{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:F564JJFRJGM7RBXUJN642K6ONW","short_pith_number":"pith:F564JJFR","canonical_record":{"source":{"id":"1905.03826","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-09T19:32:48Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"de9bda44125529585f88063f8e16c1b093ddcf6af78bfa32afa39ff1f30d784a","abstract_canon_sha256":"18791943e3b41ff583724f2aad34eed507a3afa3b7bd0f233b30468a6f3512ef"},"schema_version":"1.0"},"canonical_sha256":"2f7dc4a4b14999f886f44b7dcd2bce6d98a29f2abd528fd2014a03b12586f877","source":{"kind":"arxiv","id":"1905.03826","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.03826","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.03826v2","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03826","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"F564JJFRJGM7","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"F564JJFRJGM7RBXU","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"F564JJFR","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:F564JJFRJGM7RBXUJN642K6ONW","target":"record","payload":{"canonical_record":{"source":{"id":"1905.03826","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-09T19:32:48Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"de9bda44125529585f88063f8e16c1b093ddcf6af78bfa32afa39ff1f30d784a","abstract_canon_sha256":"18791943e3b41ff583724f2aad34eed507a3afa3b7bd0f233b30468a6f3512ef"},"schema_version":"1.0"},"canonical_sha256":"2f7dc4a4b14999f886f44b7dcd2bce6d98a29f2abd528fd2014a03b12586f877","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:25.447211Z","signature_b64":"9UQxurO901zzgeHm9+PkWb2ycRRAp9QOsraZyNYHWqnHQrFuUEjeRV2hUKjUF9oIsBx2l7G6/qT8ShKTu7cqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f7dc4a4b14999f886f44b7dcd2bce6d98a29f2abd528fd2014a03b12586f877","last_reissued_at":"2026-05-17T23:46:25.446589Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:25.446589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.03826","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HG1RqYZkDFuFJTGtuZeTI2VDi9NmRF+SH/6S6lA2BJ3/K0DbfLFcdGyC/sJ45Tag02BMMcWE/uHBC6RaRRW4Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T04:32:49.983083Z"},"content_sha256":"a51ebb48d1de07a89274b1a28751c5ad6de54edf5f9abf537153ead3c37ab039","schema_version":"1.0","event_id":"sha256:a51ebb48d1de07a89274b1a28751c5ad6de54edf5f9abf537153ead3c37ab039"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:F564JJFRJGM7RBXUJN642K6ONW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Random Function Priors for Correlation Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aonan Zhang, John Paisley","submitted_at":"2019-05-09T19:32:48Z","abstract_excerpt":"The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\\theta)$, where $\\theta\\mathrel{\\mathop:}=(\\theta_k)_{k\\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\\theta_k$ used to express $X_n$. In this paper, we introduce random function priors for $Z_n$ for modeling correlations among its $K$ dimensions $Z_{n1}$ through $Z_{nK}$, which we call \\textit{population random measure embedding} (PRME). Our model can be viewed a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03826","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ngMIjBLU6smvulOziyGRdQnVOJ258bQVBcMriWu/nwiLlgfSQtHvR/vpLTEu+gRsHzbAtF+uHBB2MTrfl0KpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T04:32:49.983459Z"},"content_sha256":"fe5f0a416ffd9beb9f860166a156c57644bfcb5e464734d0f7697e47902db400","schema_version":"1.0","event_id":"sha256:fe5f0a416ffd9beb9f860166a156c57644bfcb5e464734d0f7697e47902db400"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F564JJFRJGM7RBXUJN642K6ONW/bundle.json","state_url":"https://pith.science/pith/F564JJFRJGM7RBXUJN642K6ONW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F564JJFRJGM7RBXUJN642K6ONW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T04:32:49Z","links":{"resolver":"https://pith.science/pith/F564JJFRJGM7RBXUJN642K6ONW","bundle":"https://pith.science/pith/F564JJFRJGM7RBXUJN642K6ONW/bundle.json","state":"https://pith.science/pith/F564JJFRJGM7RBXUJN642K6ONW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F564JJFRJGM7RBXUJN642K6ONW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F564JJFRJGM7RBXUJN642K6ONW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"18791943e3b41ff583724f2aad34eed507a3afa3b7bd0f233b30468a6f3512ef","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-09T19:32:48Z","title_canon_sha256":"de9bda44125529585f88063f8e16c1b093ddcf6af78bfa32afa39ff1f30d784a"},"schema_version":"1.0","source":{"id":"1905.03826","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.03826","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.03826v2","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03826","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"F564JJFRJGM7","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"F564JJFRJGM7RBXU","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"F564JJFR","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:fe5f0a416ffd9beb9f860166a156c57644bfcb5e464734d0f7697e47902db400","target":"graph","created_at":"2026-05-17T23:46:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\\theta)$, where $\\theta\\mathrel{\\mathop:}=(\\theta_k)_{k\\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\\theta_k$ used to express $X_n$. In this paper, we introduce random function priors for $Z_n$ for modeling correlations among its $K$ dimensions $Z_{n1}$ through $Z_{nK}$, which we call \\textit{population random measure embedding} (PRME). Our model can be viewed a","authors_text":"Aonan Zhang, John Paisley","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-09T19:32:48Z","title":"Random Function Priors for Correlation Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03826","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a51ebb48d1de07a89274b1a28751c5ad6de54edf5f9abf537153ead3c37ab039","target":"record","created_at":"2026-05-17T23:46:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"18791943e3b41ff583724f2aad34eed507a3afa3b7bd0f233b30468a6f3512ef","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-09T19:32:48Z","title_canon_sha256":"de9bda44125529585f88063f8e16c1b093ddcf6af78bfa32afa39ff1f30d784a"},"schema_version":"1.0","source":{"id":"1905.03826","kind":"arxiv","version":2}},"canonical_sha256":"2f7dc4a4b14999f886f44b7dcd2bce6d98a29f2abd528fd2014a03b12586f877","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f7dc4a4b14999f886f44b7dcd2bce6d98a29f2abd528fd2014a03b12586f877","first_computed_at":"2026-05-17T23:46:25.446589Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:25.446589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9UQxurO901zzgeHm9+PkWb2ycRRAp9QOsraZyNYHWqnHQrFuUEjeRV2hUKjUF9oIsBx2l7G6/qT8ShKTu7cqCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:25.447211Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.03826","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a51ebb48d1de07a89274b1a28751c5ad6de54edf5f9abf537153ead3c37ab039","sha256:fe5f0a416ffd9beb9f860166a156c57644bfcb5e464734d0f7697e47902db400"],"state_sha256":"435b89d864bf559ce55ba1bdc9085da7bfa5b18fc0f11a60b850e65a9860c51c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yYTVNvZwV+PTGh2XD+ZGyRbilpWDbwfSo419oDrznbkAexa1WtfgKL4F0BxuLQh+PXhbuf/RKHJ8W0AaFoekAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T04:32:49.985993Z","bundle_sha256":"4755f314dcad6d94390730888674baa4ee754777bf8220641b580607606b0294"}}