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Paper Citation Record · LEDGER

AI Generalisation Gap In Comorbid Sleep Disorder Staging

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2603.23582.

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

pith.paper-citation-record.v1
2603.23582 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T19:43:02.198818Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

45 of 45 outbound references displayed

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  • unresolved45
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2add1eb-676e-404e-852d-0ce33198bda9 · outbound

This paper cites Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: A review.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: A review

Reference 1

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:06a2130405e90591fe7ddd0f2c49efd5ab60d8c56fdee4c3313fdf96c959f841

Observation b62b3cb5-207b-408f-9b96-2dc09df8ad37 · outbound

This paper cites Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances

Reference 2

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:6a36fd6ba021f5626688261bdec64cfdebbb8b57b2e3be5496830acf24d77b3f

Observation 569bbc1c-ceb6-4d80-9401-31ca87b45b7c · outbound

This paper cites Financial fraud detection using graph neural networks: A systematic review.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Financial fraud detection using graph neural networks: A systematic review

Reference 3

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:c08a154fca9f48abd3da8aaa75c3d22fe56a4cb170edb4be12a3d132d80073f6

Observation 34e5d7cd-76e3-4e9b-90cc-9264a538d741 · outbound

This paper cites HoloNets: Spectral Convolutions do extend to Directed Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging HoloNets: Spectral Convolutions do extend to Directed Graphs

Reference 4

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:92aafbaf21f23af8d95bc2848815a3fd835929b759da82658edcab49d3299859

Observation e7dc2054-0783-4117-b7d5-7a6bc800fc44 · outbound

This paper cites SigMaNet: One laplacian to rule them all.

AI Generalisation Gap In Comorbid Sleep Disorder Staging SigMaNet: One laplacian to rule them all

Reference 5

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:31d77a9d14bbaf19de21f49784d6a8296596221293bac5f12afe13600f187d79

Observation a30559b0-bdb7-40a2-9a76-ebb7eedb3187 · outbound

This paper cites MagNet: A Neural Network for Directed Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging MagNet: A Neural Network for Directed Graphs

Reference 6

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:59fe30300597e28bedb50681b9a651791387ccc047684ddba66bd3a2fa719daa

Observation 41e8ee86-980c-4dc9-b7ef-687d09ad26c9 · outbound

This paper cites Digraph Inception Convolutional Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Digraph Inception Convolutional Networks

Reference 7

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:a96e0adc64e6d38430857e1cc96603747e542853714c2fca8bcf39b27cec76ca

Observation aad7a9c1-fb36-431d-806e-7bfd63dcfe0e · outbound

This paper cites Directed Graph Convolutional Network.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Directed Graph Convolutional Network

Reference 8

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:db6bf49b1dba40e324667d4583571be3fd9d53e909d75b74e8307cce7b3105de

Observation 3c588b99-b6ad-45df-8cb5-84d380c622fc · outbound

This paper cites Spectral-based Graph Convolutional Network for Directed Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Spectral-based Graph Convolutional Network for Directed Graphs

Reference 9

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:34103ca18c268c0a8a7c35bebdfa3cab6417fb25a57fa8d083f820a72e62e87b

Observation fd381012-9917-4102-a36c-28ca045d2112 · outbound

This paper cites MotifNet: a motif-based Graph Convolutional Network for directed graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging MotifNet: a motif-based Graph Convolutional Network for directed graphs

Reference 10

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:068ff0566574162508042eeff16ddc3d266de22ea2d4e6071a9182d8a17a3b88

Observation afc3480d-f8b8-404e-8ad4-59c62be59921 · outbound

This paper cites Edge Directionality Improves Learning on Heterophilic Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Edge Directionality Improves Learning on Heterophilic Graphs

Reference 11

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:ddcc9d1fa21e5dd177402d3701e1b423f24c7aab750335fbdd1432af882c043a

Observation 7c7725f4-7549-432b-ae85-a72b503c2721 · outbound

This paper cites Directed Acyclic Graph Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Directed Acyclic Graph Neural Networks

Reference 12

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:d020ea8db188abe22d4338d10eaa4ef3331d899766183da746ff5eea2deb3ed7

Observation 53922cfe-917d-41c6-afd1-bdcea5032fcd · outbound

This paper cites Gated Graph Sequence Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Gated Graph Sequence Neural Networks

Reference 13

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:8069ddac28c716a425b3a62b3423db792f19b73a65798c5f8969ee26095994ff

Observation c4370c56-ef6f-4703-8cee-40e32ce05061 · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Neural Message Passing for Quantum Chemistry

Reference 14

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:33f19ad4c8777dabb4fc5dd4fb4952382d9e27ca3cc9edf14f1d25ec6334c83f

Observation 7bae4feb-17dd-4171-97d4-b088f3ab0881 · outbound

This paper cites The Graph Neural Network Model.

