{"as_of":"2026-08-17T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e1ad19b819bfed702f08d248dd86ac82d1edbca9f8af512fb010675e57145d2","coverage":[{"denominator":117,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:19:28.369000Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.05831/citation-record","integrity":"/paper/2508.05831/integrity","json":"/paper/2508.05831/citation-record.json","paper":"/paper/2508.05831"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.922083Z","title":"Deep Learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.922083Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:f22378577695fd9426c3cd1dd38aadcb34f018dc1fdeda49f5252aa943cb15e0","observation_id":"4ad45623-94e8-4351-81b7-34d8a9be5bd2","resolution":{"observed_at":"2026-08-05T23:19:27.922083Z","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":"10.1002/9780470685853.ch7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:07:33.414567Z","title":"Surrogate and reduced-order modeling: a comparison of approaches for large- scale statistical inverse problems","venue":null,"work_id":"0b6590f1-c1b2-471c-bf50-83ffc17e659c","year":2010},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.927492Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:db9cb652718b56f4cfc015f51c003758a14cb8e1af8a9b07f6e0dec3ac9aa829","observation_id":"aace6cfe-24a6-45d3-8dc1-526333d78908","resolution":{"observed_at":"2026-08-05T23:19:28.926991Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.931822Z","title":"Kernel methods for surrogate modeling","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.931822Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:07a7c2daaf4e5c644a953c198dbb2506cd6302a115fda6186e6a6e1031f20b32","observation_id":"32d1f40b-862d-4c6d-b8fd-c6fec96ea879","resolution":{"observed_at":"2026-08-05T23:19:27.931822Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.936023Z","title":"Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.936023Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:5f1b107ab97385cd43256091df695353994f2a452f1dd867dd026ddbf30d6e6b","observation_id":"5204a460-7c39-4688-a721-86e048240123","resolution":{"observed_at":"2026-08-05T23:19:27.936023Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.940518Z","title":"Scientific machine learning through physics–informed neural networks: where we are and what’s next","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.940518Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:7fa01e8d0a2131472de2b0e8ce06897961dec6c00389093d9ec6e38dcf3fdfc3","observation_id":"77744e11-ac9b-4670-b5be-45c8408b9d7d","resolution":{"observed_at":"2026-08-05T23:19:27.940518Z","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":"10.3934/nhm.2020011","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Deep neural network approach to forward-inverse problems","venue":"Networks and Heterogeneous Media","work_id":"3983c131-4177-4eac-9c13-13fce3b62f30","year":2020},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.944641Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:2fa91f55bafc1a0973f14953c4abf7527faac3e2ef4656134acb0db8d644327f","observation_id":"55ba8118-48d7-414a-8bbf-77cb77896637","resolution":{"observed_at":"2026-08-05T23:19:28.909077Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.10574","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.701766Z","title":"Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks","venue":null,"work_id":"2e9a537b-d1d4-407b-bb19-89bf08e7d8b0","year":2024},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.949171Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:004e419aa60403067236dd45acec0a077823b7be41f4873a14072510f3f2e16d","observation_id":"9ccbf369-0789-43c1-b602-4010cfbf442d","resolution":{"observed_at":"2026-08-05T23:19:31.707394Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.953274Z","title":"Inverse Problem Theory and Methods for Model Parameter Estimation","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.953274Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:02251248cd59581e8cf15951617ff6afa610b742ef6c12b79a9b20c34ba5fae9","observation_id":"4f23d69a-6df3-459b-b621-1dc7c9e99818","resolution":{"observed_at":"2026-08-05T23:19:27.953274Z","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":"10.1137/1.9780898718836","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Discrete Inverse Problems: Insight and Algorithms","venue":null,"work_id":"aff15ef5-b509-4b69-bcc6-926bfacf9420","year":2010},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.956856Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9ab803997c5995ee6bce5571fe93102cace90616454695ae376fbe965da26c53","observation_id":"0a557606-b968-4e34-8850-f798a967b257","resolution":{"observed_at":"2026-08-05T23:19:28.892263Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.961213Z","title":"Sur les probl` emes aux d´ eriv´ ees partielles et leur signification physique","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.961213Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:bf47ba4a67f0950ea8c7a308a354bbef34e504be910c131936e08b61c67b8a51","observation_id":"0371197c-2149-410b-b4b3-d9736388383b","resolution":{"observed_at":"2026-08-05T23:19:27.961213Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.964910Z","title":null,"venue":null,"work_id":null,"year":1961},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.964910Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:1ac06b66e38bd4acc28ad3996551a8320bf88aef926cdd5d0b4bd67bd795b90d","observation_id":"0799a102-0794-42da-93e1-93880cdac576","resolution":{"observed_at":"2026-08-05T23:19:27.964910Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.968638Z","title":"Solving inverse problems using data-driven models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.968638Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:1054be984d3b0c7ab27f04bc9ab8b3b24118ca9c52a6025a32b30341075593fb","observation_id":"0dd34bde-f9fb-4d6d-988a-61881f68cdf1","resolution":{"observed_at":"2026-08-05T23:19:27.968638Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.972953Z","title":"Modern regularization methods for inverse problems","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.972953Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:a24ef7189a8852a365b611f14f21d469aca6f29f1288057c870d52458039b710","observation_id":"3688fec6-9955-4adb-a2dd-1bb4a1c32ccd","resolution":{"observed_at":"2026-08-05T23:19:27.972953Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.976707Z","title":"Variational