{"as_of":"2026-08-14T21:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f30cba3502ad9d6bbdb7dd431670033c643bb1f3832c3332c7b7f91459783bd7","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":"2026-08-14T06:32:32.682623+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":"2026-08-10T23:46:51.775224Z","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":"2026-05-20T13:03:58.207640Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.04922","last_updated":"2022-03-28T13:32:41Z","snapshot_observed_at":"2026-08-14T20:32:18.090947Z","submitted_at":"2021-03-08T17:34:03Z","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","version":4},"cited_work":{"arxiv_id":"2103.04922","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.04922","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2021 , journal =","venue":null,"work_id":"06e25382-b13a-4d4a-b5f7-70a4ce85d86b","year":2021},"citing_paper":{"arxiv_id":"2308.08089","last_updated":"2023-08-16T01:43:41Z","snapshot_observed_at":"2026-07-06T16:06:38.424057Z","submitted_at":"2023-08-16T01:43:41Z","title":"DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-20T13:03:57.828598Z"},"links":{"cited_paper":"/paper/2103.04922","citing_paper":"/paper/2308.08089"},"observation_digest":"sha256:fdec3d2d78830e231722c7690f9654594ffd1b99cec76d337405d13fc424f915","observation_id":"ab64e0ef-609a-498f-bc9e-50db8a6dd7ae","resolution":{"observed_at":"2026-05-20T13:03:58.209406Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.04922","last_updated":"2022-03-28T13:32:41Z","snapshot_observed_at":"2026-08-14T20:32:18.090947Z","submitted_at":"2021-03-08T17:34:03Z","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.04922","snapshot_observed_at":"2026-08-10T23:46:51.775224Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19999","last_updated":"2024-12-28T03:50:56Z","snapshot_observed_at":"2026-08-14T07:57:32.344667Z","submitted_at":"2024-12-28T03:50:56Z","title":"Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T23:46:51.775224Z"},"links":{"cited_paper":"/paper/2103.04922","citing_paper":"/paper/2412.19999"},"observation_digest":"sha256:fb8c6c7143d0c4c35f9135851081ed1318d765ddea1c43b7744e14f9170dd442","observation_id":"77036ac3-c00b-4de9-a4d4-e93da6eec5c6","resolution":{"observed_at":"2026-08-10T23:46:51.775224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.04922","last_updated":"2022-03-28T13:32:41Z","snapshot_observed_at":"2026-08-14T20:32:18.090947Z","submitted_at":"2021-03-08T17:34:03Z","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.04922","snapshot_observed_at":"2026-08-09T17:44:26.065739Z","title":"Deep generative modelling: A comparative review of vaes, gans, nor- malizing flows, energy-based and autoregressive models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00800","last_updated":"2025-02-02T13:50:38Z","snapshot_observed_at":"2026-08-14T19:44:13.409866Z","submitted_at":"2025-02-02T13:50:38Z","title":"Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T17:44:26.065739Z"},"links":{"cited_paper":"/paper/2103.04922","citing_paper":"/paper/2502.00800"},"observation_digest":"sha256:f813ca751da89f92b1af580e0f71584b780f9d31aa466a236b310127ce79acef","observation_id":"39c5c5b8-7132-4219-b876-1d0f2a5c61a2","resolution":{"observed_at":"2026-08-09T17:44:26.065739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.04922","last_updated":"2022-03-28T13:32:41Z","snapshot_observed_at":"2026-08-14T20:32:18.090947Z","submitted_at":"2021-03-08T17:34:03Z","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","version":4},"cited_work":{"arxiv_id":"2103.04922","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.04922","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2021 , journal =","venue":null,"work_id":"06e25382-b13a-4d4a-b5f7-70a4ce85d86b","year":2021},"citing_paper":{"arxiv_id":"2605.08645","last_updated":"2026-05-09T03:29:03Z","snapshot_observed_at":"2026-07-06T23:20:52.521341Z","submitted_at":"2026-05-09T03:29:03Z","title":"Energy-based models for diagnostic reconstruction and analysis in a laboratory plasma device","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-12T01:12:40.071020Z"},"links":{"cited_paper":"/paper/2103.04922","citing_paper":"/paper/2605.08645"},"observation_digest":"sha256:6af8f2d91ef15edb5cf0f47c0db26d7f2c785aa0b8dadf3b87f041cd7c8f1e71","observation_id":"f7667107-aabf-49d7-960a-8a5203627c64","resolution":{"observed_at":"2026-05-12T08:21:25.933697Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.04922/citation-record","integrity":"/paper/2103.04922/integrity","json":"/paper/2103.04922/citation-record.json","paper":"/paper/2103.04922"},"outbound":[],"paper":{"arxiv_id":"2103.04922","last_updated":"2022-03-28T13:32:41Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T20:32:18.090947Z","submitted_at":"2021-03-08T17:34:03Z","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2103.04922."}