{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:J2MFPQ4AYQG4APIJP7JZCDXLVP","short_pith_number":"pith:J2MFPQ4A","schema_version":"1.0","canonical_sha256":"4e9857c380c40dc03d097fd3910eebabc9e9b9509394b1c0ed300780c74ce969","source":{"kind":"arxiv","id":"2008.10548","version":1},"attestation_state":"computed","paper":{"title":"Certainty Pooling for Multiple Instance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eldad Klaiman, Ido Ben-Shaul, Jacob Gildenblat, Zvi Lapp","submitted_at":"2020-08-24T16:38:46Z","abstract_excerpt":"Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings. We present a novel pooling operator called \\textbf{Certainty Pooling} which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. We compare our proposed method with other pooling operators in controlled experiment"},"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":"2008.10548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-24T16:38:46Z","cross_cats_sorted":[],"title_canon_sha256":"e7358015e10b7ba5d99013ec4076f6158f65230258646b92a5437a2db7558ea0","abstract_canon_sha256":"0b73fb647992fba0a2527a3d8638746a375da5413efd66b408f8b115a678000f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:29:25.435568Z","signature_b64":"Z90RXop7opS0VRrVwGQIad+PDmIbmk96u0VOLqgqB5s05z/EOf1M8CxAHOkoLvXZTMsQUoHEFO9mbiTTEwlvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e9857c380c40dc03d097fd3910eebabc9e9b9509394b1c0ed300780c74ce969","last_reissued_at":"2026-07-05T01:29:25.435124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:29:25.435124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Certainty Pooling for Multiple Instance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eldad Klaiman, Ido Ben-Shaul, Jacob Gildenblat, Zvi Lapp","submitted_at":"2020-08-24T16:38:46Z","abstract_excerpt":"Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings. We present a novel pooling operator called \\textbf{Certainty Pooling} which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. We compare our proposed method with other pooling operators in controlled experiment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.10548","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/2008.10548/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":"2008.10548","created_at":"2026-07-05T01:29:25.435177+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.10548v1","created_at":"2026-07-05T01:29:25.435177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.10548","created_at":"2026-07-05T01:29:25.435177+00:00"},{"alias_kind":"pith_short_12","alias_value":"J2MFPQ4AYQG4","created_at":"2026-07-05T01:29:25.435177+00:00"},{"alias_kind":"pith_short_16","alias_value":"J2MFPQ4AYQG4APIJ","created_at":"2026-07-05T01:29:25.435177+00:00"},{"alias_kind":"pith_short_8","alias_value":"J2MFPQ4A","created_at":"2026-07-05T01:29:25.435177+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/J2MFPQ4AYQG4APIJP7JZCDXLVP","json":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP.json","graph_json":"https://pith.science/api/pith-number/J2MFPQ4AYQG4APIJP7JZCDXLVP/graph.json","events_json":"https://pith.science/api/pith-number/J2MFPQ4AYQG4APIJP7JZCDXLVP/events.json","paper":"https://pith.science/paper/J2MFPQ4A"},"agent_actions":{"view_html":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP","download_json":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP.json","view_paper":"https://pith.science/paper/J2MFPQ4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.10548&json=true","fetch_graph":"https://pith.science/api/pith-number/J2MFPQ4AYQG4APIJP7JZCDXLVP/graph.json","fetch_events":"https://pith.science/api/pith-number/J2MFPQ4AYQG4APIJP7JZCDXLVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP/action/storage_attestation","attest_author":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP/action/author_attestation","sign_citation":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP/action/citation_signature","submit_replication":"https://pith.science/pith/J2MFPQ4AYQG4APIJP7JZCDXLVP/action/replication_record"}},"created_at":"2026-07-05T01:29:25.435177+00:00","updated_at":"2026-07-05T01:29:25.435177+00:00"}