regularization in inverse problems and machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.976707Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:460031ddfd5a2fe18e48c87c1f181cb1d4b4a4061d112563c80c7e138a0b718c","observation_id":"427926df-4b44-4579-aacc-61e26d7ebcbc","resolution":{"observed_at":"2026-08-05T23:19:27.976707Z","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":"10.1088/1361-6420/ab1d71","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Solution paths of variational regularization methods for inverse problems","venue":"Inverse Problems","work_id":"581f1975-fb6b-45c6-815f-a994fa38e4c5","year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.981034Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:879b2ca92e914ed60f2e5ca092aecf925872ebccf83e5ef4c0bfcf14be050505","observation_id":"c3e36ae3-418d-4cf0-b542-998eb4aaa9c9","resolution":{"observed_at":"2026-08-05T23:19:28.856931Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:27.984589Z","title":"Iterative regularization with a general penalty term-theory and application to L1 and TV regularization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.984589Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:be7ac24c7a756414da694f7c2738df34f59865f87b110467949bd04e0148cddc","observation_id":"f0c8f78f-5311-4dfc-8478-5da0569408c6","resolution":{"observed_at":"2026-08-05T23:19:27.984589Z","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":"10.1088/0266-5611/25/10/105004","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Iterative total variation schemes for nonlinear inverse prob- lems","venue":"Inverse Problems","work_id":"434c9c23-1816-41c0-bdfa-36f24c4158b1","year":2009},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.988204Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:d7367371e117f1aa9d204e013c16d482aab793a7921374a90df12e7e10713321","observation_id":"aebf2278-7df0-4154-b819-38618535fd13","resolution":{"observed_at":"2026-08-05T23:19:28.844887Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.apacoust","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.824699Z","title":"Empirical Bayesian regularization of the inverse acoustic problem","venue":null,"work_id":"c5b22c52-14ca-4462-bf91-5979e268696f","year":2015},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.992051Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:988a2db9a49e69f1b78ee181d0a2745ac09ea609a6361da03d99a12494c190c5","observation_id":"69570893-2ef1-491d-b9d6-4dc89032bb1d","resolution":{"observed_at":"2026-08-05T23:19:28.829184Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/wics","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.813107Z","title":"Inverse problems: from regularization to Bayesian inference","venue":null,"work_id":"07144d5f-08f6-49e0-8c70-9cc0d4f1e315","year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:27.996334Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:72030d07d2bb1e426ddab10d17579994bcb63cdaf2813f5365d4cf2fb39d21dc","observation_id":"6fab6bf0-95f7-4891-aa9b-5fa4e2ce40fc","resolution":{"observed_at":"2026-08-05T23:19:28.817094Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.29915","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.630508Z","title":"Deep learning techniques for inverse problems in imaging","venue":null,"work_id":"2b9e6bf6-723b-4d85-b1a5-7b1e89f820ba","year":2020},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.000349Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:ed73e16c345e49ae7ca4aa763988b350d528f4b9fff5d946c401d5b2cac434a1","observation_id":"70c4e035-c7be-401f-9fd0-1d1f6bf141cb","resolution":{"observed_at":"2026-08-05T23:19:31.635605Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7717/peerj-cs.951","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Deep learning methods for inverse problems","venue":"PeerJ Computer Science","work_id":"badc2cc4-12c9-469c-88ca-0196be49293b","year":2022},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.004374Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:c7893647bdc4e01df0eaa44d802fcc4534844e923572bd3514db666642439e1b","observation_id":"353c0c39-54af-49e5-9e08-8e77534d4282","resolution":{"observed_at":"2026-08-05T23:19:28.805672Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.008338Z","title":"Learning regularization parameters of inverse problems via deep neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.008338Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:03bace78c4ec748711342a28e1a63d6e129c5b39fe6c39b68aaaad255a6d637e","observation_id":"13d1ca5f-dac9-49b0-9c17-df32477c0cd9","resolution":{"observed_at":"2026-08-05T23:19:28.008338Z","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":"2017.27130","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.519738Z","title":"Deep convolutional neural network for inverse problems in imaging","venue":null,"work_id":"a1725b4c-21bf-4a72-af12-4281cf970e9c","year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.013489Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:a58d06396797ae748552b3edd380254d44c6883c488732851fee72477c26dd31","observation_id":"348ce7f8-618f-46f5-96e1-92cd88a76199","resolution":{"observed_at":"2026-08-05T23:19:31.525631Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08005","last_updated":"2022-06-16T00:01:39Z","snapshot_observed_at":"2026-08-16T17:41:52.898038Z","submitted_at":"2021-11-15T05:41:12Z","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08005","snapshot_observed_at":"2026-08-05T23:19:28.019948Z","title":"Solving inverse problems in medical imaging with score-based generative models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.019948Z"},"links":{"cited_paper":"/paper/2111.08005","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:ddddbd2bad4c3bc7eb523eba796647b318bea21ab5b200718084aa5c774c995d","observation_id":"ae93c4af-55b6-4115-b454-4b0fe9f6722a","resolution":{"observed_at":"2026-08-05T23:19:28.019948Z","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":"2017.27392","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.443411Z","title":"Convolutional neural networks for inverse problems in imaging: a