{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GCH7E3Q4ED2XIUE2U4OC3RGTXD","short_pith_number":"pith:GCH7E3Q4","schema_version":"1.0","canonical_sha256":"308ff26e1c20f574509aa71c2dc4d3b8df17cfb41b1d3b3bf13a1e7e0b1ba541","source":{"kind":"arxiv","id":"1801.01799","version":2},"attestation_state":"computed","paper":{"title":"Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alberto Lumbreras, C\\'edric F\\'evotte, Louis Filstroff","submitted_at":"2018-01-05T15:50:39Z","abstract_excerpt":"We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data. We show that GaP can be rewritten free of the score/activation matrix. This gives us new insights about the estimation of the topic/dictionary matrix by maximum marginal likelihood estimation. In particular, this explains the robustness of this estimator to over-specified values of the factorization rank, especially its ability to automatically prune irrelevant dictionary columns, as empirically observed in previous work. The marginalization of the activation matrix lead"},"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":"1801.01799","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-05T15:50:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c7d2def83359ca1dd079daa4b0c4b9c2c825dcf65a240c2f4fe3d23b5fa1af8c","abstract_canon_sha256":"b781e591c87a82478de3ee6992d77b5c60a715a1fe0cd645bf598e521323dc7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:34.127236Z","signature_b64":"+YFlpKwvlrSxEwk6UPCj0PVF/T6IXLApgKiMtqtWTc0i9T3785pUL+27e9+93/j5wJg93RyppZqdBcMveUM2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"308ff26e1c20f574509aa71c2dc4d3b8df17cfb41b1d3b3bf13a1e7e0b1ba541","last_reissued_at":"2026-05-18T00:14:34.126596Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:34.126596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alberto Lumbreras, C\\'edric F\\'evotte, Louis Filstroff","submitted_at":"2018-01-05T15:50:39Z","abstract_excerpt":"We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data. We show that GaP can be rewritten free of the score/activation matrix. This gives us new insights about the estimation of the topic/dictionary matrix by maximum marginal likelihood estimation. In particular, this explains the robustness of this estimator to over-specified values of the factorization rank, especially its ability to automatically prune irrelevant dictionary columns, as empirically observed in previous work. The marginalization of the activation matrix lead"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01799","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":"1801.01799","created_at":"2026-05-18T00:14:34.126696+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.01799v2","created_at":"2026-05-18T00:14:34.126696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01799","created_at":"2026-05-18T00:14:34.126696+00:00"},{"alias_kind":"pith_short_12","alias_value":"GCH7E3Q4ED2X","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GCH7E3Q4ED2XIUE2","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GCH7E3Q4","created_at":"2026-05-18T12:32:25.280505+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/GCH7E3Q4ED2XIUE2U4OC3RGTXD","json":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD.json","graph_json":"https://pith.science/api/pith-number/GCH7E3Q4ED2XIUE2U4OC3RGTXD/graph.json","events_json":"https://pith.science/api/pith-number/GCH7E3Q4ED2XIUE2U4OC3RGTXD/events.json","paper":"https://pith.science/paper/GCH7E3Q4"},"agent_actions":{"view_html":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD","download_json":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD.json","view_paper":"https://pith.science/paper/GCH7E3Q4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.01799&json=true","fetch_graph":"https://pith.science/api/pith-number/GCH7E3Q4ED2XIUE2U4OC3RGTXD/graph.json","fetch_events":"https://pith.science/api/pith-number/GCH7E3Q4ED2XIUE2U4OC3RGTXD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD/action/storage_attestation","attest_author":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD/action/author_attestation","sign_citation":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD/action/citation_signature","submit_replication":"https://pith.science/pith/GCH7E3Q4ED2XIUE2U4OC3RGTXD/action/replication_record"}},"created_at":"2026-05-18T00:14:34.126696+00:00","updated_at":"2026-05-18T00:14:34.126696+00:00"}