainty in Graph Neural Networks: A Survey.

GNN's Uncertainty Quantification using Self-Distillation Uncertainty in Graph Neural Networks: A Survey

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence44(8), 4388–4403 (2022).

GNN's Uncertainty Quantification using Self-Distillation IEEE Transactions on Pattern Analysis and Machine Intelligence44(8), 4388–4403 (2022)

Reference 24

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Observation 724bb582-b320-48a2-afc0-24a2422a1efe · outbound

This paper cites In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data.

GNN's Uncertainty Quantification using Self-Distillation In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Reference 25

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GNN's Uncertainty Quantification using Self-Distillation Unresolved cited work

Reference 26

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This paper cites In: Proceedings of the Conference on Health, Inference, and Learning.

GNN's Uncertainty Quantification using Self-Distillation In: Proceedings of the Conference on Health, Inference, and Learning

Reference 27

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Pith citing papers

No inbound Pith citation observations are available.