Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T13:07:09.539291Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2605.28428.
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Source: paper_references, paper_reference_links, observed 2026-06-29T13:07:09.539291Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
38 of 38 outbound references displayed
External citation measurements
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Observation 1aea8058-6720-4dd9-8c50-b16f9d9598c6 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Graph based anomaly detection and description: a survey.Data mining and knowledge discovery, 2015
Reference 1
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Observation d477c3e7-0df1-44f5-aa4d-d93c4e835987 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Pni: industrial anomaly detection using position and neighborhood infor- mation
Reference 2
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Observation 8a9cc63c-ff40-4b76-834f-f3c2073ea038 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Man- ifold regularization: A geometric framework for learning from labeled and unlabeled examples.Journal of machine learning research, 2006
Reference 3
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Observation 4d64a8ec-bc83-4502-847e-909983e196e6 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Reference 4
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Observation 1a4e88c2-8689-466c-a0a4-d454444686c9 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Reference 5
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Observation e4f54506-f9f9-494b-b9c1-0d71b2e27a53 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Vision transformers need registers
Reference 6
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Observation 56c9fa7c-f19d-450d-9232-78d6575a16fa · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Padim: a patch distribution modeling framework for anomaly detection and localization
Reference 7
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Observation 20c52ad8-84d0-4d60-8c45-53ba46298d84 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Deep anomaly detection on attributed networks
Reference 8
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Observation db03bea2-7963-409d-a0b1-627aebf9824f · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 9
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Observation 652731c1-bfd2-4b49-aa30-b663882fadf5 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Graph random neural networks for semi-supervised learning on graphs
Reference 10
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Observation 4d648e7a-ed8f-46c3-bf4c-f4c12ff33592 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Registration based few-shot anomaly detection
Reference 11
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Observation 4056df33-a01d-4a2a-8754-9b6a6acf8d94 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Winclip: Zero- /few-shot anomaly classification and segmentation
Reference 12
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Observation 945c77f0-a17e-48a2-a476-7a3efa5c953a · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Identifying medical diagnoses and treatable diseases by image-based deep learning.cell, 2018
Reference 13
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Observation 4c243d60-04b7-4011-9eb3-3ef8e2043996 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Graph anomaly detection with graph neural networks: Current status and challenges.IEEe Access, 2022
Reference 14
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Observation efc18cee-5566-40ba-ac50-d63f8de36b8b · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Semi-supervised classification with graph convo- lutional networks
Reference 15
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Observation 774cdbab-2e94-4702-ac69-719080cb4de8 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Continuous memory representation for anomaly detection
Reference 16
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Observation 436bfc71-a0f0-499c-9b38-0dae723c2233 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Deeper insights into graph convolutional networks for semi-supervised learn- ing
Reference 17
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Observation 2a28af20-73c7-4910-a025-f6fcfcee8409 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection
Reference 18
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Observation 9aba8abf-5611-40d1-a921-fbed4f2fda9b · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Exploring intrinsic normal prototypes within a single image for universal anomaly detection
Reference 19
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Observation 02384ddf-b6a0-44a5-8dc5-3fb60431a883 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Deep generative model us- ing unregularized score for anomaly detection with hetero- geneous complexity.IEEETCYB
Reference 20
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Observation 22fa7718-ef24-4440-bbaa-a4ab584a11b1 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization k-nnn: nearest neighbors of neighbors for anomaly detection
Reference 21
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Observation a5bbf4e5-f9be-4c24-92ac-7b2f73d29a04 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Anomalous: A joint modeling approach for anomaly detection on attributed networks
Reference 22
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Observation 89d3399b-25d6-44d6-9ab8-37288bdeeed3 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Learn- ing transferable visual models from natural language super- vision
Reference 23
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Observation 55c57d17-4347-4139-b5fd-4a72e500d1df · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Towards total recall in industrial anomaly detection
Reference 24
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Observation 1405c41b-5a2b-456a-b6a1-6409a78ee581 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization DINOv3
Reference 25
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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ec4e29fa-1b0a-4f70-851d-17880523c616 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Rethink- ing graph neural networks for anomaly detection
Reference 26
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Observation 80491375-e3ad-4b84-a460-f8ef1903458e · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Kernel-aware graph prompt learning for few-shot anomaly detection
Reference 27
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Observation cdd15927-197f-4731-9ddf-6696bc11d619 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Graph laplacian for image anomaly detection.Machine Vision and Applications,
Reference 28
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Observation 1c8d2b94-08af-4b98-87ed-e77ba6d051f3 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion
Reference 29
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Observation e544423a-2188-469c-9ccb-7edb70b3fbec · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Pushing the limits of fewshot anomaly detec- tion in industry vision: Graphcore
Reference 30
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Observation 0ac88359-95af-4118-b9e1-067c9d40a7d5 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Ad-dinov3: Enhancing dinov3 for zero-shot anomaly detection with anomaly-aware calibration.arXiv preprint arXiv:2509.14084, 2025
Reference 31
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Observation cd23cb7e-18ce-4db0-b082-46f4bb717033 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Wide residual net- works
Reference 32
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Observation 11f9f4a6-60a1-48eb-a69f-66d7d0a89a5f · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Error-bounded graph anomaly loss for gnns
Reference 33
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Observation ebb7bc7b-ae2e-4058-8ead-57663d8fcc94 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Graph neural networks: A review of methods and applications.AI open, 2020
Reference 34
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Observation f6408d0e-4fc0-4609-b3f0-039e418f9521 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts
Reference 35
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Observation c3e7fdf1-ee30-42d9-92d0-a0a13549a10e · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Beyond homophily in graph neural networks: Current limitations and effective designs
Reference 36
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Observation bc6ed3ca-251f-43f7-816a-4aac0934b4a1 · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Spot-the-difference self-supervised pre- training for anomaly detection and segmentation
Reference 37
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Observation 161e551c-d75f-4840-b473-63bc363e79dc · outbound
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization 11 S2.Ablation on the query feature stabilization coeffi- cientΛ q
Reference 38
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No inbound Pith citation observations are available.