{"as_of":"2026-08-05T08:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1a398a39b4d4b82f04899f42cafb6be8e31ae6d2ab3e89800a4aa2ce201cd444","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":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":22,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T01:02:12.345562Z","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":"2026-07-04T08:19:44.786891Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1906.08652","last_updated":"2019-06-20T14:15:38Z","snapshot_observed_at":"2026-07-06T08:01:41.630447Z","submitted_at":"2019-06-20T14:15:38Z","title":"Disentangling Influence: Using Disentangled Representations to Audit Model Predictions","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T19:44:09.919663Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1906.08652"},"observation_digest":"sha256:ddf1c01c5be01e2b053a2436d16d1be7413f9d60942c9a86226b9161a4b3b3ca","observation_id":"ac426523-4447-4961-a019-ddb51fae8970","resolution":{"observed_at":"2026-05-25T19:46:10.388664Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1907.06119","last_updated":"2019-07-13T19:23:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-07-13T19:23:42Z","title":"Understanding Deep Learning Techniques for Image Segmentation","version":1},"reference_index":136,"source":"pdf_text","source_observed_at":"2026-05-24T21:46:17.736097Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1907.06119"},"observation_digest":"sha256:b3d7bf7b7cf7a525d4bc9985b7a78a82b0348c503af0b41943660e5f5c29c09e","observation_id":"152bfb91-57c5-4ba7-aacf-f12bae0b4d7d","resolution":{"observed_at":"2026-05-24T21:46:24.429610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1907.06286","last_updated":"2019-07-14T21:58:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-07-14T21:58:10Z","title":"Autoencoding sensory substitution","version":1},"reference_index":200,"source":"pdf_text","source_observed_at":"2026-05-24T21:24:11.898508Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1907.06286"},"observation_digest":"sha256:6b46c24ab6e4b3a648b8e455939ff80cace649801f6f8bb588a285e78bc9c201","observation_id":"85d57104-cd53-4f79-bae1-af9edd38003e","resolution":{"observed_at":"2026-05-24T21:24:57.717304Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1907.06582","last_updated":"2019-07-12T05:51:33Z","snapshot_observed_at":"2026-07-31T22:40:27.302806Z","submitted_at":"2019-07-12T05:51:33Z","title":"AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T22:50:15.989553Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1907.06582"},"observation_digest":"sha256:58e94e961949d17719efd387801dc3f1f68e8d5cdb1ccb5088f557221ed2be67","observation_id":"85169191-7871-4987-a546-93428594bc76","resolution":{"observed_at":"2026-05-24T22:55:02.996133Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1907.10949","last_updated":"2019-07-25T10:28:15Z","snapshot_observed_at":"2026-07-06T08:10:07.945422Z","submitted_at":"2019-07-25T10:28:15Z","title":"Y-Autoencoders: disentangling latent representations via sequential-encoding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T16:14:37.956137Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1907.10949"},"observation_digest":"sha256:045e684a23221acb2855aaad6319286fac9a06cf44db0fa2200a16d4d66618bd","observation_id":"524cf16e-6cdb-492f-a700-5e1af0c32e24","resolution":{"observed_at":"2026-05-24T16:14:40.291159Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"1909.01066","last_updated":"2019-09-04T09:33:20Z","snapshot_observed_at":"2026-07-06T08:18:37.267833Z","submitted_at":"2019-09-03T11:11:08Z","title":"Language Models as Knowledge Bases?","version":2},"reference_index":282,"source":"arxiv_source","source_observed_at":"2026-05-16T10:51:45.447733Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/1909.01066"},"observation_digest":"sha256:75a2e247b0da91eed24be45d7cacd9d6d9b6837d6c7047ae6bdf75a5ded90b0f","observation_id":"5b5818ed-6b74-4cde-9d98-ad527fcd2ba2","resolution":{"observed_at":"2026-05-16T10:51:45.829149Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2308.00352","last_updated":"2024-11-01T14:36:52Z","snapshot_observed_at":"2026-07-06T16:01:07.532053Z","submitted_at":"2023-08-01T07:49:10Z","title":"MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework","version":7},"reference_index":232,"source":"arxiv_source","source_observed_at":"2026-05-11T03:43:18.632292Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2308.00352"},"observation_digest":"sha256:9b9ff6b642ee00177088ce21b21e5ac8ba5b17886546754a0d0d87f32c8b2c0d","observation_id":"90d5391e-a73b-4155-8761-4788775bf852","resolution":{"observed_at":"2026-05-11T03:43:18.921627Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2406.09250","last_updated":"2026-05-22T03:11:22Z","snapshot_observed_at":"2026-08-01T08:01:02.690476Z","submitted_at":"2024-06-13T15:55:04Z","title":"MirrorCheck: Efficient Adversarial Defense for Vision-Language