{"as_of":"2026-08-09T18:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:df8861c7f3d3a89102d39cdb81b317e6edf7d062126747560d0c4879e570287e","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":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T10:42:41.761885Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":194,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"1906.08227","last_updated":"2019-06-19T17:16:54Z","snapshot_observed_at":"2026-08-01T16:13:23.412026Z","submitted_at":"2019-06-19T17:16:54Z","title":"Local Bures-Wasserstein Transport: A Practical and Fast Mapping Approximation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T20:10:48.452451Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/1906.08227"},"observation_digest":"sha256:f4cb78d1aeeb8d086c0e5324f7779619883016f23a88618b9064063f51e837ce","observation_id":"2224b58d-07be-4b0d-ae29-ace664385876","resolution":{"observed_at":"2026-05-25T20:11:11.244243Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"1907.08325","last_updated":"2019-07-19T00:37:39Z","snapshot_observed_at":"2026-08-07T08:34:01.209413Z","submitted_at":"2019-07-19T00:37:39Z","title":"Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-24T19:27:00.286138Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/1907.08325"},"observation_digest":"sha256:765549b94e6edd6aa54d1259dc8b497b2d3d1e1dd924f912a963b4ca04f88e4f","observation_id":"bacd3f33-ffc3-4177-bae3-d7b005f7bb5b","resolution":{"observed_at":"2026-05-24T19:29:50.934844Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-09T10:42:41.761885Z","title":"Wasserstein auto-encoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04364","last_updated":"2025-02-05T06:24:25Z","snapshot_observed_at":"2026-08-09T10:35:31.504278Z","submitted_at":"2025-02-05T06:24:25Z","title":"Lost in Edits? A $\\lambda$-Compass for AIGC Provenance","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T10:42:41.761885Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2502.04364"},"observation_digest":"sha256:4727f0331cce74527fa3ca9620d47e1264ed3b0ad9c0f1ea90e8433e761dbd78","observation_id":"d5486bcf-b7fc-4497-a59d-07ab4ec1558e","resolution":{"observed_at":"2026-08-09T10:42:41.761885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-09T00:18:48.039598Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.04409","last_updated":"2025-02-06T10:16:47Z","snapshot_observed_at":"2026-08-09T01:23:55.922085Z","submitted_at":"2025-02-06T10:16:47Z","title":"Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods","version":1},"reference_index":257,"source":"pdf_text","source_observed_at":"2026-08-09T00:18:48.039598Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2502.04409"},"observation_digest":"sha256:28fb52368f5224ea9799e1723122741688b0c0dcca60bb27c47697c66bfcc09f","observation_id":"f94e3ae6-0536-4bfd-b2dd-924c37dc9e05","resolution":{"observed_at":"2026-08-09T00:18:48.039598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T23:07:18.367183Z","title":"Wasserstein auto-encoders,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08960","last_updated":"2025-09-12T10:37:30Z","snapshot_observed_at":"2026-08-07T23:02:56.898444Z","submitted_at":"2025-02-13T04:53:17Z","title":"A Comprehensive Survey on Imbalanced Data Learning","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T23:07:18.367183Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2502.08960"},"observation_digest":"sha256:930621b63c59d838c69bfd039e0d7d6d880a76df22699ec8c3ab0caf924f637e","observation_id":"69af5667-1383-42e3-b31e-012af0d19084","resolution":{"observed_at":"2026-08-07T23:07:18.367183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T14:35:13.683669Z","title":"O., Bousquet, O., Gelly, S., and Sch ¨olkopf, B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.18558","last_updated":"2025-05-24T06:52:23Z","snapshot_observed_at":"2026-08-08T23:47:57.383575Z","submitted_at":"2025-05-24T06:52:23Z","title":"Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:35:13.683669Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2505.18558"},"observation_digest":"sha256:55a29aaaecbc6d707ec79cc0ff3644629b0410930245e31cccb5f61cc3d417f5","observation_id":"a8f9e09c-9478-402e-a80f-4e80ff5fd407","resolution":{"observed_at":"2026-08-07T14:35:13.683669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T12:10:15.412933Z","title":"Wasserstein