{"as_of":"2026-08-10T19:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:04d5daa727f91d2fbca43b51c819fc325c39e2af9930bb03f330376afb98c2be","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T12:29:21.401720Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.19580/citation-record","integrity":"/paper/2607.19580/integrity","json":"/paper/2607.19580/citation-record.json","paper":"/paper/2607.19580"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T12:29:19.802960Z","title":"Abadi, A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:19.802960Z"},"links":{"citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:6dfd536625bdbb0da063d5c0b5d9bc5e9dad02f32c183eeb7962528a905d2408","observation_id":"7530cdb0-fb63-4a36-9705-d3b11b0eebaf","resolution":{"observed_at":"2026-08-01T12:29:19.802960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07073","last_updated":"2023-06-20T06:26:31Z","snapshot_observed_at":"2026-08-09T16:46:56.630901Z","submitted_at":"2021-03-12T04:02:23Z","title":"DP-Image: Differential Privacy for Image Data in Feature Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.07073","snapshot_observed_at":"2026-08-01T12:29:21.078216Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:21.078216Z"},"links":{"cited_paper":"/paper/2103.07073","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:4da145ea7c86894ade163f2f11b2e4117172620662c30903a71555bcfc9994cd","observation_id":"55043cec-a913-40ce-b816-0754e15413a6","resolution":{"observed_at":"2026-08-01T12:29:21.078216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-08T14:57:17.868613Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-01T12:29:21.401720Z","title":"Zagoruyko and N","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:21.401720Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:51460f4cd2082d4c8b21da532645352014226bf75dd2eaf5d5921108cec04bff","observation_id":"537020bb-b1d3-4682-951b-b6c35be7eddb","resolution":{"observed_at":"2026-08-01T12:29:21.401720Z","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":"2026-08-01T12:29:20.748219Z","title":null,"venue":null,"work_id":null,"year":2097},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.748219Z"},"links":{"citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:173d181be6ead9293780b738acf5a60204d7058b821715150c3b55f389623d3b","observation_id":"06c34cd4-ff0f-4392-bdc8-7c11e89520e2","resolution":{"observed_at":"2026-08-01T12:29:20.748219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-01T12:29:20.317621Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.317621Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:7cc96cdbec7552bbc7e6daf3f1966f48cf45293a6d874df94dab7265e11a4e50","observation_id":"6f108615-6fa8-463c-a1d4-a74d58e0a8e2","resolution":{"observed_at":"2026-08-01T12:29:20.317621Z","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":"2026-08-01T12:29:20.063452Z","title":"Ganju, Q","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.063452Z"},"links":{"citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:4b7dd430832456ca1ecb507f58d701bc7bee0ef4103a2b6ba7d8b073229cda72","observation_id":"6e7e1fd0-2441-46fd-951f-fa1fab33170b","resolution":{"observed_at":"2026-08-01T12:29:20.063452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.04338","last_updated":"2023-06-20T16:38:13Z","snapshot_observed_at":"2026-08-05T13:29:54.004904Z","submitted_at":"2022-09-09T14:51:13Z","title":"Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.04338","snapshot_observed_at":"2026-08-01T12:29:20.190674Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.190674Z"},"links":{"cited_paper":"/paper/2209.04338","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:9d30477bf41f7c278e521e539d31649a6902b08f3c155f42373e65e3d5d136cd","observation_id":"cfdd736d-0126-48a6-ad58-6b96ff876683","resolution":{"observed_at":"2026-08-01T12:29:20.190674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10530","last_updated":"2019-08-28T03:03:25Z","snapshot_observed_at":"2026-08-07T03:15:48.518041Z","submitted_at":"2019-08-28T03:03:25Z","title":"R\\'enyi Differential Privacy of the Sampled Gaussian Mechanism","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10530","snapshot_observed_at":"2026-08-01T12:29:20.455712Z","title":"Mironov, K","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.455712Z"},"links":{"cited_paper":"/paper/1908.10530","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:fd1213a9a1760d93f229a3645cb56c6d003e42747fbcd98fb10b47e3212ee8b6","observation_id":"a1e9ad68-0d49-4442-806c-1dcf369b9a1a","resolution":{"observed_at":"2026-08-01T12:29:20.455712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-01T12:29:20.903754Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.903754Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:3b49140d11faa2998afbc4605a75feedb4e71d81cc791f775061f12ddfae4971","observation_id":"d96c49e9-21c3-486a-b975-1b6df707f5df","resolution":{"observed_at":"2026-08-01T12:29:20.903754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.13650","last_updated":"2022-06-16T17:47:42Z","snapshot_observed_at":"2026-08-09T01:53:18.372890Z","submitted_at":"2022-04-28T17:10:56Z","title":"Unlocking High-Accuracy Differentially Private Image Classification through Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.13650","snapshot_observed_at":"2026-08-01T12:29:19.931519Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:19.931519Z"},"links":{"cited_paper":"/paper/2204.13650","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:a1a215fc2149dcecdc30fcb7374a2bbfb27f7fbb473f8a03fd60debab7ab83c8","observation_id":"165f3f58-950a-4ecc-972c-f5770c9a5fcd","resolution":{"observed_at":"2026-08-01T12:29:19.931519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.12298","last_updated":"2022-08-22T20:24:50Z","snapshot_observed_at":"2026-08-06T15:46:33.397187Z","submitted_at":"2021-09-25T07:10:54Z","title":"Opacus: User-Friendly Differential Privacy Library in PyTorch","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.12298","snapshot_observed_at":"2026-08-01T12:29:21.244346Z","title":"Yousefpour, I","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:21.244346Z"},"links":{"cited_paper":"/paper/2109.12298","citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:2c4015dc388f0c0c23a92f59aaa2cb6112ab4c5b50941a7494b8fb67dba6d082","observation_id":"a34267c1-6f2d-469c-980c-eb164a627db0","resolution":{"observed_at":"2026-08-01T12:29:21.244346Z","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":"2026-08-01T12:29:20.609900Z","title":"Netzer, T","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T12:29:20.609900Z"},"links":{"citing_paper":"/paper/2607.19580"},"observation_digest":"sha256:2227b4a1bf0754b3ab43a91d2b8bcabf6135f77d813798b0f8d3525f54b75db4","observation_id":"95899387-8bb9-4ac4-8938-1b8a886aa122","resolution":{"observed_at":"2026-08-01T12:29:20.609900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.19580","last_updated":"2026-07-21T21:15:55Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T14:56:41.104189Z","submitted_at":"2026-07-21T21:15:55Z","title":"End-to-End Differential Privacy in Training Deep Neural Network Classifiers"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.19580."}