{"as_of":"2026-08-10T03:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8b492ab33be18a5bdbcef0197428525ef23c6ee827db3f781688ab92892ac02","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T21:55:55.793497Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.09352/citation-record","integrity":"/paper/2502.09352/integrity","json":"/paper/2502.09352/citation-record.json","paper":"/paper/2502.09352"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T21:55:55.924234Z","title":"Howard and S","venue":null,"work_id":"ff26de8a-940b-43a1-8d07-111d4bca466b","year":2018},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.759727Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:f43e566f131f6fa62532d890f0348688389c88235193d30bc94877fc34a1081e","observation_id":"982e348e-fd2c-4f7d-bcb4-ed597ea6213b","resolution":{"observed_at":"2026-08-07T21:55:55.928122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T21:55:55.913115Z","title":"Kornblith, J","venue":null,"work_id":"ed6b09a9-c204-4cb6-9eca-1ae7f7851f31","year":2019},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.767402Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:aa81ab9c414f186a1f0541c75d67a59eee1d15e34b5874e35dc455d96ecc31bc","observation_id":"729f344f-9575-4e3f-9c0e-3f7bd274194c","resolution":{"observed_at":"2026-08-07T21:55:55.916637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T21:55:55.778095Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.778095Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:578f31b14195426a667efd5dd4d9e5cc1ef5dff980b2876756c86b2cb6188266","observation_id":"399aeaa7-e9c2-4a91-b430-0ec6475ebba3","resolution":{"observed_at":"2026-08-07T21:55:55.778095Z","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":"2026-08-07T21:55:55.891598Z","title":"Tzeng, J","venue":null,"work_id":"4b859e5a-807c-467d-9535-cdea00352565","year":2017},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.782234Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:125f4f49d617571c6ee02cf41ce7afa7d886963356ed6bc50c01952a1d3c153a","observation_id":"405ce307-c41c-40fe-be4e-2800f01a0ea3","resolution":{"observed_at":"2026-08-07T21:55:55.895007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T21:55:55.881000Z","title":"Yosinski, J","venue":null,"work_id":"311f0b25-85fa-4587-890c-674dba200062","year":2014},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.789486Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:7e88a67e2161b888517bb22a958830064c16444b1318c8fd6decab658238656a","observation_id":"64b6c871-5f19-41ad-a7ad-7107d02a0b1b","resolution":{"observed_at":"2026-08-07T21:55:55.884697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T21:55:55.867920Z","title":"We report other pre-trained networks considered in the paper under different training configuration","venue":null,"work_id":"a0cf619c-0c6c-47b4-b28f-a3068ca7af08","year":2020},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.793497Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:347326cb0491f34996120dad6807f9e785addeb8f1b9aee3fe4ff7f76a3489e4","observation_id":"657f7237-0e86-4f79-b082-4968cc432a07","resolution":{"observed_at":"2026-08-07T21:55:55.873666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03593","last_updated":"2021-03-30T08:08:12Z","snapshot_observed_at":"2026-07-06T10:02:27.415825Z","submitted_at":"2020-10-07T18:19:09Z","title":"Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03593","snapshot_observed_at":"2026-08-07T21:55:55.755629Z","title":"Gowal, C","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.755629Z"},"links":{"cited_paper":"/paper/2010.03593","citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:c42ce8a5903c6855af064b8b60f8cba4c79dc4dc5576c6600dfc0e4f54bb81c1","observation_id":"527d8183-1634-4068-a6db-4f4c551226e8","resolution":{"observed_at":"2026-08-07T21:55:55.755629Z","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":"2026-08-07T21:55:55.774368Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.774368Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:07a5ce51c4bb485d298dec41ddccf5eec053f235684aaf4d65e713756d67cdd2","observation_id":"cb3312f0-582e-48b3-9567-12aa91eb7b6b","resolution":{"observed_at":"2026-08-07T21:55:55.774368Z","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":"2026-08-07T21:55:55.785860Z","title":"2017.316","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.785860Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:d38938d5d7310c06c035e1f8adedd7e4b7874fb1a142e366f176649df64ca511","observation_id":"ed26a3a5-ff96-4189-955b-93014fa6d8d9","resolution":{"observed_at":"2026-08-07T21:55:55.785860Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T21:55:55.763333Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.763333Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:87b0656d3fb0a8bd22b7780a5d64fc91edacdae277ca38dc2d34636ea7f756e9","observation_id":"1b73c896-5814-4d8e-bdec-ee55b15b0bc9","resolution":{"observed_at":"2026-08-07T21:55:55.763333Z","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":"2026-08-07T21:55:55.902606Z","title":"Li and D","venue":null,"work_id":"f371d166-eba4-4c1c-aa23-77025238d695","year":2016},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.770941Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:153cc18d7b452e46fbdb526920210c9386c20fff290c5af848ac9453ed71ac01","observation_id":"a67da373-e71b-45d2-8626-d38237db2f9c","resolution":{"observed_at":"2026-08-07T21:55:55.906260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13948","last_updated":"2024-10-27T08:32:11Z","snapshot_observed_at":"2026-08-06T16:12:42.070710Z","submitted_at":"2023-05-23T11:17:45Z","title":"Decoupled Kullback-Leibler Divergence Loss","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13948","snapshot_observed_at":"2026-08-07T21:55:55.751430Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.751430Z"},"links":{"cited_paper":"/paper/2305.13948","citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:19b78dd7b4bc8d59963ed4065c2254c71350a197f7ff2f81f599cb3feabdedbf","observation_id":"43bb6588-caf1-4760-a862-b61e32aa370d","resolution":{"observed_at":"2026-08-07T21:55:55.751430Z","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":"2026-08-07T21:55:55.934906Z","title":"Carlini and D","venue":null,"work_id":"70d54544-1048-48bb-8c0c-0fff86616700","year":2017},"citing_paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T21:55:55.747149Z"},"links":{"citing_paper":"/paper/2502.09352"},"observation_digest":"sha256:53b454da6c3d0fe47b37565e1b6af70b6bafca1c6a433176e9cfa680f96a2d7b","observation_id":"beac9420-4d2e-4ae5-a98e-1c958e55e3da","resolution":{"observed_at":"2026-08-07T21:55:55.938282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.09352","last_updated":"2025-02-13T14:18:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T17:03:54.877404Z","submitted_at":"2025-02-13T14:18:41Z","title":"Wasserstein distributional adversarial training for deep neural networks"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":13},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2502.09352."}