{"as_of":"2026-08-10T16:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cefeebaa86d29098daf4d6df4ac4d678cff3235a51c24d68131f13e6571b9328","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:58:42.539518Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.04216/citation-record","integrity":"/paper/2507.04216/integrity","json":"/paper/2507.04216/citation-record.json","paper":"/paper/2507.04216"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1705.07111","last_updated":"2017-05-19T17:45:19Z","snapshot_observed_at":"2026-08-09T08:48:15.299354Z","submitted_at":"2017-05-19T17:45:19Z","title":"The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07111","snapshot_observed_at":"2026-08-06T19:58:41.166200Z","title":"The kernel mix- ture network: A nonparametric method for conditional density estimation of continuous random variables","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.166200Z"},"links":{"cited_paper":"/paper/1705.07111","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:0a12216306c2727592e487e65e24f44c68f31d8bedc06219c723af80a270c53e","observation_id":"285be720-3ee8-4275-b0ba-641f6e9f9f36","resolution":{"observed_at":"2026-08-06T19:58:41.166200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-06T19:58:41.932909Z","title":"Sgdr: Stochastic gradient descent with warm restarts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.932909Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:187bb51b267961e1878fd9437308fb16ae9523d77d4a6004bd47099a0ed7dd05","observation_id":"9ed2e27a-4843-44da-9f7a-5a92e24b0d36","resolution":{"observed_at":"2026-08-06T19:58:41.932909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-06T19:58:42.010984Z","title":"Umap: Uniform manifold approximation and projection for dimension reduction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":1979,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.010984Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:f17e4467d7f49680248eda4b032bd299dd8af0c04a87fc31b5f8cd990f3b151a","observation_id":"d45714ff-5cb3-4b6d-93c0-77de76bd69bb","resolution":{"observed_at":"2026-08-06T19:58:42.010984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:58:42.953623Z","title":"Generalized outlier detection with flexible kernel density estimates","venue":null,"work_id":"44c400c6-607a-4b52-b32f-134dbe894104","year":2014},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":1997,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.289462Z"},"links":{"citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:aeae6ac5029388d8d78ae2a668704fdd51a87f5665960314c6e0288f326cc071","observation_id":"776da2ea-3dde-4cd2-b73c-b8d37ffe0054","resolution":{"observed_at":"2026-08-06T19:58:43.039898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:58:43.150366Z","title":"Aop: An anti-overfitting pretreatment for practical image-based plant diagnosis","venue":null,"work_id":"fe51e4bf-df91-42ef-b24e-2cea8d81c001","year":2019},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.219027Z"},"links":{"citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:c858fb8eee0e695f4f1d278281f66012c6aa3096f342c1b7e5b7fe42f281f117","observation_id":"9b5ca177-5ddb-4ff9-837c-8cfff8ab1fcf","resolution":{"observed_at":"2026-08-06T19:58:43.264773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08803","last_updated":"2017-02-27T23:21:10Z","snapshot_observed_at":"2026-08-10T10:35:06.780085Z","submitted_at":"2016-05-27T21:24:32Z","title":"Density estimation using Real NVP","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08803","snapshot_observed_at":"2026-08-06T19:58:41.249049Z","title":"Density estimation using real NVP","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.249049Z"},"links":{"cited_paper":"/paper/1605.08803","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:ea8fa3746f498610ee5f270fcbb908a2cd7d1785ccafec51c3f61a134e7972b1","observation_id":"38d833e8-cf5a-4858-b44b-5862d7dd831d","resolution":{"observed_at":"2026-08-06T19:58:41.249049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01367","last_updated":"2018-10-22T17:56:45Z","snapshot_observed_at":"2026-07-06T07:05:41.488600Z","submitted_at":"2018-10-02T16:56:37Z","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01367","snapshot_observed_at":"2026-08-06T19:58:41.396582Z","title":"Ffjord: Free-form continuous dynamics for scalable reversible generative