{"as_of":"2026-08-19T20:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:def70ef12d8c6109112f9cd3e4aa8efe2570f57eab5ba046dd972f9012a2a714","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T10:58:29.041992Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/1908.10059/citation-record","integrity":"/paper/1908.10059/integrity","json":"/paper/1908.10059/citation-record.json","paper":"/paper/1908.10059"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:30.802641Z","title":"Is object localization for free? - weakly-supervised learning with convolutional neural networks,","venue":null,"work_id":"c81c2ab0-0d5d-4885-ba0e-f2e3c08536f9","year":2015},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.006369Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:43db0d830299c9e3066f0a97f723b35140ed3c089bb895b4179122f3d5a10c96","observation_id":"f98a3161-940c-437c-a71e-3b33233182b3","resolution":{"observed_at":"2026-08-14T10:58:30.807491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.698457Z","title":"WILDCAT: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation,","venue":null,"work_id":"0a4da90e-448d-4392-a59f-ed7578244086","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.050408Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:992eb4e0fec5f94138a775b37fe3c08196da2e4af28a375bf2068511f7125bf7","observation_id":"fea526a3-b9fa-4bf7-bbbf-7f28b7e3c9a5","resolution":{"observed_at":"2026-08-14T10:58:30.777907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.618481Z","title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning,","venue":null,"work_id":"6223b36e-5b83-4f45-98cf-2c7c37d038cf","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.182150Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:46b85aabd5bd0fbc1ed742a3482da25522f97869295b0683bda4c412d071ff40","observation_id":"3fc2e577-d981-48b5-b984-a16e64bc4fd1","resolution":{"observed_at":"2026-08-14T10:58:30.644119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.602112Z","title":"Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,","venue":null,"work_id":"2668abb3-52ba-475e-8d27-29df9df265f1","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.222232Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:72a2b92d3c5522df5e25424e2b8bf102f2d99fdb88fb79030780c70674c49e35","observation_id":"6603088f-a410-4772-b68a-470ced2b0215","resolution":{"observed_at":"2026-08-14T10:58:30.608046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-14T10:58:28.227176Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.227176Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:9d888fb9c519cd22f9694f556502e86b54f91b5ee40b151bb127ee0e4c4c42f5","observation_id":"f758659b-5439-412a-8645-058e923a69f3","resolution":{"observed_at":"2026-08-14T10:58:28.227176Z","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":"2026-08-14T10:58:30.586399Z","title":"Shake-shake regularization of 3-branch residual networks,","venue":null,"work_id":"84ec028d-5d81-4e37-87d5-07fbd1dfd83f","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.232203Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:1b31200570f01a2830fc2207410b2d814c398fc20ee9f86b32ee7fc74d82ac2b","observation_id":"f060f4a7-7606-4927-80ee-fb6814385431","resolution":{"observed_at":"2026-08-14T10:58:30.591736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.570834Z","title":"Dropout: a simple way to prevent neural networks from overfitting,","venue":null,"work_id":"54a2f4e6-73fb-4c86-a569-b8023c57474b","year":1929},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.237974Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:c7599c5aca937a332947f5a3bdf0dcfc06190a4a3aefa3fba0d552ee23aa07df","observation_id":"0b421099-8faa-454d-b16f-3f38ac027a70","resolution":{"observed_at":"2026-08-14T10:58:30.576080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.556485Z","title":"Imagenet classification with deep convolutional neural networks,","venue":null,"work_id":"e0b0a0d9-a91f-4a7a-9979-df8cc09408bd","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.242109Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:ffdd80c27c1ba431a0e76f363a14c357c15ee2e714c06206a1058d1b1d4c2fbc","observation_id":"8569c9ca-68e7-4651-b8cb-c320c7730e55","resolution":{"observed_at":"2026-08-14T10:58:30.560607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-14T10:58:28.246067Z","title":"mixup: Beyond empirical risk