auto- encoders","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00573","last_updated":"2025-05-31T14:10:27Z","snapshot_observed_at":"2026-08-07T12:00:05.761836Z","submitted_at":"2025-05-31T14:10:27Z","title":"Neural Estimation for Scaling Entropic Multimarginal Optimal Transport","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:15.412933Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2506.00573"},"observation_digest":"sha256:1294afaf3719cf70074739d52ea117f7ec4d809bf78de2c0fd85e5384513bc08","observation_id":"d4758bc0-c549-4ad9-83be-6c4cc27bb9c3","resolution":{"observed_at":"2026-08-07T12:10:15.412933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T12:06:39.458453Z","title":"Wasserstein auto-encoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00849","last_updated":"2025-06-01T06:11:38Z","snapshot_observed_at":"2026-08-08T15:43:46.944980Z","submitted_at":"2025-06-01T06:11:38Z","title":"Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:06:39.458453Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2506.00849"},"observation_digest":"sha256:ae0b63670a84e0d234bedcb8a8e0ade86ace0637f0eba31e1ea3338f045304b1","observation_id":"a705d7cc-f7d5-48cd-81a1-9b80257cca7b","resolution":{"observed_at":"2026-08-07T12:06:39.458453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T05:57:05.262779Z","title":"& Schoelkopf, B","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06598","last_updated":"2025-06-07T00:16:03Z","snapshot_observed_at":"2026-08-07T05:51:14.984306Z","submitted_at":"2025-06-07T00:16:03Z","title":"Imaging 3D polarization dynamics via deep learning 4D-STEM","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T05:57:05.262779Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2506.06598"},"observation_digest":"sha256:2685957c4e9763466e7cdce81207b0c9ee6605aa1ec500671cf3612b571a21fb","observation_id":"90f2d40a-20ed-4eb5-8988-8b802f25af2d","resolution":{"observed_at":"2026-08-07T05:57:05.262779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-07T05:45:58.008164Z","title":"13 Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07504","last_updated":"2025-06-09T07:28:00Z","snapshot_observed_at":"2026-08-07T05:29:42.078030Z","submitted_at":"2025-06-09T07:28:00Z","title":"Minimax Optimal Rates for Regression on Manifolds and Distributions","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T05:45:58.008164Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2506.07504"},"observation_digest":"sha256:757b7319f3e1948aea5337d69eccd7b7b7a48f0b57aff14f40d4f36fc48c0c79","observation_id":"bcba965b-2753-430c-bd71-34e0aec5429b","resolution":{"observed_at":"2026-08-07T05:45:58.008164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-06T14:59:14.375337Z","title":"Arash Vahdat, William G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.17255","last_updated":"2025-08-21T12:51:26Z","snapshot_observed_at":"2026-08-09T16:29:48.430297Z","submitted_at":"2025-07-23T06:52:00Z","title":"From Points to Spheres: A Geometric Reinterpretation of Variational Autoencoders","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:59:14.375337Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2507.17255"},"observation_digest":"sha256:3944548f985c93f0b93ea7693514cc580c6643dbc349a95da2d9e5eb239d7195","observation_id":"99966cc8-5299-4cb0-b59f-3e82ba28b61a","resolution":{"observed_at":"2026-08-06T14:59:14.375337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-06T13:49:06.602716Z","title":"Wasserstein