{"as_of":"2026-08-21T00:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5d7b2f5176de47c0e1a1caeafc9131980c06d00b14936febb0e345acc388e9b","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":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":24,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:47:05.734888Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":122,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-09T03:26:47.731344Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/1412.6980"},"observation_digest":"sha256:4c0cd25568ba9e60f1100d88c09ca6ff56a69fd44cc6a7282b529722acbd8d89","observation_id":"3c84a749-b772-4c69-aa9a-f24bdac7fbca","resolution":{"observed_at":"2026-05-09T05:45:20.982852Z","resolver_source":"arxiv_id","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-12T19:30:55.053335Z","title":"Revisiting natural gradient for deep networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.10696","last_updated":"2024-11-16T04:27:22Z","snapshot_observed_at":"2026-08-18T22:46:32.735066Z","submitted_at":"2024-11-16T04:27:22Z","title":"HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-12T19:30:55.053335Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2411.10696"},"observation_digest":"sha256:e3e2f126509d5c577d3056607afbbdc8c0045f3b4d4388afc99bc30a87c69f3b","observation_id":"35639dbe-e937-4e93-b59f-6cddc2f9caa9","resolution":{"observed_at":"2026-08-12T19:30:55.053335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-12T19:14:24.165923Z","title":"Revisiting natural gra- dient for deep networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10928","last_updated":"2024-11-17T01:16:37Z","snapshot_observed_at":"2026-08-16T23:08:01.128154Z","submitted_at":"2024-11-17T01:16:37Z","title":"Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T19:14:24.165923Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2411.10928"},"observation_digest":"sha256:0f65f50010a921b2edc14b35b4f52491445316c2b9c57d1d20ffa5d3fc04957b","observation_id":"42759223-e75a-43ad-9cfa-8872dfbf07ee","resolution":{"observed_at":"2026-08-12T19:14:24.165923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-09T17:46:07.275178Z","title":"Revisiting natural gradient for deep networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00802","last_updated":"2025-02-02T13:54:47Z","snapshot_observed_at":"2026-08-16T03:48:31.402251Z","submitted_at":"2025-02-02T13:54:47Z","title":"Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T17:46:07.275178Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2502.00802"},"observation_digest":"sha256:4f9c6eaf0a0ef76274b49337a3fa214be3401c3b3661f008051cce0f827a5ab1","observation_id":"599a5536-e801-4d65-85db-a43efc7ad4e7","resolution":{"observed_at":"2026-08-09T17:46:07.275178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2502.02345","last_updated":"2026-04-10T08:19:58Z","snapshot_observed_at":"2026-08-18T01:50:25.480135Z","submitted_at":"2025-02-04T14:27:21Z","title":"Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-23T03:39:45.167149Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2502.02345"},"observation_digest":"sha256:ef20c892597119e17569c4965c012eff6f0d7a122295eb1274768aaef9f07a25","observation_id":"293558c3-7790-43a7-9952-8511eaf46902","resolution":{"observed_at":"2026-05-23T03:42:27.756780Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-07T19:24:32.720170Z","title":", Bengio , Y","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.10131","last_updated":"2025-02-14T13:02:49Z","snapshot_observed_at":"2026-08-14T12:53:53.335461Z","submitted_at":"2025-02-14T13:02:49Z","title":"Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-07T19:24:32.720170Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2502.10131"},"observation_digest":"sha256:6cb82ab589b950bdad5787ad9eaa8dd8017124b59f93e5fea8a58f5a47128c55","observation_id":"00d9f401-f239-4490-b573-7dfdf4226d07","resolution":{"observed_at":"2026-08-07T19:24:32.720170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-08T12:04:37.415529Z","title":"Revisiting natural gradient for deep networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.15756","last_updated":"2025-02-11T15:51:59Z","snapshot_observed_at":"2026-08-14T23:14:35.311035Z","submitted_at":"2025-02-11T15:51:59Z","title":"Causal Covariate Shift Correction using Fisher information penalty","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T12:04:37.415529Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2502.15756"},"observation_digest":"sha256:92f644917c67a9aa25c30d2bf8dc81784b648828a72d4fbd4867a3735532fe55","observation_id":"4e78bbba-0ba9-410f-974f-2c298a82653d","resolution":{"observed_at":"2026-08-08T12:04:37.415529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-16T04:47:05.734888Z","title":"and Bengio, Y","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.00663","last_updated":"2025-05-01T17:07:01Z","snapshot_observed_at":"2026-08-18T22:23:55.093791Z","submitted_at":"2025-05-01T17:07:01Z","title":"Wasserstein Policy Optimization","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-16T04:47:05.734888Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2505.00663"},"observation_digest":"sha256:c8632b2703e988af915596388e4e64b57d9fe1a100ad9e72bc81973467cc05df","observation_id":"75eb8626-fb33-486c-b607-92acf35b5274","resolution":{"observed_at":"2026-08-16T04:47:05.734888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2510.04930","last_updated":"2026-05-18T15:03:38Z","snapshot_observed_at":"2026-08-13T01:25:05.882119Z","submitted_at":"2025-10-06T15:40:36Z","title":"Egalitarian Gradient Descent: A Simple Approach to Accelerated Grokking","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-21T21:13:24.267454Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2510.04930"},"observation_digest":"sha256:93d83dce6ff98a2d1a4fadda104bad96aca8f373f273acbbb0f9247f8d2437e9","observation_id":"c2235e3d-81f6-4a32-be4a-79205c16b0fb","resolution":{"observed_at":"2026-05-21T21:14:22.051983Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-03T20:20:35.726355Z","title":"Revisiting natural gra- dient for deep networks.arXiv preprint arXiv:1301.3584,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.20302","last_updated":"2026-06-17T13:44:19Z","snapshot_observed_at":"2026-08-15T06:17:52.851327Z","submitted_at":"2025-11-25T13:41:59Z","title":"CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T20:20:35.726355Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2511.20302"},"observation_digest":"sha256:759d35e8cf4b92da3d477b6901fb7d03f26628968a475263142ddc2968fe92dd","observation_id":"f5caa0a1-2b1f-455f-8c97-d197b39afa3f","resolution":{"observed_at":"2026-08-03T20:20:35.726355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-03T15:16:07.718439Z","title":"Revisiting natural gradient for deep networks, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2512.17893","last_updated":"2026-06-05T19:44:10Z","snapshot_observed_at":"2026-08-15T23:14:22.841466Z","submitted_at":"2025-12-19T18:49:33Z","title":"Exploring the Effect of Basis Rotation on NQS Performance","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:07.718439Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2512.17893"},"observation_digest":"sha256:8d409682fa6bd5c2cd5ed9be941cc7a778d5040b1f8eb4af31c14011fc67ea20","observation_id":"39466beb-46a1-409c-a9c0-97f14b89b719","resolution":{"observed_at":"2026-08-03T15:16:07.718439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-03T10:44:44.862084Z","title":"Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečn` y, Sanjiv Kumar, and H Brendan McMahan","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2601.09166","last_updated":"2026-06-20T04:27:34Z","snapshot_observed_at":"2026-08-07T13:29:04.836664Z","submitted_at":"2026-01-14T05:11:28Z","title":"DP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix","version":3},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-03T10:44:44.862084Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2601.09166"},"observation_digest":"sha256:1988117ed378a222e435516869950f80d6e14200aa2e5ff01b118fa1614d83bc","observation_id":"73e5c961-881f-45bc-a4a0-05f98a0a5ab6","resolution":{"observed_at":"2026-08-03T10:44:44.862084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2603.22347","last_updated":"2026-04-06T01:22:45Z","snapshot_observed_at":"2026-07-31T17:01:03.342336Z","submitted_at":"2026-03-22T03:37:33Z","title":"Intelligence