{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:Q542FHXHFOFIQX5WZDAX7HC42B","short_pith_number":"pith:Q542FHXH","schema_version":"1.0","canonical_sha256":"8779a29ee72b8a885fb6c8c17f9c5cd06d16b59600346089523036621389be07","source":{"kind":"arxiv","id":"2302.11874","version":1},"attestation_state":"computed","paper":{"title":"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Ido Galil, Mohammed Dabbah, Ran El-Yaniv","submitted_at":"2023-02-23T09:25:28Z","abstract_excerpt":"When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrain"},"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":"2302.11874","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-23T09:25:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5d1475e2b56904b7c59a771297cf07048820fb51fdcf6d270ae91261fc77ac12","abstract_canon_sha256":"374d020244a0bc25551bb059b43e8ca9c571eb4efa69f57e7fe63add5b85c614"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:57.518148Z","signature_b64":"sa4ptl7HQF4xSwAJetxUZ27/yDIdHervFrAhwr8UP8Pj9yF9JcW2ln90SxYlCx39KlJYKUiLE22rMX5B8GjWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8779a29ee72b8a885fb6c8c17f9c5cd06d16b59600346089523036621389be07","last_reissued_at":"2026-07-05T05:44:57.517686Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:57.517686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Ido Galil, Mohammed Dabbah, Ran El-Yaniv","submitted_at":"2023-02-23T09:25:28Z","abstract_excerpt":"When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11874","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/2302.11874/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":"2302.11874","created_at":"2026-07-05T05:44:57.517743+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.11874v1","created_at":"2026-07-05T05:44:57.517743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11874","created_at":"2026-07-05T05:44:57.517743+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q542FHXHFOFI","created_at":"2026-07-05T05:44:57.517743+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q542FHXHFOFIQX5W","created_at":"2026-07-05T05:44:57.517743+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q542FHXH","created_at":"2026-07-05T05:44:57.517743+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.07556","citing_title":"Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B","json":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B.json","graph_json":"https://pith.science/api/pith-number/Q542FHXHFOFIQX5WZDAX7HC42B/graph.json","events_json":"https://pith.science/api/pith-number/Q542FHXHFOFIQX5WZDAX7HC42B/events.json","paper":"https://pith.science/paper/Q542FHXH"},"agent_actions":{"view_html":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B","download_json":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B.json","view_paper":"https://pith.science/paper/Q542FHXH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.11874&json=true","fetch_graph":"https://pith.science/api/pith-number/Q542FHXHFOFIQX5WZDAX7HC42B/graph.json","fetch_events":"https://pith.science/api/pith-number/Q542FHXHFOFIQX5WZDAX7HC42B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B/action/storage_attestation","attest_author":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B/action/author_attestation","sign_citation":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B/action/citation_signature","submit_replication":"https://pith.science/pith/Q542FHXHFOFIQX5WZDAX7HC42B/action/replication_record"}},"created_at":"2026-07-05T05:44:57.517743+00:00","updated_at":"2026-07-05T05:44:57.517743+00:00"}