{"as_of":"2026-08-17T20:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9602cbb1dcd2674f474c373adde6542763ce36c22523036834b4f2f24a01ddc9","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:41:53.155861Z","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":"2026-07-10T19:07:35.111584Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.07540","last_updated":"2020-04-16T10:07:02Z","snapshot_observed_at":"2026-08-17T09:39:57.322659Z","submitted_at":"2018-07-19T17:12:48Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","version":5},"cited_work":{"arxiv_id":"1807.07540","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.07540","snapshot_observed_at":"2026-07-10T19:07:35.111584Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","venue":"stat.ML","work_id":"7a704bc5-e076-46b5-bd1a-f176d5155a32","year":2018},"citing_paper":{"arxiv_id":"2212.08989","last_updated":"2023-06-20T01:01:34Z","snapshot_observed_at":"2026-08-15T01:57:41.390401Z","submitted_at":"2022-12-18T02:03:00Z","title":"Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics","version":3},"reference_index":170,"source":"pdf_text","source_observed_at":"2026-05-24T10:22:00.419523Z"},"links":{"cited_paper":"/paper/1807.07540","citing_paper":"/paper/2212.08989"},"observation_digest":"sha256:0dabdd5b750dc730ece2dfb1700ae641f1be43cdbd6ddc0a123d2010de9c5a0d","observation_id":"be3f14da-21ef-4d82-bfe3-936ab9f4ac19","resolution":{"observed_at":"2026-05-24T10:24:20.130250Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07540","last_updated":"2020-04-16T10:07:02Z","snapshot_observed_at":"2026-08-17T09:39:57.322659Z","submitted_at":"2018-07-19T17:12:48Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.07540","snapshot_observed_at":"2026-08-10T22:41:53.155861Z","title":"Aitchison, A unified theory of adaptive stochastic gradient descent as bayesian filtering , arXiv: 1807.07540, (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.01013","last_updated":"2025-01-02T02:34:01Z","snapshot_observed_at":"2026-08-17T08:49:02.704460Z","submitted_at":"2025-01-02T02:34:01Z","title":"Incomplete Data Multi-Source Static Computed Tomography Reconstruction with Diffusion Priors and Implicit Neural Representation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:41:53.155861Z"},"links":{"cited_paper":"/paper/1807.07540","citing_paper":"/paper/2501.01013"},"observation_digest":"sha256:3e55affa2ba10b8ee370a68fb8b35322902968641d1550403b396fb5cbf1ea37","observation_id":"324a3e10-3ed7-4923-b843-26fe95659024","resolution":{"observed_at":"2026-08-10T22:41:53.155861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07540","last_updated":"2020-04-16T10:07:02Z","snapshot_observed_at":"2026-08-17T09:39:57.322659Z","submitted_at":"2018-07-19T17:12:48Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","version":5},"cited_work":{"arxiv_id":"1807.07540","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.07540","snapshot_observed_at":"2026-07-10T19:07:35.111584Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","venue":"stat.ML","work_id":"7a704bc5-e076-46b5-bd1a-f176d5155a32","year":2018},"citing_paper":{"arxiv_id":"2607.07756","last_updated":"2026-07-08T14:01:16Z","snapshot_observed_at":"2026-08-15T11:41:22.769624Z","submitted_at":"2026-07-08T14:01:16Z","title":"The Importance of Encoder Choice:A Tabular-Image Study","version":1},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-07-10T19:03:32.353393Z"},"links":{"cited_paper":"/paper/1807.07540","citing_paper":"/paper/2607.07756"},"observation_digest":"sha256:a90ab0a05396effd108a91c20c1a4f0f7e684c16a21efa35090fd0e52f7c0d2a","observation_id":"232c162f-ed2c-4777-8116-8ebadbf8b91a","resolution":{"observed_at":"2026-07-10T19:07:35.112807Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07540","last_updated":"2020-04-16T10:07:02Z","snapshot_observed_at":"2026-08-17T09:39:57.322659Z","submitted_at":"2018-07-19T17:12:48Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.07540","snapshot_observed_at":"2026-08-02T02:28:30.657768Z","title":"Preprint arXiv:1807.07540 , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.14361","last_updated":"2026-07-15T20:53:51Z","snapshot_observed_at":"2026-08-16T13:59:23.274873Z","submitted_at":"2026-07-15T20:53:51Z","title":"NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes","version":1},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-08-02T02:28:30.657768Z"},"links":{"cited_paper":"/paper/1807.07540","citing_paper":"/paper/2607.14361"},"observation_digest":"sha256:b408e7c69f6eb431238e55fdda64b195a9d31a3cd72d6e62d86fcfdea3b19890","observation_id":"3b78150b-2de7-4a7f-9d51-1b40211ea0ba","resolution":{"observed_at":"2026-08-02T02:28:30.657768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1807.07540/citation-record","integrity":"/paper/1807.07540/integrity","json":"/paper/1807.07540/citation-record.json","paper":"/paper/1807.07540"},"outbound":[],"paper":{"arxiv_id":"1807.07540","last_updated":"2020-04-16T10:07:02Z","latest_version":5,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-17T09:39:57.322659Z","submitted_at":"2018-07-19T17:12:48Z","title":"Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1807.07540."}