review","venue":null,"work_id":"b72ea547-6199-434b-a099-b461232f152d","year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.025778Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:456493e3ed272b250ee2f25a3ec9f2b7e91a30c15dc0a10d34e70025c51480c3","observation_id":"d34c79c4-450f-4c28-8bbb-250bebd182f9","resolution":{"observed_at":"2026-08-05T23:19:31.448727Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.030323Z","title":"Using deep neural networks for inverse problems in imaging: beyond analytical methods","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.030323Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:6b70b80c55f644e290b1e0acdb93b7b94bbc5bba645fe1eebbb4f25c6a2bf77d","observation_id":"46c8dade-e270-4bf6-8fe5-bd81a10ecdd9","resolution":{"observed_at":"2026-08-05T23:19:28.030323Z","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":"10.1109/msp.2019","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.777326Z","title":"Deep magnetic resonance image reconstruction: inverse problems meet neural networks","venue":null,"work_id":"00df15fa-e1eb-463a-abab-1effa66f0392","year":2020},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.036659Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:039b14f1ec3a2164134dffe5b1f955d1e4f6dec6069ccfdf88b9cbfd94262f5f","observation_id":"b56238f7-4133-4b63-bf8b-2c16bc54900a","resolution":{"observed_at":"2026-08-05T23:19:28.782118Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/j.1365-246x.1972.tb06115.x","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Interpretation of inaccurate, insufficient and inconsistent data","venue":"Geophysical Journal International","work_id":"d191dc6d-f6a2-49ff-a8d0-007446776a88","year":1972},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.045206Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:2a0bb7247797fbc7019f8c6e60ab9129e3559113b6110f95c4aa985c17157b20","observation_id":"483200ad-ba03-4004-a19e-cae8bf18a7b7","resolution":{"observed_at":"2026-08-05T23:19:28.769809Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/0109031","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"An application of the Wiener-Kolmogorov smoothing theory to matrix inversion","venue":"Journal of the Society for Industrial and Applied Mathematics","work_id":"933825a3-3182-406d-b0cf-8cec20fdef36","year":1961},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.049722Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:da6d17911d3636a0b04ae51820e28a8b73dc51985625188f62f6424c485e8446","observation_id":"8c4d53e4-7dc9-4619-871b-2027e65ee37e","resolution":{"observed_at":"2026-08-05T23:19:28.758949Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5555/151045","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.744087Z","title":null,"venue":null,"work_id":"6cbed9a8-057c-41a3-8563-0d19f5f8629f","year":1993},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.053383Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:7881d072fa0177966b0221edabd828de4727bfc38d661128177454a3b7e04eb0","observation_id":"beb0fcb9-615e-4aa6-90ee-5d7891ad4125","resolution":{"observed_at":"2026-08-05T23:19:28.747983Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.057543Z","title":"Extrapolation, Interpolation, and Smoothing of Stationary Time Series: with Engi- neering Applications","venue":null,"work_id":null,"year":1949},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.057543Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:93babce74f7d6c9e68f14b1aac9c273eac699e0fa37f285ed12257007c267613","observation_id":"eacd7c80-258f-45bd-91ae-c9e056795996","resolution":{"observed_at":"2026-08-05T23:19:28.057543Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.061152Z","title":"Reduced-Rank Regression for the Multivariate Linear Model","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.061152Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:881e4cd96f2bd61cf34a5d8d09d2249ff1eaeafa3a5dda36295201b8ad5858e4","observation_id":"1d00caed-750f-40d0-9f72-eb6522a68c8e","resolution":{"observed_at":"2026-08-05T23:19:28.061152Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.065944Z","title":"The Bayesian approach to inverse problems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.065944Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:e0a465dff78e03339290e74046ff95eff7b35489c34476c6a07bb06a72c5e475","observation_id":"836d307c-a94b-49d5-b937-c96f5252ae8e","resolution":{"observed_at":"2026-08-05T23:19:28.065944Z","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":"10.1002/9780470611197","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:07:33.414567Z","title":"Wiley, 2008","venue":null,"work_id":"e18972c5-7bdd-4cf5-b670-55842e9a12f2","year":2008},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.070002Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:f70da26365aa140f32738143f8de554c25c0431dad4e5a934093e9752c88f82f","observation_id":"68f95ec2-001f-4662-a565-f4a3311b6265","resolution":{"observed_at":"2026-08-05T23:19:28.709103Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.074032Z","title":"Inverse problems: a Bayesian perspective","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.074032Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:c9e89e1f5b804d08c42a052accdf8ae86cc093376cbcd3499040914c688058aa","observation_id":"101c45d7-73c1-48bb-a20e-10a8db4fe972","resolution":{"observed_at":"2026-08-05T23:19:28.074032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14636","last_updated":"2025-01-24T16:55:36Z","snapshot_observed_at":"2026-08-16T10:14:32.426016Z","submitted_at":"2025-01-24T16:55:36Z","title":"A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization","version":1},"cited_work":{"arxiv_id":"2501.14636","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.14636","snapshot_observed_at":"2026-08-05T23:19:31.380219Z","title":"A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization","venue":"cs.LG","work_id":"42f4c473-7c61-4b52-9f6a-e73e27a94b41","year":2025},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.082782Z"},"links":{"cited_paper":"/paper/2501.14636","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:cb53ab97dfa6682ab03f86d64169d597db289d535737862ad12b3c5c4ba5667d","observation_id":"b82ec691-dff5-40de-a472-de8de66bed89","resolution":{"observed_at":"2026-08-05T23:19:31.383680Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/2632-2153/ad95dd","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Paired