{"as_of":"2026-08-16T22:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b711a4f1f9739e1e418133456f213afd17fdeaca7df1ae4fe8106051ec400318","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:32:52.846731Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.07005/citation-record","integrity":"/paper/1908.07005/integrity","json":"/paper/1908.07005/citation-record.json","paper":"/paper/1908.07005"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:32:53.307846Z","title":"no free lunch","venue":null,"work_id":"7ea0f9a2-9f7d-4765-aea0-2ab20b93e73a","year":null},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.710877Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:85e1b590dc35f444514670e7d5f1082f4eb854cafe31f5fb967b935190c83c36","observation_id":"f98301ce-5698-41e4-b5c2-6287da7d0eba","resolution":{"observed_at":"2026-08-14T12:32:53.316800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.292257Z","title":"deep learning","venue":null,"work_id":"61ac14e2-392d-4096-b0d4-9b18999220b3","year":null},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.718401Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:35c934af10cf42fe7d4b2cb140cb58547e17c6964a63ea7d30e5db5e62483547","observation_id":"2248ef2e-bf1b-4771-bfc0-15ad8eee8aab","resolution":{"observed_at":"2026-08-14T12:32:53.297470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.273632Z","title":"Two main factors are involved here","venue":null,"work_id":"f1f849a4-9d86-420a-8557-7dd17e13599b","year":null},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.726133Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:6589c030839088b738da50a7d36d59b948c0c5206375dabdb6fa5d6b3c275791","observation_id":"361d2783-c496-44c9-940c-2a4619b2199b","resolution":{"observed_at":"2026-08-14T12:32:53.279876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.255466Z","title":"Let us formalize this randomness by representing it as a vector 𝑟 of random values , called noise vector","venue":null,"work_id":"824a50aa-9441-40ee-98de-06308b1f6120","year":null},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.731824Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:a1abf44e1ce6a86c721d1455e02ecb04ca958aea6f33d414124d915584116850","observation_id":"5d80bc53-b994-4623-ab69-a6f71c923d49","resolution":{"observed_at":"2026-08-14T12:32:53.262142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.230740Z","title":null,"venue":null,"work_id":"3572b9c1-3a21-4f10-9eb7-b73c7d3cfb17","year":null},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.739816Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:54ea8948cfa6a3a9f721f7c9fd4f5b1043736fb1ef476ced3eaff7b45017ae76","observation_id":"8d46b075-9197-4123-83ea-4c201da5e3b1","resolution":{"observed_at":"2026-08-14T12:32:53.236330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.211711Z","title":"Machine Learning Basics,","venue":null,"work_id":"1210596f-2d0d-4e9e-b387-9fea4eb2db0d","year":2016},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.745326Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:ff86317003a1bb2d495c765345e56f220f3b4bab0f7d8a78343e54956b943dc9","observation_id":"f3fa98be-fae6-465c-96f5-9a348224166a","resolution":{"observed_at":"2026-08-14T12:32:53.218097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.192659Z","title":"The Lack of A Priori Distinctions Between Learning Algorithms,","venue":null,"work_id":"391c8ba3-f7d1-4d5f-a633-c195a6cfd2ae","year":1996},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.751402Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:8c4cdbb86d22c59b67bcf9e8b4aef3d136f74a2c2e5cad348b96b29f6d0726df","observation_id":"e07cd2c8-b79f-4d4d-a505-3f6170bbe04c","resolution":{"observed_at":"2026-08-14T12:32:53.199198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.03530","last_updated":"2017-02-26T19:36:40Z","snapshot_observed_at":"2026-08-01T16:56:59.989486Z","submitted_at":"2016-11-10T22:02:36Z","title":"Understanding deep learning requires rethinking generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.03530","snapshot_observed_at":"2026-08-14T12:32:52.757994Z","title":"Understanding deep learning requires rethinking