{"as_of":"2026-08-23T05:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c312f7604b3f5ac696758814f95dfc4993e4c7a38e360f15333234726dcddd79","coverage":[{"denominator":108,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T22:55:22.998247Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2412.03058/citation-record","integrity":"/paper/2412.03058/integrity","json":"/paper/2412.03058/citation-record.json","paper":"/paper/2412.03058"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.725209Z","title":"Outlier detection for high dimensional data,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.725209Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:adf607172f6160296b32525ab46e51f4dc59cba963be0d80f3bdd6289ff3795b","observation_id":"5a3c9261-ad97-441f-bd69-97a4d864ad71","resolution":{"observed_at":"2026-08-11T22:55:22.725209Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.728726Z","title":"A survey of outlier detection methodologies,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.728726Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:ba8d99d7da8d70aa04606df42885da614b691db30a3e8888542ed5af8823d9a6","observation_id":"adfb6c31-75fe-427a-bd98-ab985534b83b","resolution":{"observed_at":"2026-08-11T22:55:22.728726Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.731845Z","title":"Outlier detection,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.731845Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:fbed96bcacad9464abee55cf20d68ea4f4045f6f6790786eda674d875fce287c","observation_id":"a5c63f4e-ca42-4489-b1c7-b51e9450993d","resolution":{"observed_at":"2026-08-11T22:55:22.731845Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.735096Z","title":"Progress in outlier detection techniques: A survey,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.735096Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:752ddf301a3e38df87d5f670b013acc5135f71dfc6c8ee37c223789aaf79cb78","observation_id":"327950d0-3480-4e71-9165-b577ded0c73b","resolution":{"observed_at":"2026-08-11T22:55:22.735096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06565","last_updated":"2016-07-25T17:23:29Z","snapshot_observed_at":"2026-07-06T05:00:46.434335Z","submitted_at":"2016-06-21T13:37:05Z","title":"Concrete Problems in AI Safety","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.06565","snapshot_observed_at":"2026-08-11T22:55:22.738405Z","title":"Concrete problems in AI safety,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.738405Z"},"links":{"cited_paper":"/paper/1606.06565","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d4e639f59a56cb2a568d72d0595b4ce0f68063ea481551d7b6736cedc8de1fb6","observation_id":"7432bafd-4b9a-4db5-8fcf-58c2d87b8cce","resolution":{"observed_at":"2026-08-11T22:55:22.738405Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.742259Z","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.742259Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:bfbc44fd84c1f6f21e70ebb2579b764b7cdf77ba966ee5b6e6da3d19fb4cd8c6","observation_id":"0b14da18-b55c-4f94-8c4e-50023f42c529","resolution":{"observed_at":"2026-08-11T22:55:22.742259Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.745560Z","title":"Steps toward robust artificial intelligence,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.745560Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1caf3f886af4573846a3e20aeca8cff6fcac7b958e78a133c8c6ce1377ffba3e","observation_id":"82ddacf9-cfb4-45b9-8f90-0ccd54cfe5c9","resolution":{"observed_at":"2026-08-11T22:55:22.745560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.09883","last_updated":"2017-11-28T17:40:36Z","snapshot_observed_at":"2026-08-16T22:19:47.836428Z","submitted_at":"2017-11-27T18:57:13Z","title":"AI Safety Gridworlds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.09883","snapshot_observed_at":"2026-08-11T22:55:22.748297Z","title":"AI safety gridworlds,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.748297Z"},"links":{"cited_paper":"/paper/1711.09883","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:764f9b91ae1a8534619b48defb0675ed60f763a485839b3bac33d6000b6daffd","observation_id":"221e89ae-69c7-4889-a92e-87bb69746952","resolution":{"observed_at":"2026-08-11T22:55:22.748297Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.751490Z","title":"The EU approach to ethics guidelines for trustworthy artificial intelligence,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.751490Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:19d66f2f09b0be20ce1772b8c365fbd00e14643aaa591dda2ef4985435075e2a","observation_id":"51f4e46a-9cf7-4eb2-9803-aa95c50c853f","resolution":{"observed_at":"2026-08-11T22:55:22.751490Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.754174Z","title":"Bridging the gap between ethics and practice: Guide- lines for reliable, safe, and trustworthy Human-Centered AI systems,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.754174Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d5cdc9b95753aa87528f76f3f21d2cb37957cf4932e2d981d5b39e0b0d0aab6a","observation_id":"2f74d480-1d7b-4ae9-953c-7e74e6e25a6b","resolution":{"observed_at":"2026-08-11T22:55:22.754174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04823","last_updated":"2022-03-08T00:22:37Z","snapshot_observed_at":"2026-08-20T08:55:42.442767Z","submitted_at":"2021-06-09T05:56:42Z","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","version":2},"cited_work":{"arxiv_id":"2106.04823","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.04823","snapshot_observed_at":"2026-08-11T22:55:23.155417Z","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","venue":"cs.LG","work_id":"996a3b54-9e27-4d0b-8016-66c7f7ea048c","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.757001Z"},"links":{"cited_paper":"/paper/2106.04823","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:3d06fe932248c57b88649c9b26e0c139a9a544404533444ff8f693b949c37452","observation_id":"0f33eb6a-3eff-4593-be56-092dfc7072b2","resolution":{"observed_at":"2026-08-11T22:55:23.158551Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.13916","last_updated":"2022-06-16T21:12:42Z","snapshot_observed_at":"2026-07-06T11:52:19.105210Z","submitted_at":"2021-09-28T17:59:36Z","title":"Unsolved Problems in ML Safety","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.13916","snapshot_observed_at":"2026-08-11T22:55:22.760093Z","title":"Unsolved problems in ml