auto-encoders,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20283","last_updated":"2025-07-27T13:58:23Z","snapshot_observed_at":"2026-08-06T13:48:59.895016Z","submitted_at":"2025-07-27T13:58:23Z","title":"Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T13:49:06.602716Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2507.20283"},"observation_digest":"sha256:63b504550800ad473fc915c8a68d132fb3cf8f7d775836e27b9f9593c8feca2b","observation_id":"41331513-3ead-461c-a59e-ff1a376788b7","resolution":{"observed_at":"2026-08-06T13:49:06.602716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T13:48:55.412957Z","title":"Tolstikhin, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.00311","last_updated":"2025-08-30T01:59:19Z","snapshot_observed_at":"2026-08-08T03:57:59.247604Z","submitted_at":"2025-08-30T01:59:19Z","title":"MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T13:48:55.412957Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2509.00311"},"observation_digest":"sha256:721fd7ec39b6abfb8d33fa3b6adb807ab147b7588425685a82ab6a6feadf5d22","observation_id":"0d2929f9-9b9a-46eb-a0d8-c3b2228d9000","resolution":{"observed_at":"2026-08-05T13:48:55.412957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-04T16:59:54.226403Z","title":"Tolstikhin, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11220","last_updated":"2025-09-14T11:44:43Z","snapshot_observed_at":"2026-08-09T10:08:55.588730Z","submitted_at":"2025-09-14T11:44:43Z","title":"ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T16:59:54.226403Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2509.11220"},"observation_digest":"sha256:0e1e7e7d1d59fd352a7cd8aa9418f146388dfa0849cbd70e789c26826aed91f4","observation_id":"d615a3c2-de62-452c-8c32-599f471d11ea","resolution":{"observed_at":"2026-08-04T16:59:54.226403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-04T12:47:53.073066Z","title":"Wasserstein auto-encoders","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.02168","last_updated":"2026-05-28T21:39:17Z","snapshot_observed_at":"2026-08-08T13:35:40.019136Z","submitted_at":"2025-10-02T16:15:48Z","title":"Wasserstein normalized autoencoder for anomaly detection","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T12:47:53.073066Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2510.02168"},"observation_digest":"sha256:84f2d4606d47c1a24653106d3828f6ef61ffe5e75ca6ad796d5cabf065807131","observation_id":"199244b7-301d-48d9-b58e-2b1d3f50d823","resolution":{"observed_at":"2026-08-04T12:47:53.073066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2510.18326","last_updated":"2026-05-13T22:33:07Z","snapshot_observed_at":"2026-08-08T17:58:55.579441Z","submitted_at":"2025-10-21T06:24:42Z","title":"Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-18T05:19:59.340348Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2510.18326"},"observation_digest":"sha256:e0404eb3a07725501296d0b2741dd676083d60ac35a3f09c7c8d7784aafd1833","observation_id":"6a900d76-1b06-44d2-90f2-6b1e7e5610ac","resolution":{"observed_at":"2026-05-18T05:20:54.341964Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-03T21:55:26.185485Z","title":"Tolstikhin, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.13847","last_updated":"2026-06-29T22:01:02Z","snapshot_observed_at":"2026-08-09T03:02:06.049127Z","submitted_at":"2025-11-17T19:04:28Z","title":"Convex relaxation approaches for high-dimensional optimal transport","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T21:55:26.185485Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2511.13847"},"observation_digest":"sha256:ced1fb581093267c430fd57b0e0993ae56531ea6f49bf661a4a9edf921dd25b3","observation_id":"83c7275a-6bd0-4565-8985-b5ba8652c73b","resolution":{"observed_at":"2026-08-03T21:55:26.185485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-03T05:32:40.777459Z","title":"Tolstikhin, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.02250","last_updated":"2026-06-01T08:59:01Z","snapshot_observed_at":"2026-08-06T11:10:22.201694Z","submitted_at":"2026-02-02T15:57:32Z","title":"Well-Posed KL-Regularized Control via Wasserstein and Kalman-Wasserstein KL