{"as_of":"2026-08-10T23:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:379dc636810ab703a0172e8f4feae4c8b356163092547e02b7cb98b5ac0bec8c","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:06:22.533578Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.01920/citation-record","integrity":"/paper/2502.01920/integrity","json":"/paper/2502.01920/citation-record.json","paper":"/paper/2502.01920"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:06:22.711495Z","title":"ntX i=1 zi − ˆµ(t) z T # ˆµ(t) z − ˆµ(t+1) z =","venue":null,"work_id":"123c735f-420c-47b0-b025-d9a75b7a1e30","year":2024},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.533578Z"},"links":{"citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:da158f3f5779ce2d4c0ca1e1b702d1d2d513e1ffda36d91ce648867253f6b0a6","observation_id":"045fed8d-830c-4b1c-9ec8-2461e0f4efbc","resolution":{"observed_at":"2026-08-09T14:06:22.716189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:06:22.758596Z","title":"Autoencoder-based network anomaly detection","venue":null,"work_id":"2adc1ac6-b9b4-44ae-86e5-1c8ebecd8beb","year":2018},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.459685Z"},"links":{"citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:9c1d3421af1ce5fad8d54da0cac72dbb2cb3d8a84e2b227bcf6e4a9cabf30289","observation_id":"32d143f9-0400-43fd-b24a-8e0513eb8309","resolution":{"observed_at":"2026-08-09T14:06:22.762939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:06:22.746179Z","title":"Imagenet: A large-scale hier- archical image database","venue":null,"work_id":"ad4e4e9b-7547-41d3-aa31-bddb76a8b850","year":2009},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.468956Z"},"links":{"citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:e7fb5bfa6a58440653193f74a6786f458a45b38ca2620501407a982700e3e000","observation_id":"ec790fc2-05d2-40ef-8973-cf184fab2cfc","resolution":{"observed_at":"2026-08-09T14:06:22.750353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.11632","last_updated":"2021-09-14T07:49:10Z","snapshot_observed_at":"2026-07-06T08:03:18.202161Z","submitted_at":"2019-06-27T13:38:22Z","title":"A Survey on GANs for Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.11632","snapshot_observed_at":"2026-08-09T14:06:22.472837Z","title":"A survey on gans for anomaly detection","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.472837Z"},"links":{"cited_paper":"/paper/1906.11632","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:3442921f6efa26cdf15a79d421668652c4df85ef1a395dde6cfd45f310871d7f","observation_id":"b10e21d9-8b9b-4e4f-81a5-975b4d1aa1b6","resolution":{"observed_at":"2026-08-09T14:06:22.472837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.02163","last_updated":"2017-05-12T23:52:12Z","snapshot_observed_at":"2026-08-10T07:56:39.714006Z","submitted_at":"2016-11-07T16:42:09Z","title":"Unrolled Generative Adversarial Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.02163","snapshot_observed_at":"2026-08-09T14:06:22.485398Z","title":"Unrolled generative adversarial networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.485398Z"},"links":{"cited_paper":"/paper/1611.02163","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:a0c91af922853484d3caeb31848080740b7e0bc8752fe22c4faf6b79f60819c7","observation_id":"768579ed-9e0d-40d8-b4c9-0151e41570c9","resolution":{"observed_at":"2026-08-09T14:06:22.485398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02337","last_updated":"2019-06-05T22:23:43Z","snapshot_observed_at":"2026-07-06T07:58:18.558050Z","submitted_at":"2019-06-05T22:23:43Z","title":"MNIST-C: A Robustness Benchmark for Computer Vision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02337","snapshot_observed_at":"2026-08-09T14:06:22.489835Z","title":"Mnist-c: A robustness benchmark for computer vision","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.489835Z"},"links":{"cited_paper":"/paper/1906.02337","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:d530cb32a2f430a7c352bf79bc192b097d7d83ea2820a47ea64ac472e7e4681d","observation_id":"4747ad12-1d27-4964-a4f9-587888bcbcbe","resolution":{"observed_at":"2026-08-09T14:06:22.489835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02694","last_updated":"2020-02-14T10:10:15Z","snapshot_observed_at":"2026-07-06T07:58:30.747165Z","submitted_at":"2019-06-06T16:46:56Z","title":"Deep Semi-Supervised Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02694","snapshot_observed_at":"2026-08-09T14:06:22.499173Z","title":"Deep semi-supervised anomaly