{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:GGH7MLGG6W5YVC2452KMW2JVSP","short_pith_number":"pith:GGH7MLGG","schema_version":"1.0","canonical_sha256":"318ff62cc6f5bb8a8b5cee94cb693593cfbe39b49112480e50b9c793276bc841","source":{"kind":"arxiv","id":"2006.02528","version":1},"attestation_state":"computed","paper":{"title":"Learning across label confidence distributions using Filtered Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Windemuth, Andrew E. Brereton, Benjamin Haibe-Kains, Seyed Ali Madani Tonekaboni, Stephen MacKinnon, Zhaleh Safikhani","submitted_at":"2020-06-03T21:00:11Z","abstract_excerpt":"Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve the issue of small dataset size by letting the model train on a bigger, task-related reference dataset and then fine-tune on a smaller, task-specific dataset. In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets. We propose a deep neural network method called Filtered Transfer Learning (FTL) that defines multiple tiers of data conf"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.02528","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-03T21:00:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cfc1a0f70172f7161f12327931174414fa32889ccecc4167533bee14118596ba","abstract_canon_sha256":"988d8ca9cf261436da0840577e4374f4ba0d604c72945a59d2b671e1cec6457e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:07:57.580878Z","signature_b64":"XA8aIGXWDpvcOSs2nR/2EP1xcLvLVZv9u+dvOmgtqF5EYFJ+xZrPDjAJF22jWFyiXtgC6f9d/Jn+wVRmCuakDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"318ff62cc6f5bb8a8b5cee94cb693593cfbe39b49112480e50b9c793276bc841","last_reissued_at":"2026-07-05T01:07:57.580360Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:07:57.580360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning across label confidence distributions using Filtered Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Windemuth, Andrew E. Brereton, Benjamin Haibe-Kains, Seyed Ali Madani Tonekaboni, Stephen MacKinnon, Zhaleh Safikhani","submitted_at":"2020-06-03T21:00:11Z","abstract_excerpt":"Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve the issue of small dataset size by letting the model train on a bigger, task-related reference dataset and then fine-tune on a smaller, task-specific dataset. In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets. We propose a deep neural network method called Filtered Transfer Learning (FTL) that defines multiple tiers of data conf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.02528","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.02528/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.02528","created_at":"2026-07-05T01:07:57.580423+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.02528v1","created_at":"2026-07-05T01:07:57.580423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.02528","created_at":"2026-07-05T01:07:57.580423+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGH7MLGG6W5Y","created_at":"2026-07-05T01:07:57.580423+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGH7MLGG6W5YVC24","created_at":"2026-07-05T01:07:57.580423+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGH7MLGG","created_at":"2026-07-05T01:07:57.580423+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP","json":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP.json","graph_json":"https://pith.science/api/pith-number/GGH7MLGG6W5YVC2452KMW2JVSP/graph.json","events_json":"https://pith.science/api/pith-number/GGH7MLGG6W5YVC2452KMW2JVSP/events.json","paper":"https://pith.science/paper/GGH7MLGG"},"agent_actions":{"view_html":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP","download_json":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP.json","view_paper":"https://pith.science/paper/GGH7MLGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.02528&json=true","fetch_graph":"https://pith.science/api/pith-number/GGH7MLGG6W5YVC2452KMW2JVSP/graph.json","fetch_events":"https://pith.science/api/pith-number/GGH7MLGG6W5YVC2452KMW2JVSP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP/action/storage_attestation","attest_author":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP/action/author_attestation","sign_citation":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP/action/citation_signature","submit_replication":"https://pith.science/pith/GGH7MLGG6W5YVC2452KMW2JVSP/action/replication_record"}},"created_at":"2026-07-05T01:07:57.580423+00:00","updated_at":"2026-07-05T01:07:57.580423+00:00"}