{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JNVAS6632ZWFTB625DCIUL2MG4","short_pith_number":"pith:JNVAS663","schema_version":"1.0","canonical_sha256":"4b6a097bdbd66c5987dae8c48a2f4c37325cd0b519e5a400ee897b2de516a32c","source":{"kind":"arxiv","id":"2303.01099","version":1},"attestation_state":"computed","paper":{"title":"Multi-Head Multi-Loss Model Calibration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adrian Galdran, Gustavo Carneiro, Johan Verjans, Miguel A. Gonz\\'alez Ballester","submitted_at":"2023-03-02T09:32:32Z","abstract_excerpt":"Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice. A central aspect of uncertainty quantification is the ability of a model to return predictions that are well-aligned with the actual probability of the model being correct, also known as model calibration. Although many methods have been proposed to improve calibration, no technique can match the simple, but expensive approach of training an ensemble of deep neural networks. In this paper we introduce a form of simplified ensembling that bypasses the costly "},"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":"2303.01099","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-02T09:32:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"272e762f385213b02148530b1d6b5839ea5616cb792d62c519104092225db084","abstract_canon_sha256":"05bddf451d771a12bfc2f875fc9665d879cf35972aba402f74c641300f561a27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:26.865743Z","signature_b64":"f0z0a34jGTrehn+JU/t/GVyxpdkVMFzJRNwujAThBuzalTOX56Bi3+qXLEqWc1MZEezvm7GtOwI9b2VB9PqJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b6a097bdbd66c5987dae8c48a2f4c37325cd0b519e5a400ee897b2de516a32c","last_reissued_at":"2026-07-05T05:47:26.865238Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:26.865238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Head Multi-Loss Model Calibration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adrian Galdran, Gustavo Carneiro, Johan Verjans, Miguel A. Gonz\\'alez Ballester","submitted_at":"2023-03-02T09:32:32Z","abstract_excerpt":"Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice. A central aspect of uncertainty quantification is the ability of a model to return predictions that are well-aligned with the actual probability of the model being correct, also known as model calibration. Although many methods have been proposed to improve calibration, no technique can match the simple, but expensive approach of training an ensemble of deep neural networks. In this paper we introduce a form of simplified ensembling that bypasses the costly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.01099","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/2303.01099/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":"2303.01099","created_at":"2026-07-05T05:47:26.865298+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.01099v1","created_at":"2026-07-05T05:47:26.865298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.01099","created_at":"2026-07-05T05:47:26.865298+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNVAS6632ZWF","created_at":"2026-07-05T05:47:26.865298+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNVAS6632ZWFTB62","created_at":"2026-07-05T05:47:26.865298+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNVAS663","created_at":"2026-07-05T05:47:26.865298+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/JNVAS6632ZWFTB625DCIUL2MG4","json":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4.json","graph_json":"https://pith.science/api/pith-number/JNVAS6632ZWFTB625DCIUL2MG4/graph.json","events_json":"https://pith.science/api/pith-number/JNVAS6632ZWFTB625DCIUL2MG4/events.json","paper":"https://pith.science/paper/JNVAS663"},"agent_actions":{"view_html":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4","download_json":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4.json","view_paper":"https://pith.science/paper/JNVAS663","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.01099&json=true","fetch_graph":"https://pith.science/api/pith-number/JNVAS6632ZWFTB625DCIUL2MG4/graph.json","fetch_events":"https://pith.science/api/pith-number/JNVAS6632ZWFTB625DCIUL2MG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4/action/storage_attestation","attest_author":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4/action/author_attestation","sign_citation":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4/action/citation_signature","submit_replication":"https://pith.science/pith/JNVAS6632ZWFTB625DCIUL2MG4/action/replication_record"}},"created_at":"2026-07-05T05:47:26.865298+00:00","updated_at":"2026-07-05T05:47:26.865298+00:00"}