detection","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.499173Z"},"links":{"cited_paper":"/paper/1906.02694","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:b6c6472a04a6e7bbad70b93e676298875c77ab2bc16865729cc1ab2ee923f8c9","observation_id":"29bb8254-59c8-46ea-ba49-d5bf72562ae0","resolution":{"observed_at":"2026-08-09T14:06:22.499173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:06:22.734823Z","title":"Anomaly detection using autoencoders with nonlinear dimen- sionality reduction","venue":null,"work_id":"a5fcd3d2-edcb-4e28-827f-3a8dffba34f1","year":2014},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.504082Z"},"links":{"citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:d628f181265ef3c34ba10f6d108be00eccbb10cb7b70db325d085b5466e9ffa0","observation_id":"339979fe-5831-4921-8709-15d188c6f82a","resolution":{"observed_at":"2026-08-09T14:06:22.738819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1510.01553","last_updated":"2015-10-06T12:42:55Z","snapshot_observed_at":"2026-07-06T04:32:09.490945Z","submitted_at":"2015-10-06T12:42:55Z","title":"Learning Deep Representations of Appearance and Motion for Anomalous Event Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.01553","snapshot_observed_at":"2026-08-09T14:06:22.520883Z","title":"Learning deep representations of appearance and motion for anomalous event detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.520883Z"},"links":{"cited_paper":"/paper/1510.01553","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:7d2cc5daa0fb68e7cb2acdffa3e538b30054426b7322389b3921f5b9cf521b55","observation_id":"19ef2390-7e36-49cb-86dc-8be983147561","resolution":{"observed_at":"2026-08-09T14:06:22.520883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:06:22.723497Z","title":"Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications","venue":null,"work_id":"90e27b51-11d7-4671-8e44-25b085f66faf","year":2018},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.524917Z"},"links":{"citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:fe9b8df6fcff94f43bd64119c791563e8432a52878162b65d237cedf54a62de6","observation_id":"128b6446-3b73-4d4c-a0f9-2650f49114cc","resolution":{"observed_at":"2026-08-09T14:06:22.727588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06222","last_updated":"2019-05-01T21:54:16Z","snapshot_observed_at":"2026-07-06T06:23:57.939369Z","submitted_at":"2018-02-17T11:26:53Z","title":"Efficient GAN-Based Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06222","snapshot_observed_at":"2026-08-09T14:06:22.529309Z","title":"Efficient gan-based anomaly detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.529309Z"},"links":{"cited_paper":"/paper/1802.06222","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:135ed9fdff1e03104e59740b5811db25c0347e3661f15f126c0238422f1e47c1","observation_id":"5fcb0a23-0c07-43a0-ba87-b171c11e2762","resolution":{"observed_at":"2026-08-09T14:06:22.529309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.02136","last_updated":"2017-03-02T06:28:13Z","snapshot_observed_at":"2026-08-10T10:24:43.175646Z","submitted_at":"2016-12-07T07:45:38Z","title":"Mode Regularized Generative Adversarial Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.02136","snapshot_observed_at":"2026-08-09T14:06:22.454983Z","title":"Mode regularized generative adversarial networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.454983Z"},"links":{"cited_paper":"/paper/1612.02136","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:4bb8d682fef43c5e3547b77512f76e245509f7cdf0f1bdad76a168cf6b4852c4","observation_id":"966d2229-9c1a-4567-a54e-596b63aea420","resolution":{"observed_at":"2026-08-09T14:06:22.454983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.05517","last_updated":"2017-01-19T17:29:06Z","snapshot_observed_at":"2026-08-09T02:47:46.527055Z","submitted_at":"2017-01-19T17:29:06Z","title":"PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.05517","snapshot_observed_at":"2026-08-09T14:06:22.507918Z","title":"Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.507918Z"},"links":{"cited_paper":"/paper/1701.05517","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:60eddddd287b17fa5b9fb8040e585dc35d25e84ba87b9b70b2ad24f656faa71d","observation_id":"e78363d8-6d91-45dd-bb4c-8c6c5e4046ae","resolution":{"observed_at":"2026-08-09T14:06:22.507918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-09T14:06:22.516888Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.516888Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:ad93a73dfd44d52037acb8039daed41d7174734c358f13d48f3caf2609548ded","observation_id":"681d036c-3017-4198-a166-2486309a8d30","resolution":{"observed_at":"2026-08-09T14:06:22.516888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02359","last_updated":"2020-05-05T17:44:40Z","snapshot_observed_at":"2026-08-09T20:35:30.325223Z","submitted_at":"2020-05-05T17:44:40Z","title":"Classification-Based