{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GCNH7TTMCVM76VWEO4YVH7DEBY","short_pith_number":"pith:GCNH7TTM","schema_version":"1.0","canonical_sha256":"309a7fce6c1559ff56c4773153fc640e16b12aaea41a0a855a16f4e372bc9035","source":{"kind":"arxiv","id":"2402.05806","version":4},"attestation_state":"computed","paper":{"title":"On Temperature Scaling and Conformal Prediction of Deep Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Lahav Dabah, Tom Tirer","submitted_at":"2024-02-08T16:45:12Z","abstract_excerpt":"In many classification applications, the prediction of a deep neural network (DNN) based classifier needs to be accompanied by some confidence indication. Two popular approaches for that aim are: 1) Calibration: modifies the classifier's softmax values such that the maximal value better estimates the correctness probability; and 2) Conformal Prediction (CP): produces a prediction set of candidate labels that contains the true label with a user-specified probability, guaranteeing marginal coverage but not, e.g., per class coverage. In practice, both types of indications are desirable, yet, so f"},"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":"2402.05806","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-08T16:45:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4769e8bdf8ba6bf610bc1e48c1712464f362c222d44347e5bbb6b8be6e3cc44e","abstract_canon_sha256":"1f7dce40e4aa8f2756dea955c208469627bb9b7138877d07e1ca37050a961b15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:07.344829Z","signature_b64":"zIJw52sl8oxDyYDAWfQl6rxkrWrzORIWCfMLXpYHOUMPJF6a81bKAKDRJLUQ0V3+V+L105QxFqSPfAyPvG59Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"309a7fce6c1559ff56c4773153fc640e16b12aaea41a0a855a16f4e372bc9035","last_reissued_at":"2026-07-05T11:13:07.344388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:07.344388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Temperature Scaling and Conformal Prediction of Deep Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Lahav Dabah, Tom Tirer","submitted_at":"2024-02-08T16:45:12Z","abstract_excerpt":"In many classification applications, the prediction of a deep neural network (DNN) based classifier needs to be accompanied by some confidence indication. Two popular approaches for that aim are: 1) Calibration: modifies the classifier's softmax values such that the maximal value better estimates the correctness probability; and 2) Conformal Prediction (CP): produces a prediction set of candidate labels that contains the true label with a user-specified probability, guaranteeing marginal coverage but not, e.g., per class coverage. In practice, both types of indications are desirable, yet, so f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05806","kind":"arxiv","version":4},"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/2402.05806/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":"2402.05806","created_at":"2026-07-05T11:13:07.344447+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.05806v4","created_at":"2026-07-05T11:13:07.344447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05806","created_at":"2026-07-05T11:13:07.344447+00:00"},{"alias_kind":"pith_short_12","alias_value":"GCNH7TTMCVM7","created_at":"2026-07-05T11:13:07.344447+00:00"},{"alias_kind":"pith_short_16","alias_value":"GCNH7TTMCVM76VWE","created_at":"2026-07-05T11:13:07.344447+00:00"},{"alias_kind":"pith_short_8","alias_value":"GCNH7TTM","created_at":"2026-07-05T11:13:07.344447+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/GCNH7TTMCVM76VWEO4YVH7DEBY","json":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY.json","graph_json":"https://pith.science/api/pith-number/GCNH7TTMCVM76VWEO4YVH7DEBY/graph.json","events_json":"https://pith.science/api/pith-number/GCNH7TTMCVM76VWEO4YVH7DEBY/events.json","paper":"https://pith.science/paper/GCNH7TTM"},"agent_actions":{"view_html":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY","download_json":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY.json","view_paper":"https://pith.science/paper/GCNH7TTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.05806&json=true","fetch_graph":"https://pith.science/api/pith-number/GCNH7TTMCVM76VWEO4YVH7DEBY/graph.json","fetch_events":"https://pith.science/api/pith-number/GCNH7TTMCVM76VWEO4YVH7DEBY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY/action/storage_attestation","attest_author":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY/action/author_attestation","sign_citation":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY/action/citation_signature","submit_replication":"https://pith.science/pith/GCNH7TTMCVM76VWEO4YVH7DEBY/action/replication_record"}},"created_at":"2026-07-05T11:13:07.344447+00:00","updated_at":"2026-07-05T11:13:07.344447+00:00"}