safety,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.760093Z"},"links":{"cited_paper":"/paper/2109.13916","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:46fbf3fd1e36aeffb6f63a3080c3c30a09722fac24682ee1ce712876e8c87741","observation_id":"9bba11e7-5f2a-4b03-9600-639c2516c18c","resolution":{"observed_at":"2026-08-11T22:55:22.760093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05862","last_updated":"2022-09-20T16:49:56Z","snapshot_observed_at":"2026-08-16T16:53:33.500360Z","submitted_at":"2022-06-13T00:22:50Z","title":"X-Risk Analysis for AI Research","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.05862","snapshot_observed_at":"2026-08-11T22:55:22.763326Z","title":"X-risk analysis for AI research,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.763326Z"},"links":{"cited_paper":"/paper/2206.05862","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:e8d12a9ee175e74e95a48f1b6c0b1a3e09d9ca8809414a739ee321319557396a","observation_id":"4bdfeb80-9511-466f-b76d-bbd9c8775a72","resolution":{"observed_at":"2026-08-11T22:55:22.763326Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.766358Z","title":"Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Im- ages,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.766358Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:a103bd687f8991eed1e1fd445edcbe1b769a8f5d171876dce302b719dc50f7e2","observation_id":"1c55d2c1-390c-4f58-b468-94d276ebb482","resolution":{"observed_at":"2026-08-11T22:55:22.766358Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.769091Z","title":"Why ReLU Networks Yield High-Confidence Predictions Far Away from the Training Data and How to Mitigate the Problem,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.769091Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:5020574218ffa7a2044e3b69bb1ffd348c818c1a3e149e03fb268465affe84f3","observation_id":"2bc61af5-95ee-46ac-9a03-208028d89eec","resolution":{"observed_at":"2026-08-11T22:55:22.769091Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.771705Z","title":"Standardized Max Logits: A Simple Yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.771705Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:57ac5eb6b5adc0633add019d43985cc5fc1cd44ae2e97ad8b795d40cf4465993","observation_id":"cf15df17-2cb1-4036-a3e4-552411e438b1","resolution":{"observed_at":"2026-08-11T22:55:22.771705Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.774423Z","title":"A systematic review of outliers detection techniques in medical data-preliminary study,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.774423Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:926f32b9e40fd0fcf6b440a1ae4136d6f7db03f58085c733c35e8d49900984e6","observation_id":"e8f358bd-b048-4d0c-8ca1-09633a262790","resolution":{"observed_at":"2026-08-11T22:55:22.774423Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.776982Z","title":"Outlier detection for patient monitoring and alerting,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.776982Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:2ae4efc4e254e9d3ae4a6ace07f5f5a61efca54ffbe6c874b3d9bdfff7c52203","observation_id":"55baa243-b297-4094-a9bc-fc8b66440f40","resolution":{"observed_at":"2026-08-11T22:55:22.776982Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.779661Z","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.779661Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:aa81c42ac123fef18d09b36c7a407cd55882d24c21d1487bedddbd128059c40f","observation_id":"b1495e1a-3f31-46a3-9cab-acf40e9df7a2","resolution":{"observed_at":"2026-08-11T22:55:22.779661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09301","last_updated":"2024-12-16T23:09:28Z","snapshot_observed_at":"2026-08-17T08:35:00.382598Z","submitted_at":"2023-06-15T17:28:00Z","title":"OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09301","snapshot_observed_at":"2026-08-11T22:55:22.782277Z","title":"OpenOOD v1. 5: Enhanced Benchmark for Out-of-Distribution Detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.782277Z"},"links":{"cited_paper":"/paper/2306.09301","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:0e7bcbf9ad351a481c23c83bdc73d8cae170bf694fe5c7a937f312356906a051","observation_id":"ad3feb38-2cc0-49e5-9e70-e2a2cf228598","resolution":{"observed_at":"2026-08-11T22:55:22.782277Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.785501Z","title":"Deep Anomaly Detec- tion with Outlier Exposure,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.785501Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:e4d335077b44a4b7383bd93e410d94ceafdbb0689415c83eae65338173c05c6e","observation_id":"0f18c733-55f4-48bb-a679-553dd3948db1","resolution":{"observed_at":"2026-08-11T22:55:22.785501Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.788356Z","title":"Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.788356Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:51f112af078db54f60a3df4dc4567ec6f3ec7b3f0744a0d12d4f58549fd987de","observation_id":"3be8e7cf-33cd-4ed6-a4ee-bee30f2f6db3","resolution":{"observed_at":"2026-08-11T22:55:22.788356Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.791017Z","title":"Energy-Based Out-of- Distribution Detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.791017Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:5dfc710d0360434240a744162375b041be0f212b53108b8dd57ffb7fe53ab471","observation_id":"5838cb91-73b3-4cd8-8407-9c45dc55505d","resolution":{"observed_at":"2026-08-11T22:55:22.791017Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.794184Z","title":"Self-Supervised Learning for Generalizable Out-of-Distribution Detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.794184Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:9a3e48d05eb4e4124af1690ae134691d40ea8dd4a813fca4c717da6b297eaa34","observation_id":"fa0e5474-0445-400a-9561-a098e4480aa8","resolution":{"observed_at":"2026-08-11T22:55:22.794184Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.796890Z","title":"Atom: Robustifying out- of-distribution detection using outlier mining,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.796890Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:b9529a24a47947b7a29156856b166272c4f17bb9f5a4f0e1977f9b032e841782","observation_id":"3f55671d-ae58-4831-a41a-e9f22329aecf","resolution":{"observed_at":"2026-08-11T22:55:22.796890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.07107","last_updated":"2021-05-15T00:46:11Z","snapshot_observed_at":"2026-08-20T14:05:30.165752Z","submitted_at":"2021-05-15T00:46:11Z","title":"An Effective Baseline for Robustness to Distributional