minimization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.246067Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:a9294690a060a1197a2587fc77ef92ea08e320d282779ebd8d75ff4ac1ac5ca9","observation_id":"437da6f2-0120-444b-a5db-875453192a86","resolution":{"observed_at":"2026-08-14T10:58:28.246067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.02499","last_updated":"2018-11-22T19:37:01Z","snapshot_observed_at":"2026-08-19T13:03:05.296572Z","submitted_at":"2018-09-07T14:26:17Z","title":"MixUp as Locally Linear Out-Of-Manifold Regularization","version":3},"cited_work":{"arxiv_id":"1809.02499","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.02499","snapshot_observed_at":"2026-08-14T10:58:29.149049Z","title":"MixUp as Locally Linear Out-Of-Manifold Regularization","venue":"cs.LG","work_id":"fec1d5ea-ea8a-42cc-b0f4-ea81b2665bd6","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.250291Z"},"links":{"cited_paper":"/paper/1809.02499","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:05a2a21cff715b3963b93b8a276c03a8c12dc1be9e37f6f7b16165853e0db7fc","observation_id":"a5e1bd9e-91b8-432e-a5d5-c711e6c49feb","resolution":{"observed_at":"2026-08-14T10:58:29.155546Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.440012Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks,","venue":null,"work_id":"edd190ac-d5de-4a6b-8856-0f63bb2695b6","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.348715Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:caf9224b88a85c337f40cc8f7db45bf6d3adcf49ba662a863369d3d661ab3c4f","observation_id":"5ce1439c-2f0f-4350-8c12-b106a9c9ac0b","resolution":{"observed_at":"2026-08-14T10:58:30.533417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.353988Z","title":"Practical bayesian optimiza- tion of machine learning algorithms,","venue":null,"work_id":"b7797bd1-97d9-4983-a68f-e96c530a90b0","year":2012},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.486376Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:db7cf5a1caf4558e175ffb75b3738b3ff3d425e1b6433d04507a0784d25c6267","observation_id":"5b0ea480-18da-4f9b-b958-2baa34d479c8","resolution":{"observed_at":"2026-08-14T10:58:30.372945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.00222","last_updated":"2019-02-26T05:26:00Z","snapshot_observed_at":"2026-08-14T19:30:24.800486Z","submitted_at":"2018-03-31T22:44:28Z","title":"Meta-Learning Update Rules for Unsupervised Representation Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.00222","snapshot_observed_at":"2026-08-14T10:58:28.491115Z","title":"Learning unsupervised learning rules,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.491115Z"},"links":{"cited_paper":"/paper/1804.00222","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:eedab0cd1f6a82437c804c958585b5e97afea2f08d040bff09d6ed11836c90ac","observation_id":"2ea0d9ff-f06f-4c16-a595-9fb722d3c0ca","resolution":{"observed_at":"2026-08-14T10:58:28.491115Z","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":"2026-08-14T10:58:30.338857Z","title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,","venue":null,"work_id":"1696324c-0e21-491d-b1e4-a689f84d1bbc","year":2013},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.496304Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:f54cb18df7810d2b267619dc1b0520033215993685c7329e5b6c5c28121b701a","observation_id":"9198931c-0b06-4fef-a419-f6958590dda1","resolution":{"observed_at":"2026-08-14T10:58:30.344069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02249","last_updated":"2019-10-23T18:47:34Z","snapshot_observed_at":"2026-08-15T05:14:44.745295Z","submitted_at":"2019-05-06T19:56:03Z","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02249","snapshot_observed_at":"2026-08-14T10:58:28.500406Z","title":"Mixmatch: A holistic approach to semi-supervised learning,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.500406Z"},"links":{"cited_paper":"/paper/1905.02249","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:28c9c20ee6f75247551c3de78e74aba4504fcd7fa9f6e48d6f14588352f089e2","observation_id":"9479f757-37a3-47cd-b196-2becb487f8f0","resolution":{"observed_at":"2026-08-14T10:58:28.500406Z","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":"2026-08-14T10:58:30.323652Z","title":"Keeping the neural networks simple by minimizing the description length of the weights,","venue":null,"work_id":"eac09505-50a4-4c81-a05e-601c1ef5cf9b","year":1993},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.505621Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:6aa1b2631ad7419325448277a3be3f433d6cd947093fa49a2b3e2971f9bc7319","observation_id":"7132935a-3381-4c6c-b823-47ab50724eb7","resolution":{"observed_at":"2026-08-14T10:58:30.328738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:28.510676Z","title":"Adam: