{"as_of":"2026-08-20T07:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2e0c9e9fce32097fdfb22186de880b49d763b117a44e573879c13de925af463e","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:46:22.105774Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/1908.04471/citation-record","integrity":"/paper/1908.04471/integrity","json":"/paper/1908.04471/citation-record.json","paper":"/paper/1908.04471"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1511.06530","last_updated":"2016-02-24T11:52:12Z","snapshot_observed_at":"2026-08-14T22:22:04.565322Z","submitted_at":"2015-11-20T09:20:08Z","title":"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06530","snapshot_observed_at":"2026-08-14T13:46:22.045277Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.045277Z"},"links":{"cited_paper":"/paper/1511.06530","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:1bc313e9eca0bee293bab5060fc335c203718fa621cff6a68329ef857c5217ac","observation_id":"69145c3a-4f38-4bc6-a771-6ea50eac3128","resolution":{"observed_at":"2026-08-14T13:46:22.045277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.02422","last_updated":"2021-10-18T09:43:53Z","snapshot_observed_at":"2026-08-17T09:03:20.265385Z","submitted_at":"2019-04-04T09:19:19Z","title":"Resource Efficient 3D Convolutional Neural Networks","version":5},"cited_work":{"arxiv_id":"1904.02422","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.02422","snapshot_observed_at":"2026-08-14T13:46:22.276867Z","title":"Resource Efficient 3D Convolutional Neural Networks","venue":"cs.CV","work_id":"1f5bb36f-0139-4595-8913-b3b0e6f626c3","year":2019},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.050789Z"},"links":{"cited_paper":"/paper/1904.02422","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:7b3657a260e76804c22cd3ee81faab0b0fae42f6a6711d3738a3a2079b1b7525","observation_id":"42829325-5383-4706-b4e0-d83184bf5f4d","resolution":{"observed_at":"2026-08-14T13:46:22.284181Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.0767","last_updated":"2015-10-07T01:29:12Z","snapshot_observed_at":"2026-08-14T23:09:23.492133Z","submitted_at":"2014-12-02T03:05:54Z","title":"Learning Spatiotemporal Features with 3D Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.0767","snapshot_observed_at":"2026-08-14T13:46:22.089296Z","title":"learning spatiotemporal features with 3d convolutional networks,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.089296Z"},"links":{"cited_paper":"/paper/1412.0767","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:2841c7be1d61b552053ce33b70336bc51df81b02bc69e3a96f4944a3ba71fc4d","observation_id":"ae573050-aceb-47ef-a228-8b03bc72ce7a","resolution":{"observed_at":"2026-08-14T13:46:22.089296Z","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":"2026-08-14T13:46:22.371172Z","title":null,"venue":null,"work_id":"617c17d3-4f62-45a9-a4c6-271efd0eae1d","year":2011},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.094863Z"},"links":{"citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:c6e6a5d4b7d4e33466e9192084204890bedae18bd85ea62ccc6647e48e938859","observation_id":"8f055bc8-0586-491b-9f03-edbef328dcab","resolution":{"observed_at":"2026-08-14T13:46:22.376037Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.02662","last_updated":"2019-02-09T23:31:47Z","snapshot_observed_at":"2026-08-20T00:23:39.601443Z","submitted_at":"2018-01-08T19:55:19Z","title":"Tensor network ranks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.02662","snapshot_observed_at":"2026-08-14T13:46:22.100230Z","title":"Ye and L.-H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.100230Z"},"links":{"cited_paper":"/paper/1801.02662","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:7bb76912ec309016b1ac8b30950e055e749e32cd8616116e9e680a1e2b54f59e","observation_id":"66d4ef87-fd63-4e96-a526-1edbd483a50c","resolution":{"observed_at":"2026-08-14T13:46:22.100230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01578","last_updated":"2017-02-15T05:28:05Z","snapshot_observed_at":"2026-08-14T21:31:52.294004Z","submitted_at":"2016-11-05T00:41:37Z","title":"Neural Architecture Search with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01578","snapshot_observed_at":"2026-08-14T13:46:22.105774Z","title":"Zoph and Q","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.105774Z"},"links":{"cited_paper":"/paper/1611.01578","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:f1b2e0f68230b29578a178556f3d41b85a579ae266aec045f106281c666dce7e","observation_id":"28e591bb-80fd-43f9-9037-34388934859e","resolution":{"observed_at":"2026-08-14T13:46:22.105774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-14T13:46:22.038871Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":1927,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.038871Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:07210be6c30424f3930efea4cac6dc9d42b18b34997d86a1eace5a7c8a8e4247","observation_id":"0507197b-ca81-4610-887e-0a8ca9150630","resolution":{"observed_at":"2026-08-14T13:46:22.038871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03268","last_updated":"2018-02-12T03:34:00Z","snapshot_observed_at":"2026-08-17T09:00:10.263175Z","submitted_at":"2018-02-09T14:14:37Z","title":"Efficient Neural Architecture Search via Parameter