AI Generalisation Gap In Comorbid Sleep Disorder Staging The Graph Neural Network Model

Reference 15

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:0c8396460fb7a40276bd08596f9db902a4029cc0aa1d2d23d325e00de42b7a54

Observation 48fb4b08-94b7-401f-ae4a-40db4974426c · outbound

This paper cites Screen the Account for Suspicious Indicators: Recognition of a Suspicious Activity Indicator or Indicators.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Screen the Account for Suspicious Indicators: Recognition of a Suspicious Activity Indicator or Indicators

Reference 16

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:64bb6986722bea978faac283a60130bdf62d4ca6ea865ad491024fc11865615b

Observation 6f943726-1b59-42c7-9c3f-3c6d2ba0e7c3 · outbound

This paper cites Fast incremental and personalized PageRank.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Fast incremental and personalized PageRank

Reference 17

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:989396091d51b27da9a17218fa68026c22be377d41b06233995709f67d2c6914

Observation 04033b7a-9d26-4e6f-81ac-ceba387736d5 · outbound

This paper cites Graph Attention Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Graph Attention Networks

Reference 18

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:2d6c6454edd8fdbb896062db27ba0614d7266606f46074097fa2596da752d5ae

Observation 4da25584-b938-4f72-a2a5-59e8ae2d368e · outbound

This paper cites Supervised Community Detection with Line Graph Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Supervised Community Detection with Line Graph Neural Networks

Reference 19

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:842857632d4f08673d16ffa22326d8c54e03fbfd7eb40c251c15600a865846ba

Observation 2dca566e-37cb-487c-b145-02eb29b80f91 · outbound

This paper cites Line Graph Neural Networks for Link Weight Prediction.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Line Graph Neural Networks for Link Weight Prediction

Reference 20

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:c09aac2767eab70cb778db08e5dfa4316476b7feb6f6f235713b893c6b94a64b

Observation bafe8319-c54c-47fa-9a11-4fe7377bfb1c · outbound

This paper cites LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction.

AI Generalisation Gap In Comorbid Sleep Disorder Staging LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction

Reference 21

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:fbeeef7da2c79df2d326e24d21bf7a624e66e6850c9470048d012474d51be2fe

Observation 7e842b00-bc9a-4066-a09d-71d972167209 · outbound

This paper cites Learning Graph Representations Through Learning and Propagating Edge Features.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Learning Graph Representations Through Learning and Propagating Edge Features

Reference 22

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:ac7e3a719f8fa140bcefa5e33e6b1095a41652ce9f1da7cbb4a5baead8890d21

Observation 2420ee70-59a3-40b5-9306-bd415349ed4b · outbound

This paper cites CensNet: Convolution with Edge-Node Switching in Graph Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging CensNet: Convolution with Edge-Node Switching in Graph Neural Networks

Reference 23

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:56e0d4f3279630a8e202d0941bf8f78eb6c3364c596294585d632b4647cc7231

Observation 7ccb9c95-63cb-42ae-85d3-1102fca9ee73 · outbound

This paper cites Line graph contrastive learning for node classification.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Line graph contrastive learning for node classification

Reference 24

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:9ca92617c831420ad3718a429d5c603d95f0245ec620cf85755d545032c948f1

Observation 14ded2fa-ed0f-4f8d-9e3a-6063aca999a4 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Semi-Supervised Classification with Graph Convolutional Networks

Reference 25

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:409c87f27034c9f1999a5ed44af9be8a2f8e0051d14afc6365f7807a22eed5a3

Observation 3e06dc16-2968-4c09-9e87-01b8f247327a · outbound

This paper cites Inductive Representation Learning on Large Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Inductive Representation Learning on Large Graphs

Reference 26

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:718b846fc6a56665b0d3070ff8de51b283003c9c1b8eb6d0318d9af0ab75f190

Observation 3e34ce57-bb0d-4592-9560-8aaadc2bf150 · outbound

This paper cites How Powerful are Graph Neural Networks? In Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May 2019.

AI Generalisation Gap In Comorbid Sleep Disorder Staging How Powerful are Graph Neural Networks? In Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May 2019

Reference 27

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:e035bd55fadc971acf1fab3545381ccc73a625c9802330c6423824e428e666f2

Observation e46a82fc-8647-41bc-9b89-b02da707aa0a · outbound

This paper cites Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification

Reference 28

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:223a2ccf74fa356558dcf8d3b6d4163d196b28a4ab7b72ca7883b4faf77be434

Observation 05cdf632-da90-4355-9050-7b3194629d73 · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Adaptive Universal Generalized PageRank Graph Neural Network

Reference 29

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:27fe964bc49f72a411b8b7f4cc25b7d35222460e549fddf72e2479f8eb28e074

Observation 64d9dc42-16b6-4532-a3e9-5a5a8cc8238b · outbound

This paper cites LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering.