autoencoders for likelihood-free estimation in inverse problems","venue":"Machine Learning Science and Technology","work_id":"a5085935-b6f1-448e-8ba9-f8d74f0ae4c6","year":2024},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.086877Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9a1bf0f6d5dc4387efc561212ac55dc5ff2314d1592e7adb76c46a3140756788","observation_id":"eb69fa6a-2405-4e39-9b23-862d67c96331","resolution":{"observed_at":"2026-08-05T23:19:28.690824Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.090914Z","title":"Optimal regularized low rank inverse approximation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.090914Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:8510501af4785ad7d850ece9a93fbb516b9461146c0b98df36409056c71cc687","observation_id":"0cf4f883-cbf3-4857-93e2-99b88443c85a","resolution":{"observed_at":"2026-08-05T23:19:28.090914Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.095122Z","title":"Optimal low-rank approximations of Bayesian linear inverse problems","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.095122Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:6521c1e8b97239d7a1cc3779ebc26661aa616f3c2b51a1cb59d7a2b6e3b83fb4","observation_id":"4be519de-8380-4b92-9fba-fa3961a5f01c","resolution":{"observed_at":"2026-08-05T23:19:28.095122Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.099839Z","title":"Solving Bayesian inverse problems via variational autoencoders","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.099839Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:4ac534225a0899f02f5ff34b5bf655957dd317bf6dcf9e1566547cc59c7fdb59","observation_id":"83a4f853-d4c5-42bb-b106-f77c0b8efe2c","resolution":{"observed_at":"2026-08-05T23:19:28.099839Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.104149Z","title":"Why are big data matrices approximately low rank?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.104149Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:0f8320d4789f7c6360bbde7fd174e5667a6ac66503cc27d776b00b3cf4461c14","observation_id":"1676c104-f904-486b-bc32-604ffcf36ff9","resolution":{"observed_at":"2026-08-05T23:19:28.104149Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10462-023-10662-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Autoencoders and their applications in machine learning: a survey","venue":"Artificial Intelligence Review","work_id":"0170abf5-602a-41bc-bead-92241a599dd8","year":2024},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.107936Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:40ef22da84cee03a2949247ffa43021ce9a70dc3f92c9a09cc8ac561d628aa9c","observation_id":"7ccac2f4-845a-460f-a4f5-443e83480008","resolution":{"observed_at":"2026-08-05T23:19:28.668576Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.111328Z","title":"Medical image denoising using convolutional denoising autoencoders","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.111328Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9b8473aa23b982d040b9c5619de1426317b0a82ad437bb0516164ac34a9f457a","observation_id":"38e0cd92-ecb6-4e23-ac5e-942c68ddd9a0","resolution":{"observed_at":"2026-08-05T23:19:28.111328Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.118799Z","title":"Stacked convolutional auto-encoders for hierarchical feature extraction","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.118799Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:8ea4c2ed9555a7a85fde0bc736c26240892749c8b2013368f33dab5aaa0759ca","observation_id":"d42d8ed0-dfce-4d67-bdc7-519ca6db9a6b","resolution":{"observed_at":"2026-08-05T23:19:28.118799Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.122410Z","title":"Multilayer feedforward networks are uni- versal approximators","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.122410Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:8f1fae2ed0858734ae54cb79c17d1e0059d95f0a113895f9de8c57e15a8812e3","observation_id":"9d1b25ea-f59a-4b33-913c-dcd5a257ec47","resolution":{"observed_at":"2026-08-05T23:19:28.122410Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.126436Z","title":"The mythos of model interpretability: in machine learning, the concept of inter- pretability is both important and slippery","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.126436Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:4c2ac24dc80cc0ea135cfbf258f3b3fdbfd25ecd9ff73223704fcfbb11b7a5c8","observation_id":"67965eb9-86be-4d7e-a15e-cf699fce399d","resolution":{"observed_at":"2026-08-05T23:19:28.126436Z","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":"2021.31006","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.289432Z","title":"A survey on neural network interpretability","venue":null,"work_id":"e054df03-4f1e-4c25-a3d6-0a4dcaf94f0c","year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.141786Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:6717b8f2b43d8152a6ca1061971748347a6bfb9eb98baee2a8cbd96e8f543d34","observation_id":"51a77e84-bbfa-4c61-93ec-00f16c1fe509","resolution":{"observed_at":"2026-08-05T23:19:31.295501Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.147084Z","title":"Neural networks and principal component analysis: learning from examples without local minima","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.147084Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:857cb9a4cdfda6cbdb4aaeaca6b149e674aaf8e5e464764242309f4902b64685","observation_id":"b84413b8-00dc-4704-9b84-53fb478075e3","resolution":{"observed_at":"2026-08-05T23:19:28.147084Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.151350Z","title":"Auto-association by multilayer perceptrons and singular value decompo- sition","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.151350Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:b22184cdd1c1d9fd2bdba52103a62fb752ecf405d8d4e327ee7d38523d5687b3","observation_id":"16aa29ed-db8d-4447-88dd-4feeca8aeac6","resolution":{"observed_at":"2026-08-05T23:19:28.151350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.10253","last_updated":"2018-12-28T19:02:12Z","snapshot_observed_at":"2026-08-14T19:21:27.202487Z","submitted_at":"2018-04-26T19:28:02Z","title":"From Principal Subspaces to Principal Components with Linear Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.10253","snapshot_observed_at":"2026-08-05T23:19:28.155306Z","title":"From principal subspaces to