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Paper Citation Record · LEDGER

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

As of 10 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 1 inbound Pith citation observation for arXiv:2501.16971.

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

pith.paper-citation-record.v1
2501.16971 v1

Coverage vector

measured 100 of 113 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:29:03.215049Z

measured 101 of 101 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:32:44.340202Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 113 outbound references displayed

  • verified exact11
  • verified fuzzy17
  • unresolved68
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa6a340e-156d-421e-92ac-156a93cfc9e1 · outbound

This paper cites write newline.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples write newline

Reference 1

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source=arxiv_source observed=2026-08-10T05:29:02.655413Z digest=sha256:e33c10e3ace1ff767a26a480762d5b566a9766dbb48c543dd7c2f7ed289e9258

Observation d6a0cb5e-d387-42ba-a634-e9efaecf54a6 · outbound

This paper cites Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection, 2023.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection, 2023

Reference 2

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source=arxiv_source observed=2026-08-10T05:29:02.661909Z digest=sha256:f1dce78e7543007557988b0c8879ffb622323da4239ab9199e3f2a565c78fcc3

Observation 314c9d90-5d9e-44fd-a48e-ea7cd22589de · outbound

This paper cites and Mian, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Mian, A

Reference 3

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source=arxiv_source observed=2026-08-10T05:29:02.667296Z digest=sha256:125d9a7f7a2954fea7179196b664e988425c89c5db86e5d0909ba1b2657a5622

Observation 63ba13d2-8ce9-4cfb-920b-52488b668305 · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Blended diffusion for text-driven editing of natural images

Reference 4

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source=arxiv_source observed=2026-08-10T05:29:02.671569Z digest=sha256:e157732c8a997b14733d0dd85fde5bfcd1eb8eed81ad49ca98320799a8db2cbd

Observation 57475e1b-faea-402d-94af-072a9a026444 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-10T05:29:02.675602Z digest=sha256:d709ad30607fbe44440731d53a8daca010443f5933e946acd8d6d682b2bb1cb0

Observation d45fef7a-027a-4bf3-9b1f-bf991b400f86 · outbound

This paper cites and Boult, T.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Boult, T

Reference 6

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source=arxiv_source observed=2026-08-10T05:29:02.679438Z digest=sha256:1aafddb3bf36ca719d26ddbc03ea4bbcc8af13e2a6c395911e5a61ff1b7275ef

Observation 4cdea055-0e3f-4fdd-9de0-88ebe06f5969 · outbound

This paper cites and Boult, T.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Boult, T

Reference 7

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source=arxiv_source observed=2026-08-10T05:29:02.683401Z digest=sha256:9594cd28c5352e37244f23768c0f926b7354a16975d663ae8a9fdff01d3f4f32

Observation 44c18701-98fc-43ee-a369-a96291d4e1de · outbound

This paper cites Deep Nearest Neighbor Anomaly Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep Nearest Neighbor Anomaly Detection

Reference 8

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source=arxiv_source observed=2026-08-10T05:29:02.687438Z digest=sha256:6247647b74e9b4f0e2c2acd79fdaf3bdc208a0d0dd8631476b4ec18a3247e161

Observation 99545157-a303-46a4-8155-31159e1997b9 · outbound

This paper cites Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection

Reference 9

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source=arxiv_source observed=2026-08-10T05:29:02.693607Z digest=sha256:8083f2ee9c171968c63f9e83ac75a0025f28fe508340e3a7574c46d260f8998f

Observation 0403a86f-2d9d-4c17-906e-b3b8bf7ca3ff · outbound

This paper cites Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Reference 10

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source=arxiv_source observed=2026-08-10T05:29:02.699004Z digest=sha256:6830c4f516a7f6491d8e4cfe2ded305cbd0c3169226f9a97f975ab6aa187d8c1

Observation 6f318043-90b3-40fc-82ad-3f04dd3f224d · outbound

This paper cites Brain tumor classification (mri), 2020.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Brain tumor classification (mri), 2020

Reference 11

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source=arxiv_source observed=2026-08-10T05:29:02.703616Z digest=sha256:2a3ca69c862811566e5f08ceaa874cfbff48ed33f6cb3694e81e528cf8fa0f35

Observation f75dfe65-3533-4906-a626-e2188b7febb0 · outbound

This paper cites and Zhang, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Zhang, Z

Reference 12

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source=arxiv_source observed=2026-08-10T05:29:02.707409Z digest=sha256:1389caf67b860e45feebea640c22d6576cf010c32207fd923db228d2a4b928a0

Observation 3bf41bd9-7178-4714-b31d-a43e60d32f7d · outbound

This paper cites Robust Out-of-distribution Detection for Neural Networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust Out-of-distribution Detection for Neural Networks

