{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:X3JYHZXBSG7CI7RFZJWZAL7KEI","short_pith_number":"pith:X3JYHZXB","canonical_record":{"source":{"id":"2001.10995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-29T18:08:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"db1586d614508c84a5a9f6e23717f54f9e5da3b052ac5bf6483eb18f8e576093","abstract_canon_sha256":"d1c32793325ca6fee103a544ec09dccdb62948779cbe8a3d436c77385d2c13d7"},"schema_version":"1.0"},"canonical_sha256":"bed383e6e191be247e25ca6d902fea2204ad45ee7c09902f980bfa70863a3840","source":{"kind":"arxiv","id":"2001.10995","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.10995","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"arxiv_version","alias_value":"2001.10995v1","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.10995","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_12","alias_value":"X3JYHZXBSG7C","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_16","alias_value":"X3JYHZXBSG7CI7RF","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_8","alias_value":"X3JYHZXB","created_at":"2026-07-05T00:37:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:X3JYHZXBSG7CI7RFZJWZAL7KEI","target":"record","payload":{"canonical_record":{"source":{"id":"2001.10995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-29T18:08:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"db1586d614508c84a5a9f6e23717f54f9e5da3b052ac5bf6483eb18f8e576093","abstract_canon_sha256":"d1c32793325ca6fee103a544ec09dccdb62948779cbe8a3d436c77385d2c13d7"},"schema_version":"1.0"},"canonical_sha256":"bed383e6e191be247e25ca6d902fea2204ad45ee7c09902f980bfa70863a3840","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:14.180670Z","signature_b64":"g1qd8d1fHNv4p2jePEpu5WSPACnzdN6+1w61OZ3xgW4+38qSkPCufk0UNapobml+V73/T+F8oeK5dlXbN8FfBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bed383e6e191be247e25ca6d902fea2204ad45ee7c09902f980bfa70863a3840","last_reissued_at":"2026-07-05T00:37:14.180256Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:14.180256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.10995","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:37:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gfE43oAdDAGi9IcoNGSFdu37V6GWWESrdHxeB0W1+ag2J/e4UZnkhD2NfHU4wCcyKLEDH5sy+Iv93aKGj3SYDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:36:31.192607Z"},"content_sha256":"2365bded07b23536253e4a305e0a22990a6edddb208062cd1676a6d75ab14fd4","schema_version":"1.0","event_id":"sha256:2365bded07b23536253e4a305e0a22990a6edddb208062cd1676a6d75ab14fd4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:X3JYHZXBSG7CI7RFZJWZAL7KEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Case for Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Gordon Wilson","submitted_at":"2020-01-29T18:08:52Z","abstract_excerpt":"The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks. (1) Neural networks are typically underspecified by the data, and can represent many different but high performing models corresponding to different settings of parameters, which is exactly when marginalization will make the biggest difference for both calibration and accuracy. (2) Deep ensembles have been mistaken as competing approaches to Bayesian methods, but can be seen as approximate Bayesian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.10995","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/2001.10995/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:37:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EUnmiKOk3DxYZAm1kuzp0TT2nP/k3oNYh4QkP5WC5mgNpPOTlaDZ62/+aZtDT8uPqmqpye/lXHjwAdAN1BjaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:36:31.193461Z"},"content_sha256":"f7b4a2d5f8df14dfe0da0473759862981dfafc4717f550d89a6b239ca6ec9468","schema_version":"1.0","event_id":"sha256:f7b4a2d5f8df14dfe0da0473759862981dfafc4717f550d89a6b239ca6ec9468"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/bundle.json","state_url":"https://pith.science/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/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":"2026-08-08T23:36:31Z","links":{"resolver":"https://pith.science/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI","bundle":"https://pith.science/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/bundle.json","state":"https://pith.science/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X3JYHZXBSG7CI7RFZJWZAL7KEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:X3JYHZXBSG7CI7RFZJWZAL7KEI","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":"d1c32793325ca6fee103a544ec09dccdb62948779cbe8a3d436c77385d2c13d7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-29T18:08:52Z","title_canon_sha256":"db1586d614508c84a5a9f6e23717f54f9e5da3b052ac5bf6483eb18f8e576093"},"schema_version":"1.0","source":{"id":"2001.10995","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.10995","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"arxiv_version","alias_value":"2001.10995v1","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.10995","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_12","alias_value":"X3JYHZXBSG7C","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_16","alias_value":"X3JYHZXBSG7CI7RF","created_at":"2026-07-05T00:37:14Z"},{"alias_kind":"pith_short_8","alias_value":"X3JYHZXB","created_at":"2026-07-05T00:37:14Z"}],"graph_snapshots":[{"event_id":"sha256:f7b4a2d5f8df14dfe0da0473759862981dfafc4717f550d89a6b239ca6ec9468","target":"graph","created_at":"2026-07-05T00:37:14Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2001.10995/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks. (1) Neural networks are typically underspecified by the data, and can represent many different but high performing models corresponding to different settings of parameters, which is exactly when marginalization will make the biggest difference for both calibration and accuracy. (2) Deep ensembles have been mistaken as competing approaches to Bayesian methods, but can be seen as approximate Bayesian","authors_text":"Andrew Gordon Wilson","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-29T18:08:52Z","title":"The Case for Bayesian Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.10995","kind":"arxiv","version":1},"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":"sha256:2365bded07b23536253e4a305e0a22990a6edddb208062cd1676a6d75ab14fd4","target":"record","created_at":"2026-07-05T00:37:14Z","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":"d1c32793325ca6fee103a544ec09dccdb62948779cbe8a3d436c77385d2c13d7","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-29T18:08:52Z","title_canon_sha256":"db1586d614508c84a5a9f6e23717f54f9e5da3b052ac5bf6483eb18f8e576093"},"schema_version":"1.0","source":{"id":"2001.10995","kind":"arxiv","version":1}},"canonical_sha256":"bed383e6e191be247e25ca6d902fea2204ad45ee7c09902f980bfa70863a3840","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bed383e6e191be247e25ca6d902fea2204ad45ee7c09902f980bfa70863a3840","first_computed_at":"2026-07-05T00:37:14.180256Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:37:14.180256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g1qd8d1fHNv4p2jePEpu5WSPACnzdN6+1w61OZ3xgW4+38qSkPCufk0UNapobml+V73/T+F8oeK5dlXbN8FfBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:37:14.180670Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.10995","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2365bded07b23536253e4a305e0a22990a6edddb208062cd1676a6d75ab14fd4","sha256:f7b4a2d5f8df14dfe0da0473759862981dfafc4717f550d89a6b239ca6ec9468"],"state_sha256":"be10fb34512282298aef8dd4054f75f20ccf6cff136d4b62c8392254ac82068a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9fDinf+wjyuf0FGvlzFGnL87GzhowslqG5TVf1yFiHy0VGHb9w8/t1kxfIJzPZyymNCKTCepxweSOL82J5mAAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:36:31.199395Z","bundle_sha256":"9bd17e1655696e7dc84bc3a5cdfa8ba0712bc3a075ed6548ce9f20e290172bb5"}}