{"as_of":"2026-08-14T19:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5a98efd88a93be8e692dcb6c215587637aa3da7c961e56956bd5fded601cd944","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T05:08:04.637073Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:22:27.428311Z","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":"2026-08-05T16:22:27.519190Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"cited_work":{"arxiv_id":"1909.02102","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.02102","snapshot_observed_at":"2026-08-05T16:22:27.519190Z","title":"Accelerated Information Gradient flow","venue":"math.OC","work_id":"60f96b9d-3364-4532-836b-bb9cce4c7697","year":2019},"citing_paper":{"arxiv_id":"2508.18764","last_updated":"2026-08-01T09:31:05Z","snapshot_observed_at":"2026-08-13T22:44:32.981937Z","submitted_at":"2025-08-26T07:45:47Z","title":"Constrained Optimization via Constraint-Induced Geometry: Implicit Feasible Dynamics and Optimality from Stationarity","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:27.428311Z"},"links":{"cited_paper":"/paper/1909.02102","citing_paper":"/paper/2508.18764"},"observation_digest":"sha256:f3a23e328dc6ae6e7b976e47644c9715171bc0de826ef1fb141cbbdd40bf3d5b","observation_id":"ef2be298-41be-41bd-ac39-f50e57062c1c","resolution":{"observed_at":"2026-08-05T16:22:27.529397Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1909.02102/citation-record","integrity":"/paper/1909.02102/integrity","json":"/paper/1909.02102/citation-record.json","paper":"/paper/1909.02102"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:08:05.242472Z","title":"Natural gradient works efficiently in learning.Neural computation, 10(2):251– 276, 1998","venue":null,"work_id":"89057ad3-b621-4862-bd6f-e2892b96ad49","year":1998},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.452936Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:b88c0ef54a3c46ad8f3292ec35d997236ceaf84c3af5975858c651fb9ae15d93","observation_id":"57da7378-8c29-4f12-a9b7-a21d3a489560","resolution":{"observed_at":"2026-08-14T05:08:05.247593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:08:04.461052Z","title":"Information geometry and its applications, volume 194","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.461052Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:2ca122e9ebafc7fc49ae2e85e2f7d34b792886b07197c87e0ea8327ccca0e1ea","observation_id":"26bf149a-357f-4554-9f70-5a2b960c7eaf","resolution":{"observed_at":"2026-08-14T05:08:04.461052Z","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":"2026-08-14T05:08:05.216089Z","title":"Differential geometry in statistical inference","venue":null,"work_id":"d810f29a-d9a7-45d3-a0d1-140e9f8ca32e","year":1987},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.465807Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:c9a415889d78a7aefea79a8d3aa2b1a0e455034a6a1234123f0788adc4782fe3","observation_id":"5a5c74bb-e49f-4776-9dba-2b3d1dfd40c5","resolution":{"observed_at":"2026-08-14T05:08:05.222301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.201042Z","title":"Langevin Monte Carlo and JKO splitting","venue":null,"work_id":"259bb1c0-af7d-47ad-b3ec-6e6a6cf4d13a","year":2018},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.470616Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:fe2616be57020fca1ce421bf4c419b22bcef763287935bcb6210a1b02a0f0390","observation_id":"8d38f36d-b53f-47ab-be09-2685d2bd9946","resolution":{"observed_at":"2026-08-14T05:08:05.205966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.186595Z","title":"Convergence to equilibrium in Wasserstein distance for damped Euler equations with interaction forces","venue":null,"work_id":"8684afc8-c152-4a47-8c58-4a0525b33cc5","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.476576Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:bd95542cbb00059c87aa6e2ef2580e7774bca212e060d2e16975aa6669bff0f6","observation_id":"d0e7a436-913c-400f-af68-841d2d65d14c","resolution":{"observed_at":"2026-08-14T05:08:05.191463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03663","last_updated":"2018-01-26T21:56:51Z","snapshot_observed_at":"2026-08-14T17:31:45.786963Z","submitted_at":"2017-07-12T12:08:55Z","title":"Underdamped