Reference 13

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source=arxiv_source observed=2026-08-10T05:29:02.711273Z digest=sha256:ed2aa8e4cf8cf4fa545d07d04c62810c9ab7a86152b4eaee284eebaf0ba284ee

Observation 4a4d8129-5caf-44dd-b1d2-fff53d2219d1 · outbound

This paper cites Atom: Robustifying out-of-distribution detection using outlier mining.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Atom: Robustifying out-of-distribution detection using outlier mining

Reference 14

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Observation 4324a6cd-df27-4ec2-9926-1d80f6bed3f1 · outbound

This paper cites P., Morrison, P., and Dao, L.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples P., Morrison, P., and Dao, L

Reference 15

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source=arxiv_source observed=2026-08-10T05:29:02.719840Z digest=sha256:1870de9c5c3e1060cd415d1dcb44bb0e8e59abcec3f52bf6803ca40be94cfdc8

Observation 2d0966bb-3bcc-49f0-8b5a-a51303a36fbc · outbound

This paper cites Transformaly -- Two (Feature Spaces) Are Better Than One.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Transformaly -- Two (Feature Spaces) Are Better Than One

Reference 16

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

source=arxiv_source observed=2026-08-10T05:29:02.724297Z digest=sha256:0d14303d611d8b0367aa4ecc3b3d526646b42f16be4a5e1cbbda3ba6a650c4d2

Observation 0b497d9d-73c8-4d69-aebb-29969929b617 · outbound

This paper cites and Vapnik, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Vapnik, V

Reference 17

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source=arxiv_source observed=2026-08-10T05:29:02.732173Z digest=sha256:83a276fe97166820b47af9130199c7a0495d445d882853ea48c9935bcecaf53f

Observation f5183e84-8bba-4ad6-9dd0-cf91420bdf6f · outbound

This paper cites and Hein, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Hein, M

Reference 18

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source=arxiv_source observed=2026-08-10T05:29:02.740846Z digest=sha256:6fbf75ab703ad7cf8069fb45715cd805a1132dcc0d2000130bb0dd44f6e04869

Observation b7f102db-ae21-4ff3-bf94-f7e6e88268c3 · outbound

This paper cites T., and Shah, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples T., and Shah, M

Reference 19

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source=arxiv_source observed=2026-08-10T05:29:02.748922Z digest=sha256:90cb4e2d9c9ea8d145dfdfd2f8230dafe106c6907bed8ffcb2d6e60966fc4999

Observation b9e055dc-8c71-45ff-a721-0dafc94c53ba · outbound

This paper cites Imagenet: a large-scale hierarchical image database.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Imagenet: a large-scale hierarchical image database

Reference 20

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source=arxiv_source observed=2026-08-10T05:29:02.752816Z digest=sha256:55b8dc06833af4ea8513cb71a4d0bb6143f94b2b8bcb7aaf010636a2fcd67b42

Observation a55fcca0-f267-43a0-a600-4d3e885063a6 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 21

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Observation 96871c94-2689-438f-b148-a0c68907b521 · outbound

This paper cites and Nichol, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Nichol, A

Reference 22

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source=arxiv_source observed=2026-08-10T05:29:02.762564Z digest=sha256:5e210619ad58d5626b02602ee5dbace03575f2af6bf2c422dea84b679b039271

Observation aa76f562-fd3e-424c-8d5b-b31842b310b8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

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source=arxiv_source observed=2026-08-10T05:29:02.784749Z digest=sha256:9758b52fa72ba71fa2f5fa981e16364c1f8a2d3b09876afd35381bb6ca0f17a9

Observation 747a7fec-a962-43bf-a3c2-57268a5383f0 · outbound

This paper cites and Shearer, R.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Shearer, R

Reference 24

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source=arxiv_source observed=2026-08-10T05:29:02.789427Z digest=sha256:c7d308ab727ebaa1dd6b74603c6523bc6c311b0a5904e23dccaa12098d8aa08f

Observation 92863cf3-78d5-41ce-905c-17fe87d0cc83 · outbound

This paper cites VOS: Learning What You Don't Know by Virtual Outlier Synthesis.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Reference 25

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source=arxiv_source observed=2026-08-10T05:29:02.794449Z digest=sha256:72191c03c9c1349004a765f1473a316e11dce6de61ad5e108ba9bd530dea5803

Observation c55a9007-cf7f-451f-918d-fc4f70bd22cc · outbound

This paper cites Dream the Impossible: Outlier Imagination with Diffusion Models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Dream the Impossible: Outlier Imagination with Diffusion Models

Reference 26

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source=arxiv_source observed=2026-08-10T05:29:02.798955Z digest=sha256:8a7a0ba49fa30c0166c950dce987fea00a4ee0127a44dd60f19f164a814a9fe0