Shift","version":1},"cited_work":{"arxiv_id":"2105.07107","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.07107","snapshot_observed_at":"2026-08-11T22:55:23.123782Z","title":"An Effective Baseline for Robustness to Distributional Shift","venue":"cs.LG","work_id":"2391aff0-23a8-490f-803d-d38f95cd1a53","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.799515Z"},"links":{"cited_paper":"/paper/2105.07107","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d75f353e85a570d7accceda1ce8a92176072b812cb6900989b6ad297cf32426c","observation_id":"ea52676a-b33f-4fcc-940c-fae5aad395d0","resolution":{"observed_at":"2026-08-11T22:55:23.127710Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.802693Z","title":"Background data resampling for outlier- aware classification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.802693Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:997439805c296df1c46076183e65448c4920a7e6be22a42c23624168088056c2","observation_id":"750d06f8-1126-46d2-84a1-bb36934fb5c5","resolution":{"observed_at":"2026-08-11T22:55:22.802693Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.805263Z","title":"Outlier Exposure with Confidence Control for Out-of-Distribution Detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.805263Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:9aef7f758fc98b97240bf1a9c6dbb973515c5b395e47d70a44a76083472db63f","observation_id":"50eb676c-33e9-49ec-a3fc-28049f00c241","resolution":{"observed_at":"2026-08-11T22:55:22.805263Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.807996Z","title":"Poem: Out-of-distribution detection with posterior sampling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.807996Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f7fc492126608e6b73ed4c6b19ce720a32dc4ba152e3a9d117154e7cd82dc4b4","observation_id":"8cc67f3d-c8af-4684-8952-9fd6b0943716","resolution":{"observed_at":"2026-08-11T22:55:22.807996Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.810568Z","title":"Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.810568Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:9f13f2c99839777d8b4833f75a59ee00f30768abbb938ada80419d5f317b31ae","observation_id":"3eab03be-675d-4109-b304-3b9809db174c","resolution":{"observed_at":"2026-08-11T22:55:22.810568Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.813030Z","title":"Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.813030Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d4d5ab82d24f46c274d7304698dca5d09f0778204240545e5187a4ab144ccd5a","observation_id":"f4d5ba76-340e-4d57-b7ce-dbc9b55c7317","resolution":{"observed_at":"2026-08-11T22:55:22.813030Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.816212Z","title":"Learning to augment distributions for out-of-distribution detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.816212Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f17e304e1a8100727ae922ce09ae77077c06b78fda7324edf39ef4ea84e35acc","observation_id":"fa1a8cda-098e-49e9-926c-851a50959bf5","resolution":{"observed_at":"2026-08-11T22:55:22.816212Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.819328Z","title":"A Unifying Review of Deep and Shallow Anomaly Detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.819328Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:49cd00a271cb158cc563b4e9a92e51e5547651dbf39e3c847b3b888d0311f01f","observation_id":"dd5f42f0-f5ac-44bd-8653-e0f0e99f9fee","resolution":{"observed_at":"2026-08-11T22:55:22.819328Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.821996Z","title":"Unsupervised Representation Learning by Predicting Image Rotations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.821996Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:c109031a248160a53a9d4c3f83a941c9a6cf05d9140eedf855ff31984f4ff2d6","observation_id":"7f857ff6-ae85-4334-853f-c13cf550148a","resolution":{"observed_at":"2026-08-11T22:55:22.821996Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:55:22.824585Z","title":"Design of an Image Edge Detection Filter Using the Sobel Operator,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.824585Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:9ba0912eb0b81b23ca0dd79aaface3ae9911a7eb077bd39f7781ff38d392da4b","observation_id":"2d306dae-4e65-48a1-b0b3-efdc3189d0b6","resolution":{"observed_at":"2026-08-11T22:55:22.824585Z","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":"2026-08-11T22:55:23.671121Z","title":"A Tu- torial on Energy-Based Learning,","venue":null,"work_id":"4713a82a-fbc6-409f-a472-59d98d12545e","year":2006},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.827244Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:ee57c9c526b09d62412870677757ca27295fc3d51fef60f2d91311c24a68b920","observation_id":"d12a4ff2-d457-45e6-a8f6-804215f3f11a","resolution":{"observed_at":"2026-08-11T22:55:23.674004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.663035Z","title":"CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances,","venue":null,"work_id":"8ae73565-d5a7-4578-a518-e54ee3a75d09","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.829867Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f632a8e30d29b161150a7dbcb83c905ffa6e3cc2ff01c3a72267ac22e0ea80e5","observation_id":"8aef4ae5-468a-4a16-a0b9-bd734879a684","resolution":{"observed_at":"2026-08-11T22:55:23.666097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.654804Z","title":"OEST: Outlier Exposure by Simple Transformations for Out-of-Distribution Detection,","venue":null,"work_id":"7430273e-8454-47cf-9851-fd871a434ca7","year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.832381Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:dff92e225222cfcf4dcac40c57dc27d56a552358728f1882fc7bea42bd68cad2","observation_id":"c3dd27b7-43e5-4826-8885-b40a6e8d49cb","resolution":{"observed_at":"2026-08-11T22:55:23.658005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.646863Z","title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks,","venue":null,"work_id":"766e6a49-74fb-404e-acaa-b85e67b641e3","year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.834862Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:75e33061ee90ad8f84a1d6cd7cbc31b99b98e3b695ce5a0828af78e3a6dadc8d","observation_id":"7cbe8029-20a9-4f08-92b1-58aaad5d8cd8","resolution":{"observed_at":"2026-08-11T22:55:23.649837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.638775Z","title":"Detecting Out-of-Distribution Examples with Gram