generalization,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.757994Z"},"links":{"cited_paper":"/paper/1611.03530","citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:fa702ee3711d18024afba3f7d9176cbaf5543677029bad672dcb26cc65b2b890","observation_id":"155732d5-c09f-4995-b4ef-54470bf74169","resolution":{"observed_at":"2026-08-14T12:32:52.757994Z","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":"2026-08-14T12:32:53.173188Z","title":"Feature selection, L 1 vs. L 2 regularization, and rotational invariance,","venue":null,"work_id":"d563baf0-f6b1-45a5-ac94-73736d5df933","year":2004},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.763816Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:7e9516737ce0138ecd7bcf32f1373c3b7c68c78d30dc69ec0b55671343eee4c6","observation_id":"cd5b442a-733c-48b8-b788-d03aae3492ca","resolution":{"observed_at":"2026-08-14T12:32:53.180087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.156104Z","title":"Dropout: a simple way to prevent neural networks from overfitting,","venue":null,"work_id":"6e5278f6-3835-4008-bcdd-a30b40ee550a","year":2014},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.769551Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:6effc2d15d1abd946031d86dcd10cc6cfdcc17b6dfb8cd5b381d9592fa51cc29","observation_id":"6ef2a8e4-180f-4974-bcb1-1a8b9620b77e","resolution":{"observed_at":"2026-08-14T12:32:53.161975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.137035Z","title":"Regularization of neural networks using dropconnect,","venue":null,"work_id":"0e1347c2-9fcc-450d-b8c1-fa5769b5510b","year":2013},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.776649Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:652d89046583d009e0e7dac37b12bd8929f0605a3c613ba74cadeb2a24ffb753","observation_id":"399c4228-2dec-414f-840e-042298a16f25","resolution":{"observed_at":"2026-08-14T12:32:53.142261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.117964Z","title":"Bagging Predictors,","venue":null,"work_id":"9edd3a30-61e3-4959-9bf7-6fc2aacbdc0b","year":1994},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.783293Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:262cacbf6fa7c9870b8247f2927801c327632f81b73f427c254fdbed61cf589f","observation_id":"3675a910-9c89-45ad-9ea5-d9fa1b83b6c0","resolution":{"observed_at":"2026-08-14T12:32:53.123066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.100408Z","title":"The Art of Data Augmentation,","venue":null,"work_id":"bd410a9a-aee0-4949-a734-d12f69ff7082","year":2001},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.789631Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:a04b5ad1ff70537cd974625a9907048e91e4e327c847cc7c0458ec2e32b0138f","observation_id":"7c947df4-f33a-4347-807f-d009fc424e72","resolution":{"observed_at":"2026-08-14T12:32:53.106019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.082252Z","title":"Convolutional deep belief networks for scalable unsupervised learning of 6 hierarchical representations,","venue":null,"work_id":"e63e6280-6f28-49de-8f6d-1f726ae4b902","year":2009},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.795331Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:999837631650245f13653e367bb1dfdf94b7d6841d6054897f906e63f32894fd","observation_id":"86c17f19-5e86-47a6-a15e-b0ff01b3871b","resolution":{"observed_at":"2026-08-14T12:32:53.088114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.058519Z","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,","venue":null,"work_id":"b43d40d2-c5e1-40b6-822f-a4e1e5403a38","year":2010},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.800799Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:6ed47ed71173ec4f85dcc9f1e7bf20b34402452c5d7e972f3c4fcd5e3edd768f","observation_id":"14e3a590-f757-470b-8bf5-82980523bb83","resolution":{"observed_at":"2026-08-14T12:32:53.066106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.039501Z","title":"Unsupervised feature learning for audio classification using convolutional deep belief