Observation 63b14096-3eee-4270-840d-147a7a8de5b2 · outbound

This paper cites Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations

Reference 27

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

source=arxiv_source observed=2026-08-10T05:29:02.803339Z digest=sha256:05891a1a841d09fd60f2b1cb511f786ba5e76f46c21c54b0d7c4659d6b0dc155

Observation 675c6d33-fba2-422b-8c54-1719dbcb627e · outbound

This paper cites Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

Reference 28

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source=arxiv_source observed=2026-08-10T05:29:02.808648Z digest=sha256:fe70aeeff79a4f420b4091f94049de1d7072c935828d79a48d139002b01b49f6

Observation c953953a-9a32-4dce-aed5-b530d961d1e3 · outbound

This paper cites Zero-shot out-of-distribution detection based on the pre-trained model clip.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Zero-shot out-of-distribution detection based on the pre-trained model clip

Reference 29

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Observation fc1dfc93-2c6c-4103-8b8b-b71d47c09f49 · outbound

This paper cites Exploring the limits of out-of-distribution detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exploring the limits of out-of-distribution detection

Reference 30

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source=arxiv_source observed=2026-08-10T05:29:02.816842Z digest=sha256:fd6437ad0021220882a2df2c443da86d2530bd9ff5d937bba1fd3c7c6e317576

Observation 31aa161f-0af8-4e2d-8584-db50758b4119 · outbound

This paper cites Exploring the limits of out-of-distribution detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exploring the limits of out-of-distribution detection

Reference 31

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source=arxiv_source observed=2026-08-10T05:29:02.820765Z digest=sha256:7b5350fd992afa8795250b5020d2795af0ae47274af5e8fe594712451180a680

Observation 99fd14b0-0b58-408a-b7bf-35325d6989a5 · outbound

This paper cites Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

Reference 32

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source=arxiv_source observed=2026-08-10T05:29:02.824534Z digest=sha256:ea215f48c1cb7690d71d20ea5ba67d4455ce7799a923b942bce8e839ff6d22f2

Observation 39798dca-5f51-4aa2-b328-518ad8eb7d73 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Explaining and Harnessing Adversarial Examples

Reference 33

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source=arxiv_source observed=2026-08-10T05:29:02.828361Z digest=sha256:309855bdb79e844178aeea40d7f913a8aa9d7207b524200df86de5b83c70f0a6

Observation 2fac2930-7020-436d-84ae-41545ed9ab10 · outbound

This paper cites K., and Ng, W.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples K., and Ng, W

Reference 34

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source=arxiv_source observed=2026-08-10T05:29:02.837805Z digest=sha256:b41507b977581a8c13573db98157928f9708c77b8922208cf089ace77601488a

Observation f374b1cf-3e5f-45ea-aa27-3b63f3faaaab · outbound

This paper cites G., and Weinberger, K.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples G., and Weinberger, K

Reference 35

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source=arxiv_source observed=2026-08-10T05:29:02.841878Z digest=sha256:7d87f381f8ec1dc7a005ed70f5073fb66b84f6a4ca9a81e9aa82048e497973d4

Observation 4bbf0a83-1940-4848-936d-28f86fe390d8 · outbound

This paper cites Deep residual learning for image recognition.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep residual learning for image recognition

Reference 36

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source=arxiv_source observed=2026-08-10T05:29:02.847473Z digest=sha256:272d194b7a96e714f846fdec28cecc1579ca6fd7f622ce8753997ec2de91505a

Observation 98416e3b-81b3-4163-9e7a-29a0a3f58704 · outbound

This paper cites and Gimpel, K.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Gimpel, K

Reference 37

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Observation 173ca468-5374-4a95-8d2e-0a24a5adfcb8 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep Anomaly Detection with Outlier Exposure

Reference 38

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source=arxiv_source observed=2026-08-10T05:29:02.862684Z digest=sha256:e639b31529111fb7fe2b767cc580c56498b6dfdb176d102e271e036c6418d3a1

Observation 99f87ab8-3200-4f6c-835a-39d859d1c92e · outbound

This paper cites Using pre-training can improve model robustness and uncertainty.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Using pre-training can improve model robustness and uncertainty

Reference 39

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source=arxiv_source observed=2026-08-10T05:29:02.866579Z digest=sha256:fd00c680c88d091fbcb9039af24c4b4fde6ea1dfcba5e75223de450d06a8a54d

Observation bbce45cd-af43-4720-8a7f-c5f09174a07c · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 40

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source=arxiv_source observed=2026-08-10T05:29:02.870742Z digest=sha256:d394cb120c8185c638dc5738e7f72ff41cd884ca2e7d65057f286b125c619881

Observation 59e08871-9803-47c5-8a58-da86e6647d1d · outbound

This paper cites Denoising diffusion probabilistic models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Denoising diffusion probabilistic models