AI Generalisation Gap In Comorbid Sleep Disorder Staging LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering

Reference 30

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:21b600de486bc69cce25c90c8f8dadb39627bb58aa089a17fff3f4f57f71b752

Observation ad2bb09c-ffdf-48a5-b86a-f5e3948409ea · outbound

This paper cites Cross-Stitch Networks for Multi-task Learning.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Cross-Stitch Networks for Multi-task Learning

Reference 31

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:24867c51e47aa0fb24d58e7a3209457d84f93567fead39ee77d8b6f190ff2e12

Observation ac5d51ee-9271-407b-a5a2-a591338c9b36 · outbound

This paper cites Deep Residual Learning for Image Recognition.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Deep Residual Learning for Image Recognition

Reference 32

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:9625ceb6032ce990394505563855540c8765ee58fe70d8c6894a9b53ad2b2d35

Observation ee5f6ba7-d104-42f2-a49f-da2988406f36 · outbound

This paper cites Ethereum Fraud Detection with Heterogeneous Graph Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Ethereum Fraud Detection with Heterogeneous Graph Neural Networks

Reference 33

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:6eda0a40a3f95ea55b584917b02a1034698a1154220b2583e9c1e89d203afda5

Observation 9d66a769-bff1-45eb-9530-7b65d0a62405 · outbound

This paper cites Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding

Reference 34

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:9167b49e4eb4d15669626136aa0fd42463d0038dba80404c73e668f8e16bb542

Observation aa005569-7053-4270-ad9a-4c94de6a2d99 · outbound

This paper cites Anomaly Detection in Networks with Application to Financial Transaction Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Anomaly Detection in Networks with Application to Financial Transaction Networks

Reference 35

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:11a0a2763ef805949f1c7527a46d2eeb1af6e1837d0cb3eae2c799ffc3fef006

Observation f56e0e80-28dc-4dce-89ba-1eedfb2d10f4 · outbound

This paper cites Principal Neighbourhood Aggregation for Graph Nets.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Principal Neighbourhood Aggregation for Graph Nets

Reference 36

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:3737e68a4b77d8280bd6f1a3d18ab5e14c16dc520ef1c92bda6aa84cb4f8cf95

Observation 7524876c-9cad-41a7-8a04-b5be604e11b7 · outbound

This paper cites Rossmann-toolbox: A deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Rossmann-toolbox: A deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins

Reference 37

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:7cfbf8d009ffd509cd6577dc9eb8e07848f3f9fe567a2be05f7c6ba485f7dda3

Observation 2317311f-d3b1-4c58-8528-31743a070df4 · outbound

This paper cites Representation Learning on Graphs with Jumping Knowledge Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Representation Learning on Graphs with Jumping Knowledge Networks

Reference 38

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:d25002904273451e591017cb4afd563a24d9b00b006b7e795d4108f0e0bd6d12

Observation e20f5fff-c1fa-4dc9-a039-7a2d8863c0f7 · outbound

This paper cites Strategies for Pre-training Graph Neural Net- works.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Strategies for Pre-training Graph Neural Net- works

Reference 39

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:3c0f04f0d3eb5ed0de589366be82dbfbd611f67d3d7f3c4dda3e933de42c14fe

Observation 952add43-0523-4b71-9c77-20f3c73e4d20 · outbound

This paper cites PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs.

AI Generalisation Gap In Comorbid Sleep Disorder Staging PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs

Reference 40

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:4f685fffe1e31526ad977d6e8b335530fb5de0aecd9111bc1822c7ae49853fb6

Observation 07612817-04c7-440f-990d-89a86d75f70b · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

AI Generalisation Gap In Comorbid Sleep Disorder Staging PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 41

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no resolver link, observed 2026-07-13T19:43:02.198818Z

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:07da0993efb0283f160712e9f66e10145727f1354fcd8156480c5b67ece82bc1

Observation 7ebccc3d-3b1f-42f2-a52a-2fabad0e2cf8 · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 42

Resolution
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no resolver link, observed 2026-07-13T19:43:02.198818Z

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:0b02e685c04376ab8033961509f8a1376e65ddc6d1d882b62b9335d141c75cfa

Observation 48d0af37-c9f0-423d-868d-11d93593c158 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Fast Graph Representation Learning with PyTorch Geometric

Reference 43

Resolution
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no resolver link, observed 2026-07-13T19:43:02.198818Z

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:357476dc583ac536694dc92bf715f4bbbfcbe2fb310600d0470a5bcbdee5fadb

Observation d86be409-aedc-496e-aa28-31627960037f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

AI Generalisation Gap In Comorbid Sleep Disorder Staging Adam: A Method for Stochastic Optimization

Reference 44

Resolution
unresolved
no resolver link, observed 2026-07-13T19:43:02.198818Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:7033ea6b67f225aed99f9c7c695660368d642273161e035e4e4b68c1eeb6afb9

Observation ca390b0c-0325-48a8-a404-7ed7648b9593 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

AI Generalisation Gap In Comorbid Sleep Disorder Staging SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 45

Resolution
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no resolver link, observed 2026-07-13T19:43:02.198818Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-13T19:43:02.198818Z digest=sha256:d0f340761d5d5f900e07da3ca0085a7977804a161031957e5b3c4f7b66968ad0

Pith citing papers

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