A method for stochastic optimiza- tion,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.510676Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:eaf8cc2befb5de30c3af0b253d0ceda5b6212248c17343d72fd0281203323227","observation_id":"5dd3e196-dcf4-44b2-98ef-32a316047903","resolution":{"observed_at":"2026-08-14T10:58:28.510676Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:28.515833Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.515833Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:e18139b2ac3155d5bec8faaeb898eafdb4765913f3a739fd93fd5b32c6cbe9fb","observation_id":"45f01717-9b41-4119-a6a8-c19748769c20","resolution":{"observed_at":"2026-08-14T10:58:28.515833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.09501","last_updated":"2019-04-11T22:39:27Z","snapshot_observed_at":"2026-08-18T21:01:51.069583Z","submitted_at":"2018-05-24T04:05:42Z","title":"AutoAugment: Learning Augmentation Policies from Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.09501","snapshot_observed_at":"2026-08-14T10:58:28.600384Z","title":"Au- toaugment: Learning augmentation policies from data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.600384Z"},"links":{"cited_paper":"/paper/1805.09501","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:3c66236340dd9bd91a65b9c87d8521ef9513ff1152a47efeb48230176e56da14","observation_id":"9c9d2cd9-5a7e-43f3-88fc-a57b7fbd24dc","resolution":{"observed_at":"2026-08-14T10:58:28.600384Z","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":"2026-08-14T10:58:30.288581Z","title":"Between-class learning for image classification,","venue":null,"work_id":"346fd6a5-7581-4dac-9a24-5f5bb08e5d9c","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.624843Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:3a12cd8a5ce6480f25d90eab2baf2c3f8cf0ba2f1759fd0661005386b18c1354","observation_id":"c0f0cf8c-fc04-48e0-b5ed-97091e4e9383","resolution":{"observed_at":"2026-08-14T10:58:30.293924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.05236","last_updated":"2019-05-11T16:50:55Z","snapshot_observed_at":"2026-08-14T20:44:42.980618Z","submitted_at":"2018-06-13T19:32:59Z","title":"Manifold Mixup: Better Representations by Interpolating Hidden States","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.05236","snapshot_observed_at":"2026-08-14T10:58:28.685058Z","title":"Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.685058Z"},"links":{"cited_paper":"/paper/1806.05236","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:b7ccbe5dd569d26fcfe67a62aa8dbdd84f35421ff88c7b257805758e41f7c2c8","observation_id":"965a8d14-5239-4b97-abc5-18c67fe82eb3","resolution":{"observed_at":"2026-08-14T10:58:28.685058Z","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":"2026-08-14T10:58:30.272793Z","title":"Learning to learn: Introduction and overview,","venue":null,"work_id":"d53bb77a-9793-4a67-b46b-557783cffaca","year":1998},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.690284Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:d8cda12733b310f00a6f96dade722acd2c0401935260b91b7129b577c481969a","observation_id":"b4b974e7-b022-46a0-85d8-24498057b466","resolution":{"observed_at":"2026-08-14T10:58:30.278108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.257981Z","title":"Bengio, S","venue":null,"work_id":"521481c2-0b31-4dbd-b267-533733dad395","year":1990},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.695691Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:37d601b0e081d929d159179106520b3e4d811fbc89793d5e4473d54e7dbe66ad","observation_id":"750c5a91-026e-4929-b61c-a746f7b7b66d","resolution":{"observed_at":"2026-08-14T10:58:30.262465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:28.700209Z","title":"Optimization as a model for few-shot learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.700209Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:7d8f5b34120fa067586630ebdb8db83727cdfe60d99b08da650e6965840f1cb2","observation_id":"c7f4c976-ffd9-4315-b6a1-f5b557679670","resolution":{"observed_at":"2026-08-14T10:58:28.700209Z","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":"2026-08-14T10:58:30.136730Z","title":"Prototypical networks for few- shot