{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5XB6F2G3V3LNJLML3ZKEPXUEP5","short_pith_number":"pith:5XB6F2G3","schema_version":"1.0","canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","source":{"kind":"arxiv","id":"1803.10586","version":1},"attestation_state":"computed","paper":{"title":"Stochastic Variational Inference with Gradient Linearization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Anne S. Wannenwetsch, Stefan Roth, Tobias Pl\\\"otz","submitted_at":"2018-03-28T13:22:57Z","abstract_excerpt":"Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benefit is that stochastic variational inference obviates the tedious process of deriving analytical expressions for closed-form variable updates. Instead, one simply needs to derive the gradient of the log-posterior, which is often much easier. Yet for certain model classes, the log-posterior itself is difficult to optimize using standard gradient techniques. One such example are random field models, where optimization b"},"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":"1803.10586","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T13:22:57Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"bbcfb897dedfb8ea7120adeaa0b88a1a32e322920135b356b847162fed637ded","abstract_canon_sha256":"6ff4583e943388bfbdd1ea36f3238d4e4d699c38223d6962686e6f2ed3f7c40c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:54.813589Z","signature_b64":"r9FoW+wEI9s7i7/xKW6T2mLhL1ysqK9PihlOzPyFIyBUdmU7jC+zR9c35/oVLvFvleND9CMM57a9vFdvL4L+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","last_reissued_at":"2026-05-18T00:19:54.812912Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:54.812912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic Variational Inference with Gradient Linearization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Anne S. Wannenwetsch, Stefan Roth, Tobias Pl\\\"otz","submitted_at":"2018-03-28T13:22:57Z","abstract_excerpt":"Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benefit is that stochastic variational inference obviates the tedious process of deriving analytical expressions for closed-form variable updates. Instead, one simply needs to derive the gradient of the log-posterior, which is often much easier. Yet for certain model classes, the log-posterior itself is difficult to optimize using standard gradient techniques. One such example are random field models, where optimization b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10586","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":"1803.10586","created_at":"2026-05-18T00:19:54.813008+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.10586v1","created_at":"2026-05-18T00:19:54.813008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10586","created_at":"2026-05-18T00:19:54.813008+00:00"},{"alias_kind":"pith_short_12","alias_value":"5XB6F2G3V3LN","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5XB6F2G3V3LNJLML","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5XB6F2G3","created_at":"2026-05-18T12:32:08.215937+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/5XB6F2G3V3LNJLML3ZKEPXUEP5","json":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5.json","graph_json":"https://pith.science/api/pith-number/5XB6F2G3V3LNJLML3ZKEPXUEP5/graph.json","events_json":"https://pith.science/api/pith-number/5XB6F2G3V3LNJLML3ZKEPXUEP5/events.json","paper":"https://pith.science/paper/5XB6F2G3"},"agent_actions":{"view_html":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5","download_json":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5.json","view_paper":"https://pith.science/paper/5XB6F2G3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.10586&json=true","fetch_graph":"https://pith.science/api/pith-number/5XB6F2G3V3LNJLML3ZKEPXUEP5/graph.json","fetch_events":"https://pith.science/api/pith-number/5XB6F2G3V3LNJLML3ZKEPXUEP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/action/storage_attestation","attest_author":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/action/author_attestation","sign_citation":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/action/citation_signature","submit_replication":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/action/replication_record"}},"created_at":"2026-05-18T00:19:54.813008+00:00","updated_at":"2026-05-18T00:19:54.813008+00:00"}