{"as_of":"2026-08-19T05:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b996e085d55dd2fd9a118eb7fc003ec3535bb846a3f883dbb9fb46ce8fc7c56f","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":"2026-08-18T06:34:40.430872+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":"2026-08-16T01:05:21.457214Z","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":"2026-08-05T19:48:07.140850Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.02506","last_updated":"2019-10-30T02:01:14Z","snapshot_observed_at":"2026-08-14T16:19:56.294549Z","submitted_at":"2019-06-06T10:22:45Z","title":"Practical Deep Learning with Bayesian Principles","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02506","snapshot_observed_at":"2026-08-14T14:15:57.366237Z","title":"Turner, Rio Yokota, and Mohammad Emtiyaz Khan","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.04847","last_updated":"2019-09-05T21:27:21Z","snapshot_observed_at":"2026-08-18T17:02:34.866722Z","submitted_at":"2019-08-09T18:50:09Z","title":"Convergence Rates of Variational Inference in Sparse Deep Learning","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-14T14:15:57.366237Z"},"links":{"cited_paper":"/paper/1906.02506","citing_paper":"/paper/1908.04847"},"observation_digest":"sha256:40dfca809b753298cf7541bce28675cf8317b78290ba491c7ac7456f34f63bd1","observation_id":"d2a6c4cf-2deb-49c0-bcd3-b1270b58dbd0","resolution":{"observed_at":"2026-08-14T14:15:57.366237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02506","last_updated":"2019-10-30T02:01:14Z","snapshot_observed_at":"2026-08-14T16:19:56.294549Z","submitted_at":"2019-06-06T10:22:45Z","title":"Practical Deep Learning with Bayesian Principles","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02506","snapshot_observed_at":"2026-08-10T14:14:10.245623Z","title":"Turner, Rio Yokota, and Mohammad Emtiyaz Khan","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.15573","last_updated":"2025-01-26T15:58:42Z","snapshot_observed_at":"2026-08-16T09:07:51.877379Z","submitted_at":"2025-01-26T15:58:42Z","title":"Approximate Message Passing for Bayesian Neural Networks","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T14:14:10.245623Z"},"links":{"cited_paper":"/paper/1906.02506","citing_paper":"/paper/2501.15573"},"observation_digest":"sha256:1933566b7e502f91352fa42e929d984afbc4df386fbaeef47d6d5f9474a2e141","observation_id":"bf9b3da2-d0eb-4eec-8369-c0efc28c96d7","resolution":{"observed_at":"2026-08-10T14:14:10.245623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02506","last_updated":"2019-10-30T02:01:14Z","snapshot_observed_at":"2026-08-14T16:19:56.294549Z","submitted_at":"2019-06-06T10:22:45Z","title":"Practical Deep Learning with Bayesian Principles","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02506","snapshot_observed_at":"2026-08-16T01:05:21.457214Z","title":"Turner, Rio Yokota, and Mohammad Emtiyaz Khan","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2505.02277","last_updated":"2025-08-18T23:40:45Z","snapshot_observed_at":"2026-08-16T00:53:58.766408Z","submitted_at":"2025-05-04T22:15:56Z","title":"Epistemic Wrapping for Uncertainty Quantification","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T01:05:21.457214Z"},"links":{"cited_paper":"/paper/1906.02506","citing_paper":"/paper/2505.02277"},"observation_digest":"sha256:caf8fefe020a955e2d2c5c8f308e8f81707270ead4ee2399ffeb364bcc2381e5","observation_id":"a9a59a63-3d7f-4df6-86d8-d41632f9c4a8","resolution":{"observed_at":"2026-08-16T01:05:21.457214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02506","last_updated":"2019-10-30T02:01:14Z","snapshot_observed_at":"2026-08-14T16:19:56.294549Z","submitted_at":"2019-06-06T10:22:45Z","title":"Practical Deep Learning with Bayesian Principles","version":2},"cited_work":{"arxiv_id":"1906.02506","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.02506","snapshot_observed_at":"2026-08-05T19:48:07.140850Z","title":"Practical Deep Learning with Bayesian Principles","venue":"stat.ML","work_id":"1f91e016-6f53-4ff2-90a6-207a4b5a55c4","year":2019},"citing_paper":{"arxiv_id":"2508.16632","last_updated":"2025-08-15T21:49:28Z","snapshot_observed_at":"2026-08-17T13:18:11.450076Z","submitted_at":"2025-08-15T21:49:28Z","title":"Adaptive Variance-Penalized Continual Learning with Fisher Regularization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:48:06.104468Z"},"links":{"cited_paper":"/paper/1906.02506","citing_paper":"/paper/2508.16632"},"observation_digest":"sha256:0a797f93e419607eb4591c992eff42fc3dcd56d7c77b7115648f3066fa7997c5","observation_id":"be1f6cbf-d71f-4c28-9ef6-b05c783f1288","resolution":{"observed_at":"2026-08-05T19:48:07.187330Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1906.02506/citation-record","integrity":"/paper/1906.02506/integrity","json":"/paper/1906.02506/citation-record.json","paper":"/paper/1906.02506"},"outbound":[],"paper":{"arxiv_id":"1906.02506","last_updated":"2019-10-30T02:01:14Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T16:19:56.294549Z","submitted_at":"2019-06-06T10:22:45Z","title":"Practical Deep Learning with Bayesian Principles"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1906.02506."}