{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NTWR5PNXXPCFRFQXH7KZQLR3LT","short_pith_number":"pith:NTWR5PNX","schema_version":"1.0","canonical_sha256":"6ced1ebdb7bbc45896173fd5982e3b5cdfdf7bb1b502551b4cd1748f6c02c5ff","source":{"kind":"arxiv","id":"1712.00287","version":5},"attestation_state":"computed","paper":{"title":"Faithful Inversion of Generative Models for Effective Amortized Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Adam Golinski, Frank Wood, N. Siddharth, Robert Zinkov, Stefan Webb, Tom Rainforth, Yee Whye Teh","submitted_at":"2017-12-01T12:08:03Z","abstract_excerpt":"Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversion of the dependency structure in the generative model, as the modeller must learn a mapping from observations to distributions approximating the posterior. Previous approaches have involved inverting the dependency structure in a heuristic way that fails to capture these dependencies correctly, thereby limiting the achievable accuracy of the resulting approximations. We introduce an algorithm for faithfully, and min"},"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":"1712.00287","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-01T12:08:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"00babd4befe23c8c3b505a5eb9535411f4a1b545b92d41b33c13a6e9ef5533b9","abstract_canon_sha256":"4fc32e352edf4ae72fa4d53f4b5a03a02e6b296e9165bc17772ddb2a53eccdc8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:39.387971Z","signature_b64":"D8bl5QUxFwGiYf+osYfUT8sjMWEFl8u+8AQYWdJU5JaUDFX1OUkm7NVIWHcYl9nbcqV8XCqEdnGBArFb3D7KCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ced1ebdb7bbc45896173fd5982e3b5cdfdf7bb1b502551b4cd1748f6c02c5ff","last_reissued_at":"2026-05-17T23:59:39.387176Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:39.387176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faithful Inversion of Generative Models for Effective Amortized Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Adam Golinski, Frank Wood, N. Siddharth, Robert Zinkov, Stefan Webb, Tom Rainforth, Yee Whye Teh","submitted_at":"2017-12-01T12:08:03Z","abstract_excerpt":"Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversion of the dependency structure in the generative model, as the modeller must learn a mapping from observations to distributions approximating the posterior. Previous approaches have involved inverting the dependency structure in a heuristic way that fails to capture these dependencies correctly, thereby limiting the achievable accuracy of the resulting approximations. We introduce an algorithm for faithfully, and min"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00287","kind":"arxiv","version":5},"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":"1712.00287","created_at":"2026-05-17T23:59:39.387307+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.00287v5","created_at":"2026-05-17T23:59:39.387307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00287","created_at":"2026-05-17T23:59:39.387307+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTWR5PNXXPCF","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTWR5PNXXPCFRFQX","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTWR5PNX","created_at":"2026-05-18T12:31:34.259226+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/NTWR5PNXXPCFRFQXH7KZQLR3LT","json":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT.json","graph_json":"https://pith.science/api/pith-number/NTWR5PNXXPCFRFQXH7KZQLR3LT/graph.json","events_json":"https://pith.science/api/pith-number/NTWR5PNXXPCFRFQXH7KZQLR3LT/events.json","paper":"https://pith.science/paper/NTWR5PNX"},"agent_actions":{"view_html":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT","download_json":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT.json","view_paper":"https://pith.science/paper/NTWR5PNX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.00287&json=true","fetch_graph":"https://pith.science/api/pith-number/NTWR5PNXXPCFRFQXH7KZQLR3LT/graph.json","fetch_events":"https://pith.science/api/pith-number/NTWR5PNXXPCFRFQXH7KZQLR3LT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT/action/storage_attestation","attest_author":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT/action/author_attestation","sign_citation":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT/action/citation_signature","submit_replication":"https://pith.science/pith/NTWR5PNXXPCFRFQXH7KZQLR3LT/action/replication_record"}},"created_at":"2026-05-17T23:59:39.387307+00:00","updated_at":"2026-05-17T23:59:39.387307+00:00"}