{"as_of":"2026-08-21T13:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1fd9f6c548e201691e44448d700c7f3890caad9e518198ed66cd5d4c656b8833","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T23:35:45.607156Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-24T23:36:27.613722Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1811.02657","last_updated":"2019-12-09T10:21:21Z","snapshot_observed_at":"2026-08-21T12:28:02.605379Z","submitted_at":"2018-11-01T01:27:37Z","title":"A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model","version":2},"cited_work":{"arxiv_id":"1811.02657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1811.02657","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"B., Anandkumar, A., Jordan, M","venue":null,"work_id":"01b7b975-8ae4-45ab-a116-b69867e42471","year":2018},"citing_paper":{"arxiv_id":"1907.04572","last_updated":"2019-07-10T08:32:53Z","snapshot_observed_at":"2026-08-19T19:35:08.709705Z","submitted_at":"2019-07-10T08:32:53Z","title":"Out-of-Distribution Detection Using Neural Rendering Generative Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T23:35:45.607156Z"},"links":{"cited_paper":"/paper/1811.02657","citing_paper":"/paper/1907.04572"},"observation_digest":"sha256:a003a3d114ace3403a4380009900bdfee0f772742f166dcaa8de02124382d9e9","observation_id":"42672e72-dbfa-432d-8613-d84a6a15902e","resolution":{"observed_at":"2026-05-24T23:36:27.616850Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1811.02657/citation-record","integrity":"/paper/1811.02657/integrity","json":"/paper/1811.02657/citation-record.json","paper":"/paper/1811.02657"},"outbound":[],"paper":{"arxiv_id":"1811.02657","last_updated":"2019-12-09T10:21:21Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-21T12:28:02.605379Z","submitted_at":"2018-11-01T01:27:37Z","title":"A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1811.02657."}