{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L6ST7WN6JL7AX4TIPHQWIZHYU5","short_pith_number":"pith:L6ST7WN6","schema_version":"1.0","canonical_sha256":"5fa53fd9be4afe0bf26879e16464f8a74d2710e3d29227c86c0305e1f3cc6c68","source":{"kind":"arxiv","id":"2305.10664","version":3},"attestation_state":"computed","paper":{"title":"Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Anindya Bhadra, Jorge Lor\\'ia","submitted_at":"2023-05-18T02:55:00Z","abstract_excerpt":"From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, when the network weights have bounded prior variance. Neal's result has been extended to networks with multiple hidden layers and to convolutional neural networks, also with Gaussian process scaling limits. The tractable properties of Gaussian processes then allow straightforward posterior inference and uncertainty quantification, considerably simplifying the study of the limit process compared to a network of finit"},"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":"2305.10664","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-18T02:55:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"85dc8acf64905dabde07026cbcdc2eaab85512f02311872a59f9fc52829353a0","abstract_canon_sha256":"ff3ef71f095a274529fa5afc8c7bea0a905c47eca3c79256725c7c223bd9120f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:22.271777Z","signature_b64":"eP3xsMxKuFMc5u3yGZ9cqKgiKmKOVYjrqgwknie1Oo3uQBI7HHRQSy+bCFB3K2FZBzZueWnj7U1MEdzOgIrOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fa53fd9be4afe0bf26879e16464f8a74d2710e3d29227c86c0305e1f3cc6c68","last_reissued_at":"2026-07-05T08:27:22.271358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:22.271358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Anindya Bhadra, Jorge Lor\\'ia","submitted_at":"2023-05-18T02:55:00Z","abstract_excerpt":"From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, when the network weights have bounded prior variance. Neal's result has been extended to networks with multiple hidden layers and to convolutional neural networks, also with Gaussian process scaling limits. The tractable properties of Gaussian processes then allow straightforward posterior inference and uncertainty quantification, considerably simplifying the study of the limit process compared to a network of finit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.10664","kind":"arxiv","version":3},"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/2305.10664/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":"2305.10664","created_at":"2026-07-05T08:27:22.271419+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.10664v3","created_at":"2026-07-05T08:27:22.271419+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.10664","created_at":"2026-07-05T08:27:22.271419+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6ST7WN6JL7A","created_at":"2026-07-05T08:27:22.271419+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6ST7WN6JL7AX4TI","created_at":"2026-07-05T08:27:22.271419+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6ST7WN6","created_at":"2026-07-05T08:27:22.271419+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/L6ST7WN6JL7AX4TIPHQWIZHYU5","json":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5.json","graph_json":"https://pith.science/api/pith-number/L6ST7WN6JL7AX4TIPHQWIZHYU5/graph.json","events_json":"https://pith.science/api/pith-number/L6ST7WN6JL7AX4TIPHQWIZHYU5/events.json","paper":"https://pith.science/paper/L6ST7WN6"},"agent_actions":{"view_html":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5","download_json":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5.json","view_paper":"https://pith.science/paper/L6ST7WN6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.10664&json=true","fetch_graph":"https://pith.science/api/pith-number/L6ST7WN6JL7AX4TIPHQWIZHYU5/graph.json","fetch_events":"https://pith.science/api/pith-number/L6ST7WN6JL7AX4TIPHQWIZHYU5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5/action/storage_attestation","attest_author":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5/action/author_attestation","sign_citation":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5/action/citation_signature","submit_replication":"https://pith.science/pith/L6ST7WN6JL7AX4TIPHQWIZHYU5/action/replication_record"}},"created_at":"2026-07-05T08:27:22.271419+00:00","updated_at":"2026-07-05T08:27:22.271419+00:00"}