{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5NL65QJNJLNCMO4JMU2YUQKUPR","short_pith_number":"pith:5NL65QJN","schema_version":"1.0","canonical_sha256":"eb57eec12d4ada263b8965358a41547c7ed2d30c4a337b71deb9b2366aadfa34","source":{"kind":"arxiv","id":"1711.08770","version":1},"attestation_state":"computed","paper":{"title":"Diversity-Promoting Bayesian Learning of Latent Variable Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric P. Xing, Jun Zhu, Pengtao Xie","submitted_at":"2017-11-23T16:44:36Z","abstract_excerpt":"To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating overfitting, several studies have been devoted to \"diversifying\" LVMs, which aim at encouraging the components in LVMs to be diverse. Most existing studies fall into a frequentist-style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to \"diversify\" LVMs in the paradigm of Bayesian learning. We propose two approaches that have complementary advantages. O"},"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":"1711.08770","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-23T16:44:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d214c734fb5208792e7bdb196fe4ceba4c84236a45953bcc3caa39b73a635a5d","abstract_canon_sha256":"3e2e4967620e74587d0a7f5e87bce119ce774d8c7288731c4bd06e273fdf375b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:44.135935Z","signature_b64":"AcIezbCymReqLRnHVbCFjD9He8qZ7KQjL4PWo5uvjElwhLXR+HarO6rNkNhkqajccvxtLW+rOaq04Y3xGI67Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb57eec12d4ada263b8965358a41547c7ed2d30c4a337b71deb9b2366aadfa34","last_reissued_at":"2026-05-18T00:29:44.135487Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:44.135487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diversity-Promoting Bayesian Learning of Latent Variable Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric P. Xing, Jun Zhu, Pengtao Xie","submitted_at":"2017-11-23T16:44:36Z","abstract_excerpt":"To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating overfitting, several studies have been devoted to \"diversifying\" LVMs, which aim at encouraging the components in LVMs to be diverse. Most existing studies fall into a frequentist-style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to \"diversify\" LVMs in the paradigm of Bayesian learning. We propose two approaches that have complementary advantages. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08770","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":"1711.08770","created_at":"2026-05-18T00:29:44.135549+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.08770v1","created_at":"2026-05-18T00:29:44.135549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08770","created_at":"2026-05-18T00:29:44.135549+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NL65QJNJLNC","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NL65QJNJLNCMO4J","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NL65QJN","created_at":"2026-05-18T12:31:00.734936+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/5NL65QJNJLNCMO4JMU2YUQKUPR","json":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR.json","graph_json":"https://pith.science/api/pith-number/5NL65QJNJLNCMO4JMU2YUQKUPR/graph.json","events_json":"https://pith.science/api/pith-number/5NL65QJNJLNCMO4JMU2YUQKUPR/events.json","paper":"https://pith.science/paper/5NL65QJN"},"agent_actions":{"view_html":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR","download_json":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR.json","view_paper":"https://pith.science/paper/5NL65QJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.08770&json=true","fetch_graph":"https://pith.science/api/pith-number/5NL65QJNJLNCMO4JMU2YUQKUPR/graph.json","fetch_events":"https://pith.science/api/pith-number/5NL65QJNJLNCMO4JMU2YUQKUPR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR/action/storage_attestation","attest_author":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR/action/author_attestation","sign_citation":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR/action/citation_signature","submit_replication":"https://pith.science/pith/5NL65QJNJLNCMO4JMU2YUQKUPR/action/replication_record"}},"created_at":"2026-05-18T00:29:44.135549+00:00","updated_at":"2026-05-18T00:29:44.135549+00:00"}