Matrices,","venue":null,"work_id":"8d98659c-6154-4e8a-bcfc-738ec0d4b6a2","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.837963Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1fd932c22fb6711cf390f8ce0253899a60f24e9d3f01ada3af68d76d9a80252a","observation_id":"eb6988d9-77d7-4fa3-b67e-54abc60828e5","resolution":{"observed_at":"2026-08-11T22:55:23.641818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.630491Z","title":"Out-of-Distribution Detection with Deep Nearest Neighbors,","venue":null,"work_id":"ec9611f5-67dc-4b3e-b1eb-05403c883002","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.840842Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:dee2950dfee1e242d92bbcaf56d4494df34c80e40dbf8efd39976700aefeab3c","observation_id":"83025099-7172-43c1-856e-381d18ad72c8","resolution":{"observed_at":"2026-08-11T22:55:23.633571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.622458Z","title":"Out-of-distribution detection based on in- distribution data patterns memorization with modern hopfield energy,","venue":null,"work_id":"e1c8de60-9372-4a31-999d-905cf14b7a8f","year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.843359Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:85f95b9e7e2e1b868cca9cc2114b9532bbb4fc5d858e31c00d08b9c7fe623150","observation_id":"2b912ff5-897f-4bb2-8bc7-ff4f0b12bc47","resolution":{"observed_at":"2026-08-11T22:55:23.625437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.614090Z","title":"Enhancing The Reliability of Out-of- Distribution Image Detection in Neural Networks,","venue":null,"work_id":"356c6304-7341-4e2b-ad5d-e534e8668fb3","year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.846336Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:4d207f8b8eb0c44df6321d1cde5940d7ed544c69d2a89294803dd8689f7cd609","observation_id":"bb4253ac-8127-452a-8409-46f135487707","resolution":{"observed_at":"2026-08-11T22:55:23.617324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.605983Z","title":"Scaling out-of-distribution detection for real-world settings,","venue":null,"work_id":"02b73719-ab92-4614-b6ed-967001dff770","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.849195Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:0a34236395924b8db65694b628da3d05602c52688c443bfdc94efbeab5c29e37","observation_id":"34778a5d-6d7a-416f-bd10-eaf167404e6a","resolution":{"observed_at":"2026-08-11T22:55:23.609216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.597152Z","title":"Mood: Multi-level out-of-distribution detection,","venue":null,"work_id":"17e17b1f-1158-4e8d-88be-7192d40d621d","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.851774Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:96bd4a68a5fa4e2e0908f5d7a6816b2152cd231814239d695cff8d731ff6d79d","observation_id":"45bd06cf-2566-4626-ac0e-c94b01bb7a2b","resolution":{"observed_at":"2026-08-11T22:55:23.600502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.589144Z","title":"Your Classifier is Secretly an Energy Based Model and You Should Treat It Like One,","venue":null,"work_id":"66ad8e62-6c36-4cce-9acb-0a984159f413","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.854335Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:a9a4087747f78e7f445c010d767aaeef8965108ca1acd7ae9524df1e690448ca","observation_id":"51ef7ff1-3a5a-4dca-a106-307310264d3f","resolution":{"observed_at":"2026-08-11T22:55:23.592127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.581165Z","title":"Provable guarantees for understanding out- of-distribution detection,","venue":null,"work_id":"558fa067-7d27-43ae-95c4-3a572970b558","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.856886Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:7fc6b405fb129ebc2fac8b1efc28ea34db567b6443a05bd388ec8148b69f84fe","observation_id":"596e5d4a-eb67-442f-a591-a84de8de4e00","resolution":{"observed_at":"2026-08-11T22:55:23.584138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.573177Z","title":"REAct: Out-of-Distribution Detection with Rectified Activations,","venue":null,"work_id":"76b46c86-3404-46cf-8f3a-f4e716081802","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.859418Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:63f567acf55d513c65c0f1e0d99aea86ba45f79f894be375198dc4a2e4d831e5","observation_id":"01739191-2749-410c-b985-20ab6b142984","resolution":{"observed_at":"2026-08-11T22:55:23.576259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.565262Z","title":"Neural mean discrepancy for efficient out-of-distribution detection,","venue":null,"work_id":"1be880e1-1d1a-4ddc-8359-dbc5e1665965","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.862265Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:55787868804f1c45359f607c72c7749589ebef3f9a89eebce02580ee2d0f4e89","observation_id":"005bd861-55d7-4078-88e1-cbd9c9a90c62","resolution":{"observed_at":"2026-08-11T22:55:23.568269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.557742Z","title":"Extremely simple activation shaping for out-of-distribution detection,","venue":null,"work_id":"a8d66c69-ae36-4a7f-881a-b1ac5f5544d0","year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.864684Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:00c541195ad429859fe713c8d940ebfade76a2d1cf2d2e195efab2f2fa343d08","observation_id":"068a5272-0a27-44d6-b1cc-9fe9296073d0","resolution":{"observed_at":"2026-08-11T22:55:23.560489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.549722Z","title":"Scaling for training time and post-hoc out-of-distribution detection enhancement,","venue":null,"work_id":"d8bc8896-550a-41cc-8c2a-d2cb710015e6","year":2024},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.867260Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:60cbec60178b4c59c17c6244bbe1a2916993907fdfc229435ef6e203134e8222","observation_id":"79ba481d-52d4-46c2-99ca-b5ac863c119c","resolution":{"observed_at":"2026-08-11T22:55:23.552593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.541721Z","title":"Out-of-distribution detection using neural activation prior,","venue":null,"work_id":"bcf47144-ba35-48b6-989f-36d5d41669ab","year":2024},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.869711Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:4eeae37a362b06304c9fb1284c9a56b2dde74be526b2c7effd0d6975b09ca6de","observation_id":"7c3bdd2b-f4f4-4596-b31e-47ff90d3288b","resolution":{"observed_at":"2026-08-11T22:55:23.544735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.532198Z","title":"Energy-based open-world uncertainty modeling for confidence