{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:DLN5B57XIJ6CNU5IOHB5WTLSR7","short_pith_number":"pith:DLN5B57X","schema_version":"1.0","canonical_sha256":"1adbd0f7f7427c26d3a871c3db4d728fdeded5bcdb85f1a88e15d4e1ff7260b2","source":{"kind":"arxiv","id":"1206.1147","version":2},"attestation_state":"computed","paper":{"title":"Memory-Efficient Topic Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Jia Zeng, Xiao-Qin Cao, Zhi-Qiang Liu","submitted_at":"2012-06-06T08:34:43Z","abstract_excerpt":"As one of the simplest probabilistic topic modeling techniques, latent Dirichlet allocation (LDA) has found many important applications in text mining, computer vision and computational biology. Recent training algorithms for LDA can be interpreted within a unified message passing framework. However, message passing requires storing previous messages with a large amount of memory space, increasing linearly with the number of documents or the number of topics. Therefore, the high memory usage is often a major problem for topic modeling of massive corpora containing a large number of topics. To "},"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":"1206.1147","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-06T08:34:43Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"2c5f73da9c0a6424d466964678835f1344611165930605778941e87f67e5d1bf","abstract_canon_sha256":"bb6013ea4f57fee0392b6bb3c2899f8a7151f37d052ee1782ff51aef58bfbe06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:54:03.822891Z","signature_b64":"ECoefyyGR50gesrgqeBqkkzTT+WuUVBEzAHux4fxNeKG2agszk9ai2M/t6Lfo7KLckF4ZB5S/yOLQR9GSEgKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1adbd0f7f7427c26d3a871c3db4d728fdeded5bcdb85f1a88e15d4e1ff7260b2","last_reissued_at":"2026-05-18T03:54:03.822312Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:54:03.822312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Memory-Efficient Topic Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Jia Zeng, Xiao-Qin Cao, Zhi-Qiang Liu","submitted_at":"2012-06-06T08:34:43Z","abstract_excerpt":"As one of the simplest probabilistic topic modeling techniques, latent Dirichlet allocation (LDA) has found many important applications in text mining, computer vision and computational biology. Recent training algorithms for LDA can be interpreted within a unified message passing framework. However, message passing requires storing previous messages with a large amount of memory space, increasing linearly with the number of documents or the number of topics. Therefore, the high memory usage is often a major problem for topic modeling of massive corpora containing a large number of topics. To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.1147","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1206.1147","created_at":"2026-05-18T03:54:03.822397+00:00"},{"alias_kind":"arxiv_version","alias_value":"1206.1147v2","created_at":"2026-05-18T03:54:03.822397+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.1147","created_at":"2026-05-18T03:54:03.822397+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLN5B57XIJ6C","created_at":"2026-05-18T12:27:04.183437+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLN5B57XIJ6CNU5I","created_at":"2026-05-18T12:27:04.183437+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLN5B57X","created_at":"2026-05-18T12:27:04.183437+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/DLN5B57XIJ6CNU5IOHB5WTLSR7","json":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7.json","graph_json":"https://pith.science/api/pith-number/DLN5B57XIJ6CNU5IOHB5WTLSR7/graph.json","events_json":"https://pith.science/api/pith-number/DLN5B57XIJ6CNU5IOHB5WTLSR7/events.json","paper":"https://pith.science/paper/DLN5B57X"},"agent_actions":{"view_html":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7","download_json":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7.json","view_paper":"https://pith.science/paper/DLN5B57X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1206.1147&json=true","fetch_graph":"https://pith.science/api/pith-number/DLN5B57XIJ6CNU5IOHB5WTLSR7/graph.json","fetch_events":"https://pith.science/api/pith-number/DLN5B57XIJ6CNU5IOHB5WTLSR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7/action/storage_attestation","attest_author":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7/action/author_attestation","sign_citation":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7/action/citation_signature","submit_replication":"https://pith.science/pith/DLN5B57XIJ6CNU5IOHB5WTLSR7/action/replication_record"}},"created_at":"2026-05-18T03:54:03.822397+00:00","updated_at":"2026-05-18T03:54:03.822397+00:00"}