{"as_of":"2026-08-09T21:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a8fbc7224bde86aca08fece60c41124f4355aa5266323362bf3f710bc5253e3","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":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":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T19:42:54.071853Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:39:51.058233Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":"1911.09785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-07-04T13:39:51.058233Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":"a5334d53-fbde-4811-8f85-88a0253295da","year":1911},"citing_paper":{"arxiv_id":"2404.08471","last_updated":"2024-02-15T18:59:11Z","snapshot_observed_at":"2026-08-07T02:30:11.447693Z","submitted_at":"2024-02-15T18:59:11Z","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-05-12T12:40:23.709098Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2404.08471"},"observation_digest":"sha256:16c879b3cd3b9f3b1ee01f78d71b7b2998e28ef7ca47bf22e595b4726be59f32","observation_id":"467f0a18-0d48-4c2b-a6d9-776256f64f5b","resolution":{"observed_at":"2026-05-12T12:40:23.991159Z","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":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-09T19:42:54.071853Z","title":"D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T19:31:53.502775Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.071853Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:4732790aae00c6b2eee2d1ac4d5965c4489e4cccf14d022ea4b7c82359944ce7","observation_id":"c4f662c4-f335-4fd6-aee4-65f8d4890af8","resolution":{"observed_at":"2026-08-09T19:42:54.071853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-07T15:28:46.192382Z","title":"Remixmatch: Semi-supervised learning with dis- tribution alignment and augmentation anchoring.arXiv:1911.09785, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.192382Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:02d1b71be512e4dee1e9c35884635fd773988e59ca01f4bd18455ab9b05d0d2f","observation_id":"9745da6a-3eda-4da7-8405-52c86e2a37fa","resolution":{"observed_at":"2026-08-07T15:28:46.192382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-07T05:26:58.890163Z","title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring.arXiv preprint arXiv:1911.09785, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2506.08005","last_updated":"2025-06-09T17:59:51Z","snapshot_observed_at":"2026-08-09T20:59:29.890282Z","submitted_at":"2025-06-09T17:59:51Z","title":"ZeroVO: Visual Odometry with Minimal Assumptions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:26:58.890163Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2506.08005"},"observation_digest":"sha256:4e8eb25b4d6aaf01b15d70407f287e41bfd3d6a66f561bfd072ed00448ad7f3e","observation_id":"4434065a-7c67-43e5-a5b7-d24d5cbcf6f9","resolution":{"observed_at":"2026-08-07T05:26:58.890163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-06T22:44:04.014468Z","title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2506.20841","last_updated":"2025-06-25T21:25:05Z","snapshot_observed_at":"2026-08-06T22:37:57.818563Z","submitted_at":"2025-06-25T21:25:05Z","title":"FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:44:04.014468Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2506.20841"},"observation_digest":"sha256:58dbbfb69d6d5d66bc097acacbc9de97284b05171efd12cd83387ec35390c0f1","observation_id":"22d0545d-4123-48e9-be43-a5f29fa2af4b","resolution":{"observed_at":"2026-08-06T22:44:04.014468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-06T13:09:50.675277Z","title":"Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.21205","last_updated":"2025-07-28T17:54:15Z","snapshot_observed_at":"2026-08-09T10:07:05.911460Z","submitted_at":"2025-07-28T17:54:15Z","title":"Learning from Limited and Imperfect Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:09:50.675277Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2507.21205"},"observation_digest":"sha256:46bbad91b8e9f5f470f0971c88163577d9df903fb45e84ff3e574ab28ff8e9e6","observation_id":"912151fd-b943-414e-baf4-04027bef0f54","resolution":{"observed_at":"2026-08-06T13:09:50.675277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-06T01:02:35.438611Z","title":"D.; Kurakin, A.; Sohn, K.; Zhang, H.; and Raffel, C","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.03997","last_updated":"2025-08-06T01:08:02Z","snapshot_observed_at":"2026-08-07T16:52:54.198706Z","submitted_at":"2025-08-06T01:08:02Z","title":"JanusNet: Hierarchical Slice-Block Shuffle and Displacement for Semi-Supervised 3D Multi-Organ Segmentation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T01:02:35.438611Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2508.03997"},"observation_digest":"sha256:00ba83ec9bed369d3226f5fcbc60fceb93bdbbb248fbbc799e06fc2a3416e99c","observation_id":"a78dee22-56bd-4691-a5b4-a9cc622f2fd9","resolution":{"observed_at":"2026-08-06T01:02:35.438611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":"1911.09785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-07-04T13:39:51.058233Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":"a5334d53-fbde-4811-8f85-88a0253295da","year":1911},"citing_paper":{"arxiv_id":"2605.05590","last_updated":"2026-05-07T02:17:17Z","snapshot_observed_at":"2026-07-06T23:18:12.631820Z","submitted_at":"2026-05-07T02:17:17Z","title":"Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T15:03:20.201082Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2605.05590"},"observation_digest":"sha256:89857ea82a055d2af791e779666b0833f0bf510ebce4d48a5ef96fb336781d5e","observation_id":"860b4be0-fa09-4b8e-8019-57a004b8d169","resolution":{"observed_at":"2026-05-11T18:36:08.211688Z","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":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":"1911.09785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-07-04T13:39:51.058233Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":"a5334d53-fbde-4811-8f85-88a0253295da","year":1911},"citing_paper":{"arxiv_id":"2605.08519","last_updated":"2026-05-08T22:03:31Z","snapshot_observed_at":"2026-08-03T01:40:58.624663Z","submitted_at":"2026-05-08T22:03:31Z","title":"SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data","version":1},"reference_index":111,"source":"arxiv_source","source_observed_at":"2026-05-12T01:31:00.029032Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2605.08519"},"observation_digest":"sha256:24d91d2370c57818b3163786e387346d617a2af9585b0256ceb497519ebbcd4e","observation_id":"4fb65ce1-f1c7-47e9-b172-60583eabdc2f","resolution":{"observed_at":"2026-05-12T07:56:27.511336Z","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":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":"1911.09785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-07-04T13:39:51.058233Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":"a5334d53-fbde-4811-8f85-88a0253295da","year":1911},"citing_paper":{"arxiv_id":"2605.15720","last_updated":"2026-08-01T06:57:42Z","snapshot_observed_at":"2026-08-06T23:11:18.100923Z","submitted_at":"2026-05-15T08:15:45Z","title":"Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-20T19:41:21.658482Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2605.15720"},"observation_digest":"sha256:93a471ba6aa89c657ac90f92c1f41086159d82e0fe8fd20ec315a8a6ca2e8a22","observation_id":"118d650f-234f-4c7f-9360-88fd652b67ec","resolution":{"observed_at":"2026-05-20T19:43:43.809667Z","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":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-04T05:09:57.747137Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2605.15720","last_updated":"2026-08-01T06:57:42Z","snapshot_observed_at":"2026-08-06T23:11:18.100923Z","submitted_at":"2026-05-15T08:15:45Z","title":"Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T05:09:57.747137Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2605.15720"},"observation_digest":"sha256:1b33460e04f939e8785cf494b72820c769ca8432c850a9fa2608df504859db94","observation_id":"08363bba-bc44-42f0-8c97-293b56b018c5","resolution":{"observed_at":"2026-08-04T05:09:57.747137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":"1911.09785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-07-04T13:39:51.058233Z","title":"arXiv preprint arXiv:1911.09785 (2019)","venue":null,"work_id":"a5334d53-fbde-4811-8f85-88a0253295da","year":1911},"citing_paper":{"arxiv_id":"2606.26973","last_updated":"2026-06-25T12:45:42Z","snapshot_observed_at":"2026-08-07T06:47:33.957579Z","submitted_at":"2026-06-25T12:45:42Z","title":"Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T05:00:28.839647Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2606.26973"},"observation_digest":"sha256:89a83b7b122a544bfcd7787aab0d36d3e901914f8620992f84757a978f9319c8","observation_id":"2b632bb9-7f25-44c8-b70a-b56ddf2c9098","resolution":{"observed_at":"2026-07-04T13:39:51.059881Z","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":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-02T05:58:41.245302Z","title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring.arXiv preprint arXiv:1911.09785, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2607.13192","last_updated":"2026-07-14T18:45:04Z","snapshot_observed_at":"2026-08-06T10:43:13.531895Z","submitted_at":"2026-07-14T18:45:04Z","title":"Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T05:58:41.245302Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2607.13192"},"observation_digest":"sha256:11a901d3057e82d15292cf252f6423b01776861ffd33b0975d6ca4e36547204b","observation_id":"75eb5ec4-e7f1-47d8-b1a7-62b7e2868c6b","resolution":{"observed_at":"2026-08-02T05:58:41.245302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1911.09785/citation-record","integrity":"/paper/1911.09785/integrity","json":"/paper/1911.09785/citation-record.json","paper":"/paper/1911.09785"},"outbound":[],"paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring"},"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 13 inbound Pith citation observations for arXiv:1911.09785."}