Langevin MCMC: A non-asymptotic analysis","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03663","snapshot_observed_at":"2026-08-14T05:08:04.481213Z","title":"Underdamped Langevin MCMC: A non-asymptotic analysis","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.481213Z"},"links":{"cited_paper":"/paper/1707.03663","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:0fb34fd37663ff4e4babf0bb186d375581db86f23870bac5a90e9b8586b0cafd","observation_id":"cad2494b-dd3b-4c05-abed-470f8e91fab0","resolution":{"observed_at":"2026-08-14T05:08:04.481213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01088","last_updated":"2019-12-16T06:47:02Z","snapshot_observed_at":"2026-08-14T17:08:01.497054Z","submitted_at":"2019-03-04T06:30:18Z","title":"Wasserstein Hamiltonian flows","version":2},"cited_work":{"arxiv_id":"1903.01088","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.01088","snapshot_observed_at":"2026-08-14T05:08:04.871947Z","title":"Wasserstein Hamiltonian flows","venue":"math.DS","work_id":"c559c4c8-f491-4be2-a751-60d382b2b44a","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.486235Z"},"links":{"cited_paper":"/paper/1903.01088","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:1b1a96bf81d2c39329d75389d724a81570d9cc2cd6e6b93b005c6de05fde74d9","observation_id":"9482f991-ce82-4305-85b7-055c2984fb5a","resolution":{"observed_at":"2026-08-14T05:08:04.877409Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.172227Z","title":"An information-theoretic approach to neural computing","venue":null,"work_id":"07a87202-bb98-4aa9-b384-ef0837953a7e","year":2012},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.491114Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:e4f3b6b8ed2a71b747bbe29f3a6cbcb111ebc43a2be9ea325d97c56111da1c7f","observation_id":"beaaf75d-f710-416f-8225-5987e63e1218","resolution":{"observed_at":"2026-08-14T05:08:05.177299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00894","last_updated":"2023-02-12T11:42:19Z","snapshot_observed_at":"2026-08-08T20:34:52.950153Z","submitted_at":"2019-12-02T16:20:05Z","title":"On the geometry of Stein variational gradient descent","version":2},"cited_work":{"arxiv_id":"1912.00894","doi":null,"metadata_source":"pith","pith_arxiv_id":"1912.00894","snapshot_observed_at":"2026-08-14T05:08:04.852542Z","title":"On the geometry of Stein variational gradient descent","venue":"stat.ML","work_id":"6724e8e8-0b89-4e0a-a97d-0fad378d9d78","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.495681Z"},"links":{"cited_paper":"/paper/1912.00894","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:a79d803bf2f79c3fac4c753d1650dd9ee22ed7a23ba285efca709852ae318398","observation_id":"69e08896-ddd0-4102-9dc0-3fc2ca430d41","resolution":{"observed_at":"2026-08-14T05:08:04.857114Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.08866","last_updated":"2019-10-16T17:43:18Z","snapshot_observed_at":"2026-08-14T16:59:00.385490Z","submitted_at":"2019-03-21T08:04:59Z","title":"Interacting Langevin Diffusions: Gradient Structure And Ensemble Kalman Sampler","version":3},"cited_work":{"arxiv_id":"1903.08866","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.08866","snapshot_observed_at":"2026-08-14T05:08:04.829253Z","title":"Interacting Langevin Diffusions: Gradient Structure And Ensemble Kalman Sampler","venue":"math.DS","work_id":"06962fd7-a74c-4704-9c05-04b5e8b7dedd","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.501877Z"},"links":{"cited_paper":"/paper/1903.08866","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:9e1172be5ca818277043e030118001700b095f8237148be65fc5143f517b4d67","observation_id":"974742ce-4cd6-42bd-bf04-ca57881d694f","resolution":{"observed_at":"2026-08-14T05:08:04.834218Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T05:08:04.507233Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.507233Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:c6fbc772bc3a3a81de4a08ae479a17538b45263a96692491f8dfd15c9a34460f","observation_id":"d5376d3a-3059-4334-aaa4-1c7d42107179","resolution":{"observed_at":"2026-08-14T05:08:04.507233Z","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":"2026-08-14T05:08:05.159202Z","title":"The