principal components with linear autoencoders","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.155306Z"},"links":{"cited_paper":"/paper/1804.10253","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:43e8d05cfbc46f9b28acf00fb0bc1c250d9b8fac4e21ff83623b0705351d2006","observation_id":"89e16129-af47-477a-876d-bc5261275c64","resolution":{"observed_at":"2026-08-05T23:19:28.155306Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.159534Z","title":"Regularized linear autoencoders recover the principal components, eventually","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.159534Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:71e58ab17fb6dea2c9b61e5ae347ca0ef977f0300125e544e919e8fbd49af706","observation_id":"3bc95dd9-7285-4770-8a25-51e34d24174f","resolution":{"observed_at":"2026-08-05T23:19:28.159534Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.163132Z","title":"The approximation of one matrix by another of lower rank","venue":null,"work_id":null,"year":1936},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.163132Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:fc22c93c7d3b58dd68c5b27e255b848e4ca2a53c0d0c68a5a870dbf14a6e2168","observation_id":"b720d563-3a40-41a9-b4a3-72bbeaa12abc","resolution":{"observed_at":"2026-08-05T23:19:28.163132Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.166592Z","title":"Symmetric gauge functions and unitarily invariant norms","venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.166592Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:d45811c515c3b5576578432c6ecc1ad6c5fe68f666aecfcc56eac98971fd542b","observation_id":"1233058e-0b5b-472f-90dc-16103cb3efa7","resolution":{"observed_at":"2026-08-05T23:19:28.166592Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.170790Z","title":"Zur theorie der linearen und nichtlinearen integralgleichungen","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.170790Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:b79ed111ee1875a7512578c8b0f30fa88a752a72cdc7fe83f14c3efd9348c23c","observation_id":"f64a5cea-fe61-4e79-8b78-0263a3732e8a","resolution":{"observed_at":"2026-08-05T23:19:28.170790Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.175207Z","title":"Generalized rank-constrained matrix approximations","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.175207Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:0a9528a76e5832a9ab8607c2d4541e1e793fe125ebd9ba93ebbb4dc7dd210e0e","observation_id":"d984cbbe-c68c-4394-ba5a-00096e422044","resolution":{"observed_at":"2026-08-05T23:19:28.175207Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.179248Z","title":"Finding structure with randomness: probabilistic algo- rithms for constructing approximate matrix decompositions","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.179248Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:729e5d121dcfd36ed4911c7f16310af7c646b4e610011a2b07e6b7754efa92a4","observation_id":"306c6905-ab37-4fe2-8496-6e7ca5a8b356","resolution":{"observed_at":"2026-08-05T23:19:28.179248Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.186489Z","title":"Near-optimal column-based matrix reconstruction","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.186489Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:57e606e51d79901e5511613da24b28343defe787a79fe5e670de682845a2a442","observation_id":"d38d423d-d345-4866-82c9-0a7703657629","resolution":{"observed_at":"2026-08-05T23:19:28.186489Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.184286Z","title":"Dimensionality reduction for k-means clustering and low rank approxi- mation","venue":null,"work_id":"fa71ba68-5243-4412-a917-3955d9dc53a3","year":2015},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.190179Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:d58f2b0b68d3bd6f62a44a33fbf8f194a057b91199a7707aed6532c9e0464dec","observation_id":"74742c1e-dab3-41ce-ae82-cc9ae4a254b4","resolution":{"observed_at":"2026-08-05T23:19:31.188214Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/tpami.2012.274","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Low-rank matrix approximation with manifold regularization","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","work_id":"9cfd28a3-6122-44d2-aa49-87259690d6b4","year":2013},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.194487Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9cc8501c4b9014900fb3b0b250b8cb21dd68dea7694f38e4e43f02c7b919dea6","observation_id":"9f2ee133-f96a-4d94-b6e5-b71829c0f036","resolution":{"observed_at":"2026-08-05T23:19:28.602524Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:31.006229Z","title":"Subspace-orbit randomized decomposi- tion for low-rank matrix approximations","venue":null,"work_id":"da956343-db20-4a04-ba3c-9e52ffd9e624","year":2018},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.198906Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:00334bde09f5d94214473269aa6cbfce7558e93645c1b1a4ef3de6118b7cea1a","observation_id":"faf06b79-b5a4-4509-9a85-28a648b9ab50","resolution":{"observed_at":"2026-08-05T23:19:31.010195Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:30.743453Z","title":"Dimensionality reduction strategy based on auto-encoder","venue":null,"work_id":"7a2be12a-75eb-484d-a37a-58885f14036e","year":2015},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.202990Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:e21b5c9cc17f01d1862af5adead5c52d6dbb26b70329fe385b4ad6655f42330f","observation_id":"ffdbaa11-be0d-42c0-9393-63c16cd2261a","resolution":{"observed_at":"2026-08-05T23:19:30.748910Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13980","last_updated":"2025-03-25T09:41:34Z","snapshot_observed_at":"2026-08-16T13:50:29.055901Z","submitted_at":"2024-05-22T20:33:09Z","title":"Rank Reduction Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13980","snapshot_observed_at":"2026-08-05T23:19:28.207186Z","title":"Rank reduction autoencoders","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.207186Z"},"links":{"cited_paper":"/paper/2405.13980","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:97e5b4bd2cd2f7290aa76419e02c8a9629718d319f3e42d1155ac8c4334a17a1","observation_id":"9bb3a4ee-3917-4ef2-9c0b-ba5d07554b6f","resolution":{"observed_at":"2026-08-05T23:19:28.207186Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:30.535446Z","title":"Learning-based low-rank