Reference 41

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source=arxiv_source observed=2026-08-10T05:29:02.885393Z digest=sha256:00ac8f33316ec9b89fc94226f4b2c8d6f7bf5cfd5c90ad1101ae69dcb873cc04

Observation 3c2df0da-4a66-4ade-b18e-f3d8f6f1e56e · outbound

This paper cites The power of few: Accelerating and enhancing data reweighting with coreset selection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples The power of few: Accelerating and enhancing data reweighting with coreset selection

Reference 42

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source=arxiv_source observed=2026-08-10T05:29:02.888965Z digest=sha256:cf3c8b360d05d220d640665c3022dd68884b0f8f79fca2fd4f7f2ee1c769a6cf

Observation 8906e185-b4ca-41b9-be81-9bb560142c60 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-10T05:29:02.896406Z digest=sha256:20c3bdc4b6922b58be2ebbb3f6a559a7bb756e71c6e49272285dc06cd4d33155

Observation f8f5b3ca-b8a9-4e76-9f08-ff5279ae7e49 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-10T05:29:02.904754Z digest=sha256:cb9904536271a49465db854db9444adde87b3488976c8aa04beda54bb4235c2e

Observation e29e52a4-0991-4f95-bf22-3561bfbec1ec · outbound

This paper cites and Ortmeier, F.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Ortmeier, F

Reference 45

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source=arxiv_source observed=2026-08-10T05:29:02.922204Z digest=sha256:7c9bf2c805d8eb26c90ff4b86a2a42f7c2bf2537c2def02a5feab7193df3975c

Observation 89e0efc9-e10d-4167-9117-11cbafb8980b · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 46

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source=arxiv_source observed=2026-08-10T05:29:02.925977Z digest=sha256:73484b62e4e2d5a6b91491674923569596130fa70b3959b3e7489f15ea7de0fb

Observation af29a76c-c2bd-41b1-a4f4-87e3af3b4c4a · outbound

This paper cites and Ramanan, D.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Ramanan, D

Reference 47

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source=arxiv_source observed=2026-08-10T05:29:02.929673Z digest=sha256:dc67edfc08cece12139e70a90597cabde63e7ecc72c41edb22631d6051973032

Observation 364b4475-8b9e-4331-bbbd-cc14f34defcd · outbound

This paper cites Learning multiple layers of features from tiny images.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Learning multiple layers of features from tiny images

Reference 48

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source=arxiv_source observed=2026-08-10T05:29:02.933680Z digest=sha256:d06540cbe8a6bdba51a95fbdc2696e57bb78a92d46d816bad962380d7e2c58fe

Observation f5dd9adc-0b31-4365-8c23-d7b029303232 · outbound

This paper cites and Cortes, C.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Cortes, C

Reference 49

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source=arxiv_source observed=2026-08-10T05:29:02.937529Z digest=sha256:7fbb857cf6a0212c492d20b75b8f28cd4e08fbb3e068b73fe20b43456180bf6c

Observation 5e2d3d75-9056-41fa-9091-a4867a9e824a · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 50

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source=arxiv_source observed=2026-08-10T05:29:02.941751Z digest=sha256:bea2406bd763a551b5265fa52861d3b5bad290ef5dc0de10f83b93dee98d0e4e

Observation 55786025-20cb-456e-91c0-7a76fcfe67b5 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 51

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source=arxiv_source observed=2026-08-10T05:29:02.946345Z digest=sha256:deeb15dcc3bd70efd03bfe597ceb36e466a72e6f3d368f26441a38e6c63294e3

Observation 645079b4-b06d-4a0b-956a-9bb9d9512236 · outbound

This paper cites Enhancing the reliability of out-of-distribution image detection in neural networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Enhancing the reliability of out-of-distribution image detection in neural networks

Reference 52

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source=arxiv_source observed=2026-08-10T05:29:02.960390Z digest=sha256:39cbaca1070edae09ab0861449d85008d059c2bb98d4bb6d5e12bb45bdfe460d

Observation 6d606755-26d7-4e30-9bc2-82ab39651927 · outbound

This paper cites Practical evaluation of adversarial robustness via adaptive auto attack, 2022.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Practical evaluation of adversarial robustness via adaptive auto attack, 2022

Reference 53

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raw_fallback, observed 2026-08-10T05:29:05.022627Z

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source=arxiv_source observed=2026-08-10T05:29:02.964025Z digest=sha256:6749b05792334003efd02201e2b22e670283ca128fb00b8c0f1d9da3d41b505e

Observation fa5a6ced-49d7-4702-bfa6-901d9fde3dc8 · outbound

This paper cites Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

Reference 54

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local_arxiv, observed 2026-08-10T05:29:03.983809Z