Models","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-25T09:03:31.136506Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2406.09250"},"observation_digest":"sha256:15f3aca4c06f5bf9772eab5a358283cc0b94f1f266fefa70b74f88ad8b20c90d","observation_id":"9fec9f28-4081-447b-8bd8-d9727cfdab4f","resolution":{"observed_at":"2026-05-25T09:05:35.903211Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2505.05020","last_updated":"2026-04-09T11:20:33Z","snapshot_observed_at":"2026-07-06T21:20:45.895003Z","submitted_at":"2025-05-08T07:52:37Z","title":"Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T16:21:59.840352Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2505.05020"},"observation_digest":"sha256:19b71a2ec73bcc037c6dee8206ebd3db4a04f2c6b448d1c0d35cba5850ac361f","observation_id":"61237ecd-223b-41fb-aa84-4a577d3639a4","resolution":{"observed_at":"2026-05-22T16:24:59.596802Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-04T12:47:52.924169Z","title":"Adversarial autoencoders","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.02168","last_updated":"2026-05-28T21:39:17Z","snapshot_observed_at":"2026-08-05T03:20:30.687213Z","submitted_at":"2025-10-02T16:15:48Z","title":"Wasserstein normalized autoencoder for anomaly detection","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T12:47:52.924169Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2510.02168"},"observation_digest":"sha256:f1434a95909ffcd97de7f8a63b4a907b1f1b010dd109252a7b66ad82f934f294","observation_id":"765c610d-6384-4e39-88f3-ec9bf127df0f","resolution":{"observed_at":"2026-08-04T12:47:52.924169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-03T21:47:40.816368Z","title":"Adversarial autoencoders.arXiv preprint arXiv:1511.05644,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.13899","last_updated":"2026-06-02T03:01:03Z","snapshot_observed_at":"2026-08-03T21:47:31.497669Z","submitted_at":"2025-11-17T20:49:58Z","title":"A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T21:47:40.816368Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2511.13899"},"observation_digest":"sha256:0b136d664b5aa027539aa38088e9de0f0ec5b90df027e4df08d07f820b0f33d1","observation_id":"b2c80c19-5586-4d96-aa8b-44d3c010d967","resolution":{"observed_at":"2026-08-03T21:47:40.816368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-03T17:55:07.421315Z","title":"Adversarial autoencoders","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.07778","last_updated":"2026-06-28T16:02:21Z","snapshot_observed_at":"2026-08-04T10:23:32.451665Z","submitted_at":"2025-12-08T17:59:47Z","title":"Distribution Matching Variational AutoEncoder","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T17:55:07.421315Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2512.07778"},"observation_digest":"sha256:76a3b0d0c69f3006a66fa0835d2a333da7268e2fd21e395fdc91786bdb756ac7","observation_id":"5d1491f0-bcc5-4066-8c67-25d7b0c6d2a6","resolution":{"observed_at":"2026-08-03T17:55:07.421315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-04T06:26:04.450667Z","title":"Makhzani, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.10588","last_updated":"2026-08-03T16:53:27Z","snapshot_observed_at":"2026-08-05T07:40:19.572958Z","submitted_at":"2026-01-15T16:59:40Z","title":"Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T06:26:04.450667Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2601.10588"},"observation_digest":"sha256:fb43f4eeb47f1a087a43b05111620ae96906dbab1fed7e927fafade98e5f87cf","observation_id":"5994ad12-8c74-4379-97f1-115882396cb0","resolution":{"observed_at":"2026-08-04T06:26:04.450667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-02T20:48:36.647152Z","title":"Ad- versarial autoencoders.arXiv preprint, 1511.05644, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.22188","last_updated":"2026-06-23T20:13:17Z","snapshot_observed_at":"2026-08-02T20:48:33.601668Z","submitted_at":"2026-02-25T18:34:03Z","title":"Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T20:48:36.647152Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2602.22188"},"observation_digest":"sha256:bc7d1a4211d0b6d3229da97edf05d8418249c113e293c2b2f608621735edf712","observation_id":"162425e3-35c2-4f56-ad43-1a6e3be113bf","resolution":{"observed_at":"2026-08-02T20:48:36.647152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2604.21546","last_updated":"2026-04-23T11:19:39Z","snapshot_observed_at":"2026-08-02T23:40:34.092962Z","submitted_at":"2026-04-23T11:19:39Z","title":"Component-Based Out-of-Distribution Detection","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-05-09T22:54:05.971460Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2604.21546"},"observation_digest":"sha256:bb687e69884eaf243c4e3011ec7cd727e1a53a0125862336d23e3d68a2d3579d","observation_id":"a20b9a1d-c2e7-493c-b414-b1993d839a9c","resolution":{"observed_at":"2026-05-09T22:54:16.126935Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2605.20299","last_updated":"2026-05-19T12:34:16Z","snapshot_observed_at":"2026-07-06T23:30:53.900592Z","submitted_at":"2026-05-19T12:34:16Z","title":"Mechanisms