{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4NMROOHNNJ5PTI7FPX5ETTQYL2","short_pith_number":"pith:4NMROOHN","schema_version":"1.0","canonical_sha256":"e3591738ed6a7af9a3e57dfa49ce185ebddcdc96a8e5035756384aadfb489d98","source":{"kind":"arxiv","id":"2302.13536","version":2},"attestation_state":"computed","paper":{"title":"Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Michael Stanley Smith, Ruben Loaiza-Maya, Weiben Zhang, Worapree Maneesoonthorn","submitted_at":"2023-02-27T06:24:20Z","abstract_excerpt":"Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or inaccurate in high dimensions. We suggest a fast and accurate VI method for this case that employs a well-defined natural gradient variational optimization that targets the joint posterior of the global parameters and latent variables. It is a hybrid method, where at each step the global parameters are updated using the natural gradient and the latent variables are generated from their conditional posterior. A fast to "},"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":"2302.13536","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-02-27T06:24:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fa8155f0b1e3d77d1823e6e67cf4c3db5d81a711971661d6064d658c59481c61","abstract_canon_sha256":"ceec9ecaafe64b29e0912f1e6fa12c030bbf0304ff74f8e1e9c39e1706a29d0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:10.205195Z","signature_b64":"/vmcXYXND0ivw+LS5B6l5SuV5ZKsExJP/kesITvCiIi7WwNY086LIYYsaA73YHpgl5fsjJ+BFojhpbFtQyQrCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3591738ed6a7af9a3e57dfa49ce185ebddcdc96a8e5035756384aadfb489d98","last_reissued_at":"2026-07-05T08:48:10.204856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:10.204856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Michael Stanley Smith, Ruben Loaiza-Maya, Weiben Zhang, Worapree Maneesoonthorn","submitted_at":"2023-02-27T06:24:20Z","abstract_excerpt":"Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or inaccurate in high dimensions. We suggest a fast and accurate VI method for this case that employs a well-defined natural gradient variational optimization that targets the joint posterior of the global parameters and latent variables. It is a hybrid method, where at each step the global parameters are updated using the natural gradient and the latent variables are generated from their conditional posterior. A fast to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13536","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/2302.13536/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":"2302.13536","created_at":"2026-07-05T08:48:10.204911+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.13536v2","created_at":"2026-07-05T08:48:10.204911+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13536","created_at":"2026-07-05T08:48:10.204911+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NMROOHNNJ5P","created_at":"2026-07-05T08:48:10.204911+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NMROOHNNJ5PTI7F","created_at":"2026-07-05T08:48:10.204911+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NMROOHN","created_at":"2026-07-05T08:48:10.204911+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/4NMROOHNNJ5PTI7FPX5ETTQYL2","json":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2.json","graph_json":"https://pith.science/api/pith-number/4NMROOHNNJ5PTI7FPX5ETTQYL2/graph.json","events_json":"https://pith.science/api/pith-number/4NMROOHNNJ5PTI7FPX5ETTQYL2/events.json","paper":"https://pith.science/paper/4NMROOHN"},"agent_actions":{"view_html":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2","download_json":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2.json","view_paper":"https://pith.science/paper/4NMROOHN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.13536&json=true","fetch_graph":"https://pith.science/api/pith-number/4NMROOHNNJ5PTI7FPX5ETTQYL2/graph.json","fetch_events":"https://pith.science/api/pith-number/4NMROOHNNJ5PTI7FPX5ETTQYL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2/action/storage_attestation","attest_author":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2/action/author_attestation","sign_citation":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2/action/citation_signature","submit_replication":"https://pith.science/pith/4NMROOHNNJ5PTI7FPX5ETTQYL2/action/replication_record"}},"created_at":"2026-07-05T08:48:10.204911+00:00","updated_at":"2026-07-05T08:48:10.204911+00:00"}