{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HJWKUGI2WRLWB5WU5SI2EBOOLR","short_pith_number":"pith:HJWKUGI2","schema_version":"1.0","canonical_sha256":"3a6caa191ab45760f6d4ec91a205ce5c4edc6c7fc2937ab33b84b269c6890d29","source":{"kind":"arxiv","id":"1811.00073","version":3},"attestation_state":"computed","paper":{"title":"Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"B. Milan Horacek, Cameron Knight, John L. Sapp, Linwei Wang, Prashnna K Gyawali, Sandesh Ghimire","submitted_at":"2018-10-31T19:19:49Z","abstract_excerpt":"While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain. First, there is often an unknown and potentially infinite number of confounding factors coinciding in the data. Second, not all of these factors are readily observable. In this paper, we present a deep conditional generative model that learns to disentangle a task-relevant representation from an unknown number of confounding factors that may grow infinitely. This is achieved by marrying the representational"},"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":"1811.00073","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T19:19:49Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c26a7713e8435ffbd1a612cc676fd7aee1bc41a77b084ac244eee2162ae4b9f4","abstract_canon_sha256":"812653c3a8c066a282197e863964e8019bc49a0f0b9a5d91248a2874f9527ae2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:02.585046Z","signature_b64":"6CWcIXjn8/QmBipQ3PNn0q4V+bUMnsGuDtYH6emAYg6Z8WPkVWzMm5wbPp7q7n3Y1hBqgLY7bFuRkutMqWYaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a6caa191ab45760f6d4ec91a205ce5c4edc6c7fc2937ab33b84b269c6890d29","last_reissued_at":"2026-05-17T23:47:02.584505Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:02.584505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"B. Milan Horacek, Cameron Knight, John L. Sapp, Linwei Wang, Prashnna K Gyawali, Sandesh Ghimire","submitted_at":"2018-10-31T19:19:49Z","abstract_excerpt":"While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain. First, there is often an unknown and potentially infinite number of confounding factors coinciding in the data. Second, not all of these factors are readily observable. In this paper, we present a deep conditional generative model that learns to disentangle a task-relevant representation from an unknown number of confounding factors that may grow infinitely. This is achieved by marrying the representational"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00073","kind":"arxiv","version":3},"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":"1811.00073","created_at":"2026-05-17T23:47:02.584609+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00073v3","created_at":"2026-05-17T23:47:02.584609+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00073","created_at":"2026-05-17T23:47:02.584609+00:00"},{"alias_kind":"pith_short_12","alias_value":"HJWKUGI2WRLW","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HJWKUGI2WRLWB5WU","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HJWKUGI2","created_at":"2026-05-18T12:32:28.185984+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/HJWKUGI2WRLWB5WU5SI2EBOOLR","json":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR.json","graph_json":"https://pith.science/api/pith-number/HJWKUGI2WRLWB5WU5SI2EBOOLR/graph.json","events_json":"https://pith.science/api/pith-number/HJWKUGI2WRLWB5WU5SI2EBOOLR/events.json","paper":"https://pith.science/paper/HJWKUGI2"},"agent_actions":{"view_html":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR","download_json":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR.json","view_paper":"https://pith.science/paper/HJWKUGI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00073&json=true","fetch_graph":"https://pith.science/api/pith-number/HJWKUGI2WRLWB5WU5SI2EBOOLR/graph.json","fetch_events":"https://pith.science/api/pith-number/HJWKUGI2WRLWB5WU5SI2EBOOLR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR/action/storage_attestation","attest_author":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR/action/author_attestation","sign_citation":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR/action/citation_signature","submit_replication":"https://pith.science/pith/HJWKUGI2WRLWB5WU5SI2EBOOLR/action/replication_record"}},"created_at":"2026-05-17T23:47:02.584609+00:00","updated_at":"2026-05-17T23:47:02.584609+00:00"}