approximations","venue":null,"work_id":"33ac1e58-b605-4216-9bb8-24019db74e18","year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.212118Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:caac294e41418df8fe9245346c0004ed34cde5a4e321b9cd719a38f3b49aa9b4","observation_id":"3df537d1-afe8-4bcd-ac4c-0950e88c7812","resolution":{"observed_at":"2026-08-05T23:19:30.541373Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:30.338831Z","title":"Sparse Bayesian methods for low-rank matrix estimation","venue":null,"work_id":"7c8196b2-1f11-4164-bb81-cbdd86564b59","year":2012},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.215868Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:53f84b97ce83f579b342b67bcc16556f847d1b7ca1c5f35cb77d4539b2f30cb9","observation_id":"ebdc24f5-dc3b-4315-8e9f-db1a67938cb7","resolution":{"observed_at":"2026-08-05T23:19:30.343825Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:30.143062Z","title":"Learning low-rank latent spaces with simple deterministic autoencoder: theoretical and empirical insights","venue":null,"work_id":"8dce5ad2-6738-4074-bf83-ff7289bfa666","year":2024},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.219866Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:20a14cf89819ac43601fe32531534c2f5e737eb4fd3744556e41616c7f072e2e","observation_id":"a1476b53-ce2d-4a14-93af-ce0b915bc6b8","resolution":{"observed_at":"2026-08-05T23:19:30.149615Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1023/a:1018577817064","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Bayes and empirical Bayes methods for data analysis","venue":"Statistics and Computing","work_id":"6448cc75-eba5-4560-95e6-23c961e410a4","year":1997},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.225342Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:e54f412a8906ab9d070b361dbefdc56c6ee51193ad1cde1e3f43dd920c714f1d","observation_id":"edee38da-8cef-4b26-b8ca-41ecc82f0c8f","resolution":{"observed_at":"2026-08-05T23:19:28.590698Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.230993Z","title":"Computing optimal low-rank matrix approximations for image processing","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.230993Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:c6b55be6717503a1e49640155e8cb05295d0fb40bc6359b5f0ca5e9014f31d0a","observation_id":"db191785-201b-4fc8-a3bb-425f8744ba31","resolution":{"observed_at":"2026-08-05T23:19:28.230993Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.241432Z","title":"Optimal regularized inverse matrices for inverse problems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.241432Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9b8fb68b0fa51bf271affcedcaf1ba150a0d8243898eaba4afd73b20246664f4","observation_id":"d9b26abe-bad2-43dc-921c-45c6fe64ae72","resolution":{"observed_at":"2026-08-05T23:19:28.241432Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.246101Z","title":"An efficient approach for computing optimal low-rank regu- larized inverse matrices","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.246101Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:bae84772d996c0221ab676655790c16458b47b1aaa278b463467ca4a8d3fa999","observation_id":"36d69015-4f04-4952-bb38-46725af1692a","resolution":{"observed_at":"2026-08-05T23:19:28.246101Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-05T23:19:28.250024Z","title":"Auto-encoding variational Bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.250024Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:df297f7742b3699515bc188d4bba86ad1a530872a9f32346b26af11243996347","observation_id":"88299100-6890-4802-9451-164bbe25dc34","resolution":{"observed_at":"2026-08-05T23:19:28.250024Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.254158Z","title":"On the reciprocal of the general algebraic matrix","venue":null,"work_id":null,"year":1920},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.254158Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:8475bf25ea976ebfa85532081e689069a5aa53c5142f4f4aa2552a8a7d7349b2","observation_id":"c1189294-9b7f-4b7a-9b74-906747bc3f38","resolution":{"observed_at":"2026-08-05T23:19:28.254158Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.257793Z","title":"A generalized inverse for matrices","venue":null,"work_id":null,"year":1955},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.257793Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:2392b0b7d53681ae0c15a9effe21f1a5db2c35bbf25dbd1afaf0a8530f21abe1","observation_id":"da8be438-505f-47db-a565-33a0baa673ef","resolution":{"observed_at":"2026-08-05T23:19:28.257793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06549","last_updated":"2025-08-18T23:57:40Z","snapshot_observed_at":"2026-08-17T15:23:01.321133Z","submitted_at":"2025-05-10T07:31:09Z","title":"Good Things Come in Pairs: Paired Autoencoders for Inverse Problems","version":2},"cited_work":{"arxiv_id":"2505.06549","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.06549","snapshot_observed_at":"2026-08-05T23:19:29.555120Z","title":"Good Things Come in Pairs: Paired Autoencoders for Inverse Problems","venue":"cs.LG","work_id":"18bf9cbc-1d71-40d6-bcdf-4c39d65ece3e","year":2025},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.261181Z"},"links":{"cited_paper":"/paper/2505.06549","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:80f2f1419c7b1b9fa39381b22d3db5e0db8724a50e488f302bd80070c795f90a","observation_id":"7709f85f-57dd-4766-a8ea-c9532302ffb7","resolution":{"observed_at":"2026-08-05T23:19:29.559480Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.264934Z","title":"The Elements of Statistical Learning","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.264934Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:24efb1376b47302579b25737fd4edff70e17f88f02ea97f135cf75cf946408a3","observation_id":"ba1a2999-3d9c-4c89-97bf-a95b83d07f1c","resolution":{"observed_at":"2026-08-05T23:19:28.264934Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.268269Z","title":"Williams and Carl E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.268269Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:f41a76ef36b0606a86406b230cd2383cf527d39a492ab965abf9acbb5bfb3d89","observation_id":"9cb23628-acc6-4211-bdb7-e09f19e882d7","resolution":{"observed_at":"2026-08-05T23:19:28.268269Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.275365Z","title":"MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.275365Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:5165edb86cb4fe932a0625a98fb8d5d898644aae7bd56dfd785f2f32518668c1","observation_id":"b196d93d-392b-4488-a164-72d054d9b94d","resolution":{"observed_at":"2026-08-05T23:19:28.275365Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.278804Z","title":"Annotated