calibration,","venue":null,"work_id":"471aa62e-3b3e-4362-a7e4-49bccfa93084","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.875004Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d264111a9d0dcae45615d3c8c24921f510ff4c70f87c7dad7c3ac5b573919b45","observation_id":"fb9b4937-a621-4ed2-b5a5-1a212989d6a0","resolution":{"observed_at":"2026-08-11T22:55:23.535627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.524058Z","title":"Generalized ODIN: Detecting Out-of-Distribution Image Without Learning from Out-of-Distribution Data,","venue":null,"work_id":"320dbbc9-2c18-441a-8c4e-d03b024656b8","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.877448Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:66a13bdc3034f1a59274bd5c58281217bd8b48005585702136dcf2cfde226f41","observation_id":"6d26f47e-c3bf-48b0-aba1-f237459fe3cd","resolution":{"observed_at":"2026-08-11T22:55:23.527109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.516264Z","title":"Mitigating Neural Network Overconfidence with Logit Normalization,","venue":null,"work_id":"4145092c-4a59-4bb8-952e-cb88235580dd","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.879950Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:cf4ec0527160b99cef3e6359766adc18d9a9f201b34aa61c0c60a60b48f4baa7","observation_id":"00ae1702-b12c-4924-92f5-022f8dc30e55","resolution":{"observed_at":"2026-08-11T22:55:23.519229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.508526Z","title":"Certifiably adversarially robust detection of out-of-distribution data,","venue":null,"work_id":"fd294f28-0486-4499-bda2-48b40ab9adbf","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.882356Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:66451a8488abfcb35b505d30c73b54d7f50466d8fc3c66a1ff55a1844b918d67","observation_id":"c24d36a5-7423-4d13-b595-285df2af5097","resolution":{"observed_at":"2026-08-11T22:55:23.511532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.09711","last_updated":"2021-12-09T01:15:42Z","snapshot_observed_at":"2026-08-21T21:07:25.568621Z","submitted_at":"2020-03-21T17:46:28Z","title":"Robust Out-of-distribution Detection for Neural Networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.09711","snapshot_observed_at":"2026-08-11T22:55:22.885121Z","title":"Robust out-of-distribution detection for neural networks,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.885121Z"},"links":{"cited_paper":"/paper/2003.09711","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:82533de5c2d356d98609568e71cd64729b438384fb4c05b0a261aabf4296d57e","observation_id":"8f43bc54-ab27-4ca1-8c74-ac27adf1ddcd","resolution":{"observed_at":"2026-08-11T22:55:22.885121Z","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":"2026-08-11T22:55:23.501113Z","title":"Novelty detection via blurring,","venue":null,"work_id":"481a4af5-a290-4600-ae37-8c82724b0731","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.888243Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f1b0cbfe32f763db36120cf0cd4b838b2c235d67e97a0a7eb8a9d4208e4a4d89","observation_id":"3a710961-b019-48ef-92fd-fee25274b69f","resolution":{"observed_at":"2026-08-11T22:55:23.503744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.493071Z","title":"On mixup training: Improved calibration and predictive uncertainty for deep neural networks,","venue":null,"work_id":"8e7fbf7c-5e17-44b0-897a-6509adcb4de6","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.890900Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:2754be5014abd959ef102ff31faa9f3d0efe473f492d330695b97f2c1a025e46","observation_id":"8a775f5b-c8c9-4ed9-8092-1036db33a5c5","resolution":{"observed_at":"2026-08-11T22:55:23.496288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.485168Z","title":"Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,","venue":null,"work_id":"47c48ebd-f9c1-4d82-9ebf-00874d9a821e","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.893162Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1066b22735a4020b45170ab4a2317c0ec117de375c28735cfe126064891c4046","observation_id":"7faf28b2-88be-4a78-b779-1dc0e568eb2f","resolution":{"observed_at":"2026-08-11T22:55:23.488230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.04552","last_updated":"2017-11-29T14:51:40Z","snapshot_observed_at":"2026-08-13T11:54:46.030796Z","submitted_at":"2017-08-15T15:21:53Z","title":"Improved Regularization of Convolutional Neural Networks with Cutout","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.04552","snapshot_observed_at":"2026-08-11T22:55:22.895735Z","title":"Improved regularization of convolutional neural networks with cutout,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.895735Z"},"links":{"cited_paper":"/paper/1708.04552","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1b9c1f72bb1469264f26d25b812ea374b973a359ea6f0bbcc4ba2adf207494d5","observation_id":"794acf0f-1e70-4eb7-9ea2-0a0836ed3583","resolution":{"observed_at":"2026-08-11T22:55:22.895735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02781","last_updated":"2020-02-17T06:16:13Z","snapshot_observed_at":"2026-08-19T03:25:32.888684Z","submitted_at":"2019-12-05T18:18:10Z","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02781","snapshot_observed_at":"2026-08-11T22:55:22.898917Z","title":"Augmix: A simple data processing method to improve robustness and uncertainty,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.898917Z"},"links":{"cited_paper":"/paper/1912.02781","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:cfc992badacb3a9e1c2dca394c395d07be29fec8632eb73267f20459ec65d98d","observation_id":"82f3d42f-0718-4d81-8ab8-6b1bff1cc76c","resolution":{"observed_at":"2026-08-11T22:55:22.898917Z","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":"2026-08-11T22:55:23.477290Z","title":"PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures,","venue":null,"work_id":"cd358735-a5be-4d3d-ba62-d992ea94d105","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.902164Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:234eb0ca70c7e43e1a15ab7617ede34f3b8eb4bce6ecb0c1023e8fd5a64f25df","observation_id":"4da3e2d8-d7b5-47c1-8ad0-8e90a2734f43","resolution":{"observed_at":"2026-08-11T22:55:23.480283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.469607Z","title":"Deep anomaly detection using geometric transformations,","venue":null,"work_id":"cf647f98-cf12-4d9b-9b28-aac0a0fd3574","year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.904636Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:8636af13c4278b796d9aaaf070f11057398092ed6557e736f6b8f4f22a37699d","observation_id":"8d491aa1-89df-4645-ad4b-7d29e9ed193a","resolution":{"observed_at":"2026-08-11T22:55:23.472505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.461099Z","title":"Using