density manifold and configuration space quantization","venue":null,"work_id":"74d02f4b-714b-48a6-9aae-5cbf1bd5497b","year":1988},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.512565Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:6decab3079286bcb6dc1088a3fe94518611446d11f0195c7904d946a53ad701f","observation_id":"7451093e-4dba-40a5-b283-8703792aca4f","resolution":{"observed_at":"2026-08-14T05:08:05.163677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.141360Z","title":"Understanding and accelerating particle-based variational inference","venue":null,"work_id":"e67c110a-d558-4964-8bd2-c468e8ade2e8","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.518163Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:b691f960a523c1061dcdfd4e9453bcba1000a7ba82f6726cd2a30dbc6d516576","observation_id":"2d91a1f0-48cd-4f45-9c10-26ead821b5c7","resolution":{"observed_at":"2026-08-14T05:08:05.146575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01750","last_updated":"2019-07-16T16:23:33Z","snapshot_observed_at":"2026-08-14T18:55:38.099826Z","submitted_at":"2018-07-04T19:17:36Z","title":"Understanding and Accelerating Particle-Based Variational Inference","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.01750","snapshot_observed_at":"2026-08-14T05:08:04.523549Z","title":"Ac- celerated first-order methods on the Wasserstein space for Bayesian inference","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.523549Z"},"links":{"cited_paper":"/paper/1807.01750","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:b10b0179cc0c1c8f9db761ab9e728c1b5cd5964d089271949bbff1f239c3e31f","observation_id":"527cec84-059e-44ba-b18d-ed8eaa5da589","resolution":{"observed_at":"2026-08-14T05:08:04.523549Z","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":"2026-08-14T05:08:05.122842Z","title":"Stein variational gradient descent as gradient flow","venue":null,"work_id":"0cab0787-c4e3-4ca7-8b67-b51cd63454a2","year":2017},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.528780Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:03c81e83a86c63b4f0e57a4377fd6e7d6b8a232b82686e1f605a13b15f2e9a83","observation_id":"420ec71c-ecc7-4b2b-a8d0-811682688375","resolution":{"observed_at":"2026-08-14T05:08:05.127396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.108575Z","title":"Stein variational gradient descent: A general purpose bayesian inference algorithm","venue":null,"work_id":"8d4f64ef-ba7c-4b63-bd60-29e3222f4b52","year":2016},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.534369Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:1c77d919233e04266eb8d8cfba88b6f4f72ea5bb644265ba2ad3a9a2ddd7bc5b","observation_id":"dec2147b-1c2a-491a-a572-37abb090855a","resolution":{"observed_at":"2026-08-14T05:08:05.112998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.096469Z","title":"Accelerated first-order methods for geodesically convex optimization on Riemannian manifolds","venue":null,"work_id":"18cfe43b-ed5e-460e-9467-3abe4a2a50ac","year":2017},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.539654Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:7385798af3f47d1a793646ab60080af3935c36702fe6c972d06fe2d52c6bee6e","observation_id":"793b4b33-b90c-441e-95ad-dbe74b036552","resolution":{"observed_at":"2026-08-14T05:08:05.100231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.00996","last_updated":"2019-10-22T05:55:37Z","snapshot_observed_at":"2026-08-14T17:21:37.064781Z","submitted_at":"2019-02-04T00:22:32Z","title":"Is There an Analog of Nesterov Acceleration for MCMC?","version":2},"cited_work":{"arxiv_id":"1902.00996","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.00996","snapshot_observed_at":"2026-08-14T05:08:04.784660Z","title":"Is There an Analog of Nesterov Acceleration for MCMC?","venue":"stat.ML","work_id":"46b5c4f5-0403-499c-be47-6179b0bd72c9","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.543209Z"},"links":{"cited_paper":"/paper/1902.00996","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:0cd7a97a40b0d4d603cdf9be2a7bbdabb4c3a9089f07d7c2de57e4eaec93df36","observation_id":"cd507619-1f68-4631-b90f-ce5208e588fb","resolution":{"observed_at":"2026-08-14T05:08:04.790258Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.05042","last_updated":"2018-09-13T16:21:11Z","snapshot_observed_at":"2026-08-14T18:28:46.863503Z","submitted_at":"2018-09-13T16:21:11Z","title":"Hamiltonian