{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:22SQ6MXFNBDFOIXWHFTPF5NYSC","short_pith_number":"pith:22SQ6MXF","canonical_record":{"source":{"id":"1507.05016","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-17T16:14:54Z","cross_cats_sorted":[],"title_canon_sha256":"4d925d0d68cfb7dfa8223a56bbb22a8fb2ce2ceee722b32239e6e3463aba5a79","abstract_canon_sha256":"a73f0121f78feb238d360646d942fa4a21e75325f2f3752766bbb073f9fc8574"},"schema_version":"1.0"},"canonical_sha256":"d6a50f32e568465722f63966f2f5b890a5e5e3c8e3d8074a4eab90a79fc230f6","source":{"kind":"arxiv","id":"1507.05016","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.05016","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"arxiv_version","alias_value":"1507.05016v2","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.05016","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"pith_short_12","alias_value":"22SQ6MXFNBDF","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"22SQ6MXFNBDFOIXW","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"22SQ6MXF","created_at":"2026-05-18T12:28:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:22SQ6MXFNBDFOIXWHFTPF5NYSC","target":"record","payload":{"canonical_record":{"source":{"id":"1507.05016","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-17T16:14:54Z","cross_cats_sorted":[],"title_canon_sha256":"4d925d0d68cfb7dfa8223a56bbb22a8fb2ce2ceee722b32239e6e3463aba5a79","abstract_canon_sha256":"a73f0121f78feb238d360646d942fa4a21e75325f2f3752766bbb073f9fc8574"},"schema_version":"1.0"},"canonical_sha256":"d6a50f32e568465722f63966f2f5b890a5e5e3c8e3d8074a4eab90a79fc230f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:27.129880Z","signature_b64":"FunC8phCxm9A89pzKPcmsXIOjdH98xY/QwM7U3CJG3aBiYL0H4Xar/Gq4lD7wWQDEW+pMF4BFpexbeccRSxtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6a50f32e568465722f63966f2f5b890a5e5e3c8e3d8074a4eab90a79fc230f6","last_reissued_at":"2026-05-18T01:36:27.129216Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:27.129216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.05016","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:36:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fk2ogh9eRZxGSSA9RpwDV7W7kR7w6EqQ0O5rc0QE+PAliU2sUkAdSC9+LFfFGeWlqiAAWNMdKDsIZg+rJz9eAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:16:21.695741Z"},"content_sha256":"7c79c62ffdfcc1dc111f1ea4f58196884a2e08a6fdf61bdbfdf57d96afc3622d","schema_version":"1.0","event_id":"sha256:7c79c62ffdfcc1dc111f1ea4f58196884a2e08a6fdf61bdbfdf57d96afc3622d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:22SQ6MXFNBDFOIXWHFTPF5NYSC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incremental Variational Inference for Latent Dirichlet Allocation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Beyza Ermis, Cedric Archambeau","submitted_at":"2015-07-17T16:14:54Z","abstract_excerpt":"We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning rate, converges faster to a local optimum of the variational bound and enjoys the attractive property of monotonically increasing it. We study the performance of incremental LDA on large benchmark data sets. We further introduce a stochastic approximation of incremental variation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.05016","kind":"arxiv","version":2},"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":"2026-05-18T01:36:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GPvyVxhjTwB28lHebHrfszkt1mVynE6IIa3QTcRQ1jioPGsLJAT1Le/1IEjwrawsdoEX0Pf2gqmfDzxmdQWXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T13:16:21.696449Z"},"content_sha256":"0bd52c76f5f915efef0c2f8f072a3fc333fedaf4460b0788b32b9c766a28b754","schema_version":"1.0","event_id":"sha256:0bd52c76f5f915efef0c2f8f072a3fc333fedaf4460b0788b32b9c766a28b754"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/bundle.json","state_url":"https://pith.science/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/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":"2026-05-25T13:16:21Z","links":{"resolver":"https://pith.science/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC","bundle":"https://pith.science/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/bundle.json","state":"https://pith.science/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/22SQ6MXFNBDFOIXWHFTPF5NYSC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:22SQ6MXFNBDFOIXWHFTPF5NYSC","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":"a73f0121f78feb238d360646d942fa4a21e75325f2f3752766bbb073f9fc8574","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-17T16:14:54Z","title_canon_sha256":"4d925d0d68cfb7dfa8223a56bbb22a8fb2ce2ceee722b32239e6e3463aba5a79"},"schema_version":"1.0","source":{"id":"1507.05016","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.05016","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"arxiv_version","alias_value":"1507.05016v2","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.05016","created_at":"2026-05-18T01:36:27Z"},{"alias_kind":"pith_short_12","alias_value":"22SQ6MXFNBDF","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"22SQ6MXFNBDFOIXW","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"22SQ6MXF","created_at":"2026-05-18T12:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:0bd52c76f5f915efef0c2f8f072a3fc333fedaf4460b0788b32b9c766a28b754","target":"graph","created_at":"2026-05-18T01:36:27Z","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":"We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning rate, converges faster to a local optimum of the variational bound and enjoys the attractive property of monotonically increasing it. We study the performance of incremental LDA on large benchmark data sets. We further introduce a stochastic approximation of incremental variation","authors_text":"Beyza Ermis, Cedric Archambeau","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-17T16:14:54Z","title":"Incremental Variational Inference for Latent Dirichlet Allocation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.05016","kind":"arxiv","version":2},"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":"sha256:7c79c62ffdfcc1dc111f1ea4f58196884a2e08a6fdf61bdbfdf57d96afc3622d","target":"record","created_at":"2026-05-18T01:36:27Z","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":"a73f0121f78feb238d360646d942fa4a21e75325f2f3752766bbb073f9fc8574","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-17T16:14:54Z","title_canon_sha256":"4d925d0d68cfb7dfa8223a56bbb22a8fb2ce2ceee722b32239e6e3463aba5a79"},"schema_version":"1.0","source":{"id":"1507.05016","kind":"arxiv","version":2}},"canonical_sha256":"d6a50f32e568465722f63966f2f5b890a5e5e3c8e3d8074a4eab90a79fc230f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6a50f32e568465722f63966f2f5b890a5e5e3c8e3d8074a4eab90a79fc230f6","first_computed_at":"2026-05-18T01:36:27.129216Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:36:27.129216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FunC8phCxm9A89pzKPcmsXIOjdH98xY/QwM7U3CJG3aBiYL0H4Xar/Gq4lD7wWQDEW+pMF4BFpexbeccRSxtBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:36:27.129880Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.05016","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c79c62ffdfcc1dc111f1ea4f58196884a2e08a6fdf61bdbfdf57d96afc3622d","sha256:0bd52c76f5f915efef0c2f8f072a3fc333fedaf4460b0788b32b9c766a28b754"],"state_sha256":"857ceacffbca0b92b216ea64f7839d6c273e18b0442b76bbdb6ec1d55dba7885"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"INk/qL4RkxHfSrZ7bZmN9V3WY7ZT01GJnPCX6e7sACCJsYRjlqSSMflyh9SsYG7kBoIMTgTD14N86F1XIgcpAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T13:16:21.700474Z","bundle_sha256":"e5da69dd49cfd783ee99d86c0afb6976d7484fc9fdbdae1d26b8e8ed8334535c"}}