Anomaly Detection for General Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02359","snapshot_observed_at":"2026-08-09T14:06:22.449326Z","title":"Classification-based anomaly detection for general data","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.449326Z"},"links":{"cited_paper":"/paper/2005.02359","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:587e67c399ec20bb823668db19fb429dfcb158524bf02600e79e1641f8e3ab81","observation_id":"7c9c164e-b57f-4b81-b2ca-618cdb3bbea3","resolution":{"observed_at":"2026-08-09T14:06:22.449326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00686","last_updated":"2016-12-02T14:05:49Z","snapshot_observed_at":"2026-07-06T05:21:08.258613Z","submitted_at":"2016-12-02T14:05:49Z","title":"Identifying and Categorizing Anomalies in Retinal Imaging Data","version":1},"cited_work":{"arxiv_id":"1612.00686","doi":null,"metadata_source":"pith","pith_arxiv_id":"1612.00686","snapshot_observed_at":"2026-08-09T14:06:22.593873Z","title":"Identifying and Categorizing Anomalies in Retinal Imaging Data","venue":"cs.LG","work_id":"5dabbad5-8ce8-48e0-99cc-2cf35911b0a9","year":2016},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.512609Z"},"links":{"cited_paper":"/paper/1612.00686","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:8a42f57f9cb9249ce285efa531343244af0899b10b6bb436fdd8817ffd09fc4d","observation_id":"0a204e82-7413-4431-b4f1-750e3f9e1fa5","resolution":{"observed_at":"2026-08-09T14:06:22.600847Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01392","last_updated":"2019-05-23T23:48:06Z","snapshot_observed_at":"2026-08-02T10:17:34.360048Z","submitted_at":"2018-10-02T17:32:07Z","title":"WAIC, but Why? Generative Ensembles for Robust Anomaly Detection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01392","snapshot_observed_at":"2026-08-09T14:06:22.464315Z","title":"Waic, but why? generative ensembles for robust anomaly detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.464315Z"},"links":{"cited_paper":"/paper/1810.01392","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:61f5d132f1a057c95c3473dae281412374e90809e7caf584200b29377f442259","observation_id":"11b78c3d-c3d3-4e64-a40f-da1891914477","resolution":{"observed_at":"2026-08-09T14:06:22.464315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.09136","last_updated":"2019-02-24T11:57:32Z","snapshot_observed_at":"2026-07-06T07:09:43.264415Z","submitted_at":"2018-10-22T08:32:02Z","title":"Do Deep Generative Models Know What They Don't Know?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.09136","snapshot_observed_at":"2026-08-09T14:06:22.494220Z","title":"Do deep generative models know what they don’t know? arXiv preprint arXiv:1810.09136,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.494220Z"},"links":{"cited_paper":"/paper/1810.09136","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:88f46660c4d561d1e63ee4fc293859d72652d6914dea70fee91b432272e7210b","observation_id":"d24bb0a5-2fb3-419e-a04a-9b5864704b5c","resolution":{"observed_at":"2026-08-09T14:06:22.494220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02954","last_updated":"2024-09-25T18:00:00Z","snapshot_observed_at":"2026-08-08T01:05:46.571697Z","submitted_at":"2024-04-03T18:00:00Z","title":"Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02954","snapshot_observed_at":"2026-08-09T14:06:22.481158Z","title":"Deep generative models through the lens of the manifold hypothesis: A survey and new connections","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.481158Z"},"links":{"cited_paper":"/paper/2404.02954","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:cca6626282f16306e0d1e8edbd356f126ab01d755a56386c7b6285545114467f","observation_id":"530ad0a2-2b70-421c-8944-5dd81ddfb2eb","resolution":{"observed_at":"2026-08-09T14:06:22.481158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-09T14:06:22.477136Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T14:06:22.477136Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2502.01920"},"observation_digest":"sha256:e071ee0b44d699d6ec036a6b1213ad50901d6767b301a3064bb6f71c982e3a00","observation_id":"acbccc75-1031-43b7-b7cd-c1761bddca0c","resolution":{"observed_at":"2026-08-09T14:06:22.477136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.01920","last_updated":"2025-06-11T14:05:27Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T20:36:20.945526Z","submitted_at":"2025-02-04T01:29:22Z","title":"Anomaly Detection via Autoencoder Composite Features and NCE"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"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."}