Sharing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03268","snapshot_observed_at":"2026-08-14T13:46:22.067721Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":1971,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.067721Z"},"links":{"cited_paper":"/paper/1802.03268","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:700500b1ce1981548b8f6c7c497e4ef132bf53a61ccaba9e2ac62376cae80ed8","observation_id":"b482b8a0-2b4f-49bf-9b56-1f02d79876e8","resolution":{"observed_at":"2026-08-14T13:46:22.067721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.4400","last_updated":"2014-03-04T05:15:42Z","snapshot_observed_at":"2026-08-14T23:51:30.531758Z","submitted_at":"2013-12-16T15:34:13Z","title":"Network In Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.4400","snapshot_observed_at":"2026-08-14T13:46:22.061773Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.061773Z"},"links":{"cited_paper":"/paper/1312.4400","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:6d5bcb1f95368f0e3837d01b948013601288f5fcb1f4dababb99bdc17d4ffd0c","observation_id":"f56c177a-2094-46a7-9304-c73a48612423","resolution":{"observed_at":"2026-08-14T13:46:22.061773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0706.1234","last_updated":"2007-06-08T19:53:37Z","snapshot_observed_at":"2026-08-15T10:39:09.994667Z","submitted_at":"2007-06-08T19:53:37Z","title":"Convergence of iterated Aluthge transform sequence for diagonalizable matrices II: $\\lambda$-Aluthge transform","version":1},"cited_work":{"arxiv_id":"0706.1234","doi":null,"metadata_source":"pith","pith_arxiv_id":"0706.1234","snapshot_observed_at":"2026-08-14T13:46:22.335639Z","title":"Convergence of iterated Aluthge transform sequence for diagonalizable matrices II: $\\lambda$-Aluthge transform","venue":"math.FA","work_id":"4802cf83-6e01-4304-8a0f-895ab6b1c1c3","year":2007},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.032836Z"},"links":{"cited_paper":"/paper/0706.1234","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:5fbf5a466331607ef79f8503a5efa860744b31ac3b72dc3b76a01734511f4482","observation_id":"c8d500b8-b8c9-4241-9b08-b925a4af442e","resolution":{"observed_at":"2026-08-14T13:46:22.341079Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T13:46:22.387611Z","title":"Tokui, R","venue":null,"work_id":"11437e6d-b2b0-499d-93cd-295c7ec994f9","year":2002},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.084054Z"},"links":{"citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:8e4c41c867525ecd694acab89b6652f0ccc25dbf0a867752dfc959a917bdd627","observation_id":"f6d55a51-5f8a-4463-93e2-40659829fc40","resolution":{"observed_at":"2026-08-14T13:46:22.392471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06067","last_updated":"2016-02-14T03:46:09Z","snapshot_observed_at":"2026-08-14T22:22:16.991334Z","submitted_at":"2015-11-19T06:13:55Z","title":"Convolutional neural networks with low-rank regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06067","snapshot_observed_at":"2026-08-14T13:46:22.078212Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.078212Z"},"links":{"cited_paper":"/paper/1511.06067","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:c54ba6900523fe41bc2f31847a12356e084e3e7cb098b1f7af24bfd9613c2563","observation_id":"fb640d3a-7167-4464-bdbe-1463682d8ed2","resolution":{"observed_at":"2026-08-14T13:46:22.078212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02357","last_updated":"2017-04-04T18:40:27Z","snapshot_observed_at":"2026-08-14T21:36:12.359558Z","submitted_at":"2016-10-07T17:51:51Z","title":"Xception: Deep Learning with Depthwise Separable Convolutions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02357","snapshot_observed_at":"2026-08-14T13:46:22.026944Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.026944Z"},"links":{"cited_paper":"/paper/1610.02357","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:8bcab017d7e1a38b3328fc28d4482ac453e30e72ca8387e9f8fe7357035b68e2","observation_id":"4d508852-720b-4bcc-b0be-86abbea96353","resolution":{"observed_at":"2026-08-14T13:46:22.026944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.01548","last_updated":"2019-02-16T23:28:16Z","snapshot_observed_at":"2026-08-19T19:35:55.999304Z","submitted_at":"2018-02-05T18:20:52Z","title":"Regularized Evolution for Image Classifier Architecture Search","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.01548","snapshot_observed_at":"2026-08-14T13:46:22.072462Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.072462Z"},"links":{"cited_paper":"/paper/1802.01548","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:0d4017105afc1927ba7102d7185c81734b524e024ce2cecdea77378829bc1754","observation_id":"cbc53bfc-0c29-46ce-a475-b92e2e640d9d","resolution":{"observed_at":"2026-08-14T13:46:22.072462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6553","last_updated":"2015-04-24T11:40:54Z","snapshot_observed_at":"2026-08-18T10:21:48.549450Z","submitted_at":"2014-12-19T23:02:43Z","title":"Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6553","snapshot_observed_at":"2026-08-14T13:46:22.056363Z","title":"Lebedev, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-14T13:46:22.056363Z"},"links":{"cited_paper":"/paper/1412.6553","citing_paper":"/paper/1908.04471"},"observation_digest":"sha256:9cb76318656647c02670fe16503e1ab14f3bc962c883c9430d73eb1be914f01c","observation_id":"891591b6-841f-4665-b32f-6db1017e7398","resolution":{"observed_at":"2026-08-14T13:46:22.056363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.04471","last_updated":"2019-11-27T09:08:40Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T04:34:04.134483Z","submitted_at":"2019-08-13T03:11:46Z","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":1},"total_outbound_references":15},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:1908.04471."}