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Paper Citation Record · LEDGER

Anomaly Detection via Autoencoder Composite Features and NCE

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2502.01920.

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

pith.paper-citation-record.v1
2502.01920 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:06:22.533578Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

20 of 20 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 045fed8d-830c-4b1c-9ec8-2461e0f4efbc · outbound

This paper cites ntX i=1 zi − ˆµ(t) z T # ˆµ(t) z − ˆµ(t+1) z =.

Anomaly Detection via Autoencoder Composite Features and NCE ntX i=1 zi − ˆµ(t) z T # ˆµ(t) z − ˆµ(t+1) z =

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-09T14:06:22.716189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 32d143f9-0400-43fd-b24a-8e0513eb8309 · outbound

This paper cites Autoencoder-based network anomaly detection.

Anomaly Detection via Autoencoder Composite Features and NCE Autoencoder-based network anomaly detection

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T14:06:22.459685Z digest=sha256:9c1d3421af1ce5fad8d54da0cac72dbb2cb3d8a84e2b227bcf6e4a9cabf30289

Observation ec790fc2-05d2-40ef-8973-cf184fab2cfc · outbound

This paper cites Imagenet: A large-scale hier- archical image database.

Anomaly Detection via Autoencoder Composite Features and NCE Imagenet: A large-scale hier- archical image database

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T14:06:22.468956Z digest=sha256:e7fb5bfa6a58440653193f74a6786f458a45b38ca2620501407a982700e3e000

Observation b10e21d9-8b9b-4e4f-81a5-975b4d1aa1b6 · outbound

This paper cites A Survey on GANs for Anomaly Detection.

Anomaly Detection via Autoencoder Composite Features and NCE A Survey on GANs for Anomaly Detection

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.472837Z digest=sha256:3442921f6efa26cdf15a79d421668652c4df85ef1a395dde6cfd45f310871d7f

Observation 768579ed-9e0d-40d8-b4c9-0151e41570c9 · outbound

This paper cites Unrolled Generative Adversarial Networks.

Anomaly Detection via Autoencoder Composite Features and NCE Unrolled Generative Adversarial Networks

Reference 9

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source=pdf_text observed=2026-08-09T14:06:22.485398Z digest=sha256:a0c91af922853484d3caeb31848080740b7e0bc8752fe22c4faf6b79f60819c7

Observation 4747ad12-1d27-4964-a4f9-587888bcbcbe · outbound

This paper cites MNIST-C: A Robustness Benchmark for Computer Vision.

Anomaly Detection via Autoencoder Composite Features and NCE MNIST-C: A Robustness Benchmark for Computer Vision

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.489835Z digest=sha256:d530cb32a2f430a7c352bf79bc192b097d7d83ea2820a47ea64ac472e7e4681d

Observation 29bb8254-59c8-46ea-ba49-d5bf72562ae0 · outbound

This paper cites Deep Semi-Supervised Anomaly Detection.

Anomaly Detection via Autoencoder Composite Features and NCE Deep Semi-Supervised Anomaly Detection

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.499173Z digest=sha256:b6c6472a04a6e7bbad70b93e676298875c77ab2bc16865729cc1ab2ee923f8c9

Observation 339979fe-5831-4921-8709-15d188c6f82a · outbound

This paper cites Anomaly detection using autoencoders with nonlinear dimen- sionality reduction.

Anomaly Detection via Autoencoder Composite Features and NCE Anomaly detection using autoencoders with nonlinear dimen- sionality reduction

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-09T14:06:22.738819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T14:06:22.504082Z digest=sha256:d628f181265ef3c34ba10f6d108be00eccbb10cb7b70db325d085b5466e9ffa0

Observation 19ef2390-7e36-49cb-86dc-8be983147561 · outbound

This paper cites Learning Deep Representations of Appearance and Motion for Anomalous Event Detection.

Anomaly Detection via Autoencoder Composite Features and NCE Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Reference 17

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source=pdf_text observed=2026-08-09T14:06:22.520883Z digest=sha256:7d2cc5daa0fb68e7cb2acdffa3e538b30054426b7322389b3921f5b9cf521b55

Observation 128b6446-3b73-4d4c-a0f9-2650f49114cc · outbound

This paper cites Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications.

