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Paper Citation Record · LEDGER

Deep Structured Cross-Modal Anomaly Detection

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:1908.03848.

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

pith.paper-citation-record.v1
1908.03848 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:05:29.633573Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

32 of 32 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fffd0441-19bd-408f-8841-25f38a205cfe · outbound

This paper cites Anomaly detection: A survey,.

Deep Structured Cross-Modal Anomaly Detection Anomaly detection: A survey,

Reference 1

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Observation 772cd9bd-81f7-4a39-9d07-65c26abc2e3c · outbound

This paper cites Muvir: Multi-view rare category detection.

Deep Structured Cross-Modal Anomaly Detection Muvir: Multi-view rare category detection

Reference 2

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Source-reported events for the cited work

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Observation 8100bac3-d03b-4172-b22e-c0d57bbcc865 · outbound

This paper cites A spectral framework for detecting inconsistency across multi-source object re- lationships,.

Deep Structured Cross-Modal Anomaly Detection A spectral framework for detecting inconsistency across multi-source object re- lationships,

Reference 3

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Observation a066b221-3ac2-40e4-a6f4-5fcb657c6af1 · outbound

This paper cites Canonical correlation analysis.

Deep Structured Cross-Modal Anomaly Detection Canonical correlation analysis

Reference 4

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Source-reported events for the cited work

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Observation 1b042e9b-c20a-49e3-9561-9a897ace9bac · outbound

This paper cites Kernel and nonlinear canonical correlation analysis,.

Deep Structured Cross-Modal Anomaly Detection Kernel and nonlinear canonical correlation analysis,

Reference 5

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Source-reported events for the cited work

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Observation b01d6a08-9e62-45d0-952b-b4bc8a17415b · outbound

This paper cites Multi-view low-rank analysis for outlier detection,.

Deep Structured Cross-Modal Anomaly Detection Multi-view low-rank analysis for outlier detection,

Reference 6

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Source-reported events for the cited work

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Observation 2a6f9154-ee75-42d2-8dc7-43318cddbccb · outbound

This paper cites Collaborative multi-view denoising,.

Deep Structured Cross-Modal Anomaly Detection Collaborative multi-view denoising,

Reference 7

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Source-reported events for the cited work

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Observation 99e9b04d-409c-4235-a42e-ffd98bab1153 · outbound

This paper cites Neural fraud de- tection in credit card operations,.

Deep Structured Cross-Modal Anomaly Detection Neural fraud de- tection in credit card operations,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ecf86dda-e725-4687-828f-bd2fb59ff1bf · outbound

This paper cites A survey of data mining and machine learning methods for cyber security intrusion detection,.

Deep Structured Cross-Modal Anomaly Detection A survey of data mining and machine learning methods for cyber security intrusion detection,

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 629cdcdd-24c3-4330-9163-f2f4f90e0b0d · outbound

This paper cites A survey on wearable sensor- based systems for health monitoring and prognosis,.

Deep Structured Cross-Modal Anomaly Detection A survey on wearable sensor- based systems for health monitoring and prognosis,

Reference 10

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Source-reported events for the cited work

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Observation 13713744-e174-457d-aeb5-50387498e057 · outbound

This paper cites Anomaly detection and classification for hyperspectral imagery,.

Deep Structured Cross-Modal Anomaly Detection Anomaly detection and classification for hyperspectral imagery,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cb1cfa5e-4b85-4e17-9b90-1b0440b976b1 · outbound

This paper cites Combining negative selection and classification techniques for anomaly detection,.

Deep Structured Cross-Modal Anomaly Detection Combining negative selection and classification techniques for anomaly detection,

Reference 12

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Source-reported events for the cited work

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Observation 3bfe9802-9b81-47a1-a00a-37e8531cea27 · outbound

This paper cites Intrusion detection with unlabeled data using clustering,.

Deep Structured Cross-Modal Anomaly Detection Intrusion detection with unlabeled data using clustering,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation fa910c61-a462-4c69-93c2-69fbe84df982 · outbound

This paper cites Specae: Spectral autoen- coder for anomaly detection in attributed networks,.

Deep Structured Cross-Modal Anomaly Detection Specae: Spectral autoen- coder for anomaly detection in attributed networks,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f54024ef-6096-4f20-b597-7d05bc84ae0d · outbound

This paper cites Clustering- based anomaly detection in multi-view data,.

Deep Structured Cross-Modal Anomaly Detection Clustering- based anomaly detection in multi-view data,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ed0a318b-568b-417e-a594-3b3aeefe3782 · outbound

This paper cites Exploiting Similarities among Languages for Machine Translation.