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source=arxiv_source observed=2026-08-10T05:29:02.970422Z digest=sha256:c1f27aa9cb2b4c451218fb8fd36de911b2625f29a38b63708d13414c5d6124d9

Observation b1416192-c766-4685-983e-12de5ec1fe57 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-10T05:29:02.974128Z digest=sha256:d66a919ea22552473bde1e5a77b5c276814cc900691c64db60f62bed7f24685c

Observation a82c5d7c-2049-492a-8a52-1178b7d46cdb · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 56

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source=arxiv_source observed=2026-08-10T05:29:02.980158Z digest=sha256:67d95b7af018bf8a35ffd2b0e59e3d1d8fea70f3dbcfe2baf5dbf9847e57eb20

Observation 00eb9dd2-30a9-451d-8b41-d44abc6ce80a · outbound

This paper cites Provably adversarially robust detection of out-of-distribution data (almost) for free.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Provably adversarially robust detection of out-of-distribution data (almost) for free

Reference 57

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raw_fallback, observed 2026-08-10T05:29:04.960762Z

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source=arxiv_source observed=2026-08-10T05:29:02.989474Z digest=sha256:ec66dbbac94a68466f24c437a853822a9ec7e0b597a0fdf5239076010625ff6e

Observation 25af4625-439c-42b8-86d8-1c0798f57bb7 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sdedit: Guided image synthesis and editing with stochastic differential equations

Reference 58

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raw_fallback, observed 2026-08-10T05:29:04.935013Z

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source=arxiv_source observed=2026-08-10T05:29:02.997134Z digest=sha256:11b0d14f517d6a5eb73539141fb36a4413e769406e0fd556817915540d0e9e8f

Observation f665c403-ec94-428a-843f-44c1da7e5c51 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Efficient Estimation of Word Representations in Vector Space

Reference 59

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source=arxiv_source observed=2026-08-10T05:29:03.001741Z digest=sha256:e981d1d75f45b2958b2b5114ac1e0cf49cf23e8eea4dfe9860315c2694f642a2

Observation 3206d22b-3966-46c6-a3ea-46d3729b70a0 · outbound

This paper cites Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

Reference 60

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source=arxiv_source observed=2026-08-10T05:29:03.024904Z digest=sha256:87598d2dd835a2cbe6e93d19d2a02b4255ea138b5c5e7830aef5bb47b8ef3648

Observation a19c8da9-6502-486b-bca8-6c967901d80e · outbound

This paper cites D., Nafez, M., Madadi, M., Rezaee, S., Taghavi, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples D., Nafez, M., Madadi, M., Rezaee, S., Taghavi, Z

Reference 61

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raw_fallback, observed 2026-08-10T05:29:04.905431Z

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=arxiv_source observed=2026-08-10T05:29:03.045055Z digest=sha256:d7ebdb2045118b143bbffe6476c682e6d37990e74c45d794073bf7a45a94be06

Observation 6e84eaec-f202-4fbd-abe5-ec86cddc10b6 · outbound

This paper cites Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

Reference 62

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local_arxiv, observed 2026-08-10T05:29:03.934753Z

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source=arxiv_source observed=2026-08-10T05:29:03.060490Z digest=sha256:7cd1c80fcf6f97b1f05ec5439d429495eda8dea05c02173b003c1cdba21274ad

Observation fb0f1530-0924-469d-8ee7-abb1feaf24bd · outbound

This paper cites R., Taghavi, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples R., Taghavi, Z

Reference 63

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

source=arxiv_source observed=2026-08-10T05:29:03.064770Z digest=sha256:1580c935607b87df793c973c303f274779a001b9f1e3bd0b7819b205fe6a412e

Observation a975139a-57a6-45cd-bd79-65cf0df172f9 · outbound

This paper cites B., Azizmalayeri, M., Habibi, J., Sabokrou, M., and Rohban, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples B., Azizmalayeri, M., Habibi, J., Sabokrou, M., and Rohban, M

Reference 64

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raw_fallback, observed 2026-08-10T05:29:04.861752Z

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

source=arxiv_source observed=2026-08-10T05:29:03.068203Z digest=sha256:178ba8d86310fdee237721783a89df4744f0099a99fa31bcac5a5c202f56b9be

Observation 763ec3b2-7ab1-41dd-8224-7928c5a53c28 · outbound

This paper cites Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

Reference 65

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local_arxiv, observed 2026-08-10T05:29:03.917131Z

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source=arxiv_source observed=2026-08-10T05:29:03.071894Z digest=sha256:a3e60492024ca3c71fdccf5c452b0c000a24689772899263601312352dc377b8

Observation 4798355d-b33d-496a-898d-3a2487b66f4e · outbound

This paper cites F., Oh, S.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples F., Oh, S

