{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XV3T72FT36ZCTY7WW3AXVWE46Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1ebbe2c752b9e2798cc87f8e7d0abaf7461ae4c44693d33795ef514384908d8e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T11:25:20Z","title_canon_sha256":"5442b36af073fbf758614aa9456d9c24fa92537a1c2b51c2099bb4de2700ab1e"},"schema_version":"1.0","source":{"id":"2402.08384","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.08384","created_at":"2026-07-05T08:43:26Z"},{"alias_kind":"arxiv_version","alias_value":"2402.08384v2","created_at":"2026-07-05T08:43:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08384","created_at":"2026-07-05T08:43:26Z"},{"alias_kind":"pith_short_12","alias_value":"XV3T72FT36ZC","created_at":"2026-07-05T08:43:26Z"},{"alias_kind":"pith_short_16","alias_value":"XV3T72FT36ZCTY7W","created_at":"2026-07-05T08:43:26Z"},{"alias_kind":"pith_short_8","alias_value":"XV3T72FT","created_at":"2026-07-05T08:43:26Z"}],"graph_snapshots":[{"event_id":"sha256:8e10361094b34a1f78686e3c13f219d5f431bf47a2aab7c2fd20e30fb984e752","target":"graph","created_at":"2026-07-05T08:43:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.08384/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which is characterized by learning everything presented in the training set, resulting in overconfident predictions during testing. Existing methods typically address overfitting and mitigate the miscalibration by adding a maximum-entropy regularizer to the objective function. The objective can be understood as seeking a model that fits the ground-truth labels by increasing the confidence while also maximizing the entropy","authors_text":"Changqing Zhang, Joey Tianyi Zhou, Linjun Zhang, Qinghua Hu, Yifeng Yang, Zongbo Han","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T11:25:20Z","title":"Selective Learning: Towards Robust Calibration with Dynamic Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08384","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1fecae72155313e06065655666d7b6c0bc3576e159f6489dc72124c911e4711b","target":"record","created_at":"2026-07-05T08:43:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1ebbe2c752b9e2798cc87f8e7d0abaf7461ae4c44693d33795ef514384908d8e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T11:25:20Z","title_canon_sha256":"5442b36af073fbf758614aa9456d9c24fa92537a1c2b51c2099bb4de2700ab1e"},"schema_version":"1.0","source":{"id":"2402.08384","kind":"arxiv","version":2}},"canonical_sha256":"bd773fe8b3dfb229e3f6b6c17ad89cf4094375183373e2d55a45af54e0fdcf0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd773fe8b3dfb229e3f6b6c17ad89cf4094375183373e2d55a45af54e0fdcf0d","first_computed_at":"2026-07-05T08:43:26.232237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:43:26.232237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NBMRvBIWnuSdCasG2A8KAVhuGRIGNrtm392vxqVsOSXqvEK8509ptGlO5LnRRRFvWVdWU+CL+EJSdON4rYajDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:43:26.232650Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.08384","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1fecae72155313e06065655666d7b6c0bc3576e159f6489dc72124c911e4711b","sha256:8e10361094b34a1f78686e3c13f219d5f431bf47a2aab7c2fd20e30fb984e752"],"state_sha256":"ca80e6ece5c75201b5951966b0aa2944579b3a6d76dcd5478a06a1eff3a5b468"}