{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:V3NMUMRVM5D2XOV5MUQIM3YPTA","short_pith_number":"pith:V3NMUMRV","schema_version":"1.0","canonical_sha256":"aedaca32356747abbabd6520866f0f98250202e43b6d684bf655f83a7446bd2d","source":{"kind":"arxiv","id":"2002.04033","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Thang D. Bui, Theofanis Karaletsos","submitted_at":"2020-02-10T07:19:52Z","abstract_excerpt":"Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the function space such as periodicity or dependence on contexts such as inputs. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model"},"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":"2002.04033","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T07:19:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b0baf6d5172e77147cd5abf287759e84a8d84e05084ae86bf93d1f8390778873","abstract_canon_sha256":"72e6938bb222ba3cca3e553032ed46b375d5b2f03e97d6e4466c37b354b5e8ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:39:41.600748Z","signature_b64":"F2gCUn6mfJF553CnZ/cbOFChSSJ19QcnwrqgSQdTO9+UYRKEGdlK/gc4G70yCDVutFkDExCqmFHG96tLFIXFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aedaca32356747abbabd6520866f0f98250202e43b6d684bf655f83a7446bd2d","last_reissued_at":"2026-07-05T00:39:41.600364Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:39:41.600364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Thang D. Bui, Theofanis Karaletsos","submitted_at":"2020-02-10T07:19:52Z","abstract_excerpt":"Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the function space such as periodicity or dependence on contexts such as inputs. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.04033","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/2002.04033/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":"2002.04033","created_at":"2026-07-05T00:39:41.600427+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.04033v1","created_at":"2026-07-05T00:39:41.600427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.04033","created_at":"2026-07-05T00:39:41.600427+00:00"},{"alias_kind":"pith_short_12","alias_value":"V3NMUMRVM5D2","created_at":"2026-07-05T00:39:41.600427+00:00"},{"alias_kind":"pith_short_16","alias_value":"V3NMUMRVM5D2XOV5","created_at":"2026-07-05T00:39:41.600427+00:00"},{"alias_kind":"pith_short_8","alias_value":"V3NMUMRV","created_at":"2026-07-05T00:39:41.600427+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/V3NMUMRVM5D2XOV5MUQIM3YPTA","json":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA.json","graph_json":"https://pith.science/api/pith-number/V3NMUMRVM5D2XOV5MUQIM3YPTA/graph.json","events_json":"https://pith.science/api/pith-number/V3NMUMRVM5D2XOV5MUQIM3YPTA/events.json","paper":"https://pith.science/paper/V3NMUMRV"},"agent_actions":{"view_html":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA","download_json":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA.json","view_paper":"https://pith.science/paper/V3NMUMRV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.04033&json=true","fetch_graph":"https://pith.science/api/pith-number/V3NMUMRVM5D2XOV5MUQIM3YPTA/graph.json","fetch_events":"https://pith.science/api/pith-number/V3NMUMRVM5D2XOV5MUQIM3YPTA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA/action/storage_attestation","attest_author":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA/action/author_attestation","sign_citation":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA/action/citation_signature","submit_replication":"https://pith.science/pith/V3NMUMRVM5D2XOV5MUQIM3YPTA/action/replication_record"}},"created_at":"2026-07-05T00:39:41.600427+00:00","updated_at":"2026-07-05T00:39:41.600427+00:00"}