Reference 66

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

source=arxiv_source observed=2026-08-10T05:29:03.076169Z digest=sha256:246fb7d9d4eebd18acc6727aa915f953113470bd0623cbec5514ba51c535155b

Observation 41a60ac9-e3f9-4a07-b141-9658cf59f1b6 · outbound

This paper cites Social Biases through the Text-to-Image Generation Lens.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Social Biases through the Text-to-Image Generation Lens

Reference 67

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source=arxiv_source observed=2026-08-10T05:29:03.081220Z digest=sha256:1da64d8645b8b326e361ad88e89f813c7ca11372b2dcdd2c2800dea44c9ea618

Observation ec2a6773-4aa4-4257-968e-3bdadad42507 · outbound

This paper cites Columbia object image library: Coil-100.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Columbia object image library: Coil-100

Reference 68

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source=arxiv_source observed=2026-08-10T05:29:03.087480Z digest=sha256:07b96d04f36f36c346f3d7f96e0628cfd331ebf642ac9371476731ffd395743b

Observation 77203af4-20d1-4854-89d4-b9ee2bee6f0b · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 69

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source=arxiv_source observed=2026-08-10T05:29:03.092413Z digest=sha256:f668be1964258733c10b7cd84ce7900e8ac37f7e01bff1547e121d59c53980da

Observation 52f4701d-6039-4996-98c1-efeb34a2e18b · outbound

This paper cites Brain tumor mri dataset, 2021.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Brain tumor mri dataset, 2021

Reference 70

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source=arxiv_source observed=2026-08-10T05:29:03.096709Z digest=sha256:93dd6ea3afb8f9070c9ddcff11eb79f92c1b608d6a39520d00846a49f726eeba

Observation 320479c3-1deb-4b52-a7e1-0c7dae7d0ce2 · outbound

This paper cites and Zisserman, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Zisserman, A

Reference 71

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source=arxiv_source observed=2026-08-10T05:29:03.100618Z digest=sha256:b60ad2e91b2cd1e05cb1cdcdbc49e9a6c12a9fc35384a1e2567723c93f7b7d0a

Observation c445b593-1911-43d5-a122-601854e8e21b · outbound

This paper cites Robustness and accuracy could be reconcilable by (proper) definition.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robustness and accuracy could be reconcilable by (proper) definition

Reference 72

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raw_fallback, observed 2026-08-10T05:29:04.797478Z

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source=arxiv_source observed=2026-08-10T05:29:03.104908Z digest=sha256:72e78fde5a576ea431add84955be75c72cd7289a21881b68b32d90cb19990afa

Observation cf0212ad-4d86-4414-9d49-5feb13468248 · outbound

This paper cites One-Class Classification: A Survey.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples One-Class Classification: A Survey

Reference 73

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source=arxiv_source observed=2026-08-10T05:29:03.112788Z digest=sha256:0a447e64c4031af29be16d51be65d3969e633fb173c34f896bfb02383f5b6af7

Observation 436bbf64-c79e-4192-b09c-167fa9bf3321 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 74

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source=arxiv_source observed=2026-08-10T05:29:03.116415Z digest=sha256:c0437f48382b45374fe65b589ff1401fe95b3084f41bd915852fe51aaf779d4b

Observation ce6b99f7-ffcb-470a-bda5-65deafd7cec0 · outbound

This paper cites H allu S afe at S em E val-2024 task 6: An NLI -based approach to make LLM s safer by better detecting hallucinations and overgeneration mistakes.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H allu S afe at S em E val-2024 task 6: An NLI -based approach to make LLM s safer by better detecting hallucinations and overgeneration mistakes

Reference 75

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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=arxiv_source observed=2026-08-10T05:29:03.120064Z digest=sha256:f81dfcb5eff5908f097af57011db10702b31ec619b156f040aeade54c585ebb8

Observation fd57d6e5-0317-4439-802f-5728f5ba4066 · outbound

This paper cites M., Taghavi, Z., and Sameti, H.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples M., Taghavi, Z., and Sameti, H

Reference 76

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doi, observed 2026-08-10T05:29:03.311186Z

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=arxiv_source observed=2026-08-10T05:29:03.123435Z digest=sha256:09a5e6e583ab8b6ddf612ba592866ad1b432c3938c4788fcbc1c7813ad6c8601

Observation c6218149-d55e-4e53-a367-91607f834d62 · outbound

This paper cites Mean-Shifted Contrastive Loss for Anomaly Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Mean-Shifted Contrastive Loss for Anomaly Detection

Reference 77

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local_arxiv, observed 2026-08-10T05:29:03.732014Z

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=arxiv_source observed=2026-08-10T05:29:03.127200Z digest=sha256:b5521c45789d1be61da5313d4e5773c94d7b07ce645f242e7e794742a8f9d4aa

