{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PJNOPDNHZXDPE4WIP3T4V4OOWU","short_pith_number":"pith:PJNOPDNH","schema_version":"1.0","canonical_sha256":"7a5ae78da7cdc6f272c87ee7caf1ceb50ee5160fcde3981202365ea30640505a","source":{"kind":"arxiv","id":"2406.05469","version":2},"attestation_state":"computed","paper":{"title":"On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christian Igel, Nick Hauptvogel","submitted_at":"2024-06-08T13:19:18Z","abstract_excerpt":"It is common practice to combine deep neural networks into ensembles. These deep ensembles can benefit from the cancellation of errors effect: Errors by ensemble members may average out, leading to better generalization performance than each individual network. Bayesian neural networks learn a posterior distribution over model parameters, and sampling and weighting networks according to this posterior yields an ensemble model referred to as a Bayes ensemble. This study reviews the argument that neither the sampling nor the weighting in Bayes ensembles are particularly well suited for increasin"},"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":"2406.05469","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-08T13:19:18Z","cross_cats_sorted":[],"title_canon_sha256":"90e6efcf20996c483555718add369eb93b68fb12aed1610cbd7c75a0bcd7f145","abstract_canon_sha256":"93ed222a981b01d6f6bea56105a284c838553b31bdd07569df85cb2689663e91"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:52.335304Z","signature_b64":"NrCbe6MidkhBtVwXP1LjJrZNtHX+X3IvE/UIRnfhXp2X6F40nB3/4KmxrNUCYF5goqgNJqF7FUp+n6afXWk9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a5ae78da7cdc6f272c87ee7caf1ceb50ee5160fcde3981202365ea30640505a","last_reissued_at":"2026-07-05T09:56:52.334631Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:52.334631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christian Igel, Nick Hauptvogel","submitted_at":"2024-06-08T13:19:18Z","abstract_excerpt":"It is common practice to combine deep neural networks into ensembles. These deep ensembles can benefit from the cancellation of errors effect: Errors by ensemble members may average out, leading to better generalization performance than each individual network. Bayesian neural networks learn a posterior distribution over model parameters, and sampling and weighting networks according to this posterior yields an ensemble model referred to as a Bayes ensemble. This study reviews the argument that neither the sampling nor the weighting in Bayes ensembles are particularly well suited for increasin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05469","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/2406.05469/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":"2406.05469","created_at":"2026-07-05T09:56:52.334726+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05469v2","created_at":"2026-07-05T09:56:52.334726+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05469","created_at":"2026-07-05T09:56:52.334726+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJNOPDNHZXDP","created_at":"2026-07-05T09:56:52.334726+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJNOPDNHZXDPE4WI","created_at":"2026-07-05T09:56:52.334726+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJNOPDNH","created_at":"2026-07-05T09:56:52.334726+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00455","citing_title":"Concentration and Calibration in Predictive Bayesian Inference","ref_index":215,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU","json":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU.json","graph_json":"https://pith.science/api/pith-number/PJNOPDNHZXDPE4WIP3T4V4OOWU/graph.json","events_json":"https://pith.science/api/pith-number/PJNOPDNHZXDPE4WIP3T4V4OOWU/events.json","paper":"https://pith.science/paper/PJNOPDNH"},"agent_actions":{"view_html":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU","download_json":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU.json","view_paper":"https://pith.science/paper/PJNOPDNH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05469&json=true","fetch_graph":"https://pith.science/api/pith-number/PJNOPDNHZXDPE4WIP3T4V4OOWU/graph.json","fetch_events":"https://pith.science/api/pith-number/PJNOPDNHZXDPE4WIP3T4V4OOWU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU/action/storage_attestation","attest_author":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU/action/author_attestation","sign_citation":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU/action/citation_signature","submit_replication":"https://pith.science/pith/PJNOPDNHZXDPE4WIP3T4V4OOWU/action/replication_record"}},"created_at":"2026-07-05T09:56:52.334726+00:00","updated_at":"2026-07-05T09:56:52.334726+00:00"}