learning,","venue":null,"work_id":"218416b7-ec32-4d67-8ffe-ec8623c24b9c","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.705413Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:82f586675692da016a35c1dd6b23473b749fd17324acc933200a07838ad0749f","observation_id":"939dd341-8770-4b44-8d96-9d0105c10f4f","resolution":{"observed_at":"2026-08-14T10:58:30.220008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.017709Z","title":"A simple neural attentive meta-learner,","venue":null,"work_id":"f97445fa-08eb-4200-9623-840a4445307c","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.709980Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:1dedc0d904955f0ef79c6f8b3a6b22ee015f33a83a7369f3ae8a725e0d748114","observation_id":"ece9e0df-813c-48f6-84a8-5b6565ab0d4d","resolution":{"observed_at":"2026-08-14T10:58:30.080006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:30.001311Z","title":"Rapid adapta- tion with conditionally shifted neurons,","venue":null,"work_id":"d50cdf82-fc58-4d58-bba5-e060870be871","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.713518Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:52afa8cf1a2b29bcd870097bee9b2983068efb5b5a1d0748d6031fc7833ca7b0","observation_id":"5532fd0a-9533-40ae-912e-9d42dad893c7","resolution":{"observed_at":"2026-08-14T10:58:30.008034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.982016Z","title":"Approximate least trimmed sum of squares fitting and applications in image analysis,","venue":null,"work_id":"226d8663-a388-409c-ac86-cf06374178fe","year":2013},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.717614Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:3eea6a4eb6fec62983b0ac7ae4e6896974e5e5f3bc87231fb2f12ec0aac848c3","observation_id":"ff5dc695-6b17-411c-ae58-8267844ca3ee","resolution":{"observed_at":"2026-08-14T10:58:29.988069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.969180Z","title":"Deep asymmetric pairwise hashing,","venue":null,"work_id":"99f89616-ef49-41f0-899a-3d26e43e70e0","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.721177Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:16916ca62e55ceb87d157517c13c7374d7f4b50fb4f7f92337277d53cc05716f","observation_id":"418615d3-319f-41e0-9758-eee49c82f1ec","resolution":{"observed_at":"2026-08-14T10:58:29.973553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.956452Z","title":"Supervised discrete hashing,","venue":null,"work_id":"e1703d88-ce27-408d-a98a-1b05b69b1c06","year":2015},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.725615Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:ca81f2a9ff5a21748e562b54b52e9b1895432a289a818ffb5d9e72566c05318e","observation_id":"443497f1-247f-4feb-b4d9-750aec9b1e2e","resolution":{"observed_at":"2026-08-14T10:58:29.960413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.942129Z","title":"Learning to reweight examples for robust deep learning,","venue":null,"work_id":"bb3f64cb-e39a-4345-84b9-55003698bf54","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.729350Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:57604d8be678656d4c6d8696d07f3fd1f04e6fbb1a543e8e089d90c208ba7045","observation_id":"f3691a7d-2778-4bea-8c71-1d1441a30ca1","resolution":{"observed_at":"2026-08-14T10:58:29.946962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.928870Z","title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vi- sion architectures,","venue":null,"work_id":"8147b73b-7ef5-466f-b15c-d4275e6d5671","year":2013},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.733040Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:ab965bcbc4e62692ccf6adb8a1be4b8d6966e0538cf082f3c3b2fcc2ba879bba","observation_id":"88c3d180-e4d4-4bb7-88f4-f2ff107328cc","resolution":{"observed_at":"2026-08-14T10:58:29.932804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.915136Z","title":"Auto-weka: combined selection and hyperparameter optimization of classification algorithms,","venue":null,"work_id":"2ddbc9d4-5d79-4566-951f-704de15dc4fa","year":2013},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.736957Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:a49404b0a78c0728225c3daf96d6803bb679f84ec0082364e90a26207e5bffa0","observation_id":"13782d1d-ad06-4904-abe2-9162c337f4e0","resolution":{"observed_at":"2026-08-14T10:58:29.919759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.901386Z","title":"Practical bayesian optimiza- tion of machine learning algorithms,","venue":null,"work_id":"dca90651-7220-4a28-8d34-ab9d2a17b8c3","year":2012},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.741062Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:d24571cbd6a30bdc5fae1c83c74c49ff38acdfca138afc742b725fb38b5371ae","observation_id":"6ab55e35-f244-4d72-a734-c553c7b22bef","resolution":{"observed_at":"2026-08-14T10:58:29.905980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.714151Z","title":"Hyperparameter