high-throughput mi- croscopy image sets for validation","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.278804Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:6c475eaf49ebf2164f1df8c34b09201126f1c30889a5a84684ef89b31d3b66c7","observation_id":"99c55b82-180b-4957-815f-016f50024bfa","resolution":{"observed_at":"2026-08-05T23:19:28.278804Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.282217Z","title":"ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.282217Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:6440be7972799bdc2d40601eaad6c6a43eddfb01302bebcf747e0ae52f3c761c","observation_id":"4c743a40-7f86-4022-9282-0f0f1479b7c6","resolution":{"observed_at":"2026-08-05T23:19:28.282217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04056","last_updated":"2022-11-25T09:24:35Z","snapshot_observed_at":"2026-08-14T17:31:38.744041Z","submitted_at":"2019-01-13T20:38:16Z","title":"The Liver Tumor Segmentation Benchmark (LiTS)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04056","snapshot_observed_at":"2026-08-05T23:19:28.286189Z","title":"The liver tumor segmentation benchmark (LiTS)","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.286189Z"},"links":{"cited_paper":"/paper/1901.04056","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:7c174ca78e7a1a69aa690f5f21b0226fb41bd527a1731b24194e2747b25ac4e0","observation_id":"60240d76-dcde-4878-9663-f8816cfa5e79","resolution":{"observed_at":"2026-08-05T23:19:28.286189Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.271809Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.271809Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:49abe42b07fcbc1a84b80c060b057e9e543ce3004f9f41444e1942bce0d54ca7","observation_id":"a100c4f7-a1f3-4d83-b0be-82d4707d16e4","resolution":{"observed_at":"2026-08-05T23:19:28.271809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T23:19:28.294967Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.294967Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:77bc7d2a79d95930605c7a99c82eb30702de66da52d0c518ea79f8a2681ab26f","observation_id":"079a8570-46e2-4730-85ba-fede8900a2e1","resolution":{"observed_at":"2026-08-05T23:19:28.294967Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.299144Z","title":"Common risk factors in the returns on stocks and bonds","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.299144Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:cc767ba5065f4a8690a13ee7c792b0749610648476285a423d5f0a0c44d13bfa","observation_id":"3feb3e28-5d82-4c42-b002-8b1b59b1edee","resolution":{"observed_at":"2026-08-05T23:19:28.299144Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:29.514276Z","title":"Capital asset prices: a theory of market equilibrium under conditions of risk","venue":null,"work_id":"00e6cd3c-9739-4e45-9096-692d2cb92e4a","year":1964},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.303019Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:7d53df015ff675ddace70f3d2c3c324da63605b974298bbe339dffe2390da689","observation_id":"71028f16-a573-47f3-b6f1-f947b356d77a","resolution":{"observed_at":"2026-08-05T23:19:29.518425Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:29.343430Z","title":"The cross-section of expected stock returns","venue":null,"work_id":"be135df6-7f4b-480c-a3f1-efd2d44f05b4","year":1992},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.306876Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:3c4aee3874936f220e0594469be60fa0243691b4329b24d1b8586767b5a064d2","observation_id":"745ec9ec-00c8-4afe-b37c-b6734968751f","resolution":{"observed_at":"2026-08-05T23:19:29.347481Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.290347Z","title":"Efficient multiple organ localization in CT image using 3D region proposal network","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.290347Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:d8be5d97c9ed9f45db5a195d8f9e32975a2b9d454c2b6bd67ae2a2a7cecb7070","observation_id":"0727322f-567f-43ae-a46e-93e1a6639c05","resolution":{"observed_at":"2026-08-05T23:19:28.290347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.02138","last_updated":"2018-05-10T16:53:58Z","snapshot_observed_at":"2026-08-14T20:06:20.575228Z","submitted_at":"2017-12-06T11:40:42Z","title":"A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering","version":2},"cited_work":{"arxiv_id":"1712.02138","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.02138","snapshot_observed_at":"2026-08-05T23:19:29.171100Z","title":"A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering","venue":"q-fin.ST","work_id":"ade1a6c5-c0b5-4ce8-a5ed-d30b4935fce1","year":2017},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.314983Z"},"links":{"cited_paper":"/paper/1712.02138","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:079516466bf6c01421f660bd12d634d310ebacc47a7b6be4a609c7408679cac4","observation_id":"0c1c5ee1-2ce6-4992-a470-143c2763b921","resolution":{"observed_at":"2026-08-05T23:19:29.176360Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0022-0531(76)90046-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"The arbitrage theory of capital asset pricing","venue":"Journal of Economic Theory","work_id":"81c148f7-455c-4b28-aa98-47d1606ade56","year":1976},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.319274Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:bf91287137f1736c4909839f4e27afa96483d96250ff1453d5a0c265e02e10e7","observation_id":"3801d5eb-3070-4ece-8142-895337c6f232","resolution":{"observed_at":"2026-08-05T23:19:28.550180Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.323698Z","title":"yfinance: fownload market data from Yahoo! Finance’s API","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.323698Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9ed1f7ed0625cf9515b434cb32c249e6f9227051f804b8c89f92b658eb9f4a98","observation_id":"b53df0a7-e382-4353-8ef4-a9f78dc34e92","resolution":{"observed_at":"2026-08-05T23:19:28.323698Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:29.156856Z","title":"Quant GANs: deep generation of financial