Observation 3a07f206-d92f-4a86-adba-86bbc180112d · outbound

This paper cites Panda: Adapting pretrained features for anomaly detection and segmentation.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Panda: Adapting pretrained features for anomaly detection and segmentation

Reference 78

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raw_fallback, observed 2026-08-10T05:29:04.753760Z

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=arxiv_source observed=2026-08-10T05:29:03.130972Z digest=sha256:4858bdbbd7cc6fc5343b103751440b58521d362bda1056354dd2586e258982dc

Observation f34b201a-142d-4113-9b36-327ac691bc9f · outbound

This paper cites A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 79

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T05:29:03.134515Z digest=sha256:4ecc7c84a18d312f2ff0deadeff8580817b807f95ebd0f2187fc0f312834f78d

Observation 61a9a0c1-b01d-48b5-9020-dd7b0580a9c3 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples High-resolution image synthesis with latent diffusion models

Reference 80

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source=arxiv_source observed=2026-08-10T05:29:03.138396Z digest=sha256:af8ba5b13fd574e6705eec8a36e73668ef630a41388b09b5d770f7822289203b

Observation 8f225556-84e7-4b98-9e17-0f7f2bbcb17e · outbound

This paper cites Towards total recall in industrial anomaly detection, 2021.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Towards total recall in industrial anomaly detection, 2021

Reference 81

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raw_fallback, observed 2026-08-10T05:29:04.720161Z

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=arxiv_source observed=2026-08-10T05:29:03.141906Z digest=sha256:a444f87d1e1a4f4c3e62eb00a53f73dbf3e22bd5c1d32f82b453f4ad7dc2ebcf

Observation 936f94a5-21ce-4561-852e-d68e838e8a4e · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 82

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source=arxiv_source observed=2026-08-10T05:29:03.145397Z digest=sha256:413305bc1b8aa7001a9561eda5c3c1e84d41bdfaa383cf7f3a88183d2a88f8b0

Observation 99aba3dc-84b1-4867-8d89-d489c4cf4808 · outbound

This paper cites A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Reference 83

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source=arxiv_source observed=2026-08-10T05:29:03.149066Z digest=sha256:8f65d1f5af8fc6ca31ca054007d9add5966d8bc6982cfe30b19db23b8d020813

Observation eb8c6117-d745-4369-b662-b45d36479ddd · outbound

This paper cites H., and Rabiee, H.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H., and Rabiee, H

Reference 84

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raw_fallback, observed 2026-08-10T05:29:04.695983Z

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=arxiv_source observed=2026-08-10T05:29:03.152894Z digest=sha256:f0a36eee8a2fb992bd43a3616397c04c0bcac62ee9ee3fbe68812a93dad02e9d

Observation 06fb3c8e-8a96-41df-ad50-46daea1f52ba · outbound

This paper cites Adversarially robust generalization requires more data.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adversarially robust generalization requires more data

Reference 85

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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=arxiv_source observed=2026-08-10T05:29:03.156368Z digest=sha256:94360b525d0c6df31ec9cd964f35dabfb5ce112ce9ebaee66b5efa346fa05bae

Observation 61cc1bb2-225b-48c4-b3c0-64f19acf95ba · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 86

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source=arxiv_source observed=2026-08-10T05:29:03.160647Z digest=sha256:401ef94961cdba323562b828b2c781a8534e707636f0de806bc6155f8b7cdaef

Observation e941add0-e783-4240-b195-39e78bbbc2c7 · outbound

This paper cites Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

Reference 87

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source=arxiv_source observed=2026-08-10T05:29:03.165821Z digest=sha256:fe6fec8accc3d3cb3ffb9906e399fd57b9220064af74357c3f85d91bfa0af5f7

Observation 856f7930-1917-4323-af30-a7b037572029 · outbound

This paper cites C., and Patel, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples C., and Patel, V

Reference 88

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raw_fallback, observed 2026-08-10T05:29:04.664988Z

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=arxiv_source observed=2026-08-10T05:29:03.169611Z digest=sha256:ddca852549d8edd98d26876e8e44d5fef8f8ac6dc3597dd27228c256ed398241

Observation 8453bfc6-feca-4e94-bcc8-59f60af42946 · outbound

This paper cites C., and Patel, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples C., and Patel, V

Reference 89

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raw_fallback, observed 2026-08-10T05:29:04.655321Z

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=arxiv_source observed=2026-08-10T05:29:03.172939Z digest=sha256:971b2af5bef955a7aa5375157f324573d76e5f645470088ec9f71a8b0c5b1c2d

Observation 2bf513be-996f-4c87-9c3d-2788deb0d790 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep unsupervised learning using nonequilibrium thermodynamics