Self- Supervised Learning Can Improve Model Robustness and Uncertainty,","venue":null,"work_id":"ab1b07a6-f266-419f-96cc-86f88b19010b","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.907231Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:97d40ccf14f46e0152586f54d53f7e7bd279aa5b00e2984f6f6318da43d8eabd","observation_id":"a8447d13-3c2f-4783-b7e7-3cb9b45e4b6f","resolution":{"observed_at":"2026-08-11T22:55:23.464057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.452253Z","title":"A Simple Frame- work for Contrastive Learning of Visual Representations,","venue":null,"work_id":"f74dbdb7-0889-4668-b09b-d10274c92e3e","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.909854Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:9b00b317b90fa55cdc4185f1410f3dcf65d1c7adfaa4ab3892d93946c3719f9f","observation_id":"82aadfe1-ffde-49c9-a959-5cc16bf2ae2d","resolution":{"observed_at":"2026-08-11T22:55:23.455995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.444268Z","title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples,","venue":null,"work_id":"c637e12b-f41f-4d6d-9d0c-7098fb76a8ba","year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.912333Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:01db7159213889a2b54884387ee905a649d32a38e8578bd85bf21f002a79b1a7","observation_id":"548e09f9-9502-4ddc-875c-5518694119ba","resolution":{"observed_at":"2026-08-11T22:55:23.447278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.436188Z","title":"Out-of-distribution detection in classifiers via gener- ation,","venue":null,"work_id":"4ebcd3c5-a490-4dad-b5ee-5019c8f12a18","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.914887Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:0f85686ff769550fda3edae0cba4bd2428fe865cc5f158d14fc42db02d651345","observation_id":"7f1393ba-c1a6-4b0d-9903-02d0d31c5b83","resolution":{"observed_at":"2026-08-11T22:55:23.439086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.428433Z","title":"Building robust classifiers through generation of confident out of distribution examples,","venue":null,"work_id":"a98b8f0d-b3f5-421e-9edc-b8c844833c87","year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.917803Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:2a3e2de37097f98fc252ca171f47d8a6099302cef9ba6feaad8755c11beb850c","observation_id":"c16daf78-400a-4397-a365-6ecd1563973f","resolution":{"observed_at":"2026-08-11T22:55:23.431372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.420760Z","title":"Ood-maml: Meta-learning for few-shot out- of-distribution detection and classification,","venue":null,"work_id":"486e55ef-bd28-4ec8-b78a-401892b645c6","year":2020},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.920290Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:5079db480347d28bc3dce9b59b6be96a454ab786a960d69c1cd08cddfa31f595","observation_id":"a95f7d75-6471-413b-ae17-09e184f1b4e3","resolution":{"observed_at":"2026-08-11T22:55:23.423721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.12102","last_updated":"2021-04-25T08:38:41Z","snapshot_observed_at":"2026-08-16T18:29:17.073453Z","submitted_at":"2021-04-25T08:38:41Z","title":"Unsupervised Learning of Multi-level Structures for Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2104.12102","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.12102","snapshot_observed_at":"2026-08-11T22:55:23.081588Z","title":"Unsupervised Learning of Multi-level Structures for Anomaly Detection","venue":"cs.CV","work_id":"1f97d1bf-116d-448a-b559-793190d802b0","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.922793Z"},"links":{"cited_paper":"/paper/2104.12102","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:5aa8c7e1709bb431a75cb9843342964a900c0f660c0dae6eeebc59f7d064304d","observation_id":"69d4a07e-106e-4b43-ad35-f0f718d595b7","resolution":{"observed_at":"2026-08-11T22:55:23.087271Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.412960Z","title":"Generating and reweighting dense contrastive patterns for unsupervised anomaly detection,","venue":null,"work_id":"26d82a14-4015-43b3-bdd8-8de5d03c2c38","year":2024},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.925504Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:18331a7988e743671bb29a177a91683bd0e05369116ec9e76079f0d2139b8918","observation_id":"9cb1a339-b48a-46d8-a856-ecabb7277759","resolution":{"observed_at":"2026-08-11T22:55:23.415864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.405020Z","title":"VOS: Learning What You Don’t Know by Virtual Outlier Synthesis,","venue":null,"work_id":"ec516192-2f47-4972-80c6-108f7d351efd","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.927963Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:91d73f19c24f1f159dcb2b11da6a32846fd7530b66c527b4cb9d88fc89208ea2","observation_id":"d0e4b954-2f0f-4d4c-a521-48f6f66c5a58","resolution":{"observed_at":"2026-08-11T22:55:23.408127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.397469Z","title":"Non-Parametric Outlier Synthesis,","venue":null,"work_id":"d2c47139-be03-4f98-be87-aabe0ef3e533","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.930596Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:09c04c083aede777e8592c9b86b05ec675482dad6448fc978afd06490949abdf","observation_id":"d8b163d0-0b51-4826-b595-394c5bb3b2f7","resolution":{"observed_at":"2026-08-11T22:55:23.400295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.389036Z","title":"A less biased evaluation of out-of-distribution sample detectors,","venue":null,"work_id":"1cc2f45d-46d7-48fc-8a47-c5cbbb669f98","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.933079Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:3572de35ccd53eb009bc2197ef454d1d8597cb475048f3e54b079abc9fd3aa20","observation_id":"02cb077f-d93a-4f5a-84cf-8aad74b6495e","resolution":{"observed_at":"2026-08-11T22:55:23.392572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.381159Z","title":"Do deep generative models know what they don’t know?","venue":null,"work_id":"83b58fac-970d-4387-82ac-24837ff6ac65","year":2019},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.935506Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:07a00dc4472add55bdf52b01fa1a09e737b5d11f92ce341017c045d91cb0fcbf","observation_id":"83e9c753-ec48-4d88-aedc-19ddfaa562d6","resolution":{"observed_at":"2026-08-11T22:55:23.384103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.373321Z","title":"Log-concave