optimization with approximate gradient,","venue":null,"work_id":"d2afa5bb-8ed7-4d23-aecf-83698fda33b4","year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.744860Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:3762c5d3b5e0cd5b1275232b9c54dee8644ff1c85350016a68149d727b83cdbd","observation_id":"91f9a4e8-d0f9-4587-be10-36e7209a346c","resolution":{"observed_at":"2026-08-14T10:58:29.803598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02242","last_updated":"2017-03-15T14:22:41Z","snapshot_observed_at":"2026-08-14T21:36:15.005969Z","submitted_at":"2016-10-07T12:15:42Z","title":"Temporal Ensembling for Semi-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02242","snapshot_observed_at":"2026-08-14T10:58:28.749033Z","title":"Temporal ensembling for semi-supervised learn- ing,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.749033Z"},"links":{"cited_paper":"/paper/1610.02242","citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:8f3d2fd95c70a5e946976c2a977abc351293221256e017820d3ed22be4f00c93","observation_id":"52df3d9a-631b-481c-b3dc-b8db1a561579","resolution":{"observed_at":"2026-08-14T10:58:28.749033Z","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":"2026-08-14T10:58:29.642999Z","title":"Learning classification with unlabeled data,","venue":null,"work_id":"4f7dbace-f0c7-46e5-a91f-47b1eedc1c40","year":1994},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.753329Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:d654b214c1f92cfbb420d473307d58b0f1f699a2cc8ad6c5366789520d189def","observation_id":"9f90092c-5572-4c72-b875-0075d7436dca","resolution":{"observed_at":"2026-08-14T10:58:29.647856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.629817Z","title":"Asymmetric tri-training for unsu- pervised domain adaptation,","venue":null,"work_id":"352b6c31-02a8-4ed3-bec4-40a263b10f14","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.814752Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:79cb9b09a6db5849c5e29349692a642176a877e960343b3c0dd8666ae704f532","observation_id":"7bd3fd64-967a-4a5f-962f-e68be430e25c","resolution":{"observed_at":"2026-08-14T10:58:29.634143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.615998Z","title":"Learning semantic rep- resentations for unsupervised domain adaptation,","venue":null,"work_id":"711a2412-e3e6-4b7c-bed5-06bb340e4200","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.890224Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:8899512f92c9a85cbb53989d7a80551c46c2107b1a47a8331134a8634b7217a7","observation_id":"39b5c0d1-c10c-49aa-af01-50c35ceb62aa","resolution":{"observed_at":"2026-08-14T10:58:29.620154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.600748Z","title":"Parseval networks: Improving robustness to adversarial examples,","venue":null,"work_id":"6ab4446f-ac0d-474d-9a11-a895051c3634","year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:28.996077Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:7091077211ecbc07a510359d6d794fa2092ce839d08c779d7b4f4cedd3f6ed9a","observation_id":"2fb91b3a-d0ab-406c-8afe-768b3c14dba7","resolution":{"observed_at":"2026-08-14T10:58:29.605921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.585700Z","title":"Lipschitz-margin training: Scal- able certification of perturbation invariance for deep neural networks,","venue":null,"work_id":"4685c851-2d10-4bd3-b126-6fc0b07e0576","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.000081Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:686676fc0e672900d2307f136806212eced9e235e963b7563aea509ad6cd76d8","observation_id":"c1122c88-8349-4059-ba27-ae2330c4801e","resolution":{"observed_at":"2026-08-14T10:58:29.590195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.570924Z","title":"Progressive neural architecture search,","venue":null,"work_id":"709decd7-d36a-4aea-95f6-ba02240193a8","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.003975Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:93d3d42792c2a31ce00a489be17b969715df81fb75ec7171c23069c724ba7203","observation_id":"fccd8b9c-46ff-44df-9d20-c0ee31c5e976","resolution":{"observed_at":"2026-08-14T10:58:29.575450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.555444Z","title":"An overview of bilevel