Descent Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.05042","snapshot_observed_at":"2026-08-14T05:08:04.548592Z","title":"Hamiltonian descent methods","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.548592Z"},"links":{"cited_paper":"/paper/1809.05042","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:4463fa43ea047f506e60e9c527361736859316837208e394b96401ad860b4115","observation_id":"4912b1dc-41ea-44bc-9d00-e054be39c817","resolution":{"observed_at":"2026-08-14T05:08:04.548592Z","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":"2026-08-14T05:08:05.082710Z","title":"Natural gradient, fitness modelling and model selection: A unifying perspective","venue":null,"work_id":"fbbbe4c9-c420-4168-a6b1-01e11757fbe5","year":2013},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.553659Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:af10decf5e4b607e9dc5df590aed763bc662c576542bf9237535b4df4232fb95","observation_id":"a9639db4-0ae5-4a9c-acc8-93bdd82767f1","resolution":{"observed_at":"2026-08-14T05:08:05.087046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.068257Z","title":"Optimizing neural networks with kronecker-factored ap- proximate curvature","venue":null,"work_id":"d4ad9143-0b0f-4165-a443-c83f9e58030c","year":2015},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.558626Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:99392d17a10336364dea257df6c5a9af5966c29b3f7121a001f1d73314d54d75","observation_id":"b194eb07-dfff-47b6-9535-73eb89b44e15","resolution":{"observed_at":"2026-08-14T05:08:05.072852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.050369Z","title":"A method of solving a convex programming problem with convergence rate O(1/k2)","venue":null,"work_id":"ae315ced-7fbf-4f5a-a936-8d0f5260cb9a","year":1983},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.563915Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:ef0509e3870da990237972d3cdc725fba27af4722a225e9f03036840baad9b17","observation_id":"1e879ec5-bba6-4801-ad43-f4831284442d","resolution":{"observed_at":"2026-08-14T05:08:05.056861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.032064Z","title":"The geometry of dissipative evolution equations: the porous medium equation","venue":null,"work_id":"fac69fd9-a3dc-41f6-92c3-37ef584e80fc","year":2001},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.567890Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:16d6651f9a59737d74107479678f6af9b48e0635a62cec5062e2535bfce6c605","observation_id":"38823971-8ef1-4710-b9d4-211470645a93","resolution":{"observed_at":"2026-08-14T05:08:05.037688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:05.014136Z","title":"Adaptive restart for accelerated gradient schemes","venue":null,"work_id":"002160b1-5a47-425f-9f36-8a85bf480432","year":2015},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.571855Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:2997a456ddea15ef85c2aebd479f86a6b330e7b71b24b81613358a333c1ca116","observation_id":"6a1cc0e9-dc22-4374-93a6-627110452ab8","resolution":{"observed_at":"2026-08-14T05:08:05.019405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.998867Z","title":"Information theoretic learning","venue":null,"work_id":"c0b47e48-3ba7-49e3-bcd3-ab6c076d9117","year":2000},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.575619Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:9dc2e780358e87f9424cee83502147f8f9813e228adb82ba2ea7fef3abef9280","observation_id":"9d044200-797d-41f9-90c1-231b0a53394d","resolution":{"observed_at":"2026-08-14T05:08:05.004341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.05770","last_updated":"2016-06-14T09:01:36Z","snapshot_observed_at":"2026-08-08T21:24:12.859869Z","submitted_at":"2015-05-21T15:36:37Z","title":"Variational Inference with Normalizing Flows","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.05770","snapshot_observed_at":"2026-08-14T05:08:04.579429Z","title":"Variational