Reference 90

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source=arxiv_source observed=2026-08-10T05:29:03.176307Z digest=sha256:c97eddc9e4a47fe05c06fda537c79ef3da0bad0effde0c9b5a3d941e87ae600c

Observation 67148f30-9838-4a04-b21d-93a5e9e95193 · outbound

This paper cites Disentangling adversarial robustness and generalization.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Disentangling adversarial robustness and generalization

Reference 91

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raw_fallback, observed 2026-08-10T05:29:04.638738Z

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=arxiv_source observed=2026-08-10T05:29:03.181320Z digest=sha256:6b90c2267bde7641a1ce861eee6eb7e50fe15385ce1fb626fd34e2e1a8e549d3

Observation 7e72011e-5b9a-45b6-b946-134a606644e1 · outbound

This paper cites Intriguing properties of neural networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Intriguing properties of neural networks

Reference 92

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source=arxiv_source observed=2026-08-10T05:29:03.185045Z digest=sha256:25d9232091dcb87946f50f4170703fdc349bb4c0ba59580ab95350882c2a287f

Observation 57287427-28ac-4926-ad80-6744ec409137 · outbound

This paper cites Csi: Novelty detection via contrastive learning on distributionally shifted instances.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Csi: Novelty detection via contrastive learning on distributionally shifted instances

Reference 93

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source=arxiv_source observed=2026-08-10T05:29:03.189656Z digest=sha256:db293ef03f812c3d3645341eff44c6bcd35cc7046358ba689182d22c8fb3b0c0

Observation 134118af-93c3-4689-bb11-eb40fe6397fe · outbound

This paper cites Backdooring Outlier Detection Methods: A Novel Attack Approach.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Backdooring Outlier Detection Methods: A Novel Attack Approach

Reference 94

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local_arxiv, observed 2026-08-10T05:29:03.663525Z

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=arxiv_source observed=2026-08-10T05:29:03.193034Z digest=sha256:1cf11b1fe45fff815f09d0aec701f0aa8e7063772957054217af6631f060d2d8

Observation 7ac558ab-7b9a-4c2b-b66a-cec6e7e30a8a · outbound

This paper cites H., Sadraei Javaheri, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H., Sadraei Javaheri, M

Reference 95

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doi, observed 2026-08-10T05:29:04.618639Z

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=arxiv_source observed=2026-08-10T05:29:03.196712Z digest=sha256:0a1eb35650a471c26c5953befcaef3e813cc371ae62006122c9b98d5abd48a4d

Observation f9c7de39-ed87-4cf8-a5e2-091d6f580d91 · outbound

This paper cites Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems

Reference 96

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metadata mismatch
local_arxiv, observed 2026-08-10T05:29:03.650773Z

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=arxiv_source observed=2026-08-10T05:29:03.200201Z digest=sha256:4a1f2302927d15d00785468587142d734c498b16af4ad409b886f0012a6736fc

Observation af65d2e9-43d3-4911-9bfa-b5736b756549 · outbound

This paper cites A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

Reference 97

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local_arxiv, observed 2026-08-10T05:29:03.635726Z

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=arxiv_source observed=2026-08-10T05:29:03.203813Z digest=sha256:df8cbd47454c5c2cdf864c186d4bd0c5c4fc073319723dda9ffda965187d5ff3

Observation 41d86130-b816-4e0d-b796-0ccec1620b72 · outbound

This paper cites Non-Parametric Outlier Synthesis.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Non-Parametric Outlier Synthesis

Reference 98

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source=arxiv_source observed=2026-08-10T05:29:03.207388Z digest=sha256:acb2d5b60dc49f60c377c19ded59cfb2cd19a425ae28c2a31d509d84862548d3

Observation 93c839d4-7541-4974-9ebf-d0574c0f5b51 · outbound

This paper cites Non-parametric outlier synthesis, 2023 b.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Non-parametric outlier synthesis, 2023 b

Reference 99

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raw_fallback, observed 2026-08-10T05:29:04.608718Z

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=arxiv_source observed=2026-08-10T05:29:03.211620Z digest=sha256:fea22aedab386d807974d85f76cf7141f80b334ec78e1f05ebdb8f2267daa520

Observation 8a1e89f8-897a-4003-b6e6-b21aea4a29e6 · outbound

This paper cites and Hinton, G.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Hinton, G

Reference 100

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T05:29:03.215049Z digest=sha256:69d208c51236cd8f00595c31e3fe1a55b6b872f1c5890acda2dd6c65c4d3d9c5

Pith citing papers

Observation 78ae8ff0-439e-4ea3-84f2-e5bbd487afb1 · inbound

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection cites this paper.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Reference 57

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:32:44.340202Z digest=sha256:5999e1aa57d78eac69948d060608402de81e825ba5cd9d4a6c231f4ea0320d68