{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5XB6F2G3V3LNJLML3ZKEPXUEP5","short_pith_number":"pith:5XB6F2G3","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"},"canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","source":{"kind":"arxiv","id":"1803.10586","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10586","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10586v1","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10586","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"5XB6F2G3V3LN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5XB6F2G3V3LNJLML","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5XB6F2G3","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5XB6F2G3V3LNJLML3ZKEPXUEP5","target":"record","payload":{"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"},"canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","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"},"source_kind":"arxiv","source_id":"1803.10586","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":"2026-05-18T00:19:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I0ErpPxMkmjD8cdYsFo99z2DO0ywSQ9LFUq4dpkhH2WT/WVWWq7xoMHuKfeP4VOtR0qUZmyOKB12t1qy6zdrCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:29:42.517268Z"},"content_sha256":"b82c2b689373c45527c912ab2ba8a35c5ee640cc492f6aa5f174c7fa8852db3f","schema_version":"1.0","event_id":"sha256:b82c2b689373c45527c912ab2ba8a35c5ee640cc492f6aa5f174c7fa8852db3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5XB6F2G3V3LNJLML3ZKEPXUEP5","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:19:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nLl71ZceI9EwPklABj/4dPcNMEk4ekYJSWv+LgcJIwSqjOGDPY7MZVSJWt81DDvptieGzUzcAm8wFyKhqECVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:29:42.517641Z"},"content_sha256":"7e171ce8107be7846f1e860b874a3fd46a17ace20df5c9b3c25e2f4e5831a0c8","schema_version":"1.0","event_id":"sha256:7e171ce8107be7846f1e860b874a3fd46a17ace20df5c9b3c25e2f4e5831a0c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/bundle.json","state_url":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/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":"2026-05-20T10:29:42Z","links":{"resolver":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5","bundle":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/bundle.json","state":"https://pith.science/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5XB6F2G3V3LNJLML3ZKEPXUEP5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5XB6F2G3V3LNJLML3ZKEPXUEP5","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":"6ff4583e943388bfbdd1ea36f3238d4e4d699c38223d6962686e6f2ed3f7c40c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T13:22:57Z","title_canon_sha256":"bbcfb897dedfb8ea7120adeaa0b88a1a32e322920135b356b847162fed637ded"},"schema_version":"1.0","source":{"id":"1803.10586","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10586","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10586v1","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10586","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"5XB6F2G3V3LN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5XB6F2G3V3LNJLML","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5XB6F2G3","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:7e171ce8107be7846f1e860b874a3fd46a17ace20df5c9b3c25e2f4e5831a0c8","target":"graph","created_at":"2026-05-18T00:19:54Z","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":"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","authors_text":"Anne S. Wannenwetsch, Stefan Roth, Tobias Pl\\\"otz","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T13:22:57Z","title":"Stochastic Variational Inference with Gradient Linearization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10586","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:b82c2b689373c45527c912ab2ba8a35c5ee640cc492f6aa5f174c7fa8852db3f","target":"record","created_at":"2026-05-18T00:19:54Z","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":"6ff4583e943388bfbdd1ea36f3238d4e4d699c38223d6962686e6f2ed3f7c40c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-28T13:22:57Z","title_canon_sha256":"bbcfb897dedfb8ea7120adeaa0b88a1a32e322920135b356b847162fed637ded"},"schema_version":"1.0","source":{"id":"1803.10586","kind":"arxiv","version":1}},"canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"edc3e2e8dbaed6d4ad8bde5447de847f46e63ed07e0e802ad3617c8b407b37d5","first_computed_at":"2026-05-18T00:19:54.812912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:54.812912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r9FoW+wEI9s7i7/xKW6T2mLhL1ysqK9PihlOzPyFIyBUdmU7jC+zR9c35/oVLvFvleND9CMM57a9vFdvL4L+DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:54.813589Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.10586","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b82c2b689373c45527c912ab2ba8a35c5ee640cc492f6aa5f174c7fa8852db3f","sha256:7e171ce8107be7846f1e860b874a3fd46a17ace20df5c9b3c25e2f4e5831a0c8"],"state_sha256":"4b19c972daf728b2a7bf0fc02c6779f2ca29c61851f9730f475a5a121a7dff55"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bx1oxrorg9zxVopesbyokC3nudW06oM5T7G3DETsU3oLilqa6D/wiWaesfFl6aoaaj4nGIecBDL3gUSRO8x7BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T10:29:42.519739Z","bundle_sha256":"4ba25c9871de377985d9958e88c308571fe2cfd79c2aaddf6a4e84676771536a"}}