Divergences","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T05:32:40.777459Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2602.02250"},"observation_digest":"sha256:3cb2ad9c484524d5ee2247f6f365a8e868a5ea9b1e65e848365d57a603be0789","observation_id":"0d7b4bba-5877-414f-90bb-530990ffd14f","resolution":{"observed_at":"2026-08-03T05:32:40.777459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-03T05:14:57.994716Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.02948","last_updated":"2026-07-03T15:10:40Z","snapshot_observed_at":"2026-08-08T17:32:42.581325Z","submitted_at":"2026-02-03T00:46:29Z","title":"Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T05:14:57.994716Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2602.02948"},"observation_digest":"sha256:9d1d478d2f085a0879510b77bb28c00bcf95dd7ad82ad6594c7a49fc8361f4d0","observation_id":"0dd8495f-7fea-41ca-ba95-71a785eb798d","resolution":{"observed_at":"2026-08-03T05:14:57.994716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2602.15451","last_updated":"2026-05-12T06:17:25Z","snapshot_observed_at":"2026-08-08T10:39:21.825985Z","submitted_at":"2026-02-17T09:38:11Z","title":"Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T22:15:50.671095Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2602.15451"},"observation_digest":"sha256:560bb3d06d51f1502bd192b02e2545a25c766dcc1c17ff7a20590db11836d50d","observation_id":"6e5d424a-b355-4994-8b7f-5aef1700034d","resolution":{"observed_at":"2026-05-15T22:16:42.300440Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2604.11026","last_updated":"2026-04-16T15:03:23Z","snapshot_observed_at":"2026-07-06T22:59:32.051572Z","submitted_at":"2026-04-13T05:49:59Z","title":"Optimal Stability of KL Divergence under Gaussian Perturbations","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T15:43:27.117501Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2604.11026"},"observation_digest":"sha256:de63c1bbe8fa56315e9c16591269f4a17b3564d69cd279a2240d69c2e8ae5833","observation_id":"0cdc5562-62f8-466c-baf3-27e94f0ba6a8","resolution":{"observed_at":"2026-05-11T09:56:05.113114Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2604.12912","last_updated":"2026-04-14T15:58:23Z","snapshot_observed_at":"2026-07-06T23:01:00.830207Z","submitted_at":"2026-04-14T15:58:23Z","title":"Nonlinear Stochastic Model Predictive Control with Generative Uncertainty in Homogeneous Charge Compression Ignition","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T14:42:20.407179Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2604.12912"},"observation_digest":"sha256:91b85ea94ad1fc3e020081d56475fb079580c9c4e29d139106eeb003a2a18cf7","observation_id":"f3f7e46e-b5f2-4617-b895-dd8acf5238f0","resolution":{"observed_at":"2026-05-10T14:45:40.061340Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2605.11755","last_updated":"2026-05-26T21:22:24Z","snapshot_observed_at":"2026-07-06T23:23:34.924221Z","submitted_at":"2026-05-12T08:29:44Z","title":"One-Step Generative Modeling via Wasserstein Gradient Flows","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-13T07:45:39.888214Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.11755"},"observation_digest":"sha256:87549cdda44574607325024ab28d6c8e4e5725b6787853d57aedf7ff7715325b","observation_id":"0a48941b-fcd8-4e75-b186-67afac657543","resolution":{"observed_at":"2026-05-13T07:47:33.277878Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2605.11755","last_updated":"2026-05-26T21:22:24Z","snapshot_observed_at":"2026-07-06T23:23:34.924221Z","submitted_at":"2026-05-12T08:29:44Z","title":"One-Step Generative Modeling via Wasserstein Gradient Flows","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-30T22:09:22.474650Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.11755"},"observation_digest":"sha256:cb77e187b98918dbc85af644205f04e788f1938c513e785ceebc2bcb20888096","observation_id":"9f5b6af5-c76c-43a2-a7c1-0336ded3d253","resolution":{"observed_at":"2026-07-01T14:15:47.180592Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2605.13517","last_updated":"2026-05-27T13:04:07Z","snapshot_observed_at":"2026-07-06T23:25:07.022065Z","submitted_at":"2026-05-13T13:35:16Z","title":"ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-14T20:11:48.315691Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.13517"},"observation_digest":"sha256:bf3a1324ea9fea560cd2c7b1675023d03fb3af186ef4bfceaa6b8c755518c0b2","observation_id":"7365af76-b9f3-4ac6-a527-1a6984d19938","resolution":{"observed_at":"2026-05-14T20:12:55.300305Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2605.13517","last_updated":"2026-05-27T13:04:07Z","snapshot_observed_at":"2026-07-06T23:25:07.022065Z","submitted_at":"2026-05-13T13:35:16Z","title":"ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