networks,","venue":null,"work_id":"26099f4f-0162-45f4-967b-1eb3fbfd10bc","year":2009},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.807585Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:bb6feb9efaff40e1bce1691a474071604a2ec34b3d4773372129875174a9bb47","observation_id":"1fe34a9b-0074-4c08-a0ee-c3a1c6923e7b","resolution":{"observed_at":"2026-08-14T12:32:53.044640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:53.020108Z","title":"Training with Noise is Equivalent to Tikhonov Regularization,","venue":null,"work_id":"ce7cee16-af0f-4a5d-8690-a286edb2ede0","year":1995},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.814321Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:a2e53c18a2bfbd45ed3771016f53694394e11b15f764854dcd988c9de5c09243","observation_id":"85360c9d-7e91-440f-b3d7-f8641e2a7de4","resolution":{"observed_at":"2026-08-14T12:32:53.026988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02629","last_updated":"2020-06-10T18:18:17Z","snapshot_observed_at":"2026-08-15T04:00:53.429768Z","submitted_at":"2019-06-06T15:03:11Z","title":"When Does Label Smoothing Help?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02629","snapshot_observed_at":"2026-08-14T12:32:52.820743Z","title":"When Does Label Smoothing Help?,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.820743Z"},"links":{"cited_paper":"/paper/1906.02629","citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:120536c0072795c386a042b9a24e5293e6ec11702a90edb2f2ec374a869f2e8c","observation_id":"76eee14f-259d-4501-97e9-bce27a71d6a9","resolution":{"observed_at":"2026-08-14T12:32:52.820743Z","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":"2026-08-14T12:32:53.000768Z","title":"Stenosis Detection with Deep Convolutional Neural Networks,","venue":null,"work_id":"06785b31-a584-4f58-a66e-7eedf6d238c6","year":2018},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.827521Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:4a20768bd53d0eea03abecb3669a95be27648de446e4767df0c242e581fc03aa","observation_id":"e185a014-d64b-44fd-b891-46df57558159","resolution":{"observed_at":"2026-08-14T12:32:53.007383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.11551","last_updated":"2019-01-17T08:39:06Z","snapshot_observed_at":"2026-08-14T18:46:12.927802Z","submitted_at":"2018-07-30T20:00:03Z","title":"Deep Recurrent Neural Networks for ECG Signal Denoising","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.11551","snapshot_observed_at":"2026-08-14T12:32:52.834738Z","title":"Deep Recurrent Neural Networks for ECG Signal Denoising,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.834738Z"},"links":{"cited_paper":"/paper/1807.11551","citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:65579a4f3e1d6ffb77dd1d480640c2a11b66143a52b91a63e2ea632de7f55051","observation_id":"8ace979b-806c-4257-baa6-3163d76ed164","resolution":{"observed_at":"2026-08-14T12:32:52.834738Z","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":"2026-08-14T12:32:52.979545Z","title":"Rademacher and Gaussian Co mplexities: Risk Bounds and Structural Results,","venue":null,"work_id":"904235c0-2b86-4dca-aab7-1565a780cd64","year":2002},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.841688Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:fada65a878834c8ff0de4a874071e3e28f250cffe0fcb58f758a84dd04a3dfb1","observation_id":"c835f20b-478e-44c5-816f-1eb35363e103","resolution":{"observed_at":"2026-08-14T12:32:52.986333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:32:52.958000Z","title":"On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities,","venue":null,"work_id":"f4ac1c94-67da-402d-a6e9-4c46fcc73d7a","year":1971},"citing_paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:32:52.846731Z"},"links":{"citing_paper":"/paper/1908.07005"},"observation_digest":"sha256:98f5200cfaf41ca90cf0a59af3ad792bbf4afbbc3217b3b0897c268456723b2d","observation_id":"91cfbf74-c4a0-40a2-a7e5-b5478740c090","resolution":{"observed_at":"2026-08-14T12:32:52.965502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.07005","last_updated":"2019-08-19T18:09:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T16:41:34.123919Z","submitted_at":"2019-08-19T18:09:07Z","title":"On Regularization Properties of Artificial Datasets for Deep Learning"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":22},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.07005."}