{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WP623CUQBHJ3HBZMVG2AMUDF27","short_pith_number":"pith:WP623CUQ","schema_version":"1.0","canonical_sha256":"b3fdad8a9009d3b3872ca9b4065065d7ea70a03d0c7c5498c8c1689c322dd225","source":{"kind":"arxiv","id":"1907.00865","version":4},"attestation_state":"computed","paper":{"title":"Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Michael Osborne, Sebastian Farquhar, Yarin Gal","submitted_at":"2019-07-01T15:25:50Z","abstract_excerpt":"We propose Radial Bayesian Neural Networks (BNNs): a variational approximate posterior for BNNs which scales well to large models while maintaining a distribution over weight-space with full support. Other scalable Bayesian deep learning methods, like MC dropout or deep ensembles, have discrete support-they assign zero probability to almost all of the weight-space. Unlike these discrete support methods, Radial BNNs' full support makes them suitable for use as a prior for sequential inference. In addition, they solve the conceptual challenges with the a priori implausibility of weight distribut"},"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":"1907.00865","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-01T15:25:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d2d56589386b9f258c479ffd11f1c336985f22b41f8f9d0c6434f53ae36eb008","abstract_canon_sha256":"7c9550d4997041cdddd6351b7a222d3f1e2011941fd162d83131e0bc958c9ed0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:44:30.910592Z","signature_b64":"5oSRxW41kDrs8OuE7IquyWlGNTNP0usj9gP0aq84Bp3hVKksDT+6v5+ZeWZbpfrK2eqwW0zt04xqfG2UJkS+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3fdad8a9009d3b3872ca9b4065065d7ea70a03d0c7c5498c8c1689c322dd225","last_reissued_at":"2026-07-05T02:44:30.910114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:44:30.910114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Michael Osborne, Sebastian Farquhar, Yarin Gal","submitted_at":"2019-07-01T15:25:50Z","abstract_excerpt":"We propose Radial Bayesian Neural Networks (BNNs): a variational approximate posterior for BNNs which scales well to large models while maintaining a distribution over weight-space with full support. Other scalable Bayesian deep learning methods, like MC dropout or deep ensembles, have discrete support-they assign zero probability to almost all of the weight-space. Unlike these discrete support methods, Radial BNNs' full support makes them suitable for use as a prior for sequential inference. In addition, they solve the conceptual challenges with the a priori implausibility of weight distribut"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.00865","kind":"arxiv","version":4},"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/1907.00865/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":"1907.00865","created_at":"2026-07-05T02:44:30.910168+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.00865v4","created_at":"2026-07-05T02:44:30.910168+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.00865","created_at":"2026-07-05T02:44:30.910168+00:00"},{"alias_kind":"pith_short_12","alias_value":"WP623CUQBHJ3","created_at":"2026-07-05T02:44:30.910168+00:00"},{"alias_kind":"pith_short_16","alias_value":"WP623CUQBHJ3HBZM","created_at":"2026-07-05T02:44:30.910168+00:00"},{"alias_kind":"pith_short_8","alias_value":"WP623CUQ","created_at":"2026-07-05T02:44:30.910168+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/WP623CUQBHJ3HBZMVG2AMUDF27","json":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27.json","graph_json":"https://pith.science/api/pith-number/WP623CUQBHJ3HBZMVG2AMUDF27/graph.json","events_json":"https://pith.science/api/pith-number/WP623CUQBHJ3HBZMVG2AMUDF27/events.json","paper":"https://pith.science/paper/WP623CUQ"},"agent_actions":{"view_html":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27","download_json":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27.json","view_paper":"https://pith.science/paper/WP623CUQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.00865&json=true","fetch_graph":"https://pith.science/api/pith-number/WP623CUQBHJ3HBZMVG2AMUDF27/graph.json","fetch_events":"https://pith.science/api/pith-number/WP623CUQBHJ3HBZMVG2AMUDF27/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27/action/storage_attestation","attest_author":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27/action/author_attestation","sign_citation":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27/action/citation_signature","submit_replication":"https://pith.science/pith/WP623CUQBHJ3HBZMVG2AMUDF27/action/replication_record"}},"created_at":"2026-07-05T02:44:30.910168+00:00","updated_at":"2026-07-05T02:44:30.910168+00:00"}