{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FT4LFU5AJDEEOWD5FGZ3NRFNIX","short_pith_number":"pith:FT4LFU5A","schema_version":"1.0","canonical_sha256":"2cf8b2d3a048c847587d29b3b6c4ad45eb9bd8825316b8bdb8a035b6d4af597f","source":{"kind":"arxiv","id":"1711.07168","version":3},"attestation_state":"computed","paper":{"title":"Stein Variational Message Passing for Continuous Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dilin Wang, Qiang Liu, Zhe Zeng","submitted_at":"2017-11-20T06:25:16Z","abstract_excerpt":"We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest. Our approach combines SVGD with a set of structured local kernel functions defined on the Markov blanket of each node, which alleviates the curse of high dimensionality and simultaneously yields a distributed algorithm for decentralized inference tasks. We justify our method with theoretical analysis and show that the use of local kernels can be viewed as a new type of localized ap"},"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":"1711.07168","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-20T06:25:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0d0f4d4efa12df640df5823a08775e45b36c5099b1853456e946a69c351c0c5b","abstract_canon_sha256":"6e91a7d974b3c0907a9fd08396c34c0b9097e4023fbeea2aed6a56c6e0f89cf3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:55.142412Z","signature_b64":"3yE4Td8kQMOXUWmoyEjl2WB95jyp3LQqeF29Ub4Rz/hwS1z8qzIpCkBb/pWxyBNJaAmz1lBk7ysNdQU8wVVABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cf8b2d3a048c847587d29b3b6c4ad45eb9bd8825316b8bdb8a035b6d4af597f","last_reissued_at":"2026-05-18T00:13:55.141733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:55.141733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stein Variational Message Passing for Continuous Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dilin Wang, Qiang Liu, Zhe Zeng","submitted_at":"2017-11-20T06:25:16Z","abstract_excerpt":"We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest. Our approach combines SVGD with a set of structured local kernel functions defined on the Markov blanket of each node, which alleviates the curse of high dimensionality and simultaneously yields a distributed algorithm for decentralized inference tasks. We justify our method with theoretical analysis and show that the use of local kernels can be viewed as a new type of localized ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.07168","kind":"arxiv","version":3},"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":"1711.07168","created_at":"2026-05-18T00:13:55.141870+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.07168v3","created_at":"2026-05-18T00:13:55.141870+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.07168","created_at":"2026-05-18T00:13:55.141870+00:00"},{"alias_kind":"pith_short_12","alias_value":"FT4LFU5AJDEE","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FT4LFU5AJDEEOWD5","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FT4LFU5A","created_at":"2026-05-18T12:31:15.632608+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/FT4LFU5AJDEEOWD5FGZ3NRFNIX","json":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX.json","graph_json":"https://pith.science/api/pith-number/FT4LFU5AJDEEOWD5FGZ3NRFNIX/graph.json","events_json":"https://pith.science/api/pith-number/FT4LFU5AJDEEOWD5FGZ3NRFNIX/events.json","paper":"https://pith.science/paper/FT4LFU5A"},"agent_actions":{"view_html":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX","download_json":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX.json","view_paper":"https://pith.science/paper/FT4LFU5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.07168&json=true","fetch_graph":"https://pith.science/api/pith-number/FT4LFU5AJDEEOWD5FGZ3NRFNIX/graph.json","fetch_events":"https://pith.science/api/pith-number/FT4LFU5AJDEEOWD5FGZ3NRFNIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX/action/storage_attestation","attest_author":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX/action/author_attestation","sign_citation":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX/action/citation_signature","submit_replication":"https://pith.science/pith/FT4LFU5AJDEEOWD5FGZ3NRFNIX/action/replication_record"}},"created_at":"2026-05-18T00:13:55.141870+00:00","updated_at":"2026-05-18T00:13:55.141870+00:00"}