{"as_of":"2026-08-08T04:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d7eddf10356e82c907702a1a40ebed5361ced34a0b69388f94328de5a1ba58d7","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:43:19.244192Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T08:11:17.513815Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-08-07T13:43:19.244192Z","title":null,"venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.21589","last_updated":"2025-05-27T12:22:59Z","snapshot_observed_at":"2026-08-07T13:33:24.351691Z","submitted_at":"2025-05-27T12:22:59Z","title":"Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T13:43:19.244192Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2505.21589"},"observation_digest":"sha256:a793b8668342a323a87fa1483e373266d8efef34311f88e9cd35454cac244626","observation_id":"92151362-930c-42a3-8ccf-14789ef87d96","resolution":{"observed_at":"2026-08-07T13:43:19.244192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-08-07T05:58:16.049205Z","title":"On empirical comparisons of optimizers for deep learning.arXiv preprint arXiv:1910.05446, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-07T07:16:15.494948Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.049205Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:a3155d54ef5147265918bade4ba47fa2e84a094b4488f12f13108415b212a176","observation_id":"749f7995-b674-4411-8b2c-ef67e4a55150","resolution":{"observed_at":"2026-08-07T05:58:16.049205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-08-06T05:03:21.143206Z","title":"On empirical comparisons of optimizers for deep learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2508.02448","last_updated":"2025-08-04T14:09:53Z","snapshot_observed_at":"2026-08-06T05:03:05.531743Z","submitted_at":"2025-08-04T14:09:53Z","title":"Charting 15 years of progress in deep learning for speech emotion recognition: A replication study","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T05:03:21.143206Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2508.02448"},"observation_digest":"sha256:ff7970654b12c03bb2d7e02b9c58abab53c84a57ae3c7233e97679bff8759758","observation_id":"48f6f2a4-3fb4-499a-9cf4-0f33040fa121","resolution":{"observed_at":"2026-08-06T05:03:21.143206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":"1910.05446","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:1910.05446 , year=","venue":null,"work_id":"d2da01ae-2154-4a67-86ca-7aab2dca4140","year":1910},"citing_paper":{"arxiv_id":"2604.15297","last_updated":"2026-04-17T17:48:55Z","snapshot_observed_at":"2026-07-31T08:41:17.524742Z","submitted_at":"2026-04-16T17:57:02Z","title":"Benchmarking Optimizers for MLPs in Tabular Deep Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T12:06:08.798712Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2604.15297"},"observation_digest":"sha256:49fac6e5d1bfe4fe14b22ce5dd458542ced4524f14734a14550e8b8c6bd946d1","observation_id":"97a84b44-1ef3-442d-9711-e064acf6824b","resolution":{"observed_at":"2026-05-10T12:10:21.993680Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-07-14T19:38:38.575635Z","title":"Aaron Defazio, Xingyu Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, and Ashok Cutkosky","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2604.15298","last_updated":"2026-07-13T16:57:17Z","snapshot_observed_at":"2026-07-16T23:19:34.511919Z","submitted_at":"2026-04-16T17:57:08Z","title":"Polylogarithmic-Weight Dicke States in QAC$^0$ and Arbitrary Symmetric States in QAC$^0_f$","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T19:38:38.575635Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2604.15298"},"observation_digest":"sha256:f71324d1f7aba54b019a660a2ddb6f834416b96871415c23e93c991e8424e803","observation_id":"86bfaf66-5066-43c2-92bc-212caaff6344","resolution":{"observed_at":"2026-07-14T19:38:38.575635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":"1910.05446","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:1910.05446 , year=","venue":null,"work_id":"d2da01ae-2154-4a67-86ca-7aab2dca4140","year":1910},"citing_paper":{"arxiv_id":"2605.22644","last_updated":"2026-05-21T15:50:40Z","snapshot_observed_at":"2026-08-02T21:41:54.474589Z","submitted_at":"2026-05-21T15:50:40Z","title":"Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics","version":1},"reference_index":114,"source":"arxiv_source","source_observed_at":"2026-05-22T08:06:52.309619Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2605.22644"},"observation_digest":"sha256:6cdbbde2e1c5aa96f39b28e331a4edec39cad5225fb0ad23402ddb1c2d6c2f39","observation_id":"0fc9b4ae-d8e1-44aa-93d2-c903ee6cbce0","resolution":{"observed_at":"2026-05-22T08:11:17.516358Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-07-14T15:55:25.318583Z","title":"arXiv:1910.05446 [cs.LG] , year=","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2607.09776","last_updated":"2026-07-15T15:49:55Z","snapshot_observed_at":"2026-08-02T10:22:56.853033Z","submitted_at":"2026-07-08T03:03:38Z","title":"HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation","version":1},"reference_index":280,"source":"arxiv_source","source_observed_at":"2026-07-14T15:55:25.318583Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2607.09776"},"observation_digest":"sha256:cf921390ea0fed31ebfe6d795f150f6dbbe3843a57b3f751ce73de20ffadf6e8","observation_id":"c5a98723-626f-46d9-9309-4e38acdc25c6","resolution":{"observed_at":"2026-07-14T15:55:25.318583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-08-02T08:13:31.896100Z","title":"arXiv:1910.05446 [cs.LG] , year=","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2607.09776","last_updated":"2026-07-15T15:49:55Z","snapshot_observed_at":"2026-08-02T10:22:56.853033Z","submitted_at":"2026-07-08T03:03:38Z","title":"HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation","version":2},"reference_index":280,"source":"arxiv_source","source_observed_at":"2026-08-02T08:13:31.896100Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2607.09776"},"observation_digest":"sha256:f29d669f0c742b13b20ea12ab741e4dc2b09efa3d4d25449b20da9b290b1f8a9","observation_id":"0dbfc2a7-4ba1-4d9f-892d-5f9a2fc5b31a","resolution":{"observed_at":"2026-08-02T08:13:31.896100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1910.05446/citation-record","integrity":"/paper/1910.05446/integrity","json":"/paper/1910.05446/citation-record.json","paper":"/paper/1910.05446"},"outbound":[],"paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T08:58:02.295940Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1910.05446."}