{"as_of":"2026-08-17T02:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53bdb0157ab53d482bb40beab9273a89e6d7d89ea9d130ca66c47235d99eca68","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T19:09:22.329212Z","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":"2026-08-07T11:33:20.743390Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.03653","last_updated":"2020-05-22T13:56:07Z","snapshot_observed_at":"2026-08-16T20:10:47.009438Z","submitted_at":"2018-07-10T14:00:23Z","title":"Handling Incomplete Heterogeneous Data using VAEs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03653","snapshot_observed_at":"2026-08-14T13:47:02.377449Z","title":"Handling incomplete heterogeneous data using VAEs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.04537","last_updated":"2019-08-14T09:20:57Z","snapshot_observed_at":"2026-08-16T21:27:58.795575Z","submitted_at":"2019-08-13T08:49:33Z","title":"Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-14T13:47:02.377449Z"},"links":{"cited_paper":"/paper/1807.03653","citing_paper":"/paper/1908.04537"},"observation_digest":"sha256:05fd785971a2aad028d6cc4c24d3edc1545fb8f208ce2c4886d37a6837cac7f4","observation_id":"1c4903a4-b0ae-4601-8357-500feb77d86a","resolution":{"observed_at":"2026-08-14T13:47:02.377449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03653","last_updated":"2020-05-22T13:56:07Z","snapshot_observed_at":"2026-08-16T20:10:47.009438Z","submitted_at":"2018-07-10T14:00:23Z","title":"Handling Incomplete Heterogeneous Data using VAEs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03653","snapshot_observed_at":"2026-08-07T15:05:53.119970Z","title":"M., Ghahramani, Z., and Valera, I","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16481","last_updated":"2025-08-15T09:10:03Z","snapshot_observed_at":"2026-08-17T01:51:51.126591Z","submitted_at":"2025-05-22T10:07:33Z","title":"Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:05:53.119970Z"},"links":{"cited_paper":"/paper/1807.03653","citing_paper":"/paper/2505.16481"},"observation_digest":"sha256:5e90ea593ddcae9f8797f02934edc2527a9ba598c35d22302771bedb8892e2d2","observation_id":"207c6eed-9655-4208-a01a-a54068d7978b","resolution":{"observed_at":"2026-08-07T15:05:53.119970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03653","last_updated":"2020-05-22T13:56:07Z","snapshot_observed_at":"2026-08-16T20:10:47.009438Z","submitted_at":"2018-07-10T14:00:23Z","title":"Handling Incomplete Heterogeneous Data using VAEs","version":4},"cited_work":{"arxiv_id":"1807.03653","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.03653","snapshot_observed_at":"2026-08-07T11:33:20.743390Z","title":"Handling Incomplete Heterogeneous Data using VAEs","venue":"cs.LG","work_id":"2c7e6158-4cde-4be2-a7b8-9b87d16037ec","year":2018},"citing_paper":{"arxiv_id":"2506.02306","last_updated":"2025-06-02T22:50:22Z","snapshot_observed_at":"2026-08-14T04:20:04.841994Z","submitted_at":"2025-06-02T22:50:22Z","title":"CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T11:33:20.128005Z"},"links":{"cited_paper":"/paper/1807.03653","citing_paper":"/paper/2506.02306"},"observation_digest":"sha256:54392f318e1606d66cdc1e72af9849dad1afae6ce2ece2946275fb1436ab79d5","observation_id":"0745fbcc-4131-4393-aaee-143d05b09980","resolution":{"observed_at":"2026-08-07T11:33:20.751246Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03653","last_updated":"2020-05-22T13:56:07Z","snapshot_observed_at":"2026-08-16T20:10:47.009438Z","submitted_at":"2018-07-10T14:00:23Z","title":"Handling Incomplete Heterogeneous Data using VAEs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03653","snapshot_observed_at":"2026-08-15T19:09:22.329212Z","title":"Handling in- complete heterogeneous data using vaes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.17634","last_updated":"2025-06-24T20:58:09Z","snapshot_observed_at":"2026-08-15T19:02:50.397806Z","submitted_at":"2025-06-21T08:36:34Z","title":"Scalable Machine Learning Algorithms using Path Signatures","version":2},"reference_index":197,"source":"pdf_text","source_observed_at":"2026-08-15T19:09:22.329212Z"},"links":{"cited_paper":"/paper/1807.03653","citing_paper":"/paper/2506.17634"},"observation_digest":"sha256:0aa98719f43cdfd416202044b1a68333ee5bf5fbbb427f437b20af30c3921da8","observation_id":"77d10ef9-d82d-429f-b392-21b1de146f4f","resolution":{"observed_at":"2026-08-15T19:09:22.329212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1807.03653/citation-record","integrity":"/paper/1807.03653/integrity","json":"/paper/1807.03653/citation-record.json","paper":"/paper/1807.03653"},"outbound":[],"paper":{"arxiv_id":"1807.03653","last_updated":"2020-05-22T13:56:07Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T20:10:47.009438Z","submitted_at":"2018-07-10T14:00:23Z","title":"Handling Incomplete Heterogeneous Data using VAEs"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1807.03653."}