Inertia: Physical Isomorphism and Applications","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T07:33:38.934570Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2603.22347"},"observation_digest":"sha256:163a3b8b4f67f9af31559707508feb0e74e9271a54e0722a6f48b57ae3dbdeda","observation_id":"a63e5620-0734-4c51-a3ce-2fe60ca650e5","resolution":{"observed_at":"2026-05-15T07:35:14.478422Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2605.01046","last_updated":"2026-05-26T18:00:50Z","snapshot_observed_at":"2026-08-16T03:03:35.250832Z","submitted_at":"2026-05-01T19:20:25Z","title":"Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-11T11:50:26.030339Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2605.01046"},"observation_digest":"sha256:13fcc1dc1dc511e3d1455ee98013fe64e35497d2cb97cc2a4267df7ea6922d39","observation_id":"ea1b20dd-7175-4d7c-8480-731359f26a50","resolution":{"observed_at":"2026-07-01T07:35:28.310911Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2605.04115","last_updated":"2026-05-05T07:41:15Z","snapshot_observed_at":"2026-08-15T04:44:40.526328Z","submitted_at":"2026-05-05T07:41:15Z","title":"Learning reveals invisible structure in low-rank RNNs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-08T18:38:44.820013Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2605.04115"},"observation_digest":"sha256:11853d353256595075daa9954fff1920f3e6924512f5ec431edf622ccd410d2d","observation_id":"7b08ce22-68aa-42c6-8900-654d0b0f6e4e","resolution":{"observed_at":"2026-05-09T06:15:39.623012Z","resolver_source":"arxiv_id","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2605.04230","last_updated":"2026-05-05T19:16:00Z","snapshot_observed_at":"2026-08-14T05:41:01.420894Z","submitted_at":"2026-05-05T19:16:00Z","title":"Layerwise LQR for Geometry-Aware Optimization of Deep Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-08T16:59:22.216757Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2605.04230"},"observation_digest":"sha256:2c3be44db218c20db83c30f8358edaaa33c02b7fb5e918b78c601794b1c2f5da","observation_id":"384ce381-794d-4de6-a7fd-bba52fbd94e9","resolution":{"observed_at":"2026-05-11T17:51:09.832818Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2605.15899","last_updated":"2026-05-15T12:30:07Z","snapshot_observed_at":"2026-08-17T13:00:01.293658Z","submitted_at":"2026-05-15T12:30:07Z","title":"Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-19T17:56:32.323460Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2605.15899"},"observation_digest":"sha256:4791f2f2cc5b6eeb6955b9b301575d350d7c6bda68c181ec46626ed11926e577","observation_id":"f607477f-c79b-443b-bddd-15a684f642aa","resolution":{"observed_at":"2026-05-19T17:57:42.454600Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2605.16165","last_updated":"2026-05-15T16:45:56Z","snapshot_observed_at":"2026-08-12T21:47:55.560127Z","submitted_at":"2026-05-15T16:45:56Z","title":"Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-20T19:02:02.266052Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2605.16165"},"observation_digest":"sha256:b12294db2dd05760c8c84cb9affec10ef304f6e461e685fe12064f0a059f7715","observation_id":"221cf998-0d38-469d-9b39-3aa4b48d7a87","resolution":{"observed_at":"2026-05-20T19:03:39.560386Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2606.01445","last_updated":"2026-06-05T14:32:58Z","snapshot_observed_at":"2026-08-08T14:01:28.877580Z","submitted_at":"2026-05-31T20:41:02Z","title":"Multiparameter Maximum Information States for Coherent Diffraction