inference with normalizing flows","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.579429Z"},"links":{"cited_paper":"/paper/1505.05770","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:025f4c1c192d9ca809dcc62057d20e0f8f3c08a1b314b76dec6717042a1fa507","observation_id":"d59bdf01-45f1-47a9-8344-ab6aabd5d67d","resolution":{"observed_at":"2026-08-14T05:08:04.579429Z","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":"2026-08-14T05:08:04.983856Z","title":"A Geometric Framework for Modeling and Inference using the Nonparametric Fisher–Rao metric","venue":null,"work_id":"ae52eaa9-a381-4912-b965-e1ca0642b534","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.583241Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:990065897048fc44af118d5c283aa67876edb84bfda06c5b656a18e4262e4a83","observation_id":"d3796bed-6371-4b55-9783-8a094b2022f8","resolution":{"observed_at":"2026-08-14T05:08:04.989229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.969818Z","title":"Improvement on some known nonparametric uniformly consistent estimators of derivatives of a density","venue":null,"work_id":"d41703df-fe5c-42d1-9e4b-72f33a1c71ac","year":1977},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.586980Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:d0597a92a1e599ca68fe68b2201b8487ab4307f887200ec189065d0a5b9b44ac","observation_id":"ef9d74b6-2867-44e9-b84d-4652268522b9","resolution":{"observed_at":"2026-08-14T05:08:04.974851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.956206Z","title":"Functional and shape data analysis, volume 475","venue":null,"work_id":"0fd17f34-540a-48ec-861c-726a34fbeda9","year":2016},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.591686Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:a3d590d32be49f9424ba61e755f9d5836a75a9ba792410eeeb3610f06051c47f","observation_id":"bd566c86-b2cb-4b26-b416-f928d467a93b","resolution":{"observed_at":"2026-08-14T05:08:04.960438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.942029Z","title":"Inverse problems: a Bayesian perspective","venue":null,"work_id":"36725bf0-466b-4a73-a246-7b977f422aac","year":2010},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.595566Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:0636268be73a079ebf9c0cee8265066588bd70bc11bc6ae10671e95ab5bdab73","observation_id":"fcc79e08-8544-4742-bc21-6dc421d24b65","resolution":{"observed_at":"2026-08-14T05:08:04.946892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.927394Z","title":null,"venue":null,"work_id":"dcbc1f83-381b-4044-a4a1-39c27a5e7d61","year":2016},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.599688Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:bf79967e06d98fa145245cd5bbf3d689d4bed05bc58075b1ce0d697d88f10206","observation_id":"32001eef-46a1-4f2b-be31-19be596efa13","resolution":{"observed_at":"2026-08-14T05:08:04.932429Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.03317","last_updated":"2019-01-11T02:03:30Z","snapshot_observed_at":"2026-08-14T17:32:33.373308Z","submitted_at":"2019-01-10T18:42:38Z","title":"Accelerated Flow for Probability Distributions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.03317","snapshot_observed_at":"2026-08-14T05:08:04.603430Z","title":"Accelerated flow for probability distributions","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.603430Z"},"links":{"cited_paper":"/paper/1901.03317","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:d521a53155b68f027b292c22ba2c679b91ea162931f3166ad2362dca2cad8691","observation_id":"64d19cbe-c12d-44d3-af55-102ad56c50b4","resolution":{"observed_at":"2026-08-14T05:08:04.603430Z","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":"2026-08-14T05:08:04.913784Z","title":"Topics in optimal transportation","venue":null,"work_id":"96af0a46-1aa8-4d14-a23f-23beab149ca1","year":2003},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.608152Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:0a8f649851527599ed7ae7d352da36ee3f5d4e5d7e35ea4a23b4ab6301e89e4e","observation_id":"ec34cc89-8b0a-445d-84ae-aa8fbb8b5a16","resolution":{"observed_at":"2026-08-14T05:08:04.918477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:08:04.899002Z","title":"Stein