{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TAQFXPQ2HACBQALU3CQ4X5HAPH","short_pith_number":"pith:TAQFXPQ2","schema_version":"1.0","canonical_sha256":"98205bbe1a3804180174d8a1cbf4e079e585e869941c8ea9ce04979751ca5e9e","source":{"kind":"arxiv","id":"2312.07790","version":1},"attestation_state":"computed","paper":{"title":"Characteristic Circuits","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Kristian Kersting, Martin Trapp, Zhongjie Yu","submitted_at":"2023-12-12T23:15:07Z","abstract_excerpt":"In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a prominent family of tractable probabilistic models, offer a remedy to this challenge by composing simple, tractable distributions into a high-dimensional probability distribution. However, learning PCs on heterogeneous data is challenging and densities of some parametric distributions are not available in closed form, limiting their potential use. We introduce characteristic circuits (CCs), a family of tractab"},"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":"2312.07790","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-12T23:15:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fdeb3af78f2ffa10ff2c6d91fa4e5e06d7b7ad59287e81f155d6958c12d9a0a5","abstract_canon_sha256":"e9484e902c4e00e7c63e85c63fb1d93eab19ac5009857aa99003f5497e7838ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:50.444811Z","signature_b64":"4ASS00AkMrjQzlJG2CTbrS39h3clE57bUa+gbEkBeR9qQCcSu8k62bMePHfbdvK6EQCAQABXv7SuBpuwztABAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98205bbe1a3804180174d8a1cbf4e079e585e869941c8ea9ce04979751ca5e9e","last_reissued_at":"2026-07-05T07:23:50.444362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:50.444362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characteristic Circuits","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Kristian Kersting, Martin Trapp, Zhongjie Yu","submitted_at":"2023-12-12T23:15:07Z","abstract_excerpt":"In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a prominent family of tractable probabilistic models, offer a remedy to this challenge by composing simple, tractable distributions into a high-dimensional probability distribution. However, learning PCs on heterogeneous data is challenging and densities of some parametric distributions are not available in closed form, limiting their potential use. We introduce characteristic circuits (CCs), a family of tractab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07790","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/2312.07790/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":"2312.07790","created_at":"2026-07-05T07:23:50.444417+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07790v1","created_at":"2026-07-05T07:23:50.444417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07790","created_at":"2026-07-05T07:23:50.444417+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAQFXPQ2HACB","created_at":"2026-07-05T07:23:50.444417+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAQFXPQ2HACBQALU","created_at":"2026-07-05T07:23:50.444417+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAQFXPQ2","created_at":"2026-07-05T07:23:50.444417+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/TAQFXPQ2HACBQALU3CQ4X5HAPH","json":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH.json","graph_json":"https://pith.science/api/pith-number/TAQFXPQ2HACBQALU3CQ4X5HAPH/graph.json","events_json":"https://pith.science/api/pith-number/TAQFXPQ2HACBQALU3CQ4X5HAPH/events.json","paper":"https://pith.science/paper/TAQFXPQ2"},"agent_actions":{"view_html":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH","download_json":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH.json","view_paper":"https://pith.science/paper/TAQFXPQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07790&json=true","fetch_graph":"https://pith.science/api/pith-number/TAQFXPQ2HACBQALU3CQ4X5HAPH/graph.json","fetch_events":"https://pith.science/api/pith-number/TAQFXPQ2HACBQALU3CQ4X5HAPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH/action/storage_attestation","attest_author":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH/action/author_attestation","sign_citation":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH/action/citation_signature","submit_replication":"https://pith.science/pith/TAQFXPQ2HACBQALU3CQ4X5HAPH/action/replication_record"}},"created_at":"2026-07-05T07:23:50.444417+00:00","updated_at":"2026-07-05T07:23:50.444417+00:00"}