Measurements","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-06-28T16:16:34.824122Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2606.01445"},"observation_digest":"sha256:d31939743dc51a4d7df914cbff472468a26654e40cbacf2cb34debcac2268c44","observation_id":"0b48937e-4416-4b95-9751-ae58e7dda796","resolution":{"observed_at":"2026-06-28T16:22:22.075748Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2606.03382","last_updated":"2026-06-04T22:09:00Z","snapshot_observed_at":"2026-08-02T02:33:38.414458Z","submitted_at":"2026-06-02T09:26:26Z","title":"Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-28T10:51:30.546134Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2606.03382"},"observation_digest":"sha256:b06e25697b6b537fa275c6446906e1ac0919d33c44aaceea61e41eef946c2d8f","observation_id":"076aaa75-7596-4717-936a-91886c7ee13c","resolution":{"observed_at":"2026-07-02T02:36:26.607419Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2606.04880","last_updated":"2026-06-02T17:59:57Z","snapshot_observed_at":"2026-08-13T14:45:23.831347Z","submitted_at":"2026-06-02T17:59:57Z","title":"MAOAM: Unified Object and Material Selection with Vision-Language Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-06-28T11:08:59.900161Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2606.04880"},"observation_digest":"sha256:da98ac45c72d7fadcb02d2bbb3e613ecb373191abeb2760f0184a2f066793688","observation_id":"dd4848b9-8a3f-46b9-96d0-a2592c12c881","resolution":{"observed_at":"2026-07-02T02:16:26.357922Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2606.06418","last_updated":"2026-06-04T17:22:58Z","snapshot_observed_at":"2026-08-16T10:17:57.703806Z","submitted_at":"2026-06-04T17:22:58Z","title":"Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss","version":1},"reference_index":142,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:39.845487Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2606.06418"},"observation_digest":"sha256:fa9d1d7f4707fbaa675449e3e85a6b93b891905fb464189b97c65c38ee36a2e4","observation_id":"b40840e7-c4af-4f87-a72f-4b1ecd4f6113","resolution":{"observed_at":"2026-07-02T12:06:55.435369Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":"1301.3584","doi":"10.48550/arxiv.1301.3584","metadata_source":"pith","pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Revisiting Natural Gradient for Deep Networks","venue":"cs.LG","work_id":"15af0fe8-32d5-4d66-a808-b23c8fc4f239","year":2013},"citing_paper":{"arxiv_id":"2607.07845","last_updated":"2026-07-08T18:27:16Z","snapshot_observed_at":"2026-08-17T03:00:28.171144Z","submitted_at":"2026-07-08T18:27:16Z","title":"Explaining Near-Zero Hessian Eigenvalues Through Approximate Symmetries in Neural Networks","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-10T16:51:04.235933Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2607.07845"},"observation_digest":"sha256:47119e71c519f13346d5188da4fd04121ac63860d2a3ac2354a223cab0acf6d1","observation_id":"d9e3110c-010d-4e58-868a-85cf0b89ccb9","resolution":{"observed_at":"2026-07-10T16:57:24.485080Z","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":"1301.3584","last_updated":"2014-02-17T16:29:27Z","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3584","snapshot_observed_at":"2026-08-01T02:30:36.270658Z","title":"arXiv:1301.3584 (2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.25441","last_updated":"2026-07-28T08:33:04Z","snapshot_observed_at":"2026-08-14T09:23:14.907206Z","submitted_at":"2026-07-28T08:33:04Z","title":"PIcsC: Partitioning-Induced Covariate Shift Correction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T02:30:36.270658Z"},"links":{"cited_paper":"/paper/1301.3584","citing_paper":"/paper/2607.25441"},"observation_digest":"sha256:713c99dad0647bfc44559cc2162bbf921eea13b0d2733ea1ed67136c33a58783","observation_id":"5b86f55b-ec77-490e-b91c-35aa96907dcd","resolution":{"observed_at":"2026-08-01T02:30:36.270658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1301.3584/citation-record","integrity":"/paper/1301.3584/integrity","json":"/paper/1301.3584/citation-record.json","paper":"/paper/1301.3584"},"outbound":[],"paper":{"arxiv_id":"1301.3584","last_updated":"2014-02-17T16:29:27Z","latest_version":7,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T09:38:55.579894Z","submitted_at":"2013-01-16T04:47:02Z","title":"Revisiting Natural Gradient for Deep Networks"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:1301.3584."}