{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4XDBCHRXFXTYAQGOOIEC2ETQNJ","short_pith_number":"pith:4XDBCHRX","schema_version":"1.0","canonical_sha256":"e5c6111e372de78040ce72082d12706a7f068910392838d3e1272ac429b06c13","source":{"kind":"arxiv","id":"1905.09453","version":1},"attestation_state":"computed","paper":{"title":"Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David Williams-King, Hod Lipson, Oscar Chang, Yuling Yao","submitted_at":"2019-05-23T03:58:59Z","abstract_excerpt":"Two main obstacles preventing the widespread adoption of variational Bayesian neural networks are the high parameter overhead that makes them infeasible on large networks, and the difficulty of implementation, which can be thought of as \"programming overhead.\" MC dropout [Gal and Ghahramani, 2016] is popular because it sidesteps these obstacles. Nevertheless, dropout is often harmful to model performance when used in networks with batch normalization layers [Li et al., 2018], which are an indispensable part of modern neural networks. We construct a general variational family for ensemble-based"},"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":"1905.09453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-23T03:58:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ca9d45af8681c8d0db005a9698e8779f417711afe0e04302c702c59496039002","abstract_canon_sha256":"ec63e96c865d0fee5dbaa56f2c330e6fea33db5e41f323e98c5f566bca4b7905"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:16.988106Z","signature_b64":"vRxSOBqCn+yM/LgMdYboJD/PlHXnHF03HAQ7ooh1c8kA0pIj5udripcKVcwusZTB6hKc8eLFLEf+oQn+sRGJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5c6111e372de78040ce72082d12706a7f068910392838d3e1272ac429b06c13","last_reissued_at":"2026-05-17T23:45:16.987463Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:16.987463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David Williams-King, Hod Lipson, Oscar Chang, Yuling Yao","submitted_at":"2019-05-23T03:58:59Z","abstract_excerpt":"Two main obstacles preventing the widespread adoption of variational Bayesian neural networks are the high parameter overhead that makes them infeasible on large networks, and the difficulty of implementation, which can be thought of as \"programming overhead.\" MC dropout [Gal and Ghahramani, 2016] is popular because it sidesteps these obstacles. Nevertheless, dropout is often harmful to model performance when used in networks with batch normalization layers [Li et al., 2018], which are an indispensable part of modern neural networks. We construct a general variational family for ensemble-based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.09453","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":""},"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":"1905.09453","created_at":"2026-05-17T23:45:16.987542+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.09453v1","created_at":"2026-05-17T23:45:16.987542+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.09453","created_at":"2026-05-17T23:45:16.987542+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XDBCHRXFXTY","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XDBCHRXFXTYAQGO","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XDBCHRX","created_at":"2026-05-18T12:33:10.108867+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/4XDBCHRXFXTYAQGOOIEC2ETQNJ","json":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ.json","graph_json":"https://pith.science/api/pith-number/4XDBCHRXFXTYAQGOOIEC2ETQNJ/graph.json","events_json":"https://pith.science/api/pith-number/4XDBCHRXFXTYAQGOOIEC2ETQNJ/events.json","paper":"https://pith.science/paper/4XDBCHRX"},"agent_actions":{"view_html":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ","download_json":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ.json","view_paper":"https://pith.science/paper/4XDBCHRX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.09453&json=true","fetch_graph":"https://pith.science/api/pith-number/4XDBCHRXFXTYAQGOOIEC2ETQNJ/graph.json","fetch_events":"https://pith.science/api/pith-number/4XDBCHRXFXTYAQGOOIEC2ETQNJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ/action/storage_attestation","attest_author":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ/action/author_attestation","sign_citation":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ/action/citation_signature","submit_replication":"https://pith.science/pith/4XDBCHRXFXTYAQGOOIEC2ETQNJ/action/replication_record"}},"created_at":"2026-05-17T23:45:16.987542+00:00","updated_at":"2026-05-17T23:45:16.987542+00:00"}