sampling,","venue":null,"work_id":"f25751a5-c323-4e26-8975-67e26c9d2cc9","year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.938124Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:21b9dfdc93f0729fc5076ce0e4af3c24372b89972741a67dcfbc2ae5d129a872","observation_id":"a6d5b47b-3adf-48e5-a716-cd3f29b761c4","resolution":{"observed_at":"2026-08-11T22:55:23.376025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.365259Z","title":"Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap,","venue":null,"work_id":"b3e962ef-90e4-4b66-ab16-df9b17a85bd2","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.940596Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:b13beeaa100f2b886c8440f897c09778b65ca12e9050edae4327ad26573d1d89","observation_id":"16191dbb-18ec-4023-90db-b2165014cb2e","resolution":{"observed_at":"2026-08-11T22:55:23.368354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.357464Z","title":"Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation,","venue":null,"work_id":"8625b41e-eeaf-4f7f-bcd4-34501f4654de","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.942828Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:df86c44acce1d412445c6c8aa9a3b59926ef8b6fd0f1c7abdbcad18a26e7735b","observation_id":"f23e8ba1-0aa5-4928-bb1b-d967f93d8740","resolution":{"observed_at":"2026-08-11T22:55:23.360300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.349789Z","title":"On Calibration of Modern Neural Networks,","venue":null,"work_id":"8a9ae451-546b-4ca8-a06a-61bc8f362a26","year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.945369Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d6cd8432cc99f87b05a5ca77271582a56359b05b79b868a6992a88439c3195e7","observation_id":"0b048d38-87eb-4820-8026-a23b119d4819","resolution":{"observed_at":"2026-08-11T22:55:23.352665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.342588Z","title":"GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection,","venue":null,"work_id":"9d29c8b2-1e8a-4d2b-838d-1bbeeccf89a6","year":2023},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.947845Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1343203ab99cd685aad112407bcc70fd2bf9679e77d00cb3c3bcd0c5b7eb29f1","observation_id":"8fecf4e5-2f29-4c29-8208-508c091b25f6","resolution":{"observed_at":"2026-08-11T22:55:23.345401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.334980Z","title":"MOS: Towards Scaling Out-of-Distribution De- tection for Large Semantic Space,","venue":null,"work_id":"1d75fe82-272d-43cb-8526-96d439063944","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.950246Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:d624dcc3ce9a7ab425202c9af78ac07d3a8d3e55c51bdd0c16eabbb23b206300","observation_id":"ed8aa3fc-1937-4706-8562-118f01923ca8","resolution":{"observed_at":"2026-08-11T22:55:23.337810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.326964Z","title":"Adversarial Reciprocal Points Learning for Open Set Recognition,","venue":null,"work_id":"5241bb3e-4d07-4371-b0df-d583f987f07b","year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.952909Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:03f45807396e1bcba2980746ba5da95e9167dec04ea02e65dcdae9150f816f99","observation_id":"31192afa-c951-4969-b256-25fc715c1c86","resolution":{"observed_at":"2026-08-11T22:55:23.329955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04865","last_updated":"2018-02-13T21:31:36Z","snapshot_observed_at":"2026-08-14T19:46:10.262380Z","submitted_at":"2018-02-13T21:31:36Z","title":"Learning Confidence for Out-of-Distribution Detection in Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04865","snapshot_observed_at":"2026-08-11T22:55:22.955446Z","title":"Learning Confidence for Out- of-Distribution Detection in Neural Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.955446Z"},"links":{"cited_paper":"/paper/1802.04865","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:401393b227310a6b548a303b96303051b6c22ea7fd2b37b50b219c708df69afc","observation_id":"74cbba86-c4b6-4b86-ab54-971b035a0a93","resolution":{"observed_at":"2026-08-11T22:55:22.955446Z","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":"2026-08-11T22:55:23.319896Z","title":"How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?","venue":null,"work_id":"84f88b38-0ba1-438a-b9c2-9cb1dff45eea","year":2022},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.957949Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f6d4b36ff34176c47e29f03a8c517c6178261a1f7252f915dbec524c6f82e779","observation_id":"d59af87c-6dbf-493c-b2f9-b4460cab7716","resolution":{"observed_at":"2026-08-11T22:55:23.322583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.312721Z","title":"Learning Multiple Layers of Features from Tiny Images,","venue":null,"work_id":"e3a9152d-3f0b-4a9f-997d-3cafc73464ad","year":2009},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.960489Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:8f3b2a07e2609fa6ba769aac81546d4486b3269d768f096d6d2204f276358fb7","observation_id":"7e36bc0b-dc55-4a31-a28c-958f13b96504","resolution":{"observed_at":"2026-08-11T22:55:23.315473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.303496Z","title":"Tiny ImageNet Visual Recognition Challenge,","venue":null,"work_id":"0dbff3fe-231f-44c9-b575-a9d21551a82e","year":2015},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.962982Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:be09b522b3e3f404c9c716ca0263b252c2978ce423e583458bec1934612875c1","observation_id":"12a0a20c-c44d-4617-aeae-37e9e8091768","resolution":{"observed_at":"2026-08-11T22:55:23.307035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.295396Z","title":"Gradient-Based Learning Applied to Document Recognition,","venue":null,"work_id":"193a6208-4989-4bb3-b5f4-99c65b616634","year":1998},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.965660Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:7914f470a97d25307885b617cf25c10d6046fd345d2f8455216dabc406115d21","observation_id":"709ed740-d085-4f7f-885b-cdb99572e2e3","resolution":{"observed_at":"2026-08-11T22:55:23.298912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6082","last_updated":"2014-04-14T05:25:54Z","snapshot_observed_at":"2026-08-14T23:50:45.930681Z","submitted_at":"2013-12-20T19:25:44Z","title":"Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6082","snapshot_observed_at":"2026-08-11T22:55:22.968462Z","title":"Multi- Digit Number Recognition from Street View Imagery Using Deep Con- volutional Neural Networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.968462Z"},"links":{"cited_paper":"/paper/1312.6082","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:4c8a493321b47f3e6ce2e1cd21deb4411771c2af8e57f995e6d5e2759b33d82e","observation_id":"17f15edf-bd03-47cb-8b3e-9be47709661c","resolution":{"observed_at":"2026-08-11T22:55:22.968462Z","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":"2026-08-11T22:55:23.287955Z","title":"Describing Textures in the Wild,","venue":null,"work_id":"ef5ae5d9-3abd-4bca-81ed-4477bc47913d","year":2014},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.971725Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:2d82b03971117f52cf910112f84cde018f85c07f1f372f246541bf32dacac8e6","observation_id":"952d0441-eef3-4344-955f-c1c51c5acd0d","resolution":{"observed_at":"2026-08-11T22:55:23.291141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.279983Z","title":"Places: A 10 million Image Database for Scene Recognition,","venue":null,"work_id":"9d083940-3963-454f-9a49-9b418be3c298","year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.974265Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:f13e0705ec60ea973b719f302e53d15c4800a90bb34701a1abcc8bf25c897565","observation_id":"c45394f5-2ffb-44f6-a973-dc4ac17cf1a5","resolution":{"observed_at":"2026-08-11T22:55:23.283547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01718","last_updated":"2018-12-03T12:37:31Z","snapshot_observed_at":"2026-08-14T23:13:55.892736Z","submitted_at":"2018-12-03T12:37:31Z","title":"Deep Learning for Classical Japanese Literature","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01718","snapshot_observed_at":"2026-08-11T22:55:22.977024Z","title":"Deep Learning for Classical Japanese Literature,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.977024Z"},"links":{"cited_paper":"/paper/1812.01718","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:3cbe3b755788cc6d96c54c56b35693eee5535365f3e332e579e9f11438abc7d8","observation_id":"00090f82-3a21-4a45-bde2-6b823854af3e","resolution":{"observed_at":"2026-08-11T22:55:22.977024Z","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":"2026-08-11T22:55:23.271357Z","title":"EMNIST: Extending MNIST to Handwritten Letters,","venue":null,"work_id":"199e796d-0e91-482b-b859-73719865072f","year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.980044Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:78be339ad332c2ef7e0af9cf9b7a4c970ecf9218b958e9e92fd07a50e972e1f3","observation_id":"e1650557-254e-4d51-876b-18c904c96130","resolution":{"observed_at":"2026-08-11T22:55:23.274282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.263952Z","title":"The Relationship Between Precision-Recall and ROC Curves,","venue":null,"work_id":"7fcdda52-5d78-4a36-8f99-35593a851c9e","year":2006},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.982524Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:bc9edec0fe05dd1140029e5c681c49c28f04e0bab7c5242319c438906d50b775","observation_id":"e554828d-774b-4209-9056-48e5972f01c1","resolution":{"observed_at":"2026-08-11T22:55:23.266917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.256365Z","title":"An Introduction to ROC Analysis,","venue":null,"work_id":"8e325e91-4cb4-406c-8e33-cd2b775f31bf","year":2006},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.985159Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:cdfdd1c2eb2eccb8fb8d93ea01e2cc20bc3effac845ed75794b05b9ea7441070","observation_id":"ab6f1a2a-42f6-4064-8131-fa39ae830ae4","resolution":{"observed_at":"2026-08-11T22:55:23.259620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.248837Z","title":"Towards Open Set Deep Networks,","venue":null,"work_id":"68a73ee0-942b-4a11-a477-ad7335279af8","year":2016},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.987574Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:6afe20d16f946e33c9e06865687ba94d7a552ede88f0086272f199fd998c593e","observation_id":"0603ac9e-efd7-4dd0-a982-420a81d0ca43","resolution":{"observed_at":"2026-08-11T22:55:23.251836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09022","last_updated":"2021-06-16T20:43:56Z","snapshot_observed_at":"2026-08-16T18:16:34.560199Z","submitted_at":"2021-06-16T20:43:56Z","title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09022","snapshot_observed_at":"2026-08-11T22:55:22.990484Z","title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.990484Z"},"links":{"cited_paper":"/paper/2106.09022","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:3ce4db2b55b03d397d0d9bc5d9e6b7233f1c41230d1eae0135ee082d528f9ce5","observation_id":"76ac9f1f-53eb-4bfb-8caf-806ca2a27e59","resolution":{"observed_at":"2026-08-11T22:55:22.990484Z","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":"2026-08-11T22:55:23.241119Z","title":"Deep Residual Learning for Image Recognition,","venue":null,"work_id":"135450de-f0a5-41dc-bf66-b0768466b8f3","year":2016},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.993339Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:1c6bfa809960e0ecabce25ff7e675730e4af3eef6dab4bf8bf52f76b59cb3abe","observation_id":"1dc31d5b-d671-4dad-8dc1-a827c2435856","resolution":{"observed_at":"2026-08-11T22:55:23.244058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:55:23.233354Z","title":"Wide Residual Networks,","venue":null,"work_id":"ee89acc8-9b69-429b-bb67-261ef97cd3d1","year":2016},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.995789Z"},"links":{"citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:69d521abe04b3934ee7a0a7bf2e0fa6ecfe5c7f1cec0d8cb9c1c45406f4599e6","observation_id":"2927b6b8-e90a-487e-91fe-4f1042d3f896","resolution":{"observed_at":"2026-08-11T22:55:23.236323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-08-13T15:13:33.081929Z","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":"2026-08-11T22:55:22.998247Z","title":"Fashion-MNIST: A Novel Im- age Dataset for Benchmarking Machine Learning Algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-11T22:55:22.998247Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2412.03058"},"observation_digest":"sha256:2bff462787d2805b5ba8059e55c20a7b26dd93408056ded9903f897603f827f1","observation_id":"2dcc3a9c-0142-4803-bad0-5d1dcd702d86","resolution":{"observed_at":"2026-08-11T22:55:22.998247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.03058","last_updated":"2024-12-04T06:25:26Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T02:07:05.727217Z","submitted_at":"2024-12-04T06:25:26Z","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":3,"verified_fuzzy":56},"total_outbound_references":108},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 100 of 108 outbound references and 0 inbound Pith citation observations for arXiv:2412.03058."}