time series","venue":null,"work_id":"3e3fe21e-79ae-4151-87c2-ed84846ae83b","year":2020},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.327156Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:9c849c6ab512f6f71cef692efdaa3e39a6d6ff857f785aae1523266f2b170232","observation_id":"bc08b810-3cfd-4213-b03b-5a63f8b5beaf","resolution":{"observed_at":"2026-08-05T23:19:29.160749Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-642-04898-2_455","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:07:33.414567Z","title":"Principal component analysis","venue":"International Encyclopedia of Statistical Science","work_id":"273064fc-7683-4c74-8b9f-8ae6c0c3c759","year":2011},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.311105Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:af02ff0e45a31c9b4845d06897e5ca167193101ad8f32fbf1881885a9b7a2fe8","observation_id":"006b7583-c909-46bd-a62e-eae480887a1b","resolution":{"observed_at":"2026-08-05T23:19:28.561765Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.341853Z","title":"The Varimax criterion for analytic rotation in factor analysis","venue":null,"work_id":null,"year":1958},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.341853Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:e4b8fb34a8410a3ab83fe93fcf93a663ab01ae05895791da32a926133bb87572","observation_id":"09d263cb-7cb5-43a8-adf8-4d80e1690215","resolution":{"observed_at":"2026-08-05T23:19:28.341853Z","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":"10.1007/bf02289522","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A matrix formulation of Kaiser’s Varimax criterion","venue":"Psychometrika","work_id":"d0105b61-5136-4585-ace5-03f0bbc494ea","year":1966},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.345366Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:3eeb8afa0f525126fbc01c79bb1768953fd68146e4f01462ca0208f4737e016c","observation_id":"25ae7609-b0c6-41cc-9f86-9b46447b317d","resolution":{"observed_at":"2026-08-05T23:19:28.504043Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.348877Z","title":"An overview of analytic rotation in exploratory factor analysis","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.348877Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:61c3ccb1ca9ff2260c731faed2411482e58f3049a2462d6225779c5e0eb92af5","observation_id":"533f77ac-e8d4-4dc8-8aa1-2841e3c9b829","resolution":{"observed_at":"2026-08-05T23:19:28.348877Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.352219Z","title":"Fama and Kenneth R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.352219Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:a3da4510f6cfc545dddb4af477cc6ec127c650ebfc38f09836011973ae41df49","observation_id":"5b3bddbd-a09a-441e-b624-e23634d8f9e8","resolution":{"observed_at":"2026-08-05T23:19:28.352219Z","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":"10.2307/j.ctt7skm5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Campbell, Andrew W","venue":"Princeton University Press eBooks","work_id":"e3521574-641b-4ff7-93c4-7f50c661904e","year":1997},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.335116Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:4bb92d91778400855fa119852b05f0e740834ae43d3d6bacb67faf9035bbc462","observation_id":"55e854a2-06e9-45f3-ae67-f2bb81d953e2","resolution":{"observed_at":"2026-08-05T23:19:28.529721Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.358783Z","title":"Brockwell and Richard A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.358783Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:d2a5017fa74f7007dc3989826a8961363d8725ce7a90a0496362d28e28f7928b","observation_id":"1bfdeb18-3e02-4cdf-a401-70db95a48ad1","resolution":{"observed_at":"2026-08-05T23:19:28.358783Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.362774Z","title":"Generalized autoregressive conditional heteroskedasticity","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.362774Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:02b221fda814ef2bfcf912723fdd6cca2f5c5865cde714af42c9ad63a7c5e7ec","observation_id":"1112bd7e-3579-4d79-9393-07a1b96f02a4","resolution":{"observed_at":"2026-08-05T23:19:28.362774Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.365950Z","title":"Unsupervised alignment of embeddings with Wasserstein Procrustes","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.365950Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:b260fd4d47caa2af3be07e98e29eeff8393e1ffa44bd995dd2d045ea10bcd7d6","observation_id":"07672b62-d73d-42ae-a742-650fb96c610a","resolution":{"observed_at":"2026-08-05T23:19:28.365950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.15456","last_updated":"2021-06-29T14:43:06Z","snapshot_observed_at":"2026-08-16T18:13:38.942598Z","submitted_at":"2021-06-29T14:43:06Z","title":"A Mechanism for Producing Aligned Latent Spaces with Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.15456","snapshot_observed_at":"2026-08-05T23:19:28.369000Z","title":"A mechanism for producing aligned latent spaces with autoen- coders","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.369000Z"},"links":{"cited_paper":"/paper/2106.15456","citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:a63874cc4f44b5d1333ebd14a44e16488aeac1bec515542838c8691dbe7184af","observation_id":"8c794606-57ae-4e88-a80e-59b7d9a9eaf2","resolution":{"observed_at":"2026-08-05T23:19:28.369000Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:19:28.355485Z","title":"A value for n-person games","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-05T23:19:28.355485Z"},"links":{"citing_paper":"/paper/2508.05831"},"observation_digest":"sha256:fe77d760ba53c084eceff6f30e63cb25679aba08a233a591acb3e2db3bc06d2a","observation_id":"cceb72f0-7995-479c-b16e-f72f5326335e","resolution":{"observed_at":"2026-08-05T23:19:28.355485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.05831","last_updated":"2025-08-07T20:17:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T13:24:28.624450Z","submitted_at":"2025-08-07T20:17:07Z","title":"Optimal Linear Baseline Models for Scientific Machine Learning"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":10,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":54,"verified_exact":31,"verified_fuzzy":0},"total_outbound_references":117},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 0 inbound Pith citation observations for arXiv:2508.05831."}