variational gradient descent with matrix-valued kernels","venue":null,"work_id":"a603cbbc-3d86-4197-8468-a6d87f036708","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.611991Z"},"links":{"citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:8d851348d46a7e45565d8bdfd9bff4a8af8cdccdd4f30e392ab40217055dc5e8","observation_id":"f574d092-3e92-4e72-9456-c34509e846f4","resolution":{"observed_at":"2026-08-14T05:08:04.903340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.06507","last_updated":"2019-05-16T02:39:27Z","snapshot_observed_at":"2026-08-14T16:31:30.744751Z","submitted_at":"2019-05-16T02:39:27Z","title":"The Search direction Correction makes first-order methods faster","version":1},"cited_work":{"arxiv_id":"1905.06507","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.06507","snapshot_observed_at":"2026-08-14T05:08:04.720984Z","title":"The Search direction Correction makes first-order methods faster","venue":"math.OC","work_id":"09581495-adfd-4869-8846-8c574f2dd2bb","year":2019},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.616168Z"},"links":{"cited_paper":"/paper/1905.06507","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:43593352084174ce21b749c23b0690ba6076921000a9fb0be45d10edaf62422d","observation_id":"356b0341-24cb-493b-88b8-190e4cf740fb","resolution":{"observed_at":"2026-08-14T05:08:04.727708Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04341","last_updated":"2020-08-05T01:28:44Z","snapshot_observed_at":"2026-07-06T08:50:09.743009Z","submitted_at":"2020-01-13T15:33:46Z","title":"Information Newton's flow: second-order optimization method in probability space","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04341","snapshot_observed_at":"2026-08-14T05:08:04.621208Z","title":"Information newton’s flow: second-order optimization method in probability space","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.621208Z"},"links":{"cited_paper":"/paper/2001.04341","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:bb41b651ab7b1165c2cb8dc768c17732654a459631ed8906ffc14a3d0f0daeca","observation_id":"21292b78-3483-4287-ac50-fd710cab1c00","resolution":{"observed_at":"2026-08-14T05:08:04.621208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.01469","last_updated":"2019-11-04T19:57:38Z","snapshot_observed_at":"2026-08-13T15:02:21.114312Z","submitted_at":"2019-11-04T19:57:38Z","title":"Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.01469","snapshot_observed_at":"2026-08-14T05:08:04.625771Z","title":"Proximal Langevin Algorithm: Rapid convergence under isoperimetry","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.625771Z"},"links":{"cited_paper":"/paper/1911.01469","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:e2fb60a83d12b204e2b053c375541ff9ecbefed02046ce412ab9573454e9e862","observation_id":"8c741cb9-78a7-4c95-9bc1-ae7cebbdb980","resolution":{"observed_at":"2026-08-14T05:08:04.625771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.02812","last_updated":"2018-06-07T17:45:52Z","snapshot_observed_at":"2026-08-14T19:06:20.825784Z","submitted_at":"2018-06-07T17:45:52Z","title":"Towards Riemannian Accelerated Gradient Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.02812","snapshot_observed_at":"2026-08-14T05:08:04.637073Z","title":"Towards Riemannian accelerated gradient methods","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T05:08:04.637073Z"},"links":{"cited_paper":"/paper/1806.02812","citing_paper":"/paper/1909.02102"},"observation_digest":"sha256:242feb537bfb5e2fc216eafef9963651b3b47df548a25b966d9d8eca6d9fac6f","observation_id":"dcd0826f-e21c-4d3e-bf9b-8212592b4faa","resolution":{"observed_at":"2026-08-14T05:08:04.637073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1909.02102","last_updated":"2022-06-23T20:11:02Z","latest_version":3,"primary_category":"math.OC","snapshot_observed_at":"2026-08-14T04:57:57.556179Z","submitted_at":"2019-09-04T20:43:21Z","title":"Accelerated Information Gradient flow"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":5,"verified_fuzzy":22},"total_outbound_references":38},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:1909.02102."}