{"as_of":"2026-08-22T05:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de4272ab3a1b08ed67b1f081287906b4518f4845092a3da7cf566a2f26fbd010","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:34:03.763964Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T08:14:25.678018Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.07588","snapshot_observed_at":"2026-08-11T14:34:03.763964Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11839","last_updated":"2024-12-16T15:01:53Z","snapshot_observed_at":"2026-08-11T14:29:17.348797Z","submitted_at":"2024-12-16T15:01:53Z","title":"Evaluating the Efficacy of Vectocardiographic and ECG Parameters for Efficient Tertiary Cardiology Care Allocation Using Decision Tree Analysis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:34:03.763964Z"},"links":{"cited_paper":"/paper/2007.07588","citing_paper":"/paper/2412.11839"},"observation_digest":"sha256:2551df3d372801e8a095e083cb43a3e0e49db6d895e70d8d212dc852e5a58ab9","observation_id":"b7796b14-5ab0-4a00-a207-4877d56e2fe1","resolution":{"observed_at":"2026-08-11T14:34:03.763964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.07588","snapshot_observed_at":"2026-08-07T10:45:05.032877Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.04553","last_updated":"2025-06-05T01:58:45Z","snapshot_observed_at":"2026-08-13T22:54:01.386227Z","submitted_at":"2025-06-05T01:58:45Z","title":"Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices","version":1},"reference_index":178,"source":"pdf_text","source_observed_at":"2026-08-07T10:45:05.032877Z"},"links":{"cited_paper":"/paper/2007.07588","citing_paper":"/paper/2506.04553"},"observation_digest":"sha256:a28070246314101cb25b58c453d4044e560dd17d57d66c5155c3adce3cfaebdf","observation_id":"213bf359-a321-40dd-a51e-3776b7472914","resolution":{"observed_at":"2026-08-07T10:45:05.032877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.07588","snapshot_observed_at":"2026-08-03T17:44:56.686707Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.08444","last_updated":"2026-06-28T09:38:08Z","snapshot_observed_at":"2026-08-19T05:29:00.303209Z","submitted_at":"2025-12-09T10:18:51Z","title":"Learned iterative networks: An operator learning perspective","version":2},"reference_index":146,"source":"pdf_text","source_observed_at":"2026-08-03T17:44:56.686707Z"},"links":{"cited_paper":"/paper/2007.07588","citing_paper":"/paper/2512.08444"},"observation_digest":"sha256:f6160a05da2bb2b0315720a24d354e628801961b43a3bcbd509a74679b097559","observation_id":"9e731961-161f-484c-aadf-b8ea890d7ab9","resolution":{"observed_at":"2026-08-03T17:44:56.686707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","version":1},"cited_work":{"arxiv_id":"2007.07588","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.07588","snapshot_observed_at":"2026-06-30T08:14:25.678018Z","title":"Importance of tuning hyperparameters of machine learning algorithms","venue":null,"work_id":"b996c678-5ac9-48c5-b365-2d4e6a775c3f","year":2007},"citing_paper":{"arxiv_id":"2605.06018","last_updated":"2026-05-07T11:14:17Z","snapshot_observed_at":"2026-08-13T12:23:43.753444Z","submitted_at":"2026-05-07T11:14:17Z","title":"I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks","version":1},"reference_index":123,"source":"pdf_text","source_observed_at":"2026-05-08T07:30:07.106213Z"},"links":{"cited_paper":"/paper/2007.07588","citing_paper":"/paper/2605.06018"},"observation_digest":"sha256:50a785068755ab7c63f9e9470dc08e31864ea38a81d3c16aa0f28725f9289cc3","observation_id":"b29487b9-8f69-450c-b00e-a0007a12322d","resolution":{"observed_at":"2026-05-11T21:01:10.977330Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","version":1},"cited_work":{"arxiv_id":"2007.07588","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.07588","snapshot_observed_at":"2026-06-30T08:14:25.678018Z","title":"Importance of tuning hyperparameters of machine learning algorithms","venue":null,"work_id":"b996c678-5ac9-48c5-b365-2d4e6a775c3f","year":2007},"citing_paper":{"arxiv_id":"2606.29119","last_updated":"2026-06-28T00:12:46Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-28T00:12:46Z","title":"Knowing in Advance When an Evolutionary Outer Loop Will Not Help: A Pre-Registered Cheap-Baseline Screening Rule","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T08:07:59.710459Z"},"links":{"cited_paper":"/paper/2007.07588","citing_paper":"/paper/2606.29119"},"observation_digest":"sha256:a98d04936feb3559e7c4e632f498f81e03bf1d339ac7141b7fb2f51ad1d9511a","observation_id":"fcaf1245-975a-4c45-af18-a98670230575","resolution":{"observed_at":"2026-06-30T08:14:25.680365Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2007.07588/citation-record","integrity":"/paper/2007.07588/integrity","json":"/paper/2007.07588/citation-record.json","paper":"/paper/2007.07588"},"outbound":[],"paper":{"arxiv_id":"2007.07588","last_updated":"2020-07-15T10:06:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T13:45:18.929576Z","submitted_at":"2020-07-15T10:06:59Z","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.07588."}