{"as_of":"2026-08-15T11:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bef5b7a4976ae9f3ecb8237a76e962d7a17450cee4b281d06ea1f0609803f36f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:04:22.969690Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-19T04:37:04.126189Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-14T14:04:22.969690Z","title":"Deep learning in neural networks: An overview,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03812","last_updated":"2019-08-10T21:40:25Z","snapshot_observed_at":"2026-08-14T13:59:07.857793Z","submitted_at":"2019-08-10T21:40:25Z","title":"Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:04:22.969690Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/1908.03812"},"observation_digest":"sha256:1c00eb143f5c8271b5ef741a36f140dece067ce4682099de3864d5e0d49e74f5","observation_id":"9ac58459-332d-4910-a3fd-a9ee9f3fd893","resolution":{"observed_at":"2026-08-14T14:04:22.969690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-14T12:12:33.506989Z","title":"Deep learning in neural networks: An overview doi:10.1016/j.neunet.2014.09.003,arXiv:arXiv:1404.7828","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.07701","last_updated":"2020-12-09T11:31:09Z","snapshot_observed_at":"2026-08-14T11:56:58.055828Z","submitted_at":"2019-08-21T03:55:59Z","title":"A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:33.506989Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/1908.07701"},"observation_digest":"sha256:ff7bdd3f2aa78268ca02b552214623ee189dd63673e83888c641d9a086fcc19e","observation_id":"d76816c2-93a3-40a1-a6d0-bd66929cd8b5","resolution":{"observed_at":"2026-08-14T12:12:33.506989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-11T23:57:26.945574Z","title":"Deep learning in neural networks: An overview","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02083","last_updated":"2025-03-24T14:54:27Z","snapshot_observed_at":"2026-08-14T07:30:23.344374Z","submitted_at":"2024-12-03T01:57:09Z","title":"Implementing An Artificial Quantum Perceptron","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:57:26.945574Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/2412.02083"},"observation_digest":"sha256:db0edb27a53a3fd39a11e34b89c97ff72033c34c7c0c279121dc36150b0e1971","observation_id":"76e4d4dc-0999-4b0a-891f-86a8160d20ad","resolution":{"observed_at":"2026-08-11T23:57:26.945574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-11T11:05:34.317605Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.15893","last_updated":"2024-12-20T13:48:40Z","snapshot_observed_at":"2026-08-11T10:57:40.881396Z","submitted_at":"2024-12-20T13:48:40Z","title":"Reproducibility of machine learning analyses of 21 cm reionization maps","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-11T11:05:34.317605Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/2412.15893"},"observation_digest":"sha256:c692a12590752ceb9c05f3706ee34c2e0d63bca82df3fefbc9a6dcf458406fb1","observation_id":"e1aa8594-8366-4aca-94b9-1ee1af4c3278","resolution":{"observed_at":"2026-08-11T11:05:34.317605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-11T06:00:03.019332Z","title":"Schmidhuber, Deep learning in neural networks: An overview, 2014, Available from: https: //arxiv.org/abs/1404.7828, arXiv:1404.7828","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.19836","last_updated":"2025-02-13T20:12:49Z","snapshot_observed_at":"2026-08-15T04:19:27.561024Z","submitted_at":"2024-12-22T11:28:10Z","title":"Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T06:00:03.019332Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/2412.19836"},"observation_digest":"sha256:f388dc68ef080a90c392d83379e419e194315aefdb7fe775d43939d7b73de153","observation_id":"a480a893-238a-4b71-b301-b3de4044e64f","resolution":{"observed_at":"2026-08-11T06:00:03.019332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":"1404.7828","doi":null,"metadata_source":"pith","pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep Learning in Neural Networks: An Overview","venue":"cs.NE","work_id":"ed6fadf1-bbbc-46dd-bd41-83aec96dedd8","year":2014},"citing_paper":{"arxiv_id":"2507.09040","last_updated":"2026-04-27T21:54:55Z","snapshot_observed_at":"2026-08-11T18:16:48.110574Z","submitted_at":"2025-07-11T21:36:15Z","title":"R-process heating implementation in hydrodynamic simulations with neural networks","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-19T04:34:03.950842Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/2507.09040"},"observation_digest":"sha256:64697e46d8ec4ce9649ede473b8635d7a86aa197c1558a768c024dc0d53c5658","observation_id":"bf446a2f-7fa2-4350-9920-676484cb2676","resolution":{"observed_at":"2026-05-19T04:37:04.128687Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.7828","snapshot_observed_at":"2026-08-06T15:26:37.704978Z","title":"Deep learning in neural networks: An overview","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.15976","last_updated":"2025-07-21T18:15:12Z","snapshot_observed_at":"2026-08-06T18:21:44.853672Z","submitted_at":"2025-07-21T18:15:12Z","title":"Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T15:26:37.704978Z"},"links":{"cited_paper":"/paper/1404.7828","citing_paper":"/paper/2507.15976"},"observation_digest":"sha256:b15407e321b4383ce87ca6ff68d29d4b077c71f0ed9ff936573389bc8d1c40c9","observation_id":"4e25e7ab-722d-4424-b587-a9bf1efa8c03","resolution":{"observed_at":"2026-08-06T15:26:37.704978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1404.7828/citation-record","integrity":"/paper/1404.7828/integrity","json":"/paper/1404.7828/citation-record.json","paper":"/paper/1404.7828"},"outbound":[],"paper":{"arxiv_id":"1404.7828","last_updated":"2014-10-08T10:00:38Z","latest_version":4,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-15T02:15:54.949079Z","submitted_at":"2014-04-30T18:39:00Z","title":"Deep Learning in Neural Networks: An Overview"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1404.7828."}