models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.396582Z"},"links":{"cited_paper":"/paper/1810.01367","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:5a6f7ecd27a11cfd46822922502a3416f40fedcb1afd261149e55b92af0140e5","observation_id":"cc5cdabd-e214-4d78-ab68-b2242d1e58dd","resolution":{"observed_at":"2026-08-06T19:58:41.396582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1206.4635","last_updated":"2012-06-18T15:14:57Z","snapshot_observed_at":"2026-08-03T19:07:45.988443Z","submitted_at":"2012-06-18T15:14:57Z","title":"Deep Mixtures of Factor Analysers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.4635","snapshot_observed_at":"2026-08-06T19:58:42.407289Z","title":"Deep mixtures of factor anal- ysers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.407289Z"},"links":{"cited_paper":"/paper/1206.4635","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:835a9596026c3c6129118d65dbcc1c3f73707022e58ccc3cc3abdd59937fd6d5","observation_id":"763e5c7e-dc4b-4e3f-a253-bc735088e5eb","resolution":{"observed_at":"2026-08-06T19:58:42.407289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-06T19:58:42.539518Z","title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.539518Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:23643a46ced065f378ec54a0aeb48656f87b2050c42f1fb69b86da398e1f377f","observation_id":"efe6922a-434a-46ed-9f53-65503ebdced5","resolution":{"observed_at":"2026-08-06T19:58:42.539518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T19:58:41.841479Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.841479Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:66612d958a4456f556f8d869da17432142d8355eaccc0631f5e6407f241a3f49","observation_id":"b6e8b6b5-7786-4e7d-b667-64a0e63a5446","resolution":{"observed_at":"2026-08-06T19:58:41.841479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04287","last_updated":"2022-06-09T05:39:04Z","snapshot_observed_at":"2026-07-06T13:18:57.269780Z","submitted_at":"2022-06-09T05:39:04Z","title":"Evaluating Aleatoric Uncertainty via Conditional Generative Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04287","snapshot_observed_at":"2026-08-06T19:58:41.690740Z","title":"Evaluating aleatoric uncertainty via condi- tional generative models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.690740Z"},"links":{"cited_paper":"/paper/2206.04287","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:b7b2c756c36686e708a2730bb5c52ce44681b64b8f81c16fcf80eadbab9381c5","observation_id":"b8e562e7-844e-4469-8ca7-36713aab8c82","resolution":{"observed_at":"2026-08-06T19:58:41.690740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03697","last_updated":"2021-03-19T20:33:37Z","snapshot_observed_at":"2026-08-10T11:40:54.470046Z","submitted_at":"2020-10-08T00:14:59Z","title":"A Critique of Self-Expressive Deep Subspace Clustering","version":2},"cited_work":{"arxiv_id":"2010.03697","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.03697","snapshot_observed_at":"2026-08-06T19:58:42.724434Z","title":"A Critique of Self-Expressive Deep Subspace Clustering","venue":"cs.LG","work_id":"6cadfbbb-63bd-41d2-8b96-1d2b771c860b","year":2020},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:41.545977Z"},"links":{"cited_paper":"/paper/2010.03697","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:7c548baa5561a37f0bb7e7cb434c8229a3a246822b2a49e68e03f93ef6f17e40","observation_id":"e34883e6-0f6a-4c01-9b3f-4ccd9e82b3a1","resolution":{"observed_at":"2026-08-06T19:58:42.810706Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.00954","last_updated":"2019-04-13T11:20:14Z","snapshot_observed_at":"2026-08-06T08:22:46.114258Z","submitted_at":"2019-03-03T18:15:20Z","title":"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.00954","snapshot_observed_at":"2026-08-06T19:58:42.117387Z","title":"Conditional density estimation with neural networks: Best practices and benchmarks","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:42.117387Z"},"links":{"cited_paper":"/paper/1903.00954","citing_paper":"/paper/2507.04216"},"observation_digest":"sha256:e8646e64734596b0351ae6a0d366d5d14293118e2c5650f0ac34a661ccdc3330","observation_id":"7c48e380-f5f9-4428-9c6c-2cb50279b1d7","resolution":{"observed_at":"2026-08-06T19:58:42.117387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.04216","last_updated":"2025-07-06T02:58:52Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-10T08:40:27.330701Z","submitted_at":"2025-07-06T02:58:52Z","title":"Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":13},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.04216."}