{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OCH236WXWWPKEQJ5RFVKMFHJCZ","short_pith_number":"pith:OCH236WX","schema_version":"1.0","canonical_sha256":"708fadfad7b59ea2413d896aa614e9164563c78794cfb93a34e6b9bc4a2446a0","source":{"kind":"arxiv","id":"2406.00539","version":2},"attestation_state":"computed","paper":{"title":"CONFINE: Conformal Prediction for Interpretable Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Linhui Huang, Niraj K. Jha, Sayeri Lala","submitted_at":"2024-06-01T19:34:48Z","abstract_excerpt":"Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measures of prediction uncertainty. In this study, we introduce Conformal Prediction for Interpretable Neural Networks (CONFINE), a versatile framework that generates prediction sets with statistically robust uncertainty estimates instead of point predictions to enhance model transparency and reliability. CONFINE not only provides example-based explanati"},"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":"2406.00539","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-01T19:34:48Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b6bf33b42ae6cc73dca2eac5660fd528ff5d0b99140f5cdafcc4d7bcb8591438","abstract_canon_sha256":"f65df26ebb6b42cebacf1cf041ad4436abf0d4ce82653f778ad33a8b109a9f66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:08.689132Z","signature_b64":"QCBJFx52A+tjMWu+2sDQOCuCWeyiyPJz/H0uoJws+bBK3oa7vYnJJGJD4dmdcmBRPkpssQX+/PSjIec5WZWvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"708fadfad7b59ea2413d896aa614e9164563c78794cfb93a34e6b9bc4a2446a0","last_reissued_at":"2026-07-05T10:45:08.688596Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:08.688596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CONFINE: Conformal Prediction for Interpretable Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Linhui Huang, Niraj K. Jha, Sayeri Lala","submitted_at":"2024-06-01T19:34:48Z","abstract_excerpt":"Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measures of prediction uncertainty. In this study, we introduce Conformal Prediction for Interpretable Neural Networks (CONFINE), a versatile framework that generates prediction sets with statistically robust uncertainty estimates instead of point predictions to enhance model transparency and reliability. CONFINE not only provides example-based explanati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00539","kind":"arxiv","version":2},"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/2406.00539/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":"2406.00539","created_at":"2026-07-05T10:45:08.688670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.00539v2","created_at":"2026-07-05T10:45:08.688670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00539","created_at":"2026-07-05T10:45:08.688670+00:00"},{"alias_kind":"pith_short_12","alias_value":"OCH236WXWWPK","created_at":"2026-07-05T10:45:08.688670+00:00"},{"alias_kind":"pith_short_16","alias_value":"OCH236WXWWPKEQJ5","created_at":"2026-07-05T10:45:08.688670+00:00"},{"alias_kind":"pith_short_8","alias_value":"OCH236WX","created_at":"2026-07-05T10:45:08.688670+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.08885","citing_title":"Uncertainty-Aware Transformers: Conformal Prediction for Language Models","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ","json":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ.json","graph_json":"https://pith.science/api/pith-number/OCH236WXWWPKEQJ5RFVKMFHJCZ/graph.json","events_json":"https://pith.science/api/pith-number/OCH236WXWWPKEQJ5RFVKMFHJCZ/events.json","paper":"https://pith.science/paper/OCH236WX"},"agent_actions":{"view_html":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ","download_json":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ.json","view_paper":"https://pith.science/paper/OCH236WX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.00539&json=true","fetch_graph":"https://pith.science/api/pith-number/OCH236WXWWPKEQJ5RFVKMFHJCZ/graph.json","fetch_events":"https://pith.science/api/pith-number/OCH236WXWWPKEQJ5RFVKMFHJCZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ/action/storage_attestation","attest_author":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ/action/author_attestation","sign_citation":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ/action/citation_signature","submit_replication":"https://pith.science/pith/OCH236WXWWPKEQJ5RFVKMFHJCZ/action/replication_record"}},"created_at":"2026-07-05T10:45:08.688670+00:00","updated_at":"2026-07-05T10:45:08.688670+00:00"}