opti- mization,","venue":null,"work_id":"ccc23015-1229-408b-b4dc-69f8779f1eef","year":2007},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.008010Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:11c3a0bd57e99c8d5ca86be928a21f18d3a85ad80ed21cdfd26cd5a5498ff6c3","observation_id":"22b6038b-8faa-48a8-bc1f-83bbf40ce6d7","resolution":{"observed_at":"2026-08-14T10:58:29.560574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.540259Z","title":"Scalable gradient- based tuning of continuous regularization hyperparameters,","venue":null,"work_id":"0bd1453a-a624-4cd9-af03-0c1884c02afa","year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.012659Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:5c36ec02f82974cc164f504fb265dc8bb65beed115ea15774fdd3377389a8742","observation_id":"44de3394-75b4-4ae7-8b3b-2da66c2ad57c","resolution":{"observed_at":"2026-08-14T10:58:29.545027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.522907Z","title":"Realistic evaluation of deep semi-supervised learning algorithms,","venue":null,"work_id":"56592750-d638-4dc2-af95-856631d4bf2f","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.016307Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:91a9798762254878fcd7e81ef147be1c9228d7d54d67a039576cdae42ef5d854","observation_id":"ad01e51a-0579-4051-9777-46a0be8c08ae","resolution":{"observed_at":"2026-08-14T10:58:29.530244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:29.020454Z","title":"Reading digits in natural images with unsupervised feature learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.020454Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:b64f9523b38038437fcbd52ad654b35610eefd68cdd30376a0500f5c0f609ddf","observation_id":"ba9ceed7-00b2-461d-8fee-682681db95e4","resolution":{"observed_at":"2026-08-14T10:58:29.020454Z","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":"2026-08-14T10:58:29.408619Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"963eaefa-b9d2-4c45-b7fe-4cec244b0f52","year":2015},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.024854Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:6833cd0304039c4cf0189d44b6eeec782d6e605905c87bfbe84a0ce9870932ba","observation_id":"816b7479-7971-446d-921a-c1e2efc2a773","resolution":{"observed_at":"2026-08-14T10:58:29.460927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.278068Z","title":"Identity mappings in deep residual networks,","venue":null,"work_id":"9177049b-8428-402c-ad80-b6727acb7383","year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.029544Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:cb02cf7af3f33851ffb5bbd10bbccacc6e3d3999eb348787c6243b3144a4dde3","observation_id":"203f37f4-52e9-46a5-9113-dd5f7553c91e","resolution":{"observed_at":"2026-08-14T10:58:29.339258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:58:29.034041Z","title":"SGDR: stochastic gradient descent with warm restarts,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.034041Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:651269cb76f207f6c4143f61683a6d17440d4f226d94eb02782c4419822578bd","observation_id":"c8ab7880-e703-426d-9948-bc7ee0922502","resolution":{"observed_at":"2026-08-14T10:58:29.034041Z","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":"2026-08-14T10:58:29.211398Z","title":"Wide residual networks,","venue":null,"work_id":"5c03b3ab-59d9-4837-b5bf-a060bd1309b2","year":2016},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.038112Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:4fad853cda2807593c68dc503c0261bd75c0db3b9c5f94fe34fd7f5eaedf15a3","observation_id":"a083a984-4025-4e42-845d-220026366857","resolution":{"observed_at":"2026-08-14T10:58:29.216043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:58:29.195043Z","title":"Self-ensembling for visual domain adaptation,","venue":null,"work_id":"b0c275ee-4ef5-4a0d-995f-f441a6c853c4","year":2018},"citing_paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T10:58:29.041992Z"},"links":{"citing_paper":"/paper/1908.10059"},"observation_digest":"sha256:74ab47eee201554ec2c814b29d31e14c2fbd8d08cc28b5bc089598fc709c223f","observation_id":"af6f0e90-337b-45de-b55c-b92d41333d45","resolution":{"observed_at":"2026-08-14T10:58:29.200343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.10059","last_updated":"2019-08-27T07:26:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T10:05:59.435859Z","submitted_at":"2019-08-27T07:26:35Z","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":38},"total_outbound_references":51},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.10059."}