{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JSXMIXWWMJ4EHRSX5HC27FUYWA","short_pith_number":"pith:JSXMIXWW","canonical_record":{"source":{"id":"1805.11183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-28T21:55:02Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"7261e4c7cb2ff5e16daadf69f6b91047942c032a4c7d1407f40fbfba226ddfd3","abstract_canon_sha256":"f0cc01eb44ab43ca39793f57f45f20f8517c93a0c27194be4775f019b9bfcf18"},"schema_version":"1.0"},"canonical_sha256":"4caec45ed6627843c657e9c5af9698b00f5bb4d352f5569bf45837c72fb16818","source":{"kind":"arxiv","id":"1805.11183","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.11183","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"1805.11183v1","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.11183","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"JSXMIXWWMJ4E","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JSXMIXWWMJ4EHRSX","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JSXMIXWW","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JSXMIXWWMJ4EHRSX5HC27FUYWA","target":"record","payload":{"canonical_record":{"source":{"id":"1805.11183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-28T21:55:02Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"7261e4c7cb2ff5e16daadf69f6b91047942c032a4c7d1407f40fbfba226ddfd3","abstract_canon_sha256":"f0cc01eb44ab43ca39793f57f45f20f8517c93a0c27194be4775f019b9bfcf18"},"schema_version":"1.0"},"canonical_sha256":"4caec45ed6627843c657e9c5af9698b00f5bb4d352f5569bf45837c72fb16818","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:43.639011Z","signature_b64":"xIptwnoM3kLIEVqFEhJK08oZmH7CU6/dimAZMOOttQe3AhjUb8GEJM0CP0C4Uvmt9UFeYNV3V+yBYyj8nPYxBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4caec45ed6627843c657e9c5af9698b00f5bb4d352f5569bf45837c72fb16818","last_reissued_at":"2026-05-18T00:14:43.638584Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:43.638584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.11183","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EynmIjSblOOX6MvYX0hQGhVD+KRUPaoaxMQfjO+NTrJdWtiGVkrqhrh6JAR+Ugi2RsCm2sRN8hExIh+P+Q7UDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:57:36.354984Z"},"content_sha256":"4f3fb9810f75ad384f357af479185d5f8a0ede41409dcdb98346a5241b10a211","schema_version":"1.0","event_id":"sha256:4f3fb9810f75ad384f357af479185d5f8a0ede41409dcdb98346a5241b10a211"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JSXMIXWWMJ4EHRSX5HC27FUYWA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Implicit Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Mingyuan Zhou, Mingzhang Yin","submitted_at":"2018-05-28T21:55:02Z","abstract_excerpt":"Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization. Not only does SIVI expand the variational family to incorporate highly flexible variational distributions, including implicit ones that have no analytic density functions, but also sandwiches the evidence lower bound (ELBO) between a lower bound and an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.11183","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RiKD2SyAxFsjR84zvD1wLXx3+SgEL2emex8NH6er3Y0fd6Tz0wgNO9y8BEveYymt+hqyoBE6wzHMpZygUU8/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:57:36.355685Z"},"content_sha256":"597c123a7e6a3bcbe4628e39d78dcd81a04c90347d2665c06f6ee003c928b059","schema_version":"1.0","event_id":"sha256:597c123a7e6a3bcbe4628e39d78dcd81a04c90347d2665c06f6ee003c928b059"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/bundle.json","state_url":"https://pith.science/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T16:57:36Z","links":{"resolver":"https://pith.science/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA","bundle":"https://pith.science/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/bundle.json","state":"https://pith.science/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JSXMIXWWMJ4EHRSX5HC27FUYWA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JSXMIXWWMJ4EHRSX5HC27FUYWA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f0cc01eb44ab43ca39793f57f45f20f8517c93a0c27194be4775f019b9bfcf18","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-28T21:55:02Z","title_canon_sha256":"7261e4c7cb2ff5e16daadf69f6b91047942c032a4c7d1407f40fbfba226ddfd3"},"schema_version":"1.0","source":{"id":"1805.11183","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.11183","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"1805.11183v1","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.11183","created_at":"2026-05-18T00:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"JSXMIXWWMJ4E","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JSXMIXWWMJ4EHRSX","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JSXMIXWW","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:597c123a7e6a3bcbe4628e39d78dcd81a04c90347d2665c06f6ee003c928b059","target":"graph","created_at":"2026-05-18T00:14:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization. Not only does SIVI expand the variational family to incorporate highly flexible variational distributions, including implicit ones that have no analytic density functions, but also sandwiches the evidence lower bound (ELBO) between a lower bound and an ","authors_text":"Mingyuan Zhou, Mingzhang Yin","cross_cats":["cs.LG","stat.CO","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-28T21:55:02Z","title":"Semi-Implicit Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.11183","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4f3fb9810f75ad384f357af479185d5f8a0ede41409dcdb98346a5241b10a211","target":"record","created_at":"2026-05-18T00:14:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f0cc01eb44ab43ca39793f57f45f20f8517c93a0c27194be4775f019b9bfcf18","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-28T21:55:02Z","title_canon_sha256":"7261e4c7cb2ff5e16daadf69f6b91047942c032a4c7d1407f40fbfba226ddfd3"},"schema_version":"1.0","source":{"id":"1805.11183","kind":"arxiv","version":1}},"canonical_sha256":"4caec45ed6627843c657e9c5af9698b00f5bb4d352f5569bf45837c72fb16818","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4caec45ed6627843c657e9c5af9698b00f5bb4d352f5569bf45837c72fb16818","first_computed_at":"2026-05-18T00:14:43.638584Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:43.638584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xIptwnoM3kLIEVqFEhJK08oZmH7CU6/dimAZMOOttQe3AhjUb8GEJM0CP0C4Uvmt9UFeYNV3V+yBYyj8nPYxBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:43.639011Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.11183","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f3fb9810f75ad384f357af479185d5f8a0ede41409dcdb98346a5241b10a211","sha256:597c123a7e6a3bcbe4628e39d78dcd81a04c90347d2665c06f6ee003c928b059"],"state_sha256":"7316ee2cf11aa2156b74b8e986681f384eaf16c62c341853542bc7e9cd724347"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D5TZzVlyyw3/byCyjFR62XKIqsJBsHcHPq5ZL3RJXO4U81k3yM33oJd4nUP0M//wSK5mdrcEzneyj+6RCouADw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T16:57:36.359193Z","bundle_sha256":"a08099f37ae1132ce5a6d6a24dec75ccd5d84caad925919cad1ce19a88aa966c"}}