{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:LLLBH3FOA23YFTSP5PZLGFJPY5","short_pith_number":"pith:LLLBH3FO","canonical_record":{"source":{"id":"2310.08287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-12T12:45:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"91148ce50e1d4ab3c5ec8f3d9fc68652fee45b24eda917be57c04205981babed","abstract_canon_sha256":"cfb6b3ecd30cc5683486c3324b8636e6ebd257789a4d7ce26bbff0821bf7d80f"},"schema_version":"1.0"},"canonical_sha256":"5ad613ecae06b782ce4febf2b3152fc762d43022d46eaf8c86a769f8d96efdde","source":{"kind":"arxiv","id":"2310.08287","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08287","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08287v1","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08287","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_12","alias_value":"LLLBH3FOA23Y","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_16","alias_value":"LLLBH3FOA23YFTSP","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_8","alias_value":"LLLBH3FO","created_at":"2026-07-05T07:00:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:LLLBH3FOA23YFTSP5PZLGFJPY5","target":"record","payload":{"canonical_record":{"source":{"id":"2310.08287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-12T12:45:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"91148ce50e1d4ab3c5ec8f3d9fc68652fee45b24eda917be57c04205981babed","abstract_canon_sha256":"cfb6b3ecd30cc5683486c3324b8636e6ebd257789a4d7ce26bbff0821bf7d80f"},"schema_version":"1.0"},"canonical_sha256":"5ad613ecae06b782ce4febf2b3152fc762d43022d46eaf8c86a769f8d96efdde","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:13.497176Z","signature_b64":"9AUax48cXQADNWer51t041UIiwWQIDwNyIfAOUvDAnyBhO6KkiCgJWc1E8sgBeVYoldUHMLvjCbm05w9axPZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ad613ecae06b782ce4febf2b3152fc762d43022d46eaf8c86a769f8d96efdde","last_reissued_at":"2026-07-05T07:00:13.496716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:13.496716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.08287","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":"2026-07-05T07:00:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qUGSnx/9eR5MJ5fbJ0RZeyXupwdMSjhhstfqesHnQyMDONigT+TekCZjhlNEXp79FGF/LARvNY/UxdZou+I2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:37:44.779170Z"},"content_sha256":"423d4b5db1b18d81713ffca412ae696f3ae6457ec3b5e2db2866e9077061b46d","schema_version":"1.0","event_id":"sha256:423d4b5db1b18d81713ffca412ae696f3ae6457ec3b5e2db2866e9077061b46d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:LLLBH3FOA23YFTSP5PZLGFJPY5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Emanuel Aldea, Gianni Franchi, Olivier Laurent","submitted_at":"2023-10-12T12:45:13Z","abstract_excerpt":"The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality. This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08287","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/2310.08287/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":"2026-07-05T07:00:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QynINz1Rh2dvy4XpmQS7wSVOD2/ISUbnxbPaZt0ywDiew7TRlXt7PPG0SBfglGp/p2syrZCkmrzt9sbNSp+VDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:37:44.779789Z"},"content_sha256":"69a91b486fb391d87d64bf1f72ca99c1f884edb413a7a7ce6e28fff6a1e7b6d4","schema_version":"1.0","event_id":"sha256:69a91b486fb391d87d64bf1f72ca99c1f884edb413a7a7ce6e28fff6a1e7b6d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/bundle.json","state_url":"https://pith.science/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/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":"2026-08-04T15:37:44Z","links":{"resolver":"https://pith.science/pith/LLLBH3FOA23YFTSP5PZLGFJPY5","bundle":"https://pith.science/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/bundle.json","state":"https://pith.science/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LLLBH3FOA23YFTSP5PZLGFJPY5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LLLBH3FOA23YFTSP5PZLGFJPY5","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":"cfb6b3ecd30cc5683486c3324b8636e6ebd257789a4d7ce26bbff0821bf7d80f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-12T12:45:13Z","title_canon_sha256":"91148ce50e1d4ab3c5ec8f3d9fc68652fee45b24eda917be57c04205981babed"},"schema_version":"1.0","source":{"id":"2310.08287","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08287","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08287v1","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08287","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_12","alias_value":"LLLBH3FOA23Y","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_16","alias_value":"LLLBH3FOA23YFTSP","created_at":"2026-07-05T07:00:13Z"},{"alias_kind":"pith_short_8","alias_value":"LLLBH3FO","created_at":"2026-07-05T07:00:13Z"}],"graph_snapshots":[{"event_id":"sha256:69a91b486fb391d87d64bf1f72ca99c1f884edb413a7a7ce6e28fff6a1e7b6d4","target":"graph","created_at":"2026-07-05T07:00:13Z","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/2310.08287/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality. This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on th","authors_text":"Emanuel Aldea, Gianni Franchi, Olivier Laurent","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-12T12:45:13Z","title":"A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08287","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":"sha256:423d4b5db1b18d81713ffca412ae696f3ae6457ec3b5e2db2866e9077061b46d","target":"record","created_at":"2026-07-05T07:00:13Z","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":"cfb6b3ecd30cc5683486c3324b8636e6ebd257789a4d7ce26bbff0821bf7d80f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-12T12:45:13Z","title_canon_sha256":"91148ce50e1d4ab3c5ec8f3d9fc68652fee45b24eda917be57c04205981babed"},"schema_version":"1.0","source":{"id":"2310.08287","kind":"arxiv","version":1}},"canonical_sha256":"5ad613ecae06b782ce4febf2b3152fc762d43022d46eaf8c86a769f8d96efdde","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ad613ecae06b782ce4febf2b3152fc762d43022d46eaf8c86a769f8d96efdde","first_computed_at":"2026-07-05T07:00:13.496716Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:13.496716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9AUax48cXQADNWer51t041UIiwWQIDwNyIfAOUvDAnyBhO6KkiCgJWc1E8sgBeVYoldUHMLvjCbm05w9axPZBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:13.497176Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08287","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:423d4b5db1b18d81713ffca412ae696f3ae6457ec3b5e2db2866e9077061b46d","sha256:69a91b486fb391d87d64bf1f72ca99c1f884edb413a7a7ce6e28fff6a1e7b6d4"],"state_sha256":"a498d39de4fc46af811c0709880edf2cb9900bd115c862a34b33ba96491a3e60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m3fY3EmwBts1NIAKGo6l7T4AN5KOxvuQPsEEQyRcB+zRlJouskCRHmI0+dU9umJc2QTXSbWWJxhuVCNuIGZpDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:37:44.787596Z","bundle_sha256":"ab13e085260cd8850df158f3cb880131475ef9163a05e2a4767c3fdfc695025d"}}