{"as_of":"2026-08-10T12:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d50aaa30c86528b39362fea244b5bfd4b85bb76560d31e0c98278dc33b9f39f1","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:45:31.555920Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T22:09:13.394940Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03932","snapshot_observed_at":"2026-08-07T14:45:31.555920Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.17856","last_updated":"2025-05-23T13:07:18Z","snapshot_observed_at":"2026-08-09T12:34:43.265267Z","submitted_at":"2025-05-23T13:07:18Z","title":"Stochastic Weight Sharing for Bayesian Neural Networks","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T14:45:31.555920Z"},"links":{"cited_paper":"/paper/1902.03932","citing_paper":"/paper/2505.17856"},"observation_digest":"sha256:0e115540d06bd6f876c5633dece8185e119cce4e5d53ba32e5b4af4e47eb1bfe","observation_id":"0258a54e-6ecb-4e96-a044-2879646b0726","resolution":{"observed_at":"2026-08-07T14:45:31.555920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03932","snapshot_observed_at":"2026-08-06T21:43:58.958090Z","title":"Cyclical stochastic gradient mcmc for bayesian deep learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.02959","last_updated":"2025-06-30T04:18:31Z","snapshot_observed_at":"2026-08-06T21:37:41.630741Z","submitted_at":"2025-06-30T04:18:31Z","title":"A Novel Active Learning Approach to Label One Million Unknown Malware Variants","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:43:58.958090Z"},"links":{"cited_paper":"/paper/1902.03932","citing_paper":"/paper/2507.02959"},"observation_digest":"sha256:e9da52e543bb17f772021886c3ac7794d8edbaea05e9ba9a5f51a0415e058f38","observation_id":"4048b4c3-2a9b-4016-9afc-ce3abb607051","resolution":{"observed_at":"2026-08-06T21:43:58.958090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03932","snapshot_observed_at":"2026-08-06T19:42:35.752988Z","title":"Cycli- cal stochastic gradient MCMC for Bayesian deep learning","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2507.04929","last_updated":"2025-07-07T12:25:12Z","snapshot_observed_at":"2026-08-07T04:59:41.539596Z","submitted_at":"2025-07-07T12:25:12Z","title":"ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:42:35.752988Z"},"links":{"cited_paper":"/paper/1902.03932","citing_paper":"/paper/2507.04929"},"observation_digest":"sha256:5904be0c8e2399cc48921644847563c96316fdf0e55850dfdcd672e6e1d4704a","observation_id":"67f285c1-9996-4a57-be40-54aa225831cb","resolution":{"observed_at":"2026-08-06T19:42:35.752988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","version":2},"cited_work":{"arxiv_id":"1902.03932","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.03932","snapshot_observed_at":"2026-08-05T22:09:13.394940Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","venue":"cs.LG","work_id":"e1f2404c-6847-4b0d-abc7-884c104529c4","year":2019},"citing_paper":{"arxiv_id":"2508.07556","last_updated":"2025-09-06T12:35:53Z","snapshot_observed_at":"2026-08-07T08:21:13.704669Z","submitted_at":"2025-08-11T02:33:53Z","title":"Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning","version":2},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-08-05T22:09:12.725437Z"},"links":{"cited_paper":"/paper/1902.03932","citing_paper":"/paper/2508.07556"},"observation_digest":"sha256:4e911e7c8d1204b7604330f09689346cffb9ec895d2e3ddd77e814e4231fe6ae","observation_id":"2049aa2c-c381-4a7c-9ac3-5ce486d43d92","resolution":{"observed_at":"2026-08-05T22:09:13.466241Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1902.03932/citation-record","integrity":"/paper/1902.03932/integrity","json":"/paper/1902.03932/citation-record.json","paper":"/paper/1902.03932"},"outbound":[],"paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1902.03932."}