Deep Structured Cross-Modal Anomaly Detection Exploiting Similarities among Languages for Machine Translation

Reference 16

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Source-reported events for the cited work

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Observation 6f8a77bd-6c4e-4bf1-96f0-35dc0d914a6c · outbound

This paper cites Multimodal deep learning,.

Deep Structured Cross-Modal Anomaly Detection Multimodal deep learning,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f7295599-ba26-464d-bb40-c5bbadad5dac · outbound

This paper cites Efficient learning of deep boltz- mann machines,.

Deep Structured Cross-Modal Anomaly Detection Efficient learning of deep boltz- mann machines,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 24a8bec0-0178-438a-b199-751594dfbfc4 · outbound

This paper cites Speech recognition with deep recurrent neural networks,.

Deep Structured Cross-Modal Anomaly Detection Speech recognition with deep recurrent neural networks,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation db5f7764-7a26-425f-a18d-c20ea9ffcb89 · outbound

This paper cites Graph recurrent networks with attributed random walks,.

Deep Structured Cross-Modal Anomaly Detection Graph recurrent networks with attributed random walks,

Reference 20

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Source-reported events for the cited work

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Observation 7a2fcdf3-ad75-475e-bb05-aee1a0afb314 · outbound

This paper cites Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding.

Deep Structured Cross-Modal Anomaly Detection Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 50b313b1-63b7-4d4d-8763-1135b144046c · outbound

This paper cites Dropout: a simple way to prevent neural networks from over- fitting,.

Deep Structured Cross-Modal Anomaly Detection Dropout: a simple way to prevent neural networks from over- fitting,

Reference 22

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Source-reported events for the cited work

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Observation ecf6ddd4-fc3d-489f-8885-dcee50659a9b · outbound

This paper cites MNIST handwritten digit database,.

Deep Structured Cross-Modal Anomaly Detection MNIST handwritten digit database,

Reference 23

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Source-reported events for the cited work

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Observation 27ea0360-0145-4591-81a5-2912b1cc256a · outbound

This paper cites Distributed representations of words and phrases and their composi- tionality,.

Deep Structured Cross-Modal Anomaly Detection Distributed representations of words and phrases and their composi- tionality,

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bb44a45e-883a-4a88-95ba-5a6d5bdedea4 · outbound

This paper cites Glove: Global vectors for word representation,.

Deep Structured Cross-Modal Anomaly Detection Glove: Global vectors for word representation,

Reference 25

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Source-reported events for the cited work

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Observation c6f37aa1-b41e-4730-868f-5ec43c2325ad · outbound

This paper cites A large-scale hierarchical multi- view rgb-d object dataset,.

Deep Structured Cross-Modal Anomaly Detection A large-scale hierarchical multi- view rgb-d object dataset,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 2faaa565-3b53-4c8f-a8ce-a603f05e9df8 · outbound

This paper cites A kernel method for canonical correlation analysis.

Deep Structured Cross-Modal Anomaly Detection A kernel method for canonical correlation analysis

Reference 27

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Source-reported events for the cited work

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Observation 8204d30a-66ea-45c3-8fa5-4af98840b721 · outbound

This paper cites Partial least square regression (pls regression),.

Deep Structured Cross-Modal Anomaly Detection Partial least square regression (pls regression),

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 3104f645-5a1c-43bf-9a14-d8e0c8112491 · outbound

This paper cites Learning two-branch neural networks for image-text matching tasks,.

Deep Structured Cross-Modal Anomaly Detection Learning two-branch neural networks for image-text matching tasks,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c4dc4a77-a7aa-479a-947e-c05a40f4c48e · outbound

This paper cites Learning deep structure-preserving image-text embeddings,.

Deep Structured Cross-Modal Anomaly Detection Learning deep structure-preserving image-text embeddings,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1d43407f-28c6-481a-8758-5869b58cb498 · outbound

This paper cites Deep Structured Energy Based Models for Anomaly Detection.

Deep Structured Cross-Modal Anomaly Detection Deep Structured Energy Based Models for Anomaly Detection

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation eebf73da-4c89-4ae2-b1ff-2e2ed6704193 · outbound

This paper cites Heterogeneous network embedding via deep architectures,.

Deep Structured Cross-Modal Anomaly Detection Heterogeneous network embedding via deep architectures,

Reference 32

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Pith citing papers

No inbound Pith citation observations are available.