T21:55:49.709945Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.13517"},"observation_digest":"sha256:12dff8eb60bfeb165bdfbd9eaa7879a9853522c8122cfa93f3e6908a6fb1729c","observation_id":"dabd8210-0d53-463d-898c-33a9662daf61","resolution":{"observed_at":"2026-06-30T22:05:06.165696Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"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":140,"source":"arxiv_source","source_observed_at":"2026-05-21T07:44:37.810511Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.20299"},"observation_digest":"sha256:fccc29186608a9f5af09aa281ee02ab19a26e1d7680af55bc11a8b522a9d9f23","observation_id":"a997d8ca-9045-420f-9da5-5ac512f5384b","resolution":{"observed_at":"2026-05-21T07:44:48.704806Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2605.22493","last_updated":"2026-05-21T13:45:28Z","snapshot_observed_at":"2026-07-06T23:32:49.530788Z","submitted_at":"2026-05-21T13:45:28Z","title":"Understanding Multimodal Failure in Action-Chunking Behavioral Cloning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-22T08:14:28.115848Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2605.22493"},"observation_digest":"sha256:8573d4125b61785964c9ae132f97c24605572e696dbbffb850ee83a1e663f5a7","observation_id":"33140a9c-4402-4461-8373-dfa4d5317fe4","resolution":{"observed_at":"2026-05-22T08:14:45.469451Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":"1711.01558","doi":"10.48550/arxiv.1711.01558","metadata_source":"arxiv_reference","pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tolstikhin, O","venue":"arXiv (Cornell University)","work_id":"8a71ae40-33b6-46a6-9281-259e03a927f2","year":2017},"citing_paper":{"arxiv_id":"2606.00229","last_updated":"2026-06-08T17:22:58Z","snapshot_observed_at":"2026-07-06T23:40:56.510371Z","submitted_at":"2026-05-29T18:02:09Z","title":"Continuous Reasoning for Vision-Language-Action","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-28T22:07:07.401335Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2606.00229"},"observation_digest":"sha256:6d338f84469892559085a155b26727157c3a765ece5766eb84829547dd9b5dfb","observation_id":"4912bc74-ef6a-49c4-9103-3269149ad384","resolution":{"observed_at":"2026-07-01T19:46:10.499266Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-07-11T06:15:32.320107Z","title":"Wasserstein auto-encoders.arXiv preprint arXiv:1711.01558,","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":17,"source":"pdf_text","source_observed_at":"2026-07-11T06:15:32.320107Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2607.05531"},"observation_digest":"sha256:12559761b80da5547c9ccc9e66f74ff8b676cef5b7e872289cf6f183c77db226","observation_id":"9b1dcf4b-dd2a-45c1-a886-4eddd7868495","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":"1711.01558","last_updated":"2019-12-05T10:27:44Z","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01558","snapshot_observed_at":"2026-08-08T17:22:00.310124Z","title":"arXiv preprint arXiv:1711.01558 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05243","last_updated":"2026-08-05T14:56:53Z","snapshot_observed_at":"2026-08-09T17:10:17.133506Z","submitted_at":"2026-08-05T14:56:53Z","title":"Marginal Matching Does Not License Factorized Sampling: Auditing Conditional Style Leakage in Factorized Generative Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T17:22:00.310124Z"},"links":{"cited_paper":"/paper/1711.01558","citing_paper":"/paper/2608.05243"},"observation_digest":"sha256:3561ab3939d6f4db621a6de350be3c95352bc576bb77456de7a890bd68ec8adc","observation_id":"4f364c7f-6e79-458d-810b-c06532fffe87","resolution":{"observed_at":"2026-08-08T17:22:00.310124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1711.01558/citation-record","integrity":"/paper/1711.01558/integrity","json":"/paper/1711.01558/citation-record.json","paper":"/paper/1711.01558"},"outbound":[],"paper":{"arxiv_id":"1711.01558","last_updated":"2019-12-05T10:27:44Z","latest_version":4,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T06:07:46.042790Z","submitted_at":"2017-11-05T10:18:27Z","title":"Wasserstein Auto-Encoders"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:1711.01558."}