Anomaly Detection via Autoencoder Composite Features and NCE Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T14:06:22.727588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T14:06:22.524917Z digest=sha256:fe9b8df6fcff94f43bd64119c791563e8432a52878162b65d237cedf54a62de6

Observation 5fcb0a23-0c07-43a0-ba87-b171c11e2762 · outbound

This paper cites Efficient GAN-Based Anomaly Detection.

Anomaly Detection via Autoencoder Composite Features and NCE Efficient GAN-Based Anomaly Detection

Reference 19

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source=pdf_text observed=2026-08-09T14:06:22.529309Z digest=sha256:135ed9fdff1e03104e59740b5811db25c0347e3661f15f126c0238422f1e47c1

Observation 966d2229-9c1a-4567-a54e-596b63aea420 · outbound

This paper cites Mode Regularized Generative Adversarial Networks.

Anomaly Detection via Autoencoder Composite Features and NCE Mode Regularized Generative Adversarial Networks

Reference 2009

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.454983Z digest=sha256:4bb8d682fef43c5e3547b77512f76e245509f7cdf0f1bdad76a168cf6b4852c4

Observation e78363d8-6d91-45dd-bb4c-8c6c5e4046ae · outbound

This paper cites PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications.

Anomaly Detection via Autoencoder Composite Features and NCE PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Reference 2014

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.507918Z digest=sha256:60eddddd287b17fa5b9fb8040e585dc35d25e84ba87b9b70b2ad24f656faa71d

Observation 681d036c-3017-4198-a166-2486309a8d30 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Anomaly Detection via Autoencoder Composite Features and NCE Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2015

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source=pdf_text observed=2026-08-09T14:06:22.516888Z digest=sha256:ad93a73dfd44d52037acb8039daed41d7174734c358f13d48f3caf2609548ded

Observation 7c9c164e-b57f-4b81-b2ca-618cdb3bbea3 · outbound

This paper cites Classification-Based Anomaly Detection for General Data.

Anomaly Detection via Autoencoder Composite Features and NCE Classification-Based Anomaly Detection for General Data

Reference 2016

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source=pdf_text observed=2026-08-09T14:06:22.449326Z digest=sha256:587e67c399ec20bb823668db19fb429dfcb158524bf02600e79e1641f8e3ab81

Observation 0a204e82-7413-4431-b4f1-750e3f9e1fa5 · outbound

This paper cites Identifying and Categorizing Anomalies in Retinal Imaging Data.

Anomaly Detection via Autoencoder Composite Features and NCE Identifying and Categorizing Anomalies in Retinal Imaging Data

Reference 2017

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verified exact
local_arxiv, observed 2026-08-09T14:06:22.600847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T14:06:22.512609Z digest=sha256:8a42f57f9cb9249ce285efa531343244af0899b10b6bb436fdd8817ffd09fc4d

Observation 11b78c3d-c3d3-4e64-a40f-da1891914477 · outbound

This paper cites WAIC, but Why? Generative Ensembles for Robust Anomaly Detection.

Anomaly Detection via Autoencoder Composite Features and NCE WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

Reference 2018

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.464315Z digest=sha256:61f5d132f1a057c95c3473dae281412374e90809e7caf584200b29377f442259

Observation d24bb0a5-2fb3-419e-a04a-9b5864704b5c · outbound

This paper cites Do Deep Generative Models Know What They Don't Know?.

Anomaly Detection via Autoencoder Composite Features and NCE Do Deep Generative Models Know What They Don't Know?

Reference 2019

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source=pdf_text observed=2026-08-09T14:06:22.494220Z digest=sha256:88f46660c4d561d1e63ee4fc293859d72652d6914dea70fee91b432272e7210b

Observation 530ad0a2-2b70-421c-8944-5dd81ddfb2eb · outbound

This paper cites Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections.

Anomaly Detection via Autoencoder Composite Features and NCE Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections

Reference 2023

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source=pdf_text observed=2026-08-09T14:06:22.481158Z digest=sha256:cca6626282f16306e0d1e8edbd356f126ab01d755a56386c7b6285545114467f

Observation acbccc75-1031-43b7-b7cd-c1761bddca0c · outbound

This paper cites Auto-Encoding Variational Bayes.

Anomaly Detection via Autoencoder Composite Features and NCE Auto-Encoding Variational Bayes

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:06:22.477136Z digest=sha256:e071ee0b44d699d6ec036a6b1213ad50901d6767b301a3064bb6f71c982e3a00

Pith citing papers

No inbound Pith citation observations are available.