{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6PIPPJZO4QBTC5BBYLGTLQXACD","short_pith_number":"pith:6PIPPJZO","schema_version":"1.0","canonical_sha256":"f3d0f7a72ee403317421c2cd35c2e010c9302be83b47c3e359e0b221b4f9cc9f","source":{"kind":"arxiv","id":"2605.27523","version":1},"attestation_state":"computed","paper":{"title":"Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Joseph Feldman, Yuqi Gu","submitted_at":"2026-05-26T18:00:36Z","abstract_excerpt":"Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to interpret. We introduce the Deep Discrete Encoder (DDE) Copula, an identifiable and interpretable generative model for multivariate data with arbitrary marginal distributions. The model places a hierarchical directed network of binary latent variables inside a copula framework, enabling flexible dependence modeling for mixed discrete and continuous data. Estimation is based on rank likelihoods, which decouple marginal modeling from posterior infe"},"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":"2605.27523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-05-26T18:00:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c6b76193c6870aa69dadd181b05157087b53f38249fcb124b3de98f1596f89cd","abstract_canon_sha256":"cb86e14253e951fecea441da633cfaf2a17ed4c9acf1f0408f32dbbc47abd803"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T00:05:22.782884Z","signature_b64":"McBR+tgGWYq7du3nuAZjxB6tlB54i+yOL2Pz+CyTWVEAl75vTAjDkDXhcq7lLkZPkPnPj2YqQf6eb6LKNL40CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3d0f7a72ee403317421c2cd35c2e010c9302be83b47c3e359e0b221b4f9cc9f","last_reissued_at":"2026-05-28T00:05:22.782240Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T00:05:22.782240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Joseph Feldman, Yuqi Gu","submitted_at":"2026-05-26T18:00:36Z","abstract_excerpt":"Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to interpret. We introduce the Deep Discrete Encoder (DDE) Copula, an identifiable and interpretable generative model for multivariate data with arbitrary marginal distributions. The model places a hierarchical directed network of binary latent variables inside a copula framework, enabling flexible dependence modeling for mixed discrete and continuous data. Estimation is based on rank likelihoods, which decouple marginal modeling from posterior infe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27523","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.27523/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2605.27523","created_at":"2026-05-28T00:05:22.782338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27523v1","created_at":"2026-05-28T00:05:22.782338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27523","created_at":"2026-05-28T00:05:22.782338+00:00"},{"alias_kind":"pith_short_12","alias_value":"6PIPPJZO4QBT","created_at":"2026-05-28T00:05:22.782338+00:00"},{"alias_kind":"pith_short_16","alias_value":"6PIPPJZO4QBTC5BB","created_at":"2026-05-28T00:05:22.782338+00:00"},{"alias_kind":"pith_short_8","alias_value":"6PIPPJZO","created_at":"2026-05-28T00:05:22.782338+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/6PIPPJZO4QBTC5BBYLGTLQXACD","json":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD.json","graph_json":"https://pith.science/api/pith-number/6PIPPJZO4QBTC5BBYLGTLQXACD/graph.json","events_json":"https://pith.science/api/pith-number/6PIPPJZO4QBTC5BBYLGTLQXACD/events.json","paper":"https://pith.science/paper/6PIPPJZO"},"agent_actions":{"view_html":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD","download_json":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD.json","view_paper":"https://pith.science/paper/6PIPPJZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27523&json=true","fetch_graph":"https://pith.science/api/pith-number/6PIPPJZO4QBTC5BBYLGTLQXACD/graph.json","fetch_events":"https://pith.science/api/pith-number/6PIPPJZO4QBTC5BBYLGTLQXACD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD/action/storage_attestation","attest_author":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD/action/author_attestation","sign_citation":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD/action/citation_signature","submit_replication":"https://pith.science/pith/6PIPPJZO4QBTC5BBYLGTLQXACD/action/replication_record"}},"created_at":"2026-05-28T00:05:22.782338+00:00","updated_at":"2026-05-28T00:05:22.782338+00:00"}