of Misgeneralization in Physical Sequence Modeling","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-21T07:44:37.810511Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2605.20299"},"observation_digest":"sha256:7dd942977584995fff1dbde815bbf755349ad2bf2a372e8043de977175fab48e","observation_id":"07f80376-0e09-410a-9c07-db821d00961e","resolution":{"observed_at":"2026-05-21T07:44:48.687947Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2605.21058","last_updated":"2026-05-20T11:43:46Z","snapshot_observed_at":"2026-08-03T21:36:49.518850Z","submitted_at":"2026-05-20T11:43:46Z","title":"A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-21T05:42:48.667216Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2605.21058"},"observation_digest":"sha256:832eeeb67e890006dab4e5e06caf47a6f76bfa8647d655537264b806c6f8aa2b","observation_id":"436196b4-9f41-4584-b191-c04b5047a1b0","resolution":{"observed_at":"2026-05-21T05:43:58.736296Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2606.07676","last_updated":"2026-06-04T19:06:27Z","snapshot_observed_at":"2026-08-02T10:45:28.056810Z","submitted_at":"2026-06-04T19:06:27Z","title":"Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-06-27T22:22:18.591772Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2606.07676"},"observation_digest":"sha256:52036f79826eb7a5210b0afdd8d240703239cd46259f7c42fa7371d1a9ed2f70","observation_id":"0aba5ddc-6f65-4f0c-9614-fb7c590887ef","resolution":{"observed_at":"2026-07-02T16:47:10.024954Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2606.22138","last_updated":"2026-06-20T16:38:59Z","snapshot_observed_at":"2026-07-06T23:57:04.096779Z","submitted_at":"2026-06-20T16:38:59Z","title":"BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-06-26T11:46:33.957076Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2606.22138"},"observation_digest":"sha256:439004a7476afabafc8b9457c9eee37b2dafddc940256a4a3ba7ba364565d274","observation_id":"d190eff6-7317-44bb-8c2b-76a87bf02c75","resolution":{"observed_at":"2026-07-04T08:19:44.788061Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":"1511.05644","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-04T08:19:44.786891Z","title":"Adversarial Autoencoders","venue":"cs.LG","work_id":"de1b26ff-7965-4560-a7d7-1f127462cd63","year":2015},"citing_paper":{"arxiv_id":"2607.01275","last_updated":"2026-06-30T23:05:06Z","snapshot_observed_at":"2026-08-03T05:11:37.998912Z","submitted_at":"2026-06-30T23:05:06Z","title":"eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-03T21:42:50.074527Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2607.01275"},"observation_digest":"sha256:be7ae72b8a11f646d4009d32d22b567def909570f6993c01b3fa1a92915b7480","observation_id":"6581facb-727e-4218-8771-ac55487bcc5f","resolution":{"observed_at":"2026-07-03T21:48:58.975094Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-07-11T06:15:32.320107Z","title":"Adversarial autoen- coders.arXiv preprint arXiv:1511.05644,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.05531","last_updated":"2026-07-06T18:07:46Z","snapshot_observed_at":"2026-08-05T04:15:00.332528Z","submitted_at":"2026-07-06T18:07:46Z","title":"$\\mathbf{\\lambda}$-VAE: Variance Equalization for Posterior Collapse","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-11T06:15:32.320107Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2607.05531"},"observation_digest":"sha256:5bf0000202d9fdac6824eb7df70c121f3900d90e0e9200ef4214bb0d8b0ac867","observation_id":"8191ac9b-6016-4944-a496-e9f4d2c62013","resolution":{"observed_at":"2026-07-11T06:15:32.320107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05644","snapshot_observed_at":"2026-08-05T01:02:12.345562Z","title":"ArXiv:1511.05644 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00444","last_updated":"2026-08-01T04:56:39Z","snapshot_observed_at":"2026-08-05T07:25:05.892861Z","submitted_at":"2026-08-01T04:56:39Z","title":"Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-05T01:02:12.345562Z"},"links":{"cited_paper":"/paper/1511.05644","citing_paper":"/paper/2608.00444"},"observation_digest":"sha256:cfe1942adc5210941de3c382779bb6e5900a09c08874e3eac4be64c5b04a6f1f","observation_id":"8c28e71c-00bc-4fa4-83ee-5ffb32327ecf","resolution":{"observed_at":"2026-08-05T01:02:12.345562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1511.05644/citation-record","integrity":"/paper/1511.05644/integrity","json":"/paper/1511.05644/citation-record.json","paper":"/paper/1511.05644"},"outbound":[],"paper":{"arxiv_id":"1511.05644","last_updated":"2016-05-25T00:17:45Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T04:36:52.555912Z","submitted